repo_name stringlengths 7 104 | file_path stringlengths 13 198 | context stringlengths 67 7.15k | import_statement stringlengths 16 4.43k | code stringlengths 40 6.98k | prompt stringlengths 227 8.27k | next_line stringlengths 8 795 |
|---|---|---|---|---|---|---|
difi/datahotel | src/test/java/no/difi/datahotel/resources/DatahotelExceptionMapperTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Formater.java
// public enum Formater {
//
// XML("xml", "text/xml", new XMLFormater()),
// CSV("csv", "text/plain", new CSVFormater()),
// CSVCORRECT(null, "text/csv", new CSVFormater()),
// JSON("json", "application/json", new JSONFormater()),
// JSONP("jsonp", "application/json", new JSONPFormater()),
// YAML("yaml", "text/plain", new YAMLFormater());
//
// private static Logger logger = Logger.getLogger(Formater.class.getSimpleName());
//
// private String type;
// private String mime;
// private FormaterInterface cls;
//
// private Formater(String type, String mime, FormaterInterface cls) {
// this.type = type;
// this.mime = mime;
// this.cls = cls;
// }
//
// /**
// * Gets a new dataformat based on a mime type.
// * @param type Mime type (ie. json)
// * @return Returns a new DataFormat enum.
// */
// public static Formater get(String type) {
// for (Formater t : Formater.values())
// if (type.equals(t.type))
// return t;
//
// throw new DatahotelException(404, "Format not found.");
// }
//
// /**
// * Gets the type of this DataFormat.
// * @return Returns the type of this DataFormat.
// */
// public String getType() {
// return this.type;
// }
//
// /**
// * Gets the correct Mime type for this DataFormat.
// * @return Returns the correct Mime type for this DataFormat.
// */
// public String getMime() {
// return this.mime + ";charset=UTF-8";
// }
//
// /**
// * Formats an object if support for it has been implemented.
// * @param object Object to format.
// * @param context Context.
// * @return Returns a string representation of the object supplied.
// */
// public String format(Object object, RequestContext context) {
// try
// {
// return cls.format(object, context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return formatError(e, context);
// }
// }
//
// /**
// * Formats an object into an error.
// * @param exception
// * @param context
// * @return
// */
// public String formatError(Exception exception, RequestContext context) {
// try
// {
// return cls.format(new SimpleError(exception.getMessage()), context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return "Error";
// }
// }
// }
| import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.DatahotelException;
import no.difi.datahotel.util.Formater;
import org.junit.Assert;
import org.junit.Test;
import javax.ws.rs.core.Response; | package no.difi.datahotel.resources;
public class DatahotelExceptionMapperTest extends BaseTest {
private DatahotelExceptionMapper mapper = new DatahotelExceptionMapper();
@Test
public void testNotModified() { | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Formater.java
// public enum Formater {
//
// XML("xml", "text/xml", new XMLFormater()),
// CSV("csv", "text/plain", new CSVFormater()),
// CSVCORRECT(null, "text/csv", new CSVFormater()),
// JSON("json", "application/json", new JSONFormater()),
// JSONP("jsonp", "application/json", new JSONPFormater()),
// YAML("yaml", "text/plain", new YAMLFormater());
//
// private static Logger logger = Logger.getLogger(Formater.class.getSimpleName());
//
// private String type;
// private String mime;
// private FormaterInterface cls;
//
// private Formater(String type, String mime, FormaterInterface cls) {
// this.type = type;
// this.mime = mime;
// this.cls = cls;
// }
//
// /**
// * Gets a new dataformat based on a mime type.
// * @param type Mime type (ie. json)
// * @return Returns a new DataFormat enum.
// */
// public static Formater get(String type) {
// for (Formater t : Formater.values())
// if (type.equals(t.type))
// return t;
//
// throw new DatahotelException(404, "Format not found.");
// }
//
// /**
// * Gets the type of this DataFormat.
// * @return Returns the type of this DataFormat.
// */
// public String getType() {
// return this.type;
// }
//
// /**
// * Gets the correct Mime type for this DataFormat.
// * @return Returns the correct Mime type for this DataFormat.
// */
// public String getMime() {
// return this.mime + ";charset=UTF-8";
// }
//
// /**
// * Formats an object if support for it has been implemented.
// * @param object Object to format.
// * @param context Context.
// * @return Returns a string representation of the object supplied.
// */
// public String format(Object object, RequestContext context) {
// try
// {
// return cls.format(object, context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return formatError(e, context);
// }
// }
//
// /**
// * Formats an object into an error.
// * @param exception
// * @param context
// * @return
// */
// public String formatError(Exception exception, RequestContext context) {
// try
// {
// return cls.format(new SimpleError(exception.getMessage()), context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return "Error";
// }
// }
// }
// Path: src/test/java/no/difi/datahotel/resources/DatahotelExceptionMapperTest.java
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.DatahotelException;
import no.difi.datahotel.util.Formater;
import org.junit.Assert;
import org.junit.Test;
import javax.ws.rs.core.Response;
package no.difi.datahotel.resources;
public class DatahotelExceptionMapperTest extends BaseTest {
private DatahotelExceptionMapper mapper = new DatahotelExceptionMapper();
@Test
public void testNotModified() { | Response response = mapper.toResponse(new DatahotelException(304, "Not modified").setFormater(Formater.XML)); |
difi/datahotel | src/test/java/no/difi/datahotel/resources/DatahotelExceptionMapperTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Formater.java
// public enum Formater {
//
// XML("xml", "text/xml", new XMLFormater()),
// CSV("csv", "text/plain", new CSVFormater()),
// CSVCORRECT(null, "text/csv", new CSVFormater()),
// JSON("json", "application/json", new JSONFormater()),
// JSONP("jsonp", "application/json", new JSONPFormater()),
// YAML("yaml", "text/plain", new YAMLFormater());
//
// private static Logger logger = Logger.getLogger(Formater.class.getSimpleName());
//
// private String type;
// private String mime;
// private FormaterInterface cls;
//
// private Formater(String type, String mime, FormaterInterface cls) {
// this.type = type;
// this.mime = mime;
// this.cls = cls;
// }
//
// /**
// * Gets a new dataformat based on a mime type.
// * @param type Mime type (ie. json)
// * @return Returns a new DataFormat enum.
// */
// public static Formater get(String type) {
// for (Formater t : Formater.values())
// if (type.equals(t.type))
// return t;
//
// throw new DatahotelException(404, "Format not found.");
// }
//
// /**
// * Gets the type of this DataFormat.
// * @return Returns the type of this DataFormat.
// */
// public String getType() {
// return this.type;
// }
//
// /**
// * Gets the correct Mime type for this DataFormat.
// * @return Returns the correct Mime type for this DataFormat.
// */
// public String getMime() {
// return this.mime + ";charset=UTF-8";
// }
//
// /**
// * Formats an object if support for it has been implemented.
// * @param object Object to format.
// * @param context Context.
// * @return Returns a string representation of the object supplied.
// */
// public String format(Object object, RequestContext context) {
// try
// {
// return cls.format(object, context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return formatError(e, context);
// }
// }
//
// /**
// * Formats an object into an error.
// * @param exception
// * @param context
// * @return
// */
// public String formatError(Exception exception, RequestContext context) {
// try
// {
// return cls.format(new SimpleError(exception.getMessage()), context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return "Error";
// }
// }
// }
| import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.DatahotelException;
import no.difi.datahotel.util.Formater;
import org.junit.Assert;
import org.junit.Test;
import javax.ws.rs.core.Response; | package no.difi.datahotel.resources;
public class DatahotelExceptionMapperTest extends BaseTest {
private DatahotelExceptionMapper mapper = new DatahotelExceptionMapper();
@Test
public void testNotModified() { | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Formater.java
// public enum Formater {
//
// XML("xml", "text/xml", new XMLFormater()),
// CSV("csv", "text/plain", new CSVFormater()),
// CSVCORRECT(null, "text/csv", new CSVFormater()),
// JSON("json", "application/json", new JSONFormater()),
// JSONP("jsonp", "application/json", new JSONPFormater()),
// YAML("yaml", "text/plain", new YAMLFormater());
//
// private static Logger logger = Logger.getLogger(Formater.class.getSimpleName());
//
// private String type;
// private String mime;
// private FormaterInterface cls;
//
// private Formater(String type, String mime, FormaterInterface cls) {
// this.type = type;
// this.mime = mime;
// this.cls = cls;
// }
//
// /**
// * Gets a new dataformat based on a mime type.
// * @param type Mime type (ie. json)
// * @return Returns a new DataFormat enum.
// */
// public static Formater get(String type) {
// for (Formater t : Formater.values())
// if (type.equals(t.type))
// return t;
//
// throw new DatahotelException(404, "Format not found.");
// }
//
// /**
// * Gets the type of this DataFormat.
// * @return Returns the type of this DataFormat.
// */
// public String getType() {
// return this.type;
// }
//
// /**
// * Gets the correct Mime type for this DataFormat.
// * @return Returns the correct Mime type for this DataFormat.
// */
// public String getMime() {
// return this.mime + ";charset=UTF-8";
// }
//
// /**
// * Formats an object if support for it has been implemented.
// * @param object Object to format.
// * @param context Context.
// * @return Returns a string representation of the object supplied.
// */
// public String format(Object object, RequestContext context) {
// try
// {
// return cls.format(object, context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return formatError(e, context);
// }
// }
//
// /**
// * Formats an object into an error.
// * @param exception
// * @param context
// * @return
// */
// public String formatError(Exception exception, RequestContext context) {
// try
// {
// return cls.format(new SimpleError(exception.getMessage()), context);
// } catch (Exception e)
// {
// logger.log(Level.WARNING, e.getMessage() + " - Format: " + type + " - " + e.getClass().getSimpleName() + " - " + e.getStackTrace()[0].toString());
// return "Error";
// }
// }
// }
// Path: src/test/java/no/difi/datahotel/resources/DatahotelExceptionMapperTest.java
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.DatahotelException;
import no.difi.datahotel.util.Formater;
import org.junit.Assert;
import org.junit.Test;
import javax.ws.rs.core.Response;
package no.difi.datahotel.resources;
public class DatahotelExceptionMapperTest extends BaseTest {
private DatahotelExceptionMapper mapper = new DatahotelExceptionMapper();
@Test
public void testNotModified() { | Response response = mapper.toResponse(new DatahotelException(304, "Not modified").setFormater(Formater.XML)); |
difi/datahotel | src/test/java/no/difi/datahotel/util/formater/CSVFormaterTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Result.java
// public class Result implements Serializable {
//
// private static final long serialVersionUID = -8412531397903068046L;
// private List<Map<String, String>> entries;
// private Long page = 0L, pages = 0L, posts = 0L;
//
// public Result() {
// this.setEntries(new ArrayList<Map<String, String>>());
// }
//
// /**
// * Creates a new CSVData object with the specified list of hashmaps.
// *
// * @param entries
// * A list of hashmaps. Each entry in the arraylist must be a line
// * in the CSV File, each entry in the hashmap must be column
// * header and respective value.
// */
// public Result(List<Map<String, String>> entries) {
// this.setEntries(entries);
// }
//
// /**
// * Sets the CSV data.
// *
// * @param entries
// * CSV data.
// */
// public void setEntries(List<Map<String, String>> entries) {
// this.entries = entries != null ? entries : new ArrayList<Map<String, String>>();
// }
//
// /**
// * Gets the CSV data.
// *
// * @return Returns the CSV data.
// */
// public List<Map<String, String>> getEntries() {
// return entries;
// }
//
// public Long getPosts() {
// return posts;
// }
//
// public void setPosts(long posts) {
// this.posts = posts;
// this.pages = ((posts - (posts % 100)) / 100) + (posts % 100 == 0 ? 0 : 1);
// }
//
// public Long getPages() {
// return pages;
// }
//
// public Long getPage() {
// return page;
// }
//
// public void setPage(long page) {
// this.page = page;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/formater/CSVFormater.java
// public class CSVFormater implements FormaterInterface {
//
// public String format(Object object, RequestContext context) throws Exception {
// if (object instanceof Result) {
// List<Map<String, String>> data = ((Result) object).getEntries();
//
// ByteArrayOutputStream baos = new ByteArrayOutputStream();
// CSVWriter writer = new CSVWriter(baos);
//
// writer.writeHeader(data.get(0).keySet().toArray(new String[0]));
// for (Map<String, String> row : data)
// writer.write(row.values().toArray(new String[0]));
//
// writer.close();
//
// return baos.toString("UTF-8");
// }
//
// throw new Exception("Unable to parse content.");
// }
// }
| import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Result;
import no.difi.datahotel.util.formater.CSVFormater;
import static org.junit.Assert.*;
import org.junit.Test;
| package no.difi.datahotel.util.formater;
public class CSVFormaterTest extends BaseTest {
@Test
public void testCSVData() throws Exception {
ArrayList<Map<String, String>> data = new ArrayList<Map<String, String>>();
Map<String, String> element;
element = new HashMap<String, String>();
element.put("id", "1");
element.put("name", "Ole");
data.add(element);
element = new HashMap<String, String>();
element.put("id", "2");
element.put("name", "Per");
data.add(element);
| // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Result.java
// public class Result implements Serializable {
//
// private static final long serialVersionUID = -8412531397903068046L;
// private List<Map<String, String>> entries;
// private Long page = 0L, pages = 0L, posts = 0L;
//
// public Result() {
// this.setEntries(new ArrayList<Map<String, String>>());
// }
//
// /**
// * Creates a new CSVData object with the specified list of hashmaps.
// *
// * @param entries
// * A list of hashmaps. Each entry in the arraylist must be a line
// * in the CSV File, each entry in the hashmap must be column
// * header and respective value.
// */
// public Result(List<Map<String, String>> entries) {
// this.setEntries(entries);
// }
//
// /**
// * Sets the CSV data.
// *
// * @param entries
// * CSV data.
// */
// public void setEntries(List<Map<String, String>> entries) {
// this.entries = entries != null ? entries : new ArrayList<Map<String, String>>();
// }
//
// /**
// * Gets the CSV data.
// *
// * @return Returns the CSV data.
// */
// public List<Map<String, String>> getEntries() {
// return entries;
// }
//
// public Long getPosts() {
// return posts;
// }
//
// public void setPosts(long posts) {
// this.posts = posts;
// this.pages = ((posts - (posts % 100)) / 100) + (posts % 100 == 0 ? 0 : 1);
// }
//
// public Long getPages() {
// return pages;
// }
//
// public Long getPage() {
// return page;
// }
//
// public void setPage(long page) {
// this.page = page;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/formater/CSVFormater.java
// public class CSVFormater implements FormaterInterface {
//
// public String format(Object object, RequestContext context) throws Exception {
// if (object instanceof Result) {
// List<Map<String, String>> data = ((Result) object).getEntries();
//
// ByteArrayOutputStream baos = new ByteArrayOutputStream();
// CSVWriter writer = new CSVWriter(baos);
//
// writer.writeHeader(data.get(0).keySet().toArray(new String[0]));
// for (Map<String, String> row : data)
// writer.write(row.values().toArray(new String[0]));
//
// writer.close();
//
// return baos.toString("UTF-8");
// }
//
// throw new Exception("Unable to parse content.");
// }
// }
// Path: src/test/java/no/difi/datahotel/util/formater/CSVFormaterTest.java
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Result;
import no.difi.datahotel.util.formater.CSVFormater;
import static org.junit.Assert.*;
import org.junit.Test;
package no.difi.datahotel.util.formater;
public class CSVFormaterTest extends BaseTest {
@Test
public void testCSVData() throws Exception {
ArrayList<Map<String, String>> data = new ArrayList<Map<String, String>>();
Map<String, String> element;
element = new HashMap<String, String>();
element.put("id", "1");
element.put("name", "Ole");
data.add(element);
element = new HashMap<String, String>();
element.put("id", "2");
element.put("name", "Per");
data.add(element);
| CSVFormater parser = new CSVFormater();
|
difi/datahotel | src/test/java/no/difi/datahotel/util/formater/CSVFormaterTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Result.java
// public class Result implements Serializable {
//
// private static final long serialVersionUID = -8412531397903068046L;
// private List<Map<String, String>> entries;
// private Long page = 0L, pages = 0L, posts = 0L;
//
// public Result() {
// this.setEntries(new ArrayList<Map<String, String>>());
// }
//
// /**
// * Creates a new CSVData object with the specified list of hashmaps.
// *
// * @param entries
// * A list of hashmaps. Each entry in the arraylist must be a line
// * in the CSV File, each entry in the hashmap must be column
// * header and respective value.
// */
// public Result(List<Map<String, String>> entries) {
// this.setEntries(entries);
// }
//
// /**
// * Sets the CSV data.
// *
// * @param entries
// * CSV data.
// */
// public void setEntries(List<Map<String, String>> entries) {
// this.entries = entries != null ? entries : new ArrayList<Map<String, String>>();
// }
//
// /**
// * Gets the CSV data.
// *
// * @return Returns the CSV data.
// */
// public List<Map<String, String>> getEntries() {
// return entries;
// }
//
// public Long getPosts() {
// return posts;
// }
//
// public void setPosts(long posts) {
// this.posts = posts;
// this.pages = ((posts - (posts % 100)) / 100) + (posts % 100 == 0 ? 0 : 1);
// }
//
// public Long getPages() {
// return pages;
// }
//
// public Long getPage() {
// return page;
// }
//
// public void setPage(long page) {
// this.page = page;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/formater/CSVFormater.java
// public class CSVFormater implements FormaterInterface {
//
// public String format(Object object, RequestContext context) throws Exception {
// if (object instanceof Result) {
// List<Map<String, String>> data = ((Result) object).getEntries();
//
// ByteArrayOutputStream baos = new ByteArrayOutputStream();
// CSVWriter writer = new CSVWriter(baos);
//
// writer.writeHeader(data.get(0).keySet().toArray(new String[0]));
// for (Map<String, String> row : data)
// writer.write(row.values().toArray(new String[0]));
//
// writer.close();
//
// return baos.toString("UTF-8");
// }
//
// throw new Exception("Unable to parse content.");
// }
// }
| import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Result;
import no.difi.datahotel.util.formater.CSVFormater;
import static org.junit.Assert.*;
import org.junit.Test;
| package no.difi.datahotel.util.formater;
public class CSVFormaterTest extends BaseTest {
@Test
public void testCSVData() throws Exception {
ArrayList<Map<String, String>> data = new ArrayList<Map<String, String>>();
Map<String, String> element;
element = new HashMap<String, String>();
element.put("id", "1");
element.put("name", "Ole");
data.add(element);
element = new HashMap<String, String>();
element.put("id", "2");
element.put("name", "Per");
data.add(element);
CSVFormater parser = new CSVFormater();
| // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Result.java
// public class Result implements Serializable {
//
// private static final long serialVersionUID = -8412531397903068046L;
// private List<Map<String, String>> entries;
// private Long page = 0L, pages = 0L, posts = 0L;
//
// public Result() {
// this.setEntries(new ArrayList<Map<String, String>>());
// }
//
// /**
// * Creates a new CSVData object with the specified list of hashmaps.
// *
// * @param entries
// * A list of hashmaps. Each entry in the arraylist must be a line
// * in the CSV File, each entry in the hashmap must be column
// * header and respective value.
// */
// public Result(List<Map<String, String>> entries) {
// this.setEntries(entries);
// }
//
// /**
// * Sets the CSV data.
// *
// * @param entries
// * CSV data.
// */
// public void setEntries(List<Map<String, String>> entries) {
// this.entries = entries != null ? entries : new ArrayList<Map<String, String>>();
// }
//
// /**
// * Gets the CSV data.
// *
// * @return Returns the CSV data.
// */
// public List<Map<String, String>> getEntries() {
// return entries;
// }
//
// public Long getPosts() {
// return posts;
// }
//
// public void setPosts(long posts) {
// this.posts = posts;
// this.pages = ((posts - (posts % 100)) / 100) + (posts % 100 == 0 ? 0 : 1);
// }
//
// public Long getPages() {
// return pages;
// }
//
// public Long getPage() {
// return page;
// }
//
// public void setPage(long page) {
// this.page = page;
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/formater/CSVFormater.java
// public class CSVFormater implements FormaterInterface {
//
// public String format(Object object, RequestContext context) throws Exception {
// if (object instanceof Result) {
// List<Map<String, String>> data = ((Result) object).getEntries();
//
// ByteArrayOutputStream baos = new ByteArrayOutputStream();
// CSVWriter writer = new CSVWriter(baos);
//
// writer.writeHeader(data.get(0).keySet().toArray(new String[0]));
// for (Map<String, String> row : data)
// writer.write(row.values().toArray(new String[0]));
//
// writer.close();
//
// return baos.toString("UTF-8");
// }
//
// throw new Exception("Unable to parse content.");
// }
// }
// Path: src/test/java/no/difi/datahotel/util/formater/CSVFormaterTest.java
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Result;
import no.difi.datahotel.util.formater.CSVFormater;
import static org.junit.Assert.*;
import org.junit.Test;
package no.difi.datahotel.util.formater;
public class CSVFormaterTest extends BaseTest {
@Test
public void testCSVData() throws Exception {
ArrayList<Map<String, String>> data = new ArrayList<Map<String, String>>();
Map<String, String> element;
element = new HashMap<String, String>();
element.put("id", "1");
element.put("name", "Ole");
data.add(element);
element = new HashMap<String, String>();
element.put("id", "2");
element.put("name", "Per");
data.add(element);
CSVFormater parser = new CSVFormater();
| String result = parser.format(new Result(data), null);
|
difi/datahotel | src/test/java/no/difi/datahotel/BaseTest.java | // Path: src/test/java/no/difi/datahotel/logic/ChunkBeanTest.java
// public class ChunkBeanTest extends BaseTest {
//
// private ChunkBean chunkBean;
//
// static MetadataLogger logger;
// static Metadata metadata;
//
// @Before
// public void before() throws Exception {
// chunkBean = new ChunkBean();
// logger = Mockito.mock(MetadataLogger.class);
//
// metadata = new Metadata();
// metadata.setUpdated(System.currentTimeMillis());
// metadata.setLogger(logger);
// }
//
// @Test
// public void testUpdate() {
// metadata.setLocation("difi/test/simple");
// metadata.setShortName("simple");
//
// chunkBean.update(metadata);
//
// assertTrue(Filesystem.getFile(FOLDER_CACHE_CHUNK, "difi", "test", "simple", "dataset-1.csv").exists());
// assertTrue(Filesystem.getFile(FOLDER_CACHE_CHUNK, "difi", "test", "simple", "dataset-2.csv").exists());
// }
//
// @Test
// public void testUpdateError() {
// metadata.setLocation("difi/test/simple-not-here");
// metadata.setShortName("simple-not-here");
//
// chunkBean.update(metadata);
//
// // TODO Fikse verifisert bruk av logger
// // Mockito.verify(logger).log(Level.WARNING, null);
// }
//
// @Test
// public void testGet() throws Exception {
// metadata.setLocation("difi/test/simple");
// metadata.setShortName("simple");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(19, chunkBean.get(metadata, 2).getEntries().size());
//
// assertEquals(0, chunkBean.get(metadata, 3).getEntries().size());
//
// metadata.setLocation("difi/test/simple2");
// assertEquals(0, chunkBean.get(metadata, 1).getEntries().size());
//
// // Thread.sleep(1000);
//
// // chunkEJB.delete("difi", "test", "simple2");
// }
//
// @Test
// public void testOneHundred() throws Exception {
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(0, chunkBean.get(metadata, 2).getEntries().size());
//
// assertEquals(new Long(100), chunkBean.get(metadata, 1).getPosts());
//
// metadata.setLocation("difi/test/hundred200");
// assertEquals(new Long(0), chunkBean.get(metadata, 1).getPosts());
//
// // Thread.sleep(1000);
//
// // chunkEJB.delete("difi", "test", "hundred");
// // assertFalse(Filesystem.getFolderPathF("chunk", "difi", "test",
// // "hundred").exists());
// }
//
// @Test
// public void testNoNeed() throws Exception {
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(0, chunkBean.get(metadata, 2).getEntries().size());
// long ts = Filesystem.getFile(FOLDER_CACHE_CHUNK, metadata.getLocation(), "timestamp").lastModified();
//
// Thread.sleep(1000);
//
// chunkBean.update(metadata);
// long ts2 = Filesystem.getFile(FOLDER_CACHE_CHUNK, metadata.getLocation(), "timestamp").lastModified();
// assertEquals(ts, ts2);
//
// // chunkEJB.delete("difi", "test", "hundred");
// // assertFalse(Filesystem.getFolderPathF("chunk", "difi", "test",
// // "hundred").exists());
// }
//
// @Test
// public void testReplaceGoal() throws Exception {
// File folder = Filesystem.getFolder(Filesystem.FOLDER_CACHE_CHUNK, "difi/test/hundred", "secret");
// assertTrue(folder.exists());
//
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertFalse(folder.exists());
// }
// }
| import no.difi.datahotel.logic.ChunkBeanTest;
import org.junit.BeforeClass;
import java.io.File;
| package no.difi.datahotel;
public class BaseTest {
@BeforeClass
public static void beforeClass() throws Exception {
| // Path: src/test/java/no/difi/datahotel/logic/ChunkBeanTest.java
// public class ChunkBeanTest extends BaseTest {
//
// private ChunkBean chunkBean;
//
// static MetadataLogger logger;
// static Metadata metadata;
//
// @Before
// public void before() throws Exception {
// chunkBean = new ChunkBean();
// logger = Mockito.mock(MetadataLogger.class);
//
// metadata = new Metadata();
// metadata.setUpdated(System.currentTimeMillis());
// metadata.setLogger(logger);
// }
//
// @Test
// public void testUpdate() {
// metadata.setLocation("difi/test/simple");
// metadata.setShortName("simple");
//
// chunkBean.update(metadata);
//
// assertTrue(Filesystem.getFile(FOLDER_CACHE_CHUNK, "difi", "test", "simple", "dataset-1.csv").exists());
// assertTrue(Filesystem.getFile(FOLDER_CACHE_CHUNK, "difi", "test", "simple", "dataset-2.csv").exists());
// }
//
// @Test
// public void testUpdateError() {
// metadata.setLocation("difi/test/simple-not-here");
// metadata.setShortName("simple-not-here");
//
// chunkBean.update(metadata);
//
// // TODO Fikse verifisert bruk av logger
// // Mockito.verify(logger).log(Level.WARNING, null);
// }
//
// @Test
// public void testGet() throws Exception {
// metadata.setLocation("difi/test/simple");
// metadata.setShortName("simple");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(19, chunkBean.get(metadata, 2).getEntries().size());
//
// assertEquals(0, chunkBean.get(metadata, 3).getEntries().size());
//
// metadata.setLocation("difi/test/simple2");
// assertEquals(0, chunkBean.get(metadata, 1).getEntries().size());
//
// // Thread.sleep(1000);
//
// // chunkEJB.delete("difi", "test", "simple2");
// }
//
// @Test
// public void testOneHundred() throws Exception {
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(0, chunkBean.get(metadata, 2).getEntries().size());
//
// assertEquals(new Long(100), chunkBean.get(metadata, 1).getPosts());
//
// metadata.setLocation("difi/test/hundred200");
// assertEquals(new Long(0), chunkBean.get(metadata, 1).getPosts());
//
// // Thread.sleep(1000);
//
// // chunkEJB.delete("difi", "test", "hundred");
// // assertFalse(Filesystem.getFolderPathF("chunk", "difi", "test",
// // "hundred").exists());
// }
//
// @Test
// public void testNoNeed() throws Exception {
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertEquals(100, chunkBean.get(metadata, 1).getEntries().size());
// assertEquals(0, chunkBean.get(metadata, 2).getEntries().size());
// long ts = Filesystem.getFile(FOLDER_CACHE_CHUNK, metadata.getLocation(), "timestamp").lastModified();
//
// Thread.sleep(1000);
//
// chunkBean.update(metadata);
// long ts2 = Filesystem.getFile(FOLDER_CACHE_CHUNK, metadata.getLocation(), "timestamp").lastModified();
// assertEquals(ts, ts2);
//
// // chunkEJB.delete("difi", "test", "hundred");
// // assertFalse(Filesystem.getFolderPathF("chunk", "difi", "test",
// // "hundred").exists());
// }
//
// @Test
// public void testReplaceGoal() throws Exception {
// File folder = Filesystem.getFolder(Filesystem.FOLDER_CACHE_CHUNK, "difi/test/hundred", "secret");
// assertTrue(folder.exists());
//
// metadata.setLocation("difi/test/hundred");
// metadata.setShortName("hundred");
//
// chunkBean.update(metadata);
//
// assertFalse(folder.exists());
// }
// }
// Path: src/test/java/no/difi/datahotel/BaseTest.java
import no.difi.datahotel.logic.ChunkBeanTest;
import org.junit.BeforeClass;
import java.io.File;
package no.difi.datahotel;
public class BaseTest {
@BeforeClass
public static void beforeClass() throws Exception {
| System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
|
difi/datahotel | src/main/java/no/difi/datahotel/util/MetadataLogger.java | // Path: src/main/java/no/difi/datahotel/model/Metadata.java
// @XmlRootElement
// public class Metadata implements Comparable<Metadata>, Light<MetadataLight> {
//
// private String location = "";
// private List<Metadata> children = new ArrayList<Metadata>();
// private boolean active = true;
// private boolean dataset = false;
// private Metadata parent;
// private MetadataLogger logger = new MetadataLogger(this);
// private Long version;
//
// // Values for users
// private String shortName;
// private String name;
// private String description;
// private String url;
// private Long updated;
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// public String getUrl() {
// return url;
// }
//
// public void setUrl(String url) {
// this.url = url;
// }
//
// public Long getUpdated() {
// return updated;
// }
//
// public void setUpdated(Long updated) {
// this.updated = updated;
// }
//
// public String getLocation() {
// return location;
// }
//
// public void setLocation(String location) {
// this.location = location;
// }
//
// @XmlTransient
// public List<Metadata> getChildren() {
// return children;
// }
//
// public void addChild(Metadata child) {
// this.children.add(child);
// child.parent = this;
//
// if (child.isActive() && child.updated != null)
// if (this.updated == null || this.updated < child.updated)
// this.updated = child.updated;
// }
//
// public boolean isActive() {
// return active;
// }
//
// public void setActive(boolean active) {
// this.active = active;
// }
//
// public boolean isDataset() {
// return dataset;
// }
//
// public void setDataset(boolean dataset) {
// this.dataset = dataset;
// }
//
// @XmlTransient
// public MetadataLogger getLogger() {
// return logger;
// }
//
// public void setLogger(MetadataLogger logger) {
// this.logger = logger;
// }
//
// @XmlTransient
// public Metadata getParent() {
// return parent;
// }
//
// public void setParent(Metadata parent) {
// this.parent = parent;
// }
//
// public Long getVersion() {
// return version;
// }
//
// public void setVersion(Long version) {
// this.version = version;
// }
//
// public MetadataLight light() {
// return new MetadataLight(this);
// }
//
// public void save() throws Exception {
// Disk.save(Filesystem.getFile(Filesystem.FOLDER_SLAVE, location, Filesystem.FILE_METADATA), this);
// }
//
// public static Metadata read(String location) {
// File folder = Filesystem.getFolder(FOLDER_SLAVE, location);
// Metadata metadata = (Metadata) Disk.read(Metadata.class, Filesystem.getFile(folder, FILE_METADATA));
// metadata.setLocation(location);
// metadata.setShortName(folder.getName());
// metadata.setDataset(Filesystem.getFile(folder, FILE_DATASET).exists());
//
// return metadata;
// }
//
// public static String getLocation(String... dir) {
// String location = dir[0];
// for (int i = 1; i < dir.length; i++)
// location += "/" + dir[i];
// return location;
// }
//
// @Override
// public int compareTo(Metadata other) {
// return String.valueOf(name).compareTo(String.valueOf(other.name));
// }
// }
| import no.difi.datahotel.model.Metadata;
import java.util.logging.Level;
import java.util.logging.Logger; | package no.difi.datahotel.util;
public class MetadataLogger {
private static Logger logger = Logger.getLogger(MetadataLogger.class.getSimpleName()); | // Path: src/main/java/no/difi/datahotel/model/Metadata.java
// @XmlRootElement
// public class Metadata implements Comparable<Metadata>, Light<MetadataLight> {
//
// private String location = "";
// private List<Metadata> children = new ArrayList<Metadata>();
// private boolean active = true;
// private boolean dataset = false;
// private Metadata parent;
// private MetadataLogger logger = new MetadataLogger(this);
// private Long version;
//
// // Values for users
// private String shortName;
// private String name;
// private String description;
// private String url;
// private Long updated;
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// public String getUrl() {
// return url;
// }
//
// public void setUrl(String url) {
// this.url = url;
// }
//
// public Long getUpdated() {
// return updated;
// }
//
// public void setUpdated(Long updated) {
// this.updated = updated;
// }
//
// public String getLocation() {
// return location;
// }
//
// public void setLocation(String location) {
// this.location = location;
// }
//
// @XmlTransient
// public List<Metadata> getChildren() {
// return children;
// }
//
// public void addChild(Metadata child) {
// this.children.add(child);
// child.parent = this;
//
// if (child.isActive() && child.updated != null)
// if (this.updated == null || this.updated < child.updated)
// this.updated = child.updated;
// }
//
// public boolean isActive() {
// return active;
// }
//
// public void setActive(boolean active) {
// this.active = active;
// }
//
// public boolean isDataset() {
// return dataset;
// }
//
// public void setDataset(boolean dataset) {
// this.dataset = dataset;
// }
//
// @XmlTransient
// public MetadataLogger getLogger() {
// return logger;
// }
//
// public void setLogger(MetadataLogger logger) {
// this.logger = logger;
// }
//
// @XmlTransient
// public Metadata getParent() {
// return parent;
// }
//
// public void setParent(Metadata parent) {
// this.parent = parent;
// }
//
// public Long getVersion() {
// return version;
// }
//
// public void setVersion(Long version) {
// this.version = version;
// }
//
// public MetadataLight light() {
// return new MetadataLight(this);
// }
//
// public void save() throws Exception {
// Disk.save(Filesystem.getFile(Filesystem.FOLDER_SLAVE, location, Filesystem.FILE_METADATA), this);
// }
//
// public static Metadata read(String location) {
// File folder = Filesystem.getFolder(FOLDER_SLAVE, location);
// Metadata metadata = (Metadata) Disk.read(Metadata.class, Filesystem.getFile(folder, FILE_METADATA));
// metadata.setLocation(location);
// metadata.setShortName(folder.getName());
// metadata.setDataset(Filesystem.getFile(folder, FILE_DATASET).exists());
//
// return metadata;
// }
//
// public static String getLocation(String... dir) {
// String location = dir[0];
// for (int i = 1; i < dir.length; i++)
// location += "/" + dir[i];
// return location;
// }
//
// @Override
// public int compareTo(Metadata other) {
// return String.valueOf(name).compareTo(String.valueOf(other.name));
// }
// }
// Path: src/main/java/no/difi/datahotel/util/MetadataLogger.java
import no.difi.datahotel.model.Metadata;
import java.util.logging.Level;
import java.util.logging.Logger;
package no.difi.datahotel.util;
public class MetadataLogger {
private static Logger logger = Logger.getLogger(MetadataLogger.class.getSimpleName()); | private Metadata metadata; |
difi/datahotel | src/test/java/no/difi/datahotel/model/DefinitionTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Definition.java
// @XmlRootElement
// @XmlAccessorType(XmlAccessType.NONE)
// public class Definition implements Comparable<Definition>, Light<DefinitionLight> {
// @XmlElement
// private String name;
//
// @XmlElement
// private String shortName;
//
// @XmlElement
// private String description;
//
// private List<Field> fields = new ArrayList<Field>();
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = "".equals(description) ? null : description;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public List<Field> getFields() {
// return fields;
// }
//
// public void addField(Field field) {
// fields.add(field);
// field.setDefinition(this);
// }
//
// public void removeField(Field field) {
// fields.remove(field);
// }
//
// public DefinitionLight light() {
// return new DefinitionLight(this);
// }
//
// @Override
// public boolean equals(Object o) {
// if (o == null || !(o instanceof Definition))
// return false;
//
// return String.valueOf(shortName).compareTo(String.valueOf(((Definition) o).shortName)) == 0;
// }
//
// @Override
// public int compareTo(Definition other) {
// return name.compareTo(other.name);
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/DefinitionLight.java
// @XmlRootElement
// public class DefinitionLight implements Comparable<DefinitionLight> {
//
// private String name;
// private String shortName;
// private String description;
//
// public DefinitionLight(Definition definition) {
// name = definition.getName();
// shortName = definition.getShortName();
// description = definition.getDescription();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// @Override
// public int compareTo(DefinitionLight other) {
// return name.compareTo(other.name);
// }
// }
| import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Definition;
import no.difi.datahotel.model.DefinitionLight;
import org.junit.Test;
| package no.difi.datahotel.model;
public class DefinitionTest extends BaseTest {
@Test
public void testSetGet() throws Exception {
| // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Definition.java
// @XmlRootElement
// @XmlAccessorType(XmlAccessType.NONE)
// public class Definition implements Comparable<Definition>, Light<DefinitionLight> {
// @XmlElement
// private String name;
//
// @XmlElement
// private String shortName;
//
// @XmlElement
// private String description;
//
// private List<Field> fields = new ArrayList<Field>();
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = "".equals(description) ? null : description;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public List<Field> getFields() {
// return fields;
// }
//
// public void addField(Field field) {
// fields.add(field);
// field.setDefinition(this);
// }
//
// public void removeField(Field field) {
// fields.remove(field);
// }
//
// public DefinitionLight light() {
// return new DefinitionLight(this);
// }
//
// @Override
// public boolean equals(Object o) {
// if (o == null || !(o instanceof Definition))
// return false;
//
// return String.valueOf(shortName).compareTo(String.valueOf(((Definition) o).shortName)) == 0;
// }
//
// @Override
// public int compareTo(Definition other) {
// return name.compareTo(other.name);
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/DefinitionLight.java
// @XmlRootElement
// public class DefinitionLight implements Comparable<DefinitionLight> {
//
// private String name;
// private String shortName;
// private String description;
//
// public DefinitionLight(Definition definition) {
// name = definition.getName();
// shortName = definition.getShortName();
// description = definition.getDescription();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// @Override
// public int compareTo(DefinitionLight other) {
// return name.compareTo(other.name);
// }
// }
// Path: src/test/java/no/difi/datahotel/model/DefinitionTest.java
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Definition;
import no.difi.datahotel.model.DefinitionLight;
import org.junit.Test;
package no.difi.datahotel.model;
public class DefinitionTest extends BaseTest {
@Test
public void testSetGet() throws Exception {
| Definition d = new Definition();
|
difi/datahotel | src/test/java/no/difi/datahotel/model/DefinitionTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Definition.java
// @XmlRootElement
// @XmlAccessorType(XmlAccessType.NONE)
// public class Definition implements Comparable<Definition>, Light<DefinitionLight> {
// @XmlElement
// private String name;
//
// @XmlElement
// private String shortName;
//
// @XmlElement
// private String description;
//
// private List<Field> fields = new ArrayList<Field>();
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = "".equals(description) ? null : description;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public List<Field> getFields() {
// return fields;
// }
//
// public void addField(Field field) {
// fields.add(field);
// field.setDefinition(this);
// }
//
// public void removeField(Field field) {
// fields.remove(field);
// }
//
// public DefinitionLight light() {
// return new DefinitionLight(this);
// }
//
// @Override
// public boolean equals(Object o) {
// if (o == null || !(o instanceof Definition))
// return false;
//
// return String.valueOf(shortName).compareTo(String.valueOf(((Definition) o).shortName)) == 0;
// }
//
// @Override
// public int compareTo(Definition other) {
// return name.compareTo(other.name);
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/DefinitionLight.java
// @XmlRootElement
// public class DefinitionLight implements Comparable<DefinitionLight> {
//
// private String name;
// private String shortName;
// private String description;
//
// public DefinitionLight(Definition definition) {
// name = definition.getName();
// shortName = definition.getShortName();
// description = definition.getDescription();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// @Override
// public int compareTo(DefinitionLight other) {
// return name.compareTo(other.name);
// }
// }
| import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Definition;
import no.difi.datahotel.model.DefinitionLight;
import org.junit.Test;
| package no.difi.datahotel.model;
public class DefinitionTest extends BaseTest {
@Test
public void testSetGet() throws Exception {
Definition d = new Definition();
assertNull(d.getName());
assertNull(d.getShortName());
assertNull(d.getDescription());
d.setName("Organisasjonsnummer");
d.setShortName("orgnr");
d.setDescription("Identifikator i brreg.");
assertEquals("Organisasjonsnummer", d.getName());
assertEquals("orgnr", d.getShortName());
assertEquals("Identifikator i brreg.", d.getDescription());
| // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/Definition.java
// @XmlRootElement
// @XmlAccessorType(XmlAccessType.NONE)
// public class Definition implements Comparable<Definition>, Light<DefinitionLight> {
// @XmlElement
// private String name;
//
// @XmlElement
// private String shortName;
//
// @XmlElement
// private String description;
//
// private List<Field> fields = new ArrayList<Field>();
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = "".equals(description) ? null : description;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public List<Field> getFields() {
// return fields;
// }
//
// public void addField(Field field) {
// fields.add(field);
// field.setDefinition(this);
// }
//
// public void removeField(Field field) {
// fields.remove(field);
// }
//
// public DefinitionLight light() {
// return new DefinitionLight(this);
// }
//
// @Override
// public boolean equals(Object o) {
// if (o == null || !(o instanceof Definition))
// return false;
//
// return String.valueOf(shortName).compareTo(String.valueOf(((Definition) o).shortName)) == 0;
// }
//
// @Override
// public int compareTo(Definition other) {
// return name.compareTo(other.name);
// }
// }
//
// Path: src/main/java/no/difi/datahotel/model/DefinitionLight.java
// @XmlRootElement
// public class DefinitionLight implements Comparable<DefinitionLight> {
//
// private String name;
// private String shortName;
// private String description;
//
// public DefinitionLight(Definition definition) {
// name = definition.getName();
// shortName = definition.getShortName();
// description = definition.getDescription();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// @Override
// public int compareTo(DefinitionLight other) {
// return name.compareTo(other.name);
// }
// }
// Path: src/test/java/no/difi/datahotel/model/DefinitionTest.java
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.model.Definition;
import no.difi.datahotel.model.DefinitionLight;
import org.junit.Test;
package no.difi.datahotel.model;
public class DefinitionTest extends BaseTest {
@Test
public void testSetGet() throws Exception {
Definition d = new Definition();
assertNull(d.getName());
assertNull(d.getShortName());
assertNull(d.getDescription());
d.setName("Organisasjonsnummer");
d.setShortName("orgnr");
d.setDescription("Identifikator i brreg.");
assertEquals("Organisasjonsnummer", d.getName());
assertEquals("orgnr", d.getShortName());
assertEquals("Identifikator i brreg.", d.getDescription());
| DefinitionLight dl = d.light();
|
difi/datahotel | src/main/java/no/difi/datahotel/util/RequestContext.java | // Path: src/main/java/no/difi/datahotel/model/FieldLight.java
// @XmlRootElement
// public class FieldLight {
//
// private String name;
// private String shortName;
// private boolean groupable;
// private boolean searchable;
// private boolean indexPrimaryKey;
// private String description;
// private String definition;
//
// public FieldLight(Field field) {
// name = field.getName();
// shortName = field.getShortName();
// groupable = field.getGroupable();
// searchable = field.getSearchable();
// indexPrimaryKey = field.getIndexPrimaryKey();
// description = field.getContent();
// definition = field.getDefShort();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public boolean getGroupable() {
// return groupable;
// }
//
// public void setGroupable(boolean groupable) {
// this.groupable = groupable;
// }
//
// public boolean getSearchable() {
// return searchable;
// }
//
// public void setSearchable(boolean searchable) {
// this.searchable = searchable;
// }
//
// public boolean getIndexPrimaryKey() {
// return indexPrimaryKey;
// }
//
// public void setIndexPrimaryKey(boolean indexPrimaryKey) {
// this.indexPrimaryKey = indexPrimaryKey;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// public String getDefinition() {
// return definition;
// }
//
// public void setDefinition(String definition) {
// this.definition = definition;
// }
//
// }
| import java.util.HashMap;
import java.util.List;
import java.util.Map;
import javax.ws.rs.core.MultivaluedMap;
import javax.ws.rs.core.UriInfo;
import no.difi.datahotel.model.FieldLight;
| package no.difi.datahotel.util;
public class RequestContext {
private int page = 1;
private String query = null;
private Map<String, String> lookup = new HashMap<String, String>();
private String callback;
public RequestContext() {
}
public RequestContext(UriInfo uriInfo) {
this(uriInfo, null);
}
| // Path: src/main/java/no/difi/datahotel/model/FieldLight.java
// @XmlRootElement
// public class FieldLight {
//
// private String name;
// private String shortName;
// private boolean groupable;
// private boolean searchable;
// private boolean indexPrimaryKey;
// private String description;
// private String definition;
//
// public FieldLight(Field field) {
// name = field.getName();
// shortName = field.getShortName();
// groupable = field.getGroupable();
// searchable = field.getSearchable();
// indexPrimaryKey = field.getIndexPrimaryKey();
// description = field.getContent();
// definition = field.getDefShort();
// }
//
// public String getName() {
// return name;
// }
//
// public void setName(String name) {
// this.name = name;
// }
//
// public String getShortName() {
// return shortName;
// }
//
// public void setShortName(String shortName) {
// this.shortName = shortName;
// }
//
// public boolean getGroupable() {
// return groupable;
// }
//
// public void setGroupable(boolean groupable) {
// this.groupable = groupable;
// }
//
// public boolean getSearchable() {
// return searchable;
// }
//
// public void setSearchable(boolean searchable) {
// this.searchable = searchable;
// }
//
// public boolean getIndexPrimaryKey() {
// return indexPrimaryKey;
// }
//
// public void setIndexPrimaryKey(boolean indexPrimaryKey) {
// this.indexPrimaryKey = indexPrimaryKey;
// }
//
// public String getDescription() {
// return description;
// }
//
// public void setDescription(String description) {
// this.description = description;
// }
//
// public String getDefinition() {
// return definition;
// }
//
// public void setDefinition(String definition) {
// this.definition = definition;
// }
//
// }
// Path: src/main/java/no/difi/datahotel/util/RequestContext.java
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import javax.ws.rs.core.MultivaluedMap;
import javax.ws.rs.core.UriInfo;
import no.difi.datahotel.model.FieldLight;
package no.difi.datahotel.util;
public class RequestContext {
private int page = 1;
private String query = null;
private Map<String, String> lookup = new HashMap<String, String>();
private String callback;
public RequestContext() {
}
public RequestContext(UriInfo uriInfo) {
this(uriInfo, null);
}
| public RequestContext(UriInfo uriInfo, List<FieldLight> fields) {
|
difi/datahotel | src/test/java/no/difi/datahotel/logic/MetadataBeanTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Filesystem.java
// public class Filesystem {
//
// private static final String HOME = "datahotel";
//
// public static final String FOLDER_MASTER = "master";
// public static final String FOLDER_MASTER_DEFINITION = FOLDER_MASTER + File.separator + "definition";
// public static final String FOLDER_SLAVE = "slave";
// public static final String FOLDER_CACHE = "cache";
// public static final String FOLDER_CACHE_CHUNK = FOLDER_CACHE + File.separator + "chunk";
// public static final String FOLDER_CACHE_INDEX = FOLDER_CACHE + File.separator + "index";
//
// public static final String FILE_DATASET = "dataset.csv";
// public static final String FILE_DATASET_ORIGINAL = "original.csv";
// public static final String FILE_FIELDS = "fields.xml";
// public static final String FILE_DEFINITIONS = "definitions.xml";
// public static final String FILE_METADATA = "meta.xml";
// public static final String FILE_VERSION = "version.xml";
//
// private static String home;
//
// public static String getHome() {
// if (home != null)
// return home;
//
// String dir;
//
// try {
// Context initCtx = new InitialContext();
// Context envCtx = (Context) initCtx.lookup("java:comp/env");
//
// dir = envCtx.lookup("datahotel.home") + File.separator;
// } catch (NamingException e) {
// if (System.getProperty("datahotel.home") != null)
// dir = System.getProperty("datahotel.home") + File.separator;
// else
// dir = System.getProperty("user.home") + File.separator + HOME + File.separator;
// }
//
// dir = dir.replace(File.separator + File.separator, File.separator);
// new File(dir).mkdirs();
//
// home = dir;
// return dir;
// }
//
// public static File getFolderPath(String... folder) {
// String dir = getHome();
// for (String f : folder)
// if (!"".equals(f))
// dir += f.replace("/", File.separator) + File.separator;
// return new File(dir);
// }
//
// public static File getFolder(String... folder) {
// File dir = getFolderPath(folder);
//
// if (!dir.exists())
// dir.mkdirs();
//
// return dir;
// }
//
// public static File getFile(String... uri) {
// String[] dir = new String[uri.length - 1];
// for (int i = 0; i < uri.length - 1; i++)
// dir[i] = uri[i];
//
// return new File(getFolder(dir).toString() + File.separator + uri[uri.length - 1]);
// }
//
// public static File getFile(File folder, String... uri) {
// String path = folder.toString();
// for (int i = 0; i < uri.length - 1; i++)
// path += uri[i].replace("/", File.separator) + File.separator;
//
// return new File(path + File.separator + uri[uri.length - 1]);
// }
//
// public static void delete(String folder, String location) {
// delete(getFolder(folder, location));
// }
//
// public static void delete(String folder) {
// delete(getFolder(folder));
// }
//
// public static void delete(File folder) {
// for (File f : folder.listFiles()) {
// if (f.isDirectory())
// delete(f);
// else
// f.delete();
// }
// folder.delete();
// }
// }
| import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.Filesystem;
import org.junit.Before;
import org.junit.Test;
import org.mockito.Mockito;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.lang.reflect.Field;
import java.util.logging.Level;
import java.util.logging.Logger;
import static org.junit.Assert.assertEquals;
import static org.mockito.Mockito.verify; | public MetadataBean getMetadataBean() throws Exception {
dataBean = new DataBean();
logger = Mockito.mock(Logger.class);
MetadataBean m = new MetadataBean();
m.setDataEJB(dataBean);
m.setUpdateEJB(Mockito.mock(UpdateBean.class));
Field settingsLoggerField = MetadataBean.class.getDeclaredField("logger");
settingsLoggerField.setAccessible(true);
settingsLoggerField.set(m, logger);
return m;
}
@Test
public void testReading() {
metadataBean.update();
assertEquals(1, dataBean.getChildren().size());
assertEquals("http://www.difi.no/", dataBean.getChild("difi").getUrl());
assertEquals(5, dataBean.getDatasets().size());
assertEquals(null, dataBean.getChildren("not/seen/here"));
assertEquals(2, dataBean.getChild("difi", "geo").getChildren().size());
}
@Test
public void testError() throws IOException { | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Filesystem.java
// public class Filesystem {
//
// private static final String HOME = "datahotel";
//
// public static final String FOLDER_MASTER = "master";
// public static final String FOLDER_MASTER_DEFINITION = FOLDER_MASTER + File.separator + "definition";
// public static final String FOLDER_SLAVE = "slave";
// public static final String FOLDER_CACHE = "cache";
// public static final String FOLDER_CACHE_CHUNK = FOLDER_CACHE + File.separator + "chunk";
// public static final String FOLDER_CACHE_INDEX = FOLDER_CACHE + File.separator + "index";
//
// public static final String FILE_DATASET = "dataset.csv";
// public static final String FILE_DATASET_ORIGINAL = "original.csv";
// public static final String FILE_FIELDS = "fields.xml";
// public static final String FILE_DEFINITIONS = "definitions.xml";
// public static final String FILE_METADATA = "meta.xml";
// public static final String FILE_VERSION = "version.xml";
//
// private static String home;
//
// public static String getHome() {
// if (home != null)
// return home;
//
// String dir;
//
// try {
// Context initCtx = new InitialContext();
// Context envCtx = (Context) initCtx.lookup("java:comp/env");
//
// dir = envCtx.lookup("datahotel.home") + File.separator;
// } catch (NamingException e) {
// if (System.getProperty("datahotel.home") != null)
// dir = System.getProperty("datahotel.home") + File.separator;
// else
// dir = System.getProperty("user.home") + File.separator + HOME + File.separator;
// }
//
// dir = dir.replace(File.separator + File.separator, File.separator);
// new File(dir).mkdirs();
//
// home = dir;
// return dir;
// }
//
// public static File getFolderPath(String... folder) {
// String dir = getHome();
// for (String f : folder)
// if (!"".equals(f))
// dir += f.replace("/", File.separator) + File.separator;
// return new File(dir);
// }
//
// public static File getFolder(String... folder) {
// File dir = getFolderPath(folder);
//
// if (!dir.exists())
// dir.mkdirs();
//
// return dir;
// }
//
// public static File getFile(String... uri) {
// String[] dir = new String[uri.length - 1];
// for (int i = 0; i < uri.length - 1; i++)
// dir[i] = uri[i];
//
// return new File(getFolder(dir).toString() + File.separator + uri[uri.length - 1]);
// }
//
// public static File getFile(File folder, String... uri) {
// String path = folder.toString();
// for (int i = 0; i < uri.length - 1; i++)
// path += uri[i].replace("/", File.separator) + File.separator;
//
// return new File(path + File.separator + uri[uri.length - 1]);
// }
//
// public static void delete(String folder, String location) {
// delete(getFolder(folder, location));
// }
//
// public static void delete(String folder) {
// delete(getFolder(folder));
// }
//
// public static void delete(File folder) {
// for (File f : folder.listFiles()) {
// if (f.isDirectory())
// delete(f);
// else
// f.delete();
// }
// folder.delete();
// }
// }
// Path: src/test/java/no/difi/datahotel/logic/MetadataBeanTest.java
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.Filesystem;
import org.junit.Before;
import org.junit.Test;
import org.mockito.Mockito;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.lang.reflect.Field;
import java.util.logging.Level;
import java.util.logging.Logger;
import static org.junit.Assert.assertEquals;
import static org.mockito.Mockito.verify;
public MetadataBean getMetadataBean() throws Exception {
dataBean = new DataBean();
logger = Mockito.mock(Logger.class);
MetadataBean m = new MetadataBean();
m.setDataEJB(dataBean);
m.setUpdateEJB(Mockito.mock(UpdateBean.class));
Field settingsLoggerField = MetadataBean.class.getDeclaredField("logger");
settingsLoggerField.setAccessible(true);
settingsLoggerField.set(m, logger);
return m;
}
@Test
public void testReading() {
metadataBean.update();
assertEquals(1, dataBean.getChildren().size());
assertEquals("http://www.difi.no/", dataBean.getChild("difi").getUrl());
assertEquals(5, dataBean.getDatasets().size());
assertEquals(null, dataBean.getChildren("not/seen/here"));
assertEquals(2, dataBean.getChild("difi", "geo").getChildren().size());
}
@Test
public void testError() throws IOException { | File folder = Filesystem.getFolder(Filesystem.FOLDER_SLAVE, "google"); |
difi/datahotel | src/main/java/no/difi/datahotel/util/formater/JSONFormater.java | // Path: src/main/java/no/difi/datahotel/util/FormaterInterface.java
// public interface FormaterInterface {
// public String format(Object object, RequestContext context) throws Exception;
// }
//
// Path: src/main/java/no/difi/datahotel/util/RequestContext.java
// public class RequestContext {
//
// private int page = 1;
// private String query = null;
// private Map<String, String> lookup = new HashMap<String, String>();
// private String callback;
//
// public RequestContext() {
//
// }
//
// public RequestContext(UriInfo uriInfo) {
// this(uriInfo, null);
// }
//
// public RequestContext(UriInfo uriInfo, List<FieldLight> fields) {
// MultivaluedMap<String, String> parameters = uriInfo.getQueryParameters();
//
// if (fields != null)
// for (FieldLight f : fields)
// if (f.getGroupable())
// if (parameters.containsKey(f.getShortName()))
// if (!"".equals(parameters.getFirst(f.getShortName())))
// lookup.put(f.getShortName(), parameters.getFirst(f.getShortName()));
//
// if (parameters.containsKey("query"))
// if (!"".equals(parameters.getFirst("query")))
// query = parameters.getFirst("query");
//
// if (parameters.containsKey("callback"))
// if (!"".equals(parameters.getFirst("callback")))
// callback = parameters.getFirst("callback");
//
// if (parameters.containsKey("page"))
// if (!"".equals(parameters.getFirst("page")))
// page = Integer.parseInt(parameters.getFirst("page"));
// }
//
// public int getPage() {
// return page;
// }
//
// public String getQuery() {
// return query;
// }
//
// public Map<String, String> getLookup() {
// return lookup;
// }
//
// public String getCallback() {
// return callback;
// }
//
// @Deprecated
// public void setCallback(String callback) {
// this.callback = callback;
// }
//
// public boolean isSearch() {
// return query != null || lookup.size() > 0;
// }
// }
| import no.difi.datahotel.util.FormaterInterface;
import no.difi.datahotel.util.RequestContext;
import com.google.gson.Gson;
| package no.difi.datahotel.util.formater;
/**
* Class representing JSON.
*/
public class JSONFormater implements FormaterInterface {
private static Gson gson;
public JSONFormater() {
gson = new Gson();
}
@Override
| // Path: src/main/java/no/difi/datahotel/util/FormaterInterface.java
// public interface FormaterInterface {
// public String format(Object object, RequestContext context) throws Exception;
// }
//
// Path: src/main/java/no/difi/datahotel/util/RequestContext.java
// public class RequestContext {
//
// private int page = 1;
// private String query = null;
// private Map<String, String> lookup = new HashMap<String, String>();
// private String callback;
//
// public RequestContext() {
//
// }
//
// public RequestContext(UriInfo uriInfo) {
// this(uriInfo, null);
// }
//
// public RequestContext(UriInfo uriInfo, List<FieldLight> fields) {
// MultivaluedMap<String, String> parameters = uriInfo.getQueryParameters();
//
// if (fields != null)
// for (FieldLight f : fields)
// if (f.getGroupable())
// if (parameters.containsKey(f.getShortName()))
// if (!"".equals(parameters.getFirst(f.getShortName())))
// lookup.put(f.getShortName(), parameters.getFirst(f.getShortName()));
//
// if (parameters.containsKey("query"))
// if (!"".equals(parameters.getFirst("query")))
// query = parameters.getFirst("query");
//
// if (parameters.containsKey("callback"))
// if (!"".equals(parameters.getFirst("callback")))
// callback = parameters.getFirst("callback");
//
// if (parameters.containsKey("page"))
// if (!"".equals(parameters.getFirst("page")))
// page = Integer.parseInt(parameters.getFirst("page"));
// }
//
// public int getPage() {
// return page;
// }
//
// public String getQuery() {
// return query;
// }
//
// public Map<String, String> getLookup() {
// return lookup;
// }
//
// public String getCallback() {
// return callback;
// }
//
// @Deprecated
// public void setCallback(String callback) {
// this.callback = callback;
// }
//
// public boolean isSearch() {
// return query != null || lookup.size() > 0;
// }
// }
// Path: src/main/java/no/difi/datahotel/util/formater/JSONFormater.java
import no.difi.datahotel.util.FormaterInterface;
import no.difi.datahotel.util.RequestContext;
import com.google.gson.Gson;
package no.difi.datahotel.util.formater;
/**
* Class representing JSON.
*/
public class JSONFormater implements FormaterInterface {
private static Gson gson;
public JSONFormater() {
gson = new Gson();
}
@Override
| public String format(Object object, RequestContext context) {
|
difi/datahotel | src/test/java/no/difi/datahotel/model/FieldsTest.java | // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Filesystem.java
// public class Filesystem {
//
// private static final String HOME = "datahotel";
//
// public static final String FOLDER_MASTER = "master";
// public static final String FOLDER_MASTER_DEFINITION = FOLDER_MASTER + File.separator + "definition";
// public static final String FOLDER_SLAVE = "slave";
// public static final String FOLDER_CACHE = "cache";
// public static final String FOLDER_CACHE_CHUNK = FOLDER_CACHE + File.separator + "chunk";
// public static final String FOLDER_CACHE_INDEX = FOLDER_CACHE + File.separator + "index";
//
// public static final String FILE_DATASET = "dataset.csv";
// public static final String FILE_DATASET_ORIGINAL = "original.csv";
// public static final String FILE_FIELDS = "fields.xml";
// public static final String FILE_DEFINITIONS = "definitions.xml";
// public static final String FILE_METADATA = "meta.xml";
// public static final String FILE_VERSION = "version.xml";
//
// private static String home;
//
// public static String getHome() {
// if (home != null)
// return home;
//
// String dir;
//
// try {
// Context initCtx = new InitialContext();
// Context envCtx = (Context) initCtx.lookup("java:comp/env");
//
// dir = envCtx.lookup("datahotel.home") + File.separator;
// } catch (NamingException e) {
// if (System.getProperty("datahotel.home") != null)
// dir = System.getProperty("datahotel.home") + File.separator;
// else
// dir = System.getProperty("user.home") + File.separator + HOME + File.separator;
// }
//
// dir = dir.replace(File.separator + File.separator, File.separator);
// new File(dir).mkdirs();
//
// home = dir;
// return dir;
// }
//
// public static File getFolderPath(String... folder) {
// String dir = getHome();
// for (String f : folder)
// if (!"".equals(f))
// dir += f.replace("/", File.separator) + File.separator;
// return new File(dir);
// }
//
// public static File getFolder(String... folder) {
// File dir = getFolderPath(folder);
//
// if (!dir.exists())
// dir.mkdirs();
//
// return dir;
// }
//
// public static File getFile(String... uri) {
// String[] dir = new String[uri.length - 1];
// for (int i = 0; i < uri.length - 1; i++)
// dir[i] = uri[i];
//
// return new File(getFolder(dir).toString() + File.separator + uri[uri.length - 1]);
// }
//
// public static File getFile(File folder, String... uri) {
// String path = folder.toString();
// for (int i = 0; i < uri.length - 1; i++)
// path += uri[i].replace("/", File.separator) + File.separator;
//
// return new File(path + File.separator + uri[uri.length - 1]);
// }
//
// public static void delete(String folder, String location) {
// delete(getFolder(folder, location));
// }
//
// public static void delete(String folder) {
// delete(getFolder(folder));
// }
//
// public static void delete(File folder) {
// for (File f : folder.listFiles()) {
// if (f.isDirectory())
// delete(f);
// else
// f.delete();
// }
// folder.delete();
// }
// }
| import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import java.util.ArrayList;
import java.util.List;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.Filesystem;
import org.junit.Test;
| package no.difi.datahotel.model;
public class FieldsTest extends BaseTest {
@Test
public void testSaveRead() throws Exception {
Fields f = new Fields();
List<Field> fields = new ArrayList<Field>();
fields.add(new Field("id", false));
fields.add(new Field("name", true));
f.setFields(fields);
f.save("difi", "test", "people");
| // Path: src/test/java/no/difi/datahotel/BaseTest.java
// public class BaseTest {
//
// @BeforeClass
// public static void beforeClass() throws Exception {
// System.setProperty("datahotel.home", new File(ChunkBeanTest.class.getResource("/").toURI()).toString() + File.separator + "datahotel");
// }
// }
//
// Path: src/main/java/no/difi/datahotel/util/Filesystem.java
// public class Filesystem {
//
// private static final String HOME = "datahotel";
//
// public static final String FOLDER_MASTER = "master";
// public static final String FOLDER_MASTER_DEFINITION = FOLDER_MASTER + File.separator + "definition";
// public static final String FOLDER_SLAVE = "slave";
// public static final String FOLDER_CACHE = "cache";
// public static final String FOLDER_CACHE_CHUNK = FOLDER_CACHE + File.separator + "chunk";
// public static final String FOLDER_CACHE_INDEX = FOLDER_CACHE + File.separator + "index";
//
// public static final String FILE_DATASET = "dataset.csv";
// public static final String FILE_DATASET_ORIGINAL = "original.csv";
// public static final String FILE_FIELDS = "fields.xml";
// public static final String FILE_DEFINITIONS = "definitions.xml";
// public static final String FILE_METADATA = "meta.xml";
// public static final String FILE_VERSION = "version.xml";
//
// private static String home;
//
// public static String getHome() {
// if (home != null)
// return home;
//
// String dir;
//
// try {
// Context initCtx = new InitialContext();
// Context envCtx = (Context) initCtx.lookup("java:comp/env");
//
// dir = envCtx.lookup("datahotel.home") + File.separator;
// } catch (NamingException e) {
// if (System.getProperty("datahotel.home") != null)
// dir = System.getProperty("datahotel.home") + File.separator;
// else
// dir = System.getProperty("user.home") + File.separator + HOME + File.separator;
// }
//
// dir = dir.replace(File.separator + File.separator, File.separator);
// new File(dir).mkdirs();
//
// home = dir;
// return dir;
// }
//
// public static File getFolderPath(String... folder) {
// String dir = getHome();
// for (String f : folder)
// if (!"".equals(f))
// dir += f.replace("/", File.separator) + File.separator;
// return new File(dir);
// }
//
// public static File getFolder(String... folder) {
// File dir = getFolderPath(folder);
//
// if (!dir.exists())
// dir.mkdirs();
//
// return dir;
// }
//
// public static File getFile(String... uri) {
// String[] dir = new String[uri.length - 1];
// for (int i = 0; i < uri.length - 1; i++)
// dir[i] = uri[i];
//
// return new File(getFolder(dir).toString() + File.separator + uri[uri.length - 1]);
// }
//
// public static File getFile(File folder, String... uri) {
// String path = folder.toString();
// for (int i = 0; i < uri.length - 1; i++)
// path += uri[i].replace("/", File.separator) + File.separator;
//
// return new File(path + File.separator + uri[uri.length - 1]);
// }
//
// public static void delete(String folder, String location) {
// delete(getFolder(folder, location));
// }
//
// public static void delete(String folder) {
// delete(getFolder(folder));
// }
//
// public static void delete(File folder) {
// for (File f : folder.listFiles()) {
// if (f.isDirectory())
// delete(f);
// else
// f.delete();
// }
// folder.delete();
// }
// }
// Path: src/test/java/no/difi/datahotel/model/FieldsTest.java
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import java.util.ArrayList;
import java.util.List;
import no.difi.datahotel.BaseTest;
import no.difi.datahotel.util.Filesystem;
import org.junit.Test;
package no.difi.datahotel.model;
public class FieldsTest extends BaseTest {
@Test
public void testSaveRead() throws Exception {
Fields f = new Fields();
List<Field> fields = new ArrayList<Field>();
fields.add(new Field("id", false));
fields.add(new Field("name", true));
f.setFields(fields);
f.save("difi", "test", "people");
| assertTrue(Filesystem.getFile(Filesystem.FOLDER_SLAVE, "difi", "test", "people", Filesystem.FILE_FIELDS).exists());
|
difi/datahotel | src/main/java/no/difi/datahotel/resources/DatahotelExceptionMapper.java | // Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
| import no.difi.datahotel.util.DatahotelException;
import org.springframework.stereotype.Component;
import javax.ws.rs.core.Response;
import javax.ws.rs.ext.ExceptionMapper;
import javax.ws.rs.ext.Provider; | package no.difi.datahotel.resources;
@Provider
@Component | // Path: src/main/java/no/difi/datahotel/util/DatahotelException.java
// @SuppressWarnings("serial")
// public class DatahotelException extends RuntimeException {
//
// private int status = 500;
// private Formater formater = JSON;
//
// public DatahotelException(String message) {
// super(message);
// }
//
// public DatahotelException(int status, String message) {
// super(message);
// this.status = status;
// }
//
// public int getStatus() {
// return status;
// }
//
// public Formater getFormater() {
// return formater;
// }
//
// public DatahotelException setFormater(Formater formater) {
// this.formater = formater;
// return this;
// }
// }
// Path: src/main/java/no/difi/datahotel/resources/DatahotelExceptionMapper.java
import no.difi.datahotel.util.DatahotelException;
import org.springframework.stereotype.Component;
import javax.ws.rs.core.Response;
import javax.ws.rs.ext.ExceptionMapper;
import javax.ws.rs.ext.Provider;
package no.difi.datahotel.resources;
@Provider
@Component | public class DatahotelExceptionMapper implements ExceptionMapper<DatahotelException> { |
difi/datahotel | src/main/java/no/difi/datahotel/util/formater/JSONPFormater.java | // Path: src/main/java/no/difi/datahotel/util/RequestContext.java
// public class RequestContext {
//
// private int page = 1;
// private String query = null;
// private Map<String, String> lookup = new HashMap<String, String>();
// private String callback;
//
// public RequestContext() {
//
// }
//
// public RequestContext(UriInfo uriInfo) {
// this(uriInfo, null);
// }
//
// public RequestContext(UriInfo uriInfo, List<FieldLight> fields) {
// MultivaluedMap<String, String> parameters = uriInfo.getQueryParameters();
//
// if (fields != null)
// for (FieldLight f : fields)
// if (f.getGroupable())
// if (parameters.containsKey(f.getShortName()))
// if (!"".equals(parameters.getFirst(f.getShortName())))
// lookup.put(f.getShortName(), parameters.getFirst(f.getShortName()));
//
// if (parameters.containsKey("query"))
// if (!"".equals(parameters.getFirst("query")))
// query = parameters.getFirst("query");
//
// if (parameters.containsKey("callback"))
// if (!"".equals(parameters.getFirst("callback")))
// callback = parameters.getFirst("callback");
//
// if (parameters.containsKey("page"))
// if (!"".equals(parameters.getFirst("page")))
// page = Integer.parseInt(parameters.getFirst("page"));
// }
//
// public int getPage() {
// return page;
// }
//
// public String getQuery() {
// return query;
// }
//
// public Map<String, String> getLookup() {
// return lookup;
// }
//
// public String getCallback() {
// return callback;
// }
//
// @Deprecated
// public void setCallback(String callback) {
// this.callback = callback;
// }
//
// public boolean isSearch() {
// return query != null || lookup.size() > 0;
// }
// }
| import no.difi.datahotel.util.RequestContext;
| package no.difi.datahotel.util.formater;
/**
* Class representing an JSONP.
*/
public class JSONPFormater extends JSONFormater {
@Override
| // Path: src/main/java/no/difi/datahotel/util/RequestContext.java
// public class RequestContext {
//
// private int page = 1;
// private String query = null;
// private Map<String, String> lookup = new HashMap<String, String>();
// private String callback;
//
// public RequestContext() {
//
// }
//
// public RequestContext(UriInfo uriInfo) {
// this(uriInfo, null);
// }
//
// public RequestContext(UriInfo uriInfo, List<FieldLight> fields) {
// MultivaluedMap<String, String> parameters = uriInfo.getQueryParameters();
//
// if (fields != null)
// for (FieldLight f : fields)
// if (f.getGroupable())
// if (parameters.containsKey(f.getShortName()))
// if (!"".equals(parameters.getFirst(f.getShortName())))
// lookup.put(f.getShortName(), parameters.getFirst(f.getShortName()));
//
// if (parameters.containsKey("query"))
// if (!"".equals(parameters.getFirst("query")))
// query = parameters.getFirst("query");
//
// if (parameters.containsKey("callback"))
// if (!"".equals(parameters.getFirst("callback")))
// callback = parameters.getFirst("callback");
//
// if (parameters.containsKey("page"))
// if (!"".equals(parameters.getFirst("page")))
// page = Integer.parseInt(parameters.getFirst("page"));
// }
//
// public int getPage() {
// return page;
// }
//
// public String getQuery() {
// return query;
// }
//
// public Map<String, String> getLookup() {
// return lookup;
// }
//
// public String getCallback() {
// return callback;
// }
//
// @Deprecated
// public void setCallback(String callback) {
// this.callback = callback;
// }
//
// public boolean isSearch() {
// return query != null || lookup.size() > 0;
// }
// }
// Path: src/main/java/no/difi/datahotel/util/formater/JSONPFormater.java
import no.difi.datahotel.util.RequestContext;
package no.difi.datahotel.util.formater;
/**
* Class representing an JSONP.
*/
public class JSONPFormater extends JSONFormater {
@Override
| public String format(Object object, RequestContext context) {
|
kijiproject/kiji-mapreduce | kiji-mapreduce/src/main/java/org/kiji/mapreduce/tools/KijiPivot.java | // Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/MapReduceJobOutput.java
// @ApiAudience.Public
// @ApiStability.Stable
// @Inheritance.Sealed
// public abstract class MapReduceJobOutput {
// /**
// * Initializes the job output from command-line parameters.
// *
// * @param params Initialization parameters.
// * @throws IOException on I/O error.
// */
// public abstract void initialize(Map<String, String> params) throws IOException;
//
// /**
// * Configures the output for a MapReduce job.
// *
// * @param job The job to configure.
// * @throws IOException If there is an error.
// */
// public void configure(Job job) throws IOException {
// job.setOutputFormatClass(getOutputFormatClass());
// }
//
// /**
// * Gets the Hadoop MapReduce output format class.
// *
// * @return The job output format class.
// */
// protected abstract Class<? extends OutputFormat> getOutputFormatClass();
// }
| import java.io.IOException;
import java.util.List;
import com.google.common.base.Preconditions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.kiji.annotations.ApiAudience;
import org.kiji.common.flags.Flag;
import org.kiji.mapreduce.MapReduceJobInput;
import org.kiji.mapreduce.MapReduceJobOutput;
import org.kiji.mapreduce.input.KijiTableMapReduceJobInput;
import org.kiji.mapreduce.output.DirectKijiTableMapReduceJobOutput;
import org.kiji.mapreduce.output.HFileMapReduceJobOutput;
import org.kiji.mapreduce.output.KijiTableMapReduceJobOutput;
import org.kiji.mapreduce.pivot.KijiPivotJobBuilder;
import org.kiji.mapreduce.pivot.impl.KijiPivoters;
import org.kiji.mapreduce.tools.framework.KijiJobTool;
import org.kiji.mapreduce.tools.framework.MapReduceJobInputFactory;
import org.kiji.mapreduce.tools.framework.MapReduceJobOutputFactory;
import org.kiji.schema.Kiji;
import org.kiji.schema.KijiTable;
import org.kiji.schema.tools.KijiToolLauncher;
import org.kiji.schema.tools.RequiredFlagException;
import org.kiji.schema.util.ResourceUtils; | /**
* (c) Copyright 2012 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.tools;
/**
* Runs a KijiPivoter a Kiji table.
*
* <p>
* Here is an example of command-line to launch a KijiPivoter named {@code package.SomePivoter}
* on the rows from an input table {@code kiji://.env/input_instance/input_table}
* while writing cells to another output table {@code kiji://.env/output_instance/output_table}:
* <pre><blockquote>
* $ kiji pivot \
* --pivoter='package.SomePivoter' \
* --input="format=kiji table=kiji://.env/default/input_table" \
* --output="format=kiji table=kiji://.env/default/output_table nsplits=5" \
* --lib=/path/to/libdir/
* </blockquote></pre>
* </p>
*/
@ApiAudience.Private
public final class KijiPivot extends KijiJobTool<KijiPivotJobBuilder> {
private static final Logger LOG = LoggerFactory.getLogger(KijiPivot.class);
@Flag(name="pivoter",
usage="KijiPivoter class to run over the table.")
private String mPivoter= "";
/** {@inheritDoc} */
@Override
public String getName() {
return "pivot";
}
/** {@inheritDoc} */
@Override
public String getDescription() {
return "Run a pivoter over a Kiji table.";
}
/** {@inheritDoc} */
@Override
public String getCategory() {
return "MapReduce";
}
/** Kiji instance where the output table lives. */
private Kiji mKiji;
/** KijiTable to import data into. */
private KijiTable mTable;
/** Job output. */
private KijiTableMapReduceJobOutput mOutput;
/** {@inheritDoc} */
@Override
protected void validateFlags() throws Exception {
// Parse --input and --output flags:
// --input is guaranteed to be a Kiji table, --output is not.
super.validateFlags();
if (mInputFlag.isEmpty()) {
throw new RequiredFlagException("input");
}
if (mPivoter.isEmpty()) {
throw new RequiredFlagException("pivoter");
}
if (mOutputFlag.isEmpty()) {
throw new RequiredFlagException("output");
}
| // Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/MapReduceJobOutput.java
// @ApiAudience.Public
// @ApiStability.Stable
// @Inheritance.Sealed
// public abstract class MapReduceJobOutput {
// /**
// * Initializes the job output from command-line parameters.
// *
// * @param params Initialization parameters.
// * @throws IOException on I/O error.
// */
// public abstract void initialize(Map<String, String> params) throws IOException;
//
// /**
// * Configures the output for a MapReduce job.
// *
// * @param job The job to configure.
// * @throws IOException If there is an error.
// */
// public void configure(Job job) throws IOException {
// job.setOutputFormatClass(getOutputFormatClass());
// }
//
// /**
// * Gets the Hadoop MapReduce output format class.
// *
// * @return The job output format class.
// */
// protected abstract Class<? extends OutputFormat> getOutputFormatClass();
// }
// Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/tools/KijiPivot.java
import java.io.IOException;
import java.util.List;
import com.google.common.base.Preconditions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.kiji.annotations.ApiAudience;
import org.kiji.common.flags.Flag;
import org.kiji.mapreduce.MapReduceJobInput;
import org.kiji.mapreduce.MapReduceJobOutput;
import org.kiji.mapreduce.input.KijiTableMapReduceJobInput;
import org.kiji.mapreduce.output.DirectKijiTableMapReduceJobOutput;
import org.kiji.mapreduce.output.HFileMapReduceJobOutput;
import org.kiji.mapreduce.output.KijiTableMapReduceJobOutput;
import org.kiji.mapreduce.pivot.KijiPivotJobBuilder;
import org.kiji.mapreduce.pivot.impl.KijiPivoters;
import org.kiji.mapreduce.tools.framework.KijiJobTool;
import org.kiji.mapreduce.tools.framework.MapReduceJobInputFactory;
import org.kiji.mapreduce.tools.framework.MapReduceJobOutputFactory;
import org.kiji.schema.Kiji;
import org.kiji.schema.KijiTable;
import org.kiji.schema.tools.KijiToolLauncher;
import org.kiji.schema.tools.RequiredFlagException;
import org.kiji.schema.util.ResourceUtils;
/**
* (c) Copyright 2012 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.tools;
/**
* Runs a KijiPivoter a Kiji table.
*
* <p>
* Here is an example of command-line to launch a KijiPivoter named {@code package.SomePivoter}
* on the rows from an input table {@code kiji://.env/input_instance/input_table}
* while writing cells to another output table {@code kiji://.env/output_instance/output_table}:
* <pre><blockquote>
* $ kiji pivot \
* --pivoter='package.SomePivoter' \
* --input="format=kiji table=kiji://.env/default/input_table" \
* --output="format=kiji table=kiji://.env/default/output_table nsplits=5" \
* --lib=/path/to/libdir/
* </blockquote></pre>
* </p>
*/
@ApiAudience.Private
public final class KijiPivot extends KijiJobTool<KijiPivotJobBuilder> {
private static final Logger LOG = LoggerFactory.getLogger(KijiPivot.class);
@Flag(name="pivoter",
usage="KijiPivoter class to run over the table.")
private String mPivoter= "";
/** {@inheritDoc} */
@Override
public String getName() {
return "pivot";
}
/** {@inheritDoc} */
@Override
public String getDescription() {
return "Run a pivoter over a Kiji table.";
}
/** {@inheritDoc} */
@Override
public String getCategory() {
return "MapReduce";
}
/** Kiji instance where the output table lives. */
private Kiji mKiji;
/** KijiTable to import data into. */
private KijiTable mTable;
/** Job output. */
private KijiTableMapReduceJobOutput mOutput;
/** {@inheritDoc} */
@Override
protected void validateFlags() throws Exception {
// Parse --input and --output flags:
// --input is guaranteed to be a Kiji table, --output is not.
super.validateFlags();
if (mInputFlag.isEmpty()) {
throw new RequiredFlagException("input");
}
if (mPivoter.isEmpty()) {
throw new RequiredFlagException("pivoter");
}
if (mOutputFlag.isEmpty()) {
throw new RequiredFlagException("output");
}
| final MapReduceJobOutput mrJobOutput = |
kijiproject/kiji-mapreduce | kiji-mapreduce/src/main/java/org/kiji/mapreduce/gather/impl/GatherMapper.java | // Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/gather/GathererContext.java
// @ApiAudience.Public
// @ApiStability.Stable
// @Inheritance.Sealed
// public interface GathererContext<K, V> extends KijiContext {
// /**
// * Emits a key/value pair.
// *
// * @param key Emit this key.
// * @param value Emit this value.
// * @throws IOException on I/O error.
// */
// void write(K key, V value) throws IOException;
// }
| import org.kiji.mapreduce.avro.AvroValueWriter;
import org.kiji.mapreduce.framework.JobHistoryCounters;
import org.kiji.mapreduce.framework.KijiConfKeys;
import org.kiji.mapreduce.gather.GathererContext;
import org.kiji.mapreduce.gather.KijiGatherer;
import org.kiji.mapreduce.impl.KijiTableMapper;
import org.kiji.schema.KijiDataRequest;
import org.kiji.schema.KijiRowData;
import java.io.IOException;
import com.google.common.base.Preconditions;
import org.apache.avro.Schema;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ReflectionUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.kiji.annotations.ApiAudience;
import org.kiji.mapreduce.avro.AvroKeyWriter; | /**
* (c) Copyright 2012 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.gather.impl;
/**
* Mapper that executes a gatherer over the rows of a Kiji table.
*
* @param <K> The type of the MapReduce output key.
* @param <V> The type of the MapReduce output value.
*/
@ApiAudience.Private
public final class GatherMapper<K, V>
extends KijiTableMapper<K, V>
implements AvroKeyWriter, AvroValueWriter {
private static final Logger LOG = LoggerFactory.getLogger(GatherMapper.class);
/** The gatherer to execute. */
private KijiGatherer<K, V> mGatherer;
/**
* The context object that allows the gatherer to interact with MapReduce,
* KVStores, etc.
*/ | // Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/gather/GathererContext.java
// @ApiAudience.Public
// @ApiStability.Stable
// @Inheritance.Sealed
// public interface GathererContext<K, V> extends KijiContext {
// /**
// * Emits a key/value pair.
// *
// * @param key Emit this key.
// * @param value Emit this value.
// * @throws IOException on I/O error.
// */
// void write(K key, V value) throws IOException;
// }
// Path: kiji-mapreduce/src/main/java/org/kiji/mapreduce/gather/impl/GatherMapper.java
import org.kiji.mapreduce.avro.AvroValueWriter;
import org.kiji.mapreduce.framework.JobHistoryCounters;
import org.kiji.mapreduce.framework.KijiConfKeys;
import org.kiji.mapreduce.gather.GathererContext;
import org.kiji.mapreduce.gather.KijiGatherer;
import org.kiji.mapreduce.impl.KijiTableMapper;
import org.kiji.schema.KijiDataRequest;
import org.kiji.schema.KijiRowData;
import java.io.IOException;
import com.google.common.base.Preconditions;
import org.apache.avro.Schema;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ReflectionUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.kiji.annotations.ApiAudience;
import org.kiji.mapreduce.avro.AvroKeyWriter;
/**
* (c) Copyright 2012 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.gather.impl;
/**
* Mapper that executes a gatherer over the rows of a Kiji table.
*
* @param <K> The type of the MapReduce output key.
* @param <V> The type of the MapReduce output value.
*/
@ApiAudience.Private
public final class GatherMapper<K, V>
extends KijiTableMapper<K, V>
implements AvroKeyWriter, AvroValueWriter {
private static final Logger LOG = LoggerFactory.getLogger(GatherMapper.class);
/** The gatherer to execute. */
private KijiGatherer<K, V> mGatherer;
/**
* The context object that allows the gatherer to interact with MapReduce,
* KVStores, etc.
*/ | private GathererContext<K, V> mGathererContext; |
kijiproject/kiji-mapreduce | kiji-mapreduce/src/test/java/org/kiji/mapreduce/input/TestKijiTableMapReduceJobInput.java | // Path: kiji-mapreduce/src/test/java/org/kiji/mapreduce/KijiMRTestLayouts.java
// public final class KijiMRTestLayouts {
//
// /**
// * Alias for KijiTableLayouts.getLayout(resource).
// *
// * @param resourcePath Path of the resource to load the JSON descriptor from.
// * @return the decoded TableLayoutDesc.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getLayout(String resourcePath) throws IOException {
// return KijiTableLayouts.getLayout(resourcePath);
// }
//
// /** Generic test layout with user info, all primitive types, a map-type family of strings. */
// public static final String TEST_LAYOUT = "org/kiji/mapreduce/layout/test.json";
// /** Multiple locality group test layout. */
// public static final String LG_TEST_LAYOUT = "org/kiji/mapreduce/layout/lgtest.json";
//
// /**
// * @return a generic test layout.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getTestLayout() throws IOException {
// return getLayout(TEST_LAYOUT);
// }
//
// /**
// * @param tableName For the table name in the layout.
// * @return a generic test layout.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getTestLayout(String tableName) throws IOException {
// final TableLayoutDesc desc = getLayout(TEST_LAYOUT);
// desc.setName(tableName);
// return desc;
// }
//
// /** Utility class cannot be instantiated. */
// private KijiMRTestLayouts() {
// }
// }
| import static org.junit.Assert.assertEquals;
import java.io.File;
import java.io.IOException;
import org.apache.commons.codec.binary.Base64;
import org.apache.commons.io.FileUtils;
import org.apache.commons.lang.SerializationUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.mapreduce.Job;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import org.kiji.mapreduce.KijiMRTestLayouts;
import org.kiji.mapreduce.MapReduceJobInput;
import org.kiji.mapreduce.framework.KijiConfKeys;
import org.kiji.schema.EntityId;
import org.kiji.schema.HBaseEntityId;
import org.kiji.schema.KijiClientTest;
import org.kiji.schema.KijiDataRequest;
import org.kiji.schema.KijiDataRequestBuilder;
import org.kiji.schema.KijiTable;
import org.kiji.schema.KijiURI;
import org.kiji.schema.filter.KijiRowFilter;
import org.kiji.schema.filter.StripValueRowFilter;
import org.kiji.schema.util.ResourceUtils;
import org.kiji.schema.util.TestingFileUtils; | /**
* (c) Copyright 2013 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.input;
public class TestKijiTableMapReduceJobInput extends KijiClientTest {
private File mTempDir;
private Path mTempPath;
private KijiTable mTable;
@Before
public void setUp() throws Exception {
mTempDir = TestingFileUtils.createTempDir("test", "dir");
mTempPath = new Path("file://" + mTempDir);
getConf().set("fs.defaultFS", mTempPath.toString());
getConf().set("fs.default.name", mTempPath.toString()); | // Path: kiji-mapreduce/src/test/java/org/kiji/mapreduce/KijiMRTestLayouts.java
// public final class KijiMRTestLayouts {
//
// /**
// * Alias for KijiTableLayouts.getLayout(resource).
// *
// * @param resourcePath Path of the resource to load the JSON descriptor from.
// * @return the decoded TableLayoutDesc.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getLayout(String resourcePath) throws IOException {
// return KijiTableLayouts.getLayout(resourcePath);
// }
//
// /** Generic test layout with user info, all primitive types, a map-type family of strings. */
// public static final String TEST_LAYOUT = "org/kiji/mapreduce/layout/test.json";
// /** Multiple locality group test layout. */
// public static final String LG_TEST_LAYOUT = "org/kiji/mapreduce/layout/lgtest.json";
//
// /**
// * @return a generic test layout.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getTestLayout() throws IOException {
// return getLayout(TEST_LAYOUT);
// }
//
// /**
// * @param tableName For the table name in the layout.
// * @return a generic test layout.
// * @throws IOException on I/O error.
// */
// public static TableLayoutDesc getTestLayout(String tableName) throws IOException {
// final TableLayoutDesc desc = getLayout(TEST_LAYOUT);
// desc.setName(tableName);
// return desc;
// }
//
// /** Utility class cannot be instantiated. */
// private KijiMRTestLayouts() {
// }
// }
// Path: kiji-mapreduce/src/test/java/org/kiji/mapreduce/input/TestKijiTableMapReduceJobInput.java
import static org.junit.Assert.assertEquals;
import java.io.File;
import java.io.IOException;
import org.apache.commons.codec.binary.Base64;
import org.apache.commons.io.FileUtils;
import org.apache.commons.lang.SerializationUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.mapreduce.Job;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import org.kiji.mapreduce.KijiMRTestLayouts;
import org.kiji.mapreduce.MapReduceJobInput;
import org.kiji.mapreduce.framework.KijiConfKeys;
import org.kiji.schema.EntityId;
import org.kiji.schema.HBaseEntityId;
import org.kiji.schema.KijiClientTest;
import org.kiji.schema.KijiDataRequest;
import org.kiji.schema.KijiDataRequestBuilder;
import org.kiji.schema.KijiTable;
import org.kiji.schema.KijiURI;
import org.kiji.schema.filter.KijiRowFilter;
import org.kiji.schema.filter.StripValueRowFilter;
import org.kiji.schema.util.ResourceUtils;
import org.kiji.schema.util.TestingFileUtils;
/**
* (c) Copyright 2013 WibiData, Inc.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.kiji.mapreduce.input;
public class TestKijiTableMapReduceJobInput extends KijiClientTest {
private File mTempDir;
private Path mTempPath;
private KijiTable mTable;
@Before
public void setUp() throws Exception {
mTempDir = TestingFileUtils.createTempDir("test", "dir");
mTempPath = new Path("file://" + mTempDir);
getConf().set("fs.defaultFS", mTempPath.toString());
getConf().set("fs.default.name", mTempPath.toString()); | getKiji().createTable(KijiMRTestLayouts.getTestLayout()); |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/generic/GenericJSONClient.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import java.nio.charset.Charset;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.json.JSONException;
import org.json.JSONObject;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.net.HttpURLConnection;
import java.net.URL;
import java.net.URLConnection; | /*
* Copyright (c) 2005-2011, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* WSO2 Inc. licenses this file to you under the Apache License,
* Version 2.0 (the "License"); you may not use this file except
* in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*
*/
package org.wso2.carbon.automation.test.utils.generic;
public class GenericJSONClient {
public static final Log log = LogFactory.getLog(GenericJSONClient.class);
public static final String HEADER_CONTENT_TYPE = "Content-Type";
public static final String HEADER_ACCEPT_CHARSET = "Accept-Charset";
| // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/generic/GenericJSONClient.java
import java.nio.charset.Charset;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.json.JSONException;
import org.json.JSONObject;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.net.HttpURLConnection;
import java.net.URL;
import java.net.URLConnection;
/*
* Copyright (c) 2005-2011, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* WSO2 Inc. licenses this file to you under the Apache License,
* Version 2.0 (the "License"); you may not use this file except
* in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*
*/
package org.wso2.carbon.automation.test.utils.generic;
public class GenericJSONClient {
public static final Log log = LogFactory.getLog(GenericJSONClient.class);
public static final String HEADER_CONTENT_TYPE = "Content-Type";
public static final String HEADER_ACCEPT_CHARSET = "Accept-Charset";
| public JSONObject doGet(String endpoint, String query, String contentType) throws AutomationFrameworkException, IOException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/JMeterTestManager.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.jmeter.JMeter;
import org.apache.jmeter.engine.StandardJMeterEngine;
import org.apache.jmeter.util.ShutdownClient;
import org.w3c.dom.Document;
import org.w3c.dom.Element;
import org.w3c.dom.Node;
import org.w3c.dom.NodeList;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.xml.sax.SAXException;
import javax.xml.parsers.DocumentBuilder;
import javax.xml.parsers.DocumentBuilderFactory;
import javax.xml.parsers.ParserConfigurationException;
import javax.xml.stream.XMLInputFactory;
import javax.xml.stream.XMLStreamException;
import javax.xml.stream.XMLStreamReader;
import java.io.*;
import java.lang.Thread.UncaughtExceptionHandler;
import java.nio.charset.Charset;
import java.text.DateFormat;
import java.text.SimpleDateFormat;
import java.util.*;
import java.util.regex.Pattern; | /*
* Copyright (c) 2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.wso2.carbon.automation.extensions.jmeter;
public class JMeterTestManager {
private static final Pattern PAT_ERROR = Pattern.compile(".*\\s+ERROR\\s+.*");
private static final Log log = LogFactory.getLog(JMeterTestManager.class);
private String jmeterLogLevel = "INFO";
private File testFile = null;
private File jmeterHome = null;
private File jmeterLogFile = null;
private DateFormat fmt = new SimpleDateFormat("yyMMdd-HH-mm-ss");
private File jmeterProps = null;
public void runTest(JMeterTest jMeterTest) | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/JMeterTestManager.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.jmeter.JMeter;
import org.apache.jmeter.engine.StandardJMeterEngine;
import org.apache.jmeter.util.ShutdownClient;
import org.w3c.dom.Document;
import org.w3c.dom.Element;
import org.w3c.dom.Node;
import org.w3c.dom.NodeList;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.xml.sax.SAXException;
import javax.xml.parsers.DocumentBuilder;
import javax.xml.parsers.DocumentBuilderFactory;
import javax.xml.parsers.ParserConfigurationException;
import javax.xml.stream.XMLInputFactory;
import javax.xml.stream.XMLStreamException;
import javax.xml.stream.XMLStreamReader;
import java.io.*;
import java.lang.Thread.UncaughtExceptionHandler;
import java.nio.charset.Charset;
import java.text.DateFormat;
import java.text.SimpleDateFormat;
import java.util.*;
import java.util.regex.Pattern;
/*
* Copyright (c) 2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.wso2.carbon.automation.extensions.jmeter;
public class JMeterTestManager {
private static final Pattern PAT_ERROR = Pattern.compile(".*\\s+ERROR\\s+.*");
private static final Log log = LogFactory.getLog(JMeterTestManager.class);
private String jmeterLogLevel = "INFO";
private File testFile = null;
private File jmeterHome = null;
private File jmeterLogFile = null;
private DateFormat fmt = new SimpleDateFormat("yyMMdd-HH-mm-ss");
private File jmeterProps = null;
public void runTest(JMeterTest jMeterTest) | throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/context/beans/User.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
| import org.wso2.carbon.automation.engine.FrameworkConstants;
import java.util.ArrayList;
import java.util.List; | }
public String getKey() {
return key;
}
public String getUserName() {
return userName;
}
public String getPassword() {
return password;
}
public void setPassword(String password) {
this.password = password;
}
public String getUserNameWithoutDomain() {
if (userName.contains("@")) {
return userName.substring(0, userName.lastIndexOf("@"));
} else {
return userName;
}
}
public String getUserDomain() {
if(userName.contains("@")) {
return userName.substring(userName.lastIndexOf("@") + 1);
} else { | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/context/beans/User.java
import org.wso2.carbon.automation.engine.FrameworkConstants;
import java.util.ArrayList;
import java.util.List;
}
public String getKey() {
return key;
}
public String getUserName() {
return userName;
}
public String getPassword() {
return password;
}
public void setPassword(String password) {
this.password = password;
}
public String getUserNameWithoutDomain() {
if (userName.contains("@")) {
return userName.substring(0, userName.lastIndexOf("@"));
} else {
return userName;
}
}
public String getUserDomain() {
if(userName.contains("@")) {
return userName.substring(userName.lastIndexOf("@") + 1);
} else { | return FrameworkConstants.SUPER_TENANT_DOMAIN_NAME; |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSTopicMessagePublisher.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.Properties; | public void disconnect() {
if (publisher != null) {
try {
publisher.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* this will publish the message to given topic
*
* @param messageContent
* @throws AutomationFrameworkException
*/ | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSTopicMessagePublisher.java
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.Properties;
public void disconnect() {
if (publisher != null) {
try {
publisher.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* this will publish the message to given topic
*
* @param messageContent
* @throws AutomationFrameworkException
*/ | public void publish(String messageContent) throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/http/client/HttpRequestUtil.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.*;
import java.net.HttpURLConnection;
import java.net.ProtocolException;
import java.net.URL;
import java.nio.charset.Charset;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map; |
Iterator<String> itr = conn.getHeaderFields().keySet().iterator();
Map<String, String> headers = new HashMap();
while (itr.hasNext()) {
String key = itr.next();
if (key != null) {
headers.put(key, conn.getHeaderField(key));
}
}
return new HttpResponse(sb.toString(), conn.getResponseCode(), headers);
} finally {
if (conn != null) {
conn.disconnect();
}
}
}
/**
* Reads data from the data reader and posts it to a server via POST request.
* data - The data you want to send
* endpoint - The server's address
* output - writes the server's response to output
*
* @param data Data to be sent
* @param endpoint The endpoint to which the data has to be POSTed
* @param output Output
* @throws AutomationFrameworkException If an error occurs while POSTing
*/ | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/http/client/HttpRequestUtil.java
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.*;
import java.net.HttpURLConnection;
import java.net.ProtocolException;
import java.net.URL;
import java.nio.charset.Charset;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
Iterator<String> itr = conn.getHeaderFields().keySet().iterator();
Map<String, String> headers = new HashMap();
while (itr.hasNext()) {
String key = itr.next();
if (key != null) {
headers.put(key, conn.getHeaderField(key));
}
}
return new HttpResponse(sb.toString(), conn.getResponseCode(), headers);
} finally {
if (conn != null) {
conn.disconnect();
}
}
}
/**
* Reads data from the data reader and posts it to a server via POST request.
* data - The data you want to send
* endpoint - The server's address
* output - writes the server's response to output
*
* @param data Data to be sent
* @param endpoint The endpoint to which the data has to be POSTed
* @param output Output
* @throws AutomationFrameworkException If an error occurs while POSTing
*/ | public static void sendPostRequest(Reader data, URL endpoint, Writer output) throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSTopicMessageConsumer.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties; | public void stopConsuming() {
if (consumer != null) {
try {
consumer.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* this will returns all the message received from the topic after the subscription
*
* @return
* @throws AutomationFrameworkException
*/ | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSTopicMessageConsumer.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
public void stopConsuming() {
if (consumer != null) {
try {
consumer.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* this will returns all the message received from the topic after the subscription
*
* @return
* @throws AutomationFrameworkException
*/ | public List<String> getMessages() throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/JMeterInstallationProvider.java | // Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/util/Utils.java
// public class Utils {
// public static void copyFromClassPath(String fileName, File destination) throws IOException {
//
// BufferedWriter out = null;
// try {
//
// out = new BufferedWriter
// (new OutputStreamWriter(new FileOutputStream(destination), Charset.defaultCharset()));
//
// IOUtils.copy(Thread.currentThread().getContextClassLoader().getResourceAsStream(fileName), out);
//
//
// } catch (IOException e) {
// throw new IOException("Could not create temporary saveservice.properties", e);
// } finally {
// if (out != null) {
// out.flush();
// out.close();
// }
// }
// }
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.extensions.jmeter.util.Utils;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Properties; | //creating jmeter directory
jMeterHome = new File(targetDir, "jmeter");
if (!jMeterHome.mkdirs()) {
log.error("Unable to create jmeter directory");
throw new RuntimeException("Unable to create jmeter directory");
}
reportDir = new File(jMeterHome, "reports");
// now create lib dir for jmeter fallback mode
libDir = new File(jMeterHome + File.separator + "lib");
createLibDirectory(libDir);
binDir = new File(jMeterHome + File.separator + "bin");
if (!binDir.exists()) {
if (!binDir.mkdirs()) {
log.error("unable to create bin directory");
throw new RuntimeException("unable to create bin dir for Jmeter");
}
}
//saving properties file in bin directory
saveServiceProps = new File(binDir, "saveservice.properties");
upgradeProps = new File(binDir, "upgrade.properties");
jmeterPropertyFile = new File(binDir, "jmeter.properties");
jmeterPropertyFileTemp = new File(binDir, "jmeterTemp.properties");
//copying saveservice.properties from classpath
try { | // Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/util/Utils.java
// public class Utils {
// public static void copyFromClassPath(String fileName, File destination) throws IOException {
//
// BufferedWriter out = null;
// try {
//
// out = new BufferedWriter
// (new OutputStreamWriter(new FileOutputStream(destination), Charset.defaultCharset()));
//
// IOUtils.copy(Thread.currentThread().getContextClassLoader().getResourceAsStream(fileName), out);
//
//
// } catch (IOException e) {
// throw new IOException("Could not create temporary saveservice.properties", e);
// } finally {
// if (out != null) {
// out.flush();
// out.close();
// }
// }
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/jmeter/JMeterInstallationProvider.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.extensions.jmeter.util.Utils;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Properties;
//creating jmeter directory
jMeterHome = new File(targetDir, "jmeter");
if (!jMeterHome.mkdirs()) {
log.error("Unable to create jmeter directory");
throw new RuntimeException("Unable to create jmeter directory");
}
reportDir = new File(jMeterHome, "reports");
// now create lib dir for jmeter fallback mode
libDir = new File(jMeterHome + File.separator + "lib");
createLibDirectory(libDir);
binDir = new File(jMeterHome + File.separator + "bin");
if (!binDir.exists()) {
if (!binDir.mkdirs()) {
log.error("unable to create bin directory");
throw new RuntimeException("unable to create bin dir for Jmeter");
}
}
//saving properties file in bin directory
saveServiceProps = new File(binDir, "saveservice.properties");
upgradeProps = new File(binDir, "upgrade.properties");
jmeterPropertyFile = new File(binDir, "jmeter.properties");
jmeterPropertyFileTemp = new File(binDir, "jmeterTemp.properties");
//copying saveservice.properties from classpath
try { | Utils.copyFromClassPath("bin/saveservice.properties", saveServiceProps); |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
//
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/enums/OperatingSystems.java
// public enum OperatingSystems {
// WINDOWS,
// MAC,
// UNIX,
// SOLARIS
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.FrameworkConstants;
import org.wso2.carbon.automation.engine.frameworkutils.enums.OperatingSystems;
import java.io.File; | /*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
public class FrameworkPathUtil {
public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
public static String getSystemResourceLocation() {
String resourceLocation; | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
//
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/enums/OperatingSystems.java
// public enum OperatingSystems {
// WINDOWS,
// MAC,
// UNIX,
// SOLARIS
// }
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.FrameworkConstants;
import org.wso2.carbon.automation.engine.frameworkutils.enums.OperatingSystems;
import java.io.File;
/*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
public class FrameworkPathUtil {
public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
public static String getSystemResourceLocation() {
String resourceLocation; | if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME) |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
//
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/enums/OperatingSystems.java
// public enum OperatingSystems {
// WINDOWS,
// MAC,
// UNIX,
// SOLARIS
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.FrameworkConstants;
import org.wso2.carbon.automation.engine.frameworkutils.enums.OperatingSystems;
import java.io.File; | /*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
public class FrameworkPathUtil {
public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
public static String getSystemResourceLocation() {
String resourceLocation;
if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME) | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/FrameworkConstants.java
// public class FrameworkConstants {
// public static final String SYSTEM_PROPERTY_SETTINGS_LOCATION = "automation.settings.location";
// public static final String SYSTEM_PROPERTY_BASEDIR_LOCATION = "basedir";
// public static final String SYSTEM_PROPERTY_OS_NAME = "os.name";
// public static final String SYSTEM_PROPERTY_CARBON_ZIP_LOCATION = "carbon.zip";
// public static final String SYSTEM_PROPERTY_SEC_VERIFIER_DIRECTORY = "sec.verifier.dir";
// public static final int DEFAULT_CARBON_PORT_OFFSET = 0;
// public static final String SERVICE_FILE_SEC_VERIFIER = "SecVerifier.aar";
// public static final String LIST_SUPPORTED_DATABASES = "mysql,oracle,derby,h2";
// public static final String SEVER_STARTUP_SCRIPT_NAME = "wso2server";
// public static final String SERVER_STARTUP_PORT_OFFSET_COMMAND = "-DportOffset";
// public static final String SERVER_DEFAULT_HTTPS_PORT = "9443";
// public static final String SERVER_DEFAULT_HTTP_PORT = "9763";
// public static final String SUPER_TENANT_DOMAIN_NAME = "carbon.super";
// public static final String ADMIN_ROLE = "admin";
// public static final String DEFAULT_KEY_STORE = "wso2";
// public static final String TENANT_USAGE_PLAN_DEMO = "demo";
// public static final String AUTOMATION_SCHEMA_NAME = "automationXMLSchema.xsd";
// public static final String LISTENER_INIT_METHOD = "initiate";
// public static final String LISTENER_EXECUTE_METHOD = "execution";
// public static final String DEFAULT_BACKEND_URL = "https://localhost:9443/services/";
// public static final String AUTHENTICATE_ADMIN_SERVICE_NAME = "AuthenticationAdmin";
// public static final String CONFIGURATION_FILE_NAME = "automation.xml";
// public static final String MAPPING_FILE_NAME = "automation_mapping.xsd";
// public static final String DEFAULT_PRODUCT_GROUP = "default.product.group";
// public static final String EXECUTION_MODE = "framework.execution.mode";
// public static final String ENVIRONMENT_STANDALONE = "standalone";
// public static final String ENVIRONMENT_PLATFORM = "platform";
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// public static final String SUPER_TENANT_KEY = "superTenant";
// public static final String SUPER_TENANT_ADMIN = "superAdmin";
// public static final String CARBON_HOME = "carbon.home";
// public static final String JACOCO_AGENT_JAR_NAME = "jacocoagent.jar";
// public static final String CLASS_FILE_PATTERN = "**/*.class";
//
// }
//
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/enums/OperatingSystems.java
// public enum OperatingSystems {
// WINDOWS,
// MAC,
// UNIX,
// SOLARIS
// }
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.FrameworkConstants;
import org.wso2.carbon.automation.engine.frameworkutils.enums.OperatingSystems;
import java.io.File;
/*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
public class FrameworkPathUtil {
public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
public static String getSystemResourceLocation() {
String resourceLocation;
if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME) | .toLowerCase().contains(OperatingSystems.WINDOWS.name().toLowerCase())) { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/axis2client/AxisServiceClientUtils.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.apache.axiom.om.OMAbstractFactory;
import org.apache.axiom.om.OMElement;
import org.apache.axiom.om.OMFactory;
import org.apache.axiom.om.OMNamespace;
import org.apache.axiom.om.util.AXIOMUtil;
import org.apache.axis2.AxisFault;
import org.apache.axis2.addressing.EndpointReference;
import org.apache.axis2.client.Options;
import org.apache.axis2.client.ServiceClient;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import javax.xml.stream.XMLStreamException;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.MalformedURLException;
import java.net.URL;
import java.nio.charset.Charset;
import java.util.List;
import static org.testng.Assert.assertFalse;
import static org.testng.Assert.fail; | } catch (IOException e) {
return false;
} finally {
if (in != null) {
in.close();
}
}
}
return false;
}
public static void waitForServiceDeployment(String serviceUrl) throws IOException {
int serviceTimeOut = 0;
while (!isServiceAvailable(serviceUrl)) {
if (serviceTimeOut == 0) {
} else if (serviceTimeOut > 100) { //Check for the service for 100 seconds
// if Service not available assertfalse;
fail(serviceUrl + " service is not found");
break;
}
try {
Thread.sleep(500);
serviceTimeOut++;
} catch (InterruptedException ignored) {
}
}
}
public static void sendRequest(String eprUrl, String operation, String payload,
int numberOfInstances, List<String> expectedStrings, | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/axis2client/AxisServiceClientUtils.java
import org.apache.axiom.om.OMAbstractFactory;
import org.apache.axiom.om.OMElement;
import org.apache.axiom.om.OMFactory;
import org.apache.axiom.om.OMNamespace;
import org.apache.axiom.om.util.AXIOMUtil;
import org.apache.axis2.AxisFault;
import org.apache.axis2.addressing.EndpointReference;
import org.apache.axis2.client.Options;
import org.apache.axis2.client.ServiceClient;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import javax.xml.stream.XMLStreamException;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.MalformedURLException;
import java.net.URL;
import java.nio.charset.Charset;
import java.util.List;
import static org.testng.Assert.assertFalse;
import static org.testng.Assert.fail;
} catch (IOException e) {
return false;
} finally {
if (in != null) {
in.close();
}
}
}
return false;
}
public static void waitForServiceDeployment(String serviceUrl) throws IOException {
int serviceTimeOut = 0;
while (!isServiceAvailable(serviceUrl)) {
if (serviceTimeOut == 0) {
} else if (serviceTimeOut > 100) { //Check for the service for 100 seconds
// if Service not available assertfalse;
fail(serviceUrl + " service is not found");
break;
}
try {
Thread.sleep(500);
serviceTimeOut++;
} catch (InterruptedException ignored) {
}
}
}
public static void sendRequest(String eprUrl, String operation, String payload,
int numberOfInstances, List<String> expectedStrings, | boolean twoWay) throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/TestCoverageGenerator.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.apache.commons.io.FileUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.File;
import java.io.IOException; | /*
*Copyright (c) 2015, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
/**
* Coverage generator class for multi module test project.
* This class traverse though all Jacoco data dump files in multiple modules and then merge the result
* into one file. This file will be used to generate aggregated coverage report.
*/
public class TestCoverageGenerator {
private static final Log log = LogFactory.getLog(TestCoverageGenerator.class);
private static String carbonZip;
| // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/TestCoverageGenerator.java
import org.apache.commons.io.FileUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import java.io.File;
import java.io.IOException;
/*
*Copyright (c) 2015, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.frameworkutils;
/**
* Coverage generator class for multi module test project.
* This class traverse though all Jacoco data dump files in multiple modules and then merge the result
* into one file. This file will be used to generate aggregated coverage report.
*/
public class TestCoverageGenerator {
private static final Log log = LogFactory.getLog(TestCoverageGenerator.class);
private static String carbonZip;
| public static void main(String[] args) throws AutomationFrameworkException, IOException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/axis2client/ConfigurationContextProvider.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java
// public class FrameworkPathUtil {
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
//
// public static String getSystemResourceLocation() {
// String resourceLocation;
// if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME)
// .toLowerCase().contains(OperatingSystems.WINDOWS.name().toLowerCase())) {
// resourceLocation = System.getProperty
// (SYSTEM_ARTIFACT_RESOURCE_LOCATION).replace("/", "\\");
// } else {
// resourceLocation = System.getProperty
// (SYSTEM_ARTIFACT_RESOURCE_LOCATION).replace("/", "/");
// }
// return resourceLocation;
// }
//
// public static String getSystemSettingsLocation() {
// String settingsLocation;
// if (System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION) != null) {
// if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME)
// .toLowerCase().contains(OperatingSystems.WINDOWS.name().toLowerCase())) {
// settingsLocation = System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION).replace("/", "\\");
// } else {
// settingsLocation = System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION).replace("/", "/");
// }
// } else {
// settingsLocation = getSystemResourceLocation();
// }
// return settingsLocation;
// }
//
// public static String getReportLocation() {
// String reportLocation;
// reportLocation = (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_BASEDIR_LOCATION, ".")) +
// File.separator + "target";
// return reportLocation;
// }
//
// public static String getCarbonZipLocation() {
// return System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_CARBON_ZIP_LOCATION);
// }
//
// public static String getCarbonTempLocation() {
// String extractDir = "carbontmp" + System.currentTimeMillis();
// String baseDir = (System.getProperty("basedir", ".")) + File.separator + "target";
// return new File(baseDir).getAbsolutePath() + File.separator + extractDir;
// }
//
// public static String getCarbonServerAxisServiceDirectory() {
// return getCarbonHome() + File.separator + "repository" + File.separator
// + "deployment" + File.separator + "server" + File.separator + "axis2services";
// }
//
// public static String getCarbonServerLibLocation() {
// return getCarbonHome() + File.separator + "repository" + File.separator + "components" +
// File.separator + "lib";
// }
//
// public static String getCarbonServerConfLocation() {
// return getCarbonHome() + File.separator + "repository" + File.separator + "conf";
// }
//
// public static String getCoverageDirPath() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator +
// "jacoco" + File.separator + "coverage";
// }
//
// public static String getJacocoCoverageHome() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator +
// "jacoco";
// }
//
// public static String getTargetDirectory() {
// return System.getProperty("basedir") + File.separator + "target";
// }
//
// public static String getCoverageDumpFilePath() {
// return getJacocoCoverageHome() + File.separator + "jacoco" + System.currentTimeMillis() + ".exec";
// }
//
// public static String getCoverageMergeFilePath() {
// return getJacocoCoverageHome() + File.separator + "jacoco-data-merge" + ".exec";
// }
//
// public static String getJarExtractedFilePath() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator + "jar";
// }
//
// public static String getCarbonHome() {
// if (System.getProperty(FrameworkConstants.CARBON_HOME) != null) {
// return System.getProperty(FrameworkConstants.CARBON_HOME);
// } else {
// log.error("Cannot read carbon.home property ");
// return null;
// }
// }
// }
| import org.apache.axis2.AxisFault;
import org.apache.axis2.Constants;
import org.apache.axis2.context.ConfigurationContext;
import org.apache.axis2.context.ConfigurationContextFactory;
import org.apache.axis2.transport.http.HTTPConstants;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.http.impl.client.CloseableHttpClient;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.impl.conn.PoolingHttpClientConnectionManager;
import org.wso2.carbon.automation.engine.frameworkutils.FrameworkPathUtil;
import java.io.File; | /*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.test.utils.axis2client;
public class ConfigurationContextProvider {
private static final Log log = LogFactory.getLog(ConfigurationContextProvider.class);
private static ConfigurationContext configurationContext = null;
private static ConfigurationContextProvider instance = new ConfigurationContextProvider();
private ConfigurationContextProvider() {
try {
PoolingHttpClientConnectionManager poolingHttpClientConnectionManager;
configurationContext = ConfigurationContextFactory.createConfigurationContextFromFileSystem( | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/frameworkutils/FrameworkPathUtil.java
// public class FrameworkPathUtil {
// public static final String SYSTEM_ARTIFACT_RESOURCE_LOCATION = "framework.resource.location";
// private static final Log log = LogFactory.getLog(FrameworkPathUtil.class);
//
// public static String getSystemResourceLocation() {
// String resourceLocation;
// if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME)
// .toLowerCase().contains(OperatingSystems.WINDOWS.name().toLowerCase())) {
// resourceLocation = System.getProperty
// (SYSTEM_ARTIFACT_RESOURCE_LOCATION).replace("/", "\\");
// } else {
// resourceLocation = System.getProperty
// (SYSTEM_ARTIFACT_RESOURCE_LOCATION).replace("/", "/");
// }
// return resourceLocation;
// }
//
// public static String getSystemSettingsLocation() {
// String settingsLocation;
// if (System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION) != null) {
// if (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_OS_NAME)
// .toLowerCase().contains(OperatingSystems.WINDOWS.name().toLowerCase())) {
// settingsLocation = System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION).replace("/", "\\");
// } else {
// settingsLocation = System.getProperty
// (FrameworkConstants.SYSTEM_PROPERTY_SETTINGS_LOCATION).replace("/", "/");
// }
// } else {
// settingsLocation = getSystemResourceLocation();
// }
// return settingsLocation;
// }
//
// public static String getReportLocation() {
// String reportLocation;
// reportLocation = (System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_BASEDIR_LOCATION, ".")) +
// File.separator + "target";
// return reportLocation;
// }
//
// public static String getCarbonZipLocation() {
// return System.getProperty(FrameworkConstants.SYSTEM_PROPERTY_CARBON_ZIP_LOCATION);
// }
//
// public static String getCarbonTempLocation() {
// String extractDir = "carbontmp" + System.currentTimeMillis();
// String baseDir = (System.getProperty("basedir", ".")) + File.separator + "target";
// return new File(baseDir).getAbsolutePath() + File.separator + extractDir;
// }
//
// public static String getCarbonServerAxisServiceDirectory() {
// return getCarbonHome() + File.separator + "repository" + File.separator
// + "deployment" + File.separator + "server" + File.separator + "axis2services";
// }
//
// public static String getCarbonServerLibLocation() {
// return getCarbonHome() + File.separator + "repository" + File.separator + "components" +
// File.separator + "lib";
// }
//
// public static String getCarbonServerConfLocation() {
// return getCarbonHome() + File.separator + "repository" + File.separator + "conf";
// }
//
// public static String getCoverageDirPath() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator +
// "jacoco" + File.separator + "coverage";
// }
//
// public static String getJacocoCoverageHome() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator +
// "jacoco";
// }
//
// public static String getTargetDirectory() {
// return System.getProperty("basedir") + File.separator + "target";
// }
//
// public static String getCoverageDumpFilePath() {
// return getJacocoCoverageHome() + File.separator + "jacoco" + System.currentTimeMillis() + ".exec";
// }
//
// public static String getCoverageMergeFilePath() {
// return getJacocoCoverageHome() + File.separator + "jacoco-data-merge" + ".exec";
// }
//
// public static String getJarExtractedFilePath() {
// return System.getProperty("basedir") + File.separator + "target" + File.separator + "jar";
// }
//
// public static String getCarbonHome() {
// if (System.getProperty(FrameworkConstants.CARBON_HOME) != null) {
// return System.getProperty(FrameworkConstants.CARBON_HOME);
// } else {
// log.error("Cannot read carbon.home property ");
// return null;
// }
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/axis2client/ConfigurationContextProvider.java
import org.apache.axis2.AxisFault;
import org.apache.axis2.Constants;
import org.apache.axis2.context.ConfigurationContext;
import org.apache.axis2.context.ConfigurationContextFactory;
import org.apache.axis2.transport.http.HTTPConstants;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.http.impl.client.CloseableHttpClient;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.impl.conn.PoolingHttpClientConnectionManager;
import org.wso2.carbon.automation.engine.frameworkutils.FrameworkPathUtil;
import java.io.File;
/*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.test.utils.axis2client;
public class ConfigurationContextProvider {
private static final Log log = LogFactory.getLog(ConfigurationContextProvider.class);
private static ConfigurationContext configurationContext = null;
private static ConfigurationContextProvider instance = new ConfigurationContextProvider();
private ConfigurationContextProvider() {
try {
PoolingHttpClientConnectionManager poolingHttpClientConnectionManager;
configurationContext = ConfigurationContextFactory.createConfigurationContextFromFileSystem( | FrameworkPathUtil.getSystemResourceLocation() + File.separator + "client", null); |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/http/client/HttpClientUtil.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import java.net.URL;
import java.nio.charset.Charset;
import org.apache.axiom.om.OMElement;
import org.apache.axiom.om.util.AXIOMUtil;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.testng.Assert;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import javax.xml.stream.XMLStreamException;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.net.HttpURLConnection;
import java.net.MalformedURLException;
import java.net.ProtocolException; | /*
* Copyright (c) 2012, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.wso2.carbon.automation.test.utils.http.client;
public class HttpClientUtil {
private static final Log log = LogFactory.getLog(HttpClientUtil.class);
private static final int connectionTimeOut = 30000;
| // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.test.utils/src/main/java/org/wso2/carbon/automation/test/utils/http/client/HttpClientUtil.java
import java.net.URL;
import java.nio.charset.Charset;
import org.apache.axiom.om.OMElement;
import org.apache.axiom.om.util.AXIOMUtil;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.testng.Assert;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import javax.xml.stream.XMLStreamException;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.net.HttpURLConnection;
import java.net.MalformedURLException;
import java.net.ProtocolException;
/*
* Copyright (c) 2012, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.wso2.carbon.automation.test.utils.http.client;
public class HttpClientUtil {
private static final Log log = LogFactory.getLog(HttpClientUtil.class);
private static final int connectionTimeOut = 30000;
| public OMElement get(String endpoint) throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSQueueMessageProducer.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.Properties; | public void disconnect() {
if (producer != null) {
try {
producer.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* This will send the message to the destination Queue
*
* @param messageContent returns the message contents
* @throws AutomationFrameworkException
*/ | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.extensions/src/main/java/org/wso2/carbon/automation/extensions/servers/jmsserver/client/JMSQueueMessageProducer.java
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
import org.wso2.carbon.automation.extensions.servers.jmsserver.controller.config.JMSBrokerConfiguration;
import javax.jms.*;
import javax.naming.Context;
import javax.naming.InitialContext;
import javax.naming.NamingException;
import java.util.Properties;
public void disconnect() {
if (producer != null) {
try {
producer.close();
} catch (JMSException e) {
//ignore
}
}
if (session != null) {
try {
session.close();
} catch (JMSException e) {
//ignore
}
}
if (connection != null) {
try {
connection.close();
} catch (JMSException e) {
//ignore
}
}
}
/**
* This will send the message to the destination Queue
*
* @param messageContent returns the message contents
* @throws AutomationFrameworkException
*/ | public void pushMessage(String messageContent) throws AutomationFrameworkException { |
wso2/carbon-platform-integration | test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/extensions/ExecutionListenerExtension.java | // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
| import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException; | /*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.extensions;
public abstract class ExecutionListenerExtension extends ListenerExtension {
private final Log log = LogFactory.getLog(getClass());
private final static String XPATH_TO_CLASS = "//listenerExtensions/platformExecutionManager/extentionClasses/class/name";
public ExecutionListenerExtension() {
super();
try {
setParameterMap(XPATH_TO_CLASS, getClass().getName());
} catch (Exception e) {
log.warn("Failed to initializing the Extension Class");
log.error("Error initializing the Automation Context", e);
}
}
| // Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/exceptions/AutomationFrameworkException.java
// public class AutomationFrameworkException extends Exception {
// public AutomationFrameworkException(String message) {
// super(message);
// }
//
// public AutomationFrameworkException(String message, Throwable e) {
// super(message, e);
// }
//
// public AutomationFrameworkException(Throwable e) {
// super(e);
// }
// }
// Path: test-automation-framework/org.wso2.carbon.automation.engine/src/main/java/org/wso2/carbon/automation/engine/extensions/ExecutionListenerExtension.java
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.wso2.carbon.automation.engine.exceptions.AutomationFrameworkException;
/*
*Copyright (c) 2005-2010, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
*WSO2 Inc. licenses this file to you under the Apache License,
*Version 2.0 (the "License"); you may not use this file except
*in compliance with the License.
*You may obtain a copy of the License at
*
*http://www.apache.org/licenses/LICENSE-2.0
*
*Unless required by applicable law or agreed to in writing,
*software distributed under the License is distributed on an
*"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
*KIND, either express or implied. See the License for the
*specific language governing permissions and limitations
*under the License.
*/
package org.wso2.carbon.automation.engine.extensions;
public abstract class ExecutionListenerExtension extends ListenerExtension {
private final Log log = LogFactory.getLog(getClass());
private final static String XPATH_TO_CLASS = "//listenerExtensions/platformExecutionManager/extentionClasses/class/name";
public ExecutionListenerExtension() {
super();
try {
setParameterMap(XPATH_TO_CLASS, getClass().getName());
} catch (Exception e) {
log.warn("Failed to initializing the Extension Class");
log.error("Error initializing the Automation Context", e);
}
}
| public abstract void initiate() throws AutomationFrameworkException; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa; | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa; | private final Normalizer normalizer; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final double[] breakpoints;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSize the size of the alphabet used to discretise the values
*/
public SymbolicAggregateApproximation(int segments, int alphabetSize) { | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final double[] breakpoints;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSize the size of the alphabet used to discretise the values
*/
public SymbolicAggregateApproximation(int segments, int alphabetSize) { | this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), new NormalDistributionDivider().getBreakpoints(alphabetSize)); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final double[] breakpoints;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSize the size of the alphabet used to discretise the values
*/
public SymbolicAggregateApproximation(int segments, int alphabetSize) { | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Symbolic Aggregate Approximation (SAX) algorithm.
* <p>
* Reference:
* Jessica L., Keogh E., Lonardi S., Chiu B. (2003)
* <i>A symbolic representation of time series, with implications for streaming algorithms</i>
* </p>
*
* @since 0.5
*/
public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = -2951279057715694424L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final double[] breakpoints;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSize the size of the alphabet used to discretise the values
*/
public SymbolicAggregateApproximation(int segments, int alphabetSize) { | this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), new NormalDistributionDivider().getBreakpoints(alphabetSize)); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/normalization/ZNormalizerTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.ArgumentMatchers;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.normalization;
@RunWith(MockitoJUnitRunner.class)
public class ZNormalizerTest {
@InjectMocks
private ZNormalizer normalizer;
@Mock
private Mean mean;
@Mock
private StandardDeviation standardDeviation;
@Test
public void testCalls() throws Exception {
Mockito.when(mean.evaluate(ArgumentMatchers.any(double[].class))).thenReturn(3.0);
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
normalizer.normalize(v);
Mockito.verify(mean).evaluate(v);
Mockito.verify(standardDeviation).evaluate(v, 3.0);
}
@Test
public void testNormalize() throws Exception {
normalizer = new ZNormalizer();
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
double aux = FastMath.sqrt(2);
double[] expected = {-2 / aux, -1 / aux, 0, 1 / aux, 2 / aux};
double[] out = normalizer.normalize(v);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/normalization/ZNormalizerTest.java
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.ArgumentMatchers;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.normalization;
@RunWith(MockitoJUnitRunner.class)
public class ZNormalizerTest {
@InjectMocks
private ZNormalizer normalizer;
@Mock
private Mean mean;
@Mock
private StandardDeviation standardDeviation;
@Test
public void testCalls() throws Exception {
Mockito.when(mean.evaluate(ArgumentMatchers.any(double[].class))).thenReturn(3.0);
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
normalizer.normalize(v);
Mockito.verify(mean).evaluate(v);
Mockito.verify(standardDeviation).evaluate(v, 3.0);
}
@Test
public void testNormalize() throws Exception {
normalizer = new ZNormalizer();
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
double aux = FastMath.sqrt(2);
double[] expected = {-2 / aux, -1 / aux, 0, 1 / aux, 2 / aux};
double[] out = normalizer.normalize(v);
| Assert.assertArrayEquals(expected, out, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/exception/NumberIsNotDivisibleExceptionTest.java | // Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximation.java
// public class PiecewiseAggregateApproximation implements GenericTransformer<double[], double[]> {
// private static final long serialVersionUID = -8199587096227874425L;
// private final int segments;
//
// /**
// * Creates a new instance of this class.
// *
// * @param segments the number of segments
// * @throws NumberIsTooSmallException if segments lower than 1
// */
// public PiecewiseAggregateApproximation(int segments) {
// if (segments < 1) {
// throw new NumberIsTooSmallException(segments, 1, true);
// }
//
// this.segments = segments;
// }
//
// @Override
// public double[] transform(double[] values) {
// int len = values.length;
// if (len < segments) {
// throw new ArrayLengthIsTooSmallException(len, segments, true);
// }
//
// int modulo = len % segments;
// double[] reducedValues = new double[segments];
// if (modulo == 0) {
// int segmentSize = len / segments;
// double sum = 0;
// int n = 0;
// for (int i = 0; i < len; i++) {
// sum += values[i];
// if ((i + 1) % segmentSize == 0) {
// reducedValues[n++] = sum / segmentSize;
// if (n == segments) break;
// sum = 0;
// }
// }
// } else {
// double segmentSize = len * 1.0 / segments;
// int k = 0;
// double sum = 0;
// for (int i = 0; i < segments - 1; i++) {
// double x = (i + 1) * segmentSize - 1;
//
// for (; k < x; k++) {
// sum += values[k];
// }
//
// double delta = x - (int) x;
// sum += delta * values[k];
// reducedValues[i] = sum / segmentSize;
//
// sum = (1 - delta) * values[k];
// k++;
// }
//
// for (; k < len; k++) {
// sum += values[k];
// }
// reducedValues[segments - 1] = sum / segmentSize;
// }
//
// return reducedValues;
// }
//
// public int getSegments() {
// return segments;
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.representation.PiecewiseAggregateApproximation; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
public class NumberIsNotDivisibleExceptionTest {
@Test
public void testGetFactor() throws Exception {
try { | // Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximation.java
// public class PiecewiseAggregateApproximation implements GenericTransformer<double[], double[]> {
// private static final long serialVersionUID = -8199587096227874425L;
// private final int segments;
//
// /**
// * Creates a new instance of this class.
// *
// * @param segments the number of segments
// * @throws NumberIsTooSmallException if segments lower than 1
// */
// public PiecewiseAggregateApproximation(int segments) {
// if (segments < 1) {
// throw new NumberIsTooSmallException(segments, 1, true);
// }
//
// this.segments = segments;
// }
//
// @Override
// public double[] transform(double[] values) {
// int len = values.length;
// if (len < segments) {
// throw new ArrayLengthIsTooSmallException(len, segments, true);
// }
//
// int modulo = len % segments;
// double[] reducedValues = new double[segments];
// if (modulo == 0) {
// int segmentSize = len / segments;
// double sum = 0;
// int n = 0;
// for (int i = 0; i < len; i++) {
// sum += values[i];
// if ((i + 1) % segmentSize == 0) {
// reducedValues[n++] = sum / segmentSize;
// if (n == segments) break;
// sum = 0;
// }
// }
// } else {
// double segmentSize = len * 1.0 / segments;
// int k = 0;
// double sum = 0;
// for (int i = 0; i < segments - 1; i++) {
// double x = (i + 1) * segmentSize - 1;
//
// for (; k < x; k++) {
// sum += values[k];
// }
//
// double delta = x - (int) x;
// sum += delta * values[k];
// reducedValues[i] = sum / segmentSize;
//
// sum = (1 - delta) * values[k];
// k++;
// }
//
// for (; k < len; k++) {
// sum += values[k];
// }
// reducedValues[segments - 1] = sum / segmentSize;
// }
//
// return reducedValues;
// }
//
// public int getSegments() {
// return segments;
// }
// }
// Path: src/test/java/ro/hasna/ts/math/exception/NumberIsNotDivisibleExceptionTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.representation.PiecewiseAggregateApproximation;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
public class NumberIsNotDivisibleExceptionTest {
@Test
public void testGetFactor() throws Exception {
try { | PiecewiseAggregateApproximation paa = new PiecewiseAggregateApproximation(4); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/StampTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class StampTransformerTest {
@Test
public void transform_withoutNormalization() {
StampTransformer transformer = new StampTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/StampTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class StampTransformerTest {
@Test
public void transform_withoutNormalization() {
StampTransformer transformer = new StampTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| MatrixProfile transform = transformer.transform(v); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/StampTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class StampTransformerTest {
@Test
public void transform_withoutNormalization() {
StampTransformer transformer = new StampTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/StampTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class StampTransformerTest {
@Test
public void transform_withoutNormalization() {
StampTransformer transformer = new StampTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage; | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage; | private final Normalizer normalizer; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage;
private final Normalizer normalizer;
public DynamicTimeWarpingDistance() { | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage;
private final Normalizer normalizer;
public DynamicTimeWarpingDistance() { | this(0.05, new ZNormalizer()); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
| import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage;
private final Normalizer normalizer;
public DynamicTimeWarpingDistance() {
this(0.05, new ZNormalizer());
}
/**
* Creates a new instance of this class with
*
* @param radiusPercentage Sakoe-Chiba Band width used to constraint the warping window
* @param normalizer the normalizer (it can be null if the values were normalized)
* @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
*/
public DynamicTimeWarpingDistance(double radiusPercentage, Normalizer normalizer) {
if (radiusPercentage < 0 || radiusPercentage > 1) { | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/DynamicTimeWarpingDistance.java
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import java.util.Deque;
import java.util.LinkedList;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Dynamic Time Warping.
* <p>
* Reference:
* Wikipedia https://en.wikipedia.org/wiki/Dynamic_time_warping
* </p>
*
* @since 0.10
*/
public class DynamicTimeWarpingDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 1154818905340336905L;
private final double radiusPercentage;
private final Normalizer normalizer;
public DynamicTimeWarpingDistance() {
this(0.05, new ZNormalizer());
}
/**
* Creates a new instance of this class with
*
* @param radiusPercentage Sakoe-Chiba Band width used to constraint the warping window
* @param normalizer the normalizer (it can be null if the values were normalized)
* @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
*/
public DynamicTimeWarpingDistance(double radiusPercentage, Normalizer normalizer) {
if (radiusPercentage < 0 || radiusPercentage > 1) { | throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/distribution/UniformDistributionDividerTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class UniformDistributionDividerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private UniformDistributionDivider divider;
@Before
public void setUp() throws Exception {
divider = new UniformDistributionDivider(-1, 1);
}
@After
public void tearDown() throws Exception {
divider = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooLargeException.class);
thrown.expectMessage("4 is larger than the maximum (3)");
new UniformDistributionDivider(4, 3);
}
@Test
public void testGetBreakpointsWithException() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("1 is smaller than the minimum (2)");
divider.getBreakpoints(1);
}
@Test
public void testGetBreakpoints1() throws Exception {
double[] expected = {-1.0 / 3, 1.0 / 3};
double[] v = divider.getBreakpoints(3);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/distribution/UniformDistributionDividerTest.java
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class UniformDistributionDividerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private UniformDistributionDivider divider;
@Before
public void setUp() throws Exception {
divider = new UniformDistributionDivider(-1, 1);
}
@After
public void tearDown() throws Exception {
divider = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooLargeException.class);
thrown.expectMessage("4 is larger than the maximum (3)");
new UniformDistributionDivider(4, 3);
}
@Test
public void testGetBreakpointsWithException() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("1 is smaller than the minimum (2)");
divider.getBreakpoints(1);
}
@Test
public void testGetBreakpoints1() throws Exception {
double[] expected = {-1.0 / 3, 1.0 / 3};
double[] v = divider.getBreakpoints(3);
| Assert.assertArrayEquals(expected, v, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/SaxEuclideanDistance.java | // Path: src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java
// public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
// private static final long serialVersionUID = -2951279057715694424L;
// private final PiecewiseAggregateApproximation paa;
// private final Normalizer normalizer;
// private final double[] breakpoints;
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the size of the alphabet.
// *
// * @param segments the number of segments
// * @param alphabetSize the size of the alphabet used to discretise the values
// */
// public SymbolicAggregateApproximation(int segments, int alphabetSize) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), new NormalDistributionDivider().getBreakpoints(alphabetSize));
// }
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param segments the number of segments
// * @param breakpoints the list of values that divide the distribution of the values
// * in equal areas of probability
// */
// public SymbolicAggregateApproximation(int segments, double[] breakpoints) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), breakpoints);
// }
//
// /**
// * Creates a new instance of this class with a given PAA and normalizer algorithm and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param paa the Piecewise Aggregate Approximation algorithm
// * @param normalizer the normalizer (it can be null if the values were normalized)
// * @param breakpoints the list of values that divide the distribution of the values
// * in equal areas of probability
// */
// public SymbolicAggregateApproximation(PiecewiseAggregateApproximation paa, Normalizer normalizer, double[] breakpoints) {
// this.paa = paa;
// this.normalizer = normalizer;
// this.breakpoints = breakpoints;
// }
//
// /**
// * Transform a given sequence of values using the SAX algorithm.
// *
// * @param values the sequence of values
// * @return the result of the transformation
// */
// public int[] transform(double[] values) {
// double[] copy = paa.transform(values);
//
// // NOTE: mathematically the order of PAA and normalisation doesn't matter, the result is the same,
// // but comparing the speed, running PAA and then normalisation is faster than in the reverse order
// if (normalizer != null) {
// copy = normalizer.normalize(copy);
// }
//
// int n = 0;
// int[] result = new int[copy.length];
// for (double item : copy) {
// boolean found = false;
// for (int i = 0; i < breakpoints.length && !found; i++) {
// double breakpoint = breakpoints[i];
// if (breakpoint > item) {
// result[n] = i;
// found = true;
// }
// }
// if (!found) {
// result[n] = breakpoints.length;
// }
// n++;
// }
//
// return result;
// }
//
// public double[] getBreakpoints() {
// return breakpoints;
// }
// }
| import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.representation.SymbolicAggregateApproximation; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the L<sub>2</sub> (Euclidean) distance between two vectors using the SAX representation.
*
* @since 0.7
*/
public class SaxEuclideanDistance implements GenericDistanceMeasure<int[]> {
private static final long serialVersionUID = -2580188567848020545L; | // Path: src/main/java/ro/hasna/ts/math/representation/SymbolicAggregateApproximation.java
// public class SymbolicAggregateApproximation implements GenericTransformer<double[], int[]> {
// private static final long serialVersionUID = -2951279057715694424L;
// private final PiecewiseAggregateApproximation paa;
// private final Normalizer normalizer;
// private final double[] breakpoints;
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the size of the alphabet.
// *
// * @param segments the number of segments
// * @param alphabetSize the size of the alphabet used to discretise the values
// */
// public SymbolicAggregateApproximation(int segments, int alphabetSize) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), new NormalDistributionDivider().getBreakpoints(alphabetSize));
// }
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param segments the number of segments
// * @param breakpoints the list of values that divide the distribution of the values
// * in equal areas of probability
// */
// public SymbolicAggregateApproximation(int segments, double[] breakpoints) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), breakpoints);
// }
//
// /**
// * Creates a new instance of this class with a given PAA and normalizer algorithm and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param paa the Piecewise Aggregate Approximation algorithm
// * @param normalizer the normalizer (it can be null if the values were normalized)
// * @param breakpoints the list of values that divide the distribution of the values
// * in equal areas of probability
// */
// public SymbolicAggregateApproximation(PiecewiseAggregateApproximation paa, Normalizer normalizer, double[] breakpoints) {
// this.paa = paa;
// this.normalizer = normalizer;
// this.breakpoints = breakpoints;
// }
//
// /**
// * Transform a given sequence of values using the SAX algorithm.
// *
// * @param values the sequence of values
// * @return the result of the transformation
// */
// public int[] transform(double[] values) {
// double[] copy = paa.transform(values);
//
// // NOTE: mathematically the order of PAA and normalisation doesn't matter, the result is the same,
// // but comparing the speed, running PAA and then normalisation is faster than in the reverse order
// if (normalizer != null) {
// copy = normalizer.normalize(copy);
// }
//
// int n = 0;
// int[] result = new int[copy.length];
// for (double item : copy) {
// boolean found = false;
// for (int i = 0; i < breakpoints.length && !found; i++) {
// double breakpoint = breakpoints[i];
// if (breakpoint > item) {
// result[n] = i;
// found = true;
// }
// }
// if (!found) {
// result[n] = breakpoints.length;
// }
// n++;
// }
//
// return result;
// }
//
// public double[] getBreakpoints() {
// return breakpoints;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/SaxEuclideanDistance.java
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.representation.SymbolicAggregateApproximation;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the L<sub>2</sub> (Euclidean) distance between two vectors using the SAX representation.
*
* @since 0.7
*/
public class SaxEuclideanDistance implements GenericDistanceMeasure<int[]> {
private static final long serialVersionUID = -2580188567848020545L; | private final SymbolicAggregateApproximation sax; |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | thrown.expect(ArrayLengthIsTooSmallException.class); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseAggregateApproximation paa = new PiecewiseAggregateApproximation(4);
double[] v = {1, 2, 3};
paa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseAggregateApproximation paa = new PiecewiseAggregateApproximation(2);
double[] v = {1, 2, 3, 4, 5, 6};
double[] expected = {2.0, 5.0};
double[] result = paa.transform(v);
| // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseAggregateApproximation paa = new PiecewiseAggregateApproximation(4);
double[] v = {1, 2, 3};
paa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseAggregateApproximation paa = new PiecewiseAggregateApproximation(2);
double[] v = {1, 2, 3, 4, 5, 6};
double[] expected = {2.0, 5.0};
double[] result = paa.transform(v);
| Assert.assertArrayEquals(expected, result, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.PolynomialCurveFitter;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseCurveFitterApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private AbstractCurveFitter curveFitter;
private static void assertMatrixEquals(double[][] expected, double[][] actual, double precision) {
Assert.assertEquals(expected.length, actual.length);
for (int i = 0; i < expected.length; i++) {
Assert.assertArrayEquals(expected[i], actual[i], precision);
}
}
@Before
public void setUp() throws Exception {
curveFitter = PolynomialCurveFitter.create(0).withMaxIterations(2);
}
@After
public void tearDown() throws Exception {
curveFitter = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseCurveFitterApproximation(0, curveFitter);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.PolynomialCurveFitter;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseCurveFitterApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private AbstractCurveFitter curveFitter;
private static void assertMatrixEquals(double[][] expected, double[][] actual, double precision) {
Assert.assertEquals(expected.length, actual.length);
for (int i = 0; i < expected.length; i++) {
Assert.assertArrayEquals(expected[i], actual[i], precision);
}
}
@Before
public void setUp() throws Exception {
curveFitter = PolynomialCurveFitter.create(0).withMaxIterations(2);
}
@After
public void tearDown() throws Exception {
curveFitter = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseCurveFitterApproximation(0, curveFitter);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | thrown.expect(ArrayLengthIsTooSmallException.class); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.PolynomialCurveFitter;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | curveFitter = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseCurveFitterApproximation(0, curveFitter);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseCurveFitterApproximation pcfa = new PiecewiseCurveFitterApproximation(4, curveFitter);
double[] v = {1, 2, 3};
pcfa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseCurveFitterApproximation pcfa = new PiecewiseCurveFitterApproximation(2, curveFitter);
double[] v = {1, 2, 3, 4, 5, 6};
double[][] expected = {{2.0}, {5.0}};
double[][] result = pcfa.transform(v);
| // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.PolynomialCurveFitter;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
curveFitter = null;
}
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseCurveFitterApproximation(0, curveFitter);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseCurveFitterApproximation pcfa = new PiecewiseCurveFitterApproximation(4, curveFitter);
double[] v = {1, 2, 3};
pcfa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseCurveFitterApproximation pcfa = new PiecewiseCurveFitterApproximation(2, curveFitter);
double[] v = {1, 2, 3, 4, 5, 6};
double[][] expected = {{2.0}, {5.0}};
double[][] result = pcfa.transform(v);
| assertMatrixEquals(expected, result, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | thrown.expect(ArrayLengthIsTooSmallException.class); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3};
plaa.transform(v);
}
@Test
public void testTransformSegmentsNotDivisible() throws Exception { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3};
plaa.transform(v);
}
@Test
public void testTransformSegmentsNotDivisible() throws Exception { | thrown.expect(ArrayLengthIsNotDivisibleException.class); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3};
plaa.transform(v);
}
@Test
public void testTransformSegmentsNotDivisible() throws Exception {
thrown.expect(ArrayLengthIsNotDivisibleException.class);
thrown.expectMessage("5 is not divisible with 4");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3, 4, 5};
plaa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(2);
double[] v = {1, 2, 3, 3, 2, 1}; | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class PiecewiseLinearAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new PiecewiseLinearAggregateApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3};
plaa.transform(v);
}
@Test
public void testTransformSegmentsNotDivisible() throws Exception {
thrown.expect(ArrayLengthIsNotDivisibleException.class);
thrown.expectMessage("5 is not divisible with 4");
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(4);
double[] v = {1, 2, 3, 4, 5};
plaa.transform(v);
}
@Test
public void testTransformStrict() throws Exception {
PiecewiseLinearAggregateApproximation plaa = new PiecewiseLinearAggregateApproximation(2);
double[] v = {1, 2, 3, 3, 2, 1}; | MeanSlopePair[] expected = {new MeanSlopePair(2.0, 1.0), new MeanSlopePair(2.0, -1.0)}; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/IndexableSaxEuclideanDistance.java | // Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
// public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
// private static final long serialVersionUID = -1652621695908903282L;
// private final PiecewiseAggregateApproximation paa;
// private final Normalizer normalizer;
// private final DistributionDivider distributionDivider;
// private final int[] alphabetSizes;
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the size of the alphabet.
// *
// * @param segments the number of segments
// * @param alphabetSizes the size of the alphabet used to discretise the values for every segment
// */
// public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), alphabetSizes, new NormalDistributionDivider());
// }
//
//
// /**
// * Creates a new instance of this class with a given PAA and normalizer algorithm and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param paa the Piecewise Aggregate Approximation algorithm
// * @param normalizer the normalizer (it can be null if the values were normalized)
// * @param alphabetSizes the size of the alphabet used to discretise the values for every segment
// * @param distributionDivider the divider that gets the list of breakpoints (values that divide a
// * distribution in equal areas of probability)
// * @throws DimensionMismatchException if the number of segments is different than the length of alphabetSizes
// */
// public IndexableSymbolicAggregateApproximation(PiecewiseAggregateApproximation paa, Normalizer normalizer,
// int[] alphabetSizes, DistributionDivider distributionDivider) {
// if (paa.getSegments() != alphabetSizes.length) {
// throw new DimensionMismatchException(alphabetSizes.length, paa.getSegments());
// }
//
// this.paa = paa;
// this.normalizer = normalizer;
// this.alphabetSizes = alphabetSizes;
// this.distributionDivider = distributionDivider;
// }
//
// /**
// * Transform a given sequence of values using the iSAX algorithm.
// *
// * @param values the sequence of values
// * @return the result of the transformation
// */
// public SaxPair[] transform(double[] values) {
// double[] copy = paa.transform(values);
//
// // NOTE: mathematically the order of PAA and normalisation doesn't matter, the result is the same,
// // but comparing the speed, running PAA and then normalisation is faster than in the reverse order
// if (normalizer != null) {
// copy = normalizer.normalize(copy);
// }
//
// int n = 0;
// int segments = copy.length;
// SaxPair[] result = new SaxPair[segments];
// for (int i = 0; i < segments; i++) {
// double item = copy[i];
// int alphabetSize = alphabetSizes[i];
// double[] breakpoints = distributionDivider.getBreakpoints(alphabetSize);
// boolean found = false;
// for (int j = 0; j < breakpoints.length && !found; j++) {
// double breakpoint = breakpoints[j];
// if (breakpoint > item) {
// result[n] = new SaxPair(j, alphabetSize);
// found = true;
// }
// }
// if (!found) {
// result[n] = new SaxPair(breakpoints.length, alphabetSize);
// }
// n++;
// }
//
// return result;
// }
//
// public double[] getBreakpoints(int areas) {
// return distributionDivider.getBreakpoints(areas);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.representation.IndexableSymbolicAggregateApproximation;
import ro.hasna.ts.math.type.SaxPair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the L<sub>2</sub> (Euclidean) distance between two vectors using the iSAX representation.
*
* @since 0.7
*/
public class IndexableSaxEuclideanDistance implements GenericDistanceMeasure<SaxPair[]> {
private static final long serialVersionUID = -4740907293933039859L; | // Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
// public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
// private static final long serialVersionUID = -1652621695908903282L;
// private final PiecewiseAggregateApproximation paa;
// private final Normalizer normalizer;
// private final DistributionDivider distributionDivider;
// private final int[] alphabetSizes;
//
// /**
// * Creates a new instance of this class with a given number of segments and
// * the size of the alphabet.
// *
// * @param segments the number of segments
// * @param alphabetSizes the size of the alphabet used to discretise the values for every segment
// */
// public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) {
// this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), alphabetSizes, new NormalDistributionDivider());
// }
//
//
// /**
// * Creates a new instance of this class with a given PAA and normalizer algorithm and
// * the breakpoints used to divide the distribution of the values.
// *
// * @param paa the Piecewise Aggregate Approximation algorithm
// * @param normalizer the normalizer (it can be null if the values were normalized)
// * @param alphabetSizes the size of the alphabet used to discretise the values for every segment
// * @param distributionDivider the divider that gets the list of breakpoints (values that divide a
// * distribution in equal areas of probability)
// * @throws DimensionMismatchException if the number of segments is different than the length of alphabetSizes
// */
// public IndexableSymbolicAggregateApproximation(PiecewiseAggregateApproximation paa, Normalizer normalizer,
// int[] alphabetSizes, DistributionDivider distributionDivider) {
// if (paa.getSegments() != alphabetSizes.length) {
// throw new DimensionMismatchException(alphabetSizes.length, paa.getSegments());
// }
//
// this.paa = paa;
// this.normalizer = normalizer;
// this.alphabetSizes = alphabetSizes;
// this.distributionDivider = distributionDivider;
// }
//
// /**
// * Transform a given sequence of values using the iSAX algorithm.
// *
// * @param values the sequence of values
// * @return the result of the transformation
// */
// public SaxPair[] transform(double[] values) {
// double[] copy = paa.transform(values);
//
// // NOTE: mathematically the order of PAA and normalisation doesn't matter, the result is the same,
// // but comparing the speed, running PAA and then normalisation is faster than in the reverse order
// if (normalizer != null) {
// copy = normalizer.normalize(copy);
// }
//
// int n = 0;
// int segments = copy.length;
// SaxPair[] result = new SaxPair[segments];
// for (int i = 0; i < segments; i++) {
// double item = copy[i];
// int alphabetSize = alphabetSizes[i];
// double[] breakpoints = distributionDivider.getBreakpoints(alphabetSize);
// boolean found = false;
// for (int j = 0; j < breakpoints.length && !found; j++) {
// double breakpoint = breakpoints[j];
// if (breakpoint > item) {
// result[n] = new SaxPair(j, alphabetSize);
// found = true;
// }
// }
// if (!found) {
// result[n] = new SaxPair(breakpoints.length, alphabetSize);
// }
// n++;
// }
//
// return result;
// }
//
// public double[] getBreakpoints(int areas) {
// return distributionDivider.getBreakpoints(areas);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/IndexableSaxEuclideanDistance.java
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.representation.IndexableSymbolicAggregateApproximation;
import ro.hasna.ts.math.type.SaxPair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the L<sub>2</sub> (Euclidean) distance between two vectors using the iSAX representation.
*
* @since 0.7
*/
public class IndexableSaxEuclideanDistance implements GenericDistanceMeasure<SaxPair[]> {
private static final long serialVersionUID = -4740907293933039859L; | private final IndexableSymbolicAggregateApproximation isax; |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/type/TesparSymbolTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NotPositiveException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.type;
public class TesparSymbolTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructorException() throws Exception {
thrown.expect(NotPositiveException.class);
thrown.expectMessage("-5.5 is smaller than the minimum (0)");
new TesparSymbol(10, 3, -5.5);
}
@Test
public void testGetters() throws Exception {
TesparSymbol symbol = new TesparSymbol(10, 3, 5.5);
Assert.assertEquals(10, symbol.getDuration());
Assert.assertEquals(3, symbol.getShape()); | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/type/TesparSymbolTest.java
import org.apache.commons.math3.exception.NotPositiveException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.type;
public class TesparSymbolTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructorException() throws Exception {
thrown.expect(NotPositiveException.class);
thrown.expectMessage("-5.5 is smaller than the minimum (0)");
new TesparSymbol(10, 3, -5.5);
}
@Test
public void testGetters() throws Exception {
TesparSymbol symbol = new TesparSymbol(10, 3, 5.5);
Assert.assertEquals(10, symbol.getDuration());
Assert.assertEquals(3, symbol.getShape()); | Assert.assertEquals(5.5, symbol.getAmplitude(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/StampTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import org.apache.commons.math3.complex.Complex;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STAMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Yeh C. C. M., Zhu Y., Ulanova L., Begum N., Ding Y., Dau H. A., Keogh E. (2016, December)
* <i>Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets</i>
* </p>
*
* @since 0.17
*/
public class StampTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 5919724985496947961L;
public StampTransformer(int window) {
super(window);
}
public StampTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/StampTransformer.java
import org.apache.commons.math3.complex.Complex;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STAMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Yeh C. C. M., Zhu Y., Ulanova L., Begum N., Ding Y., Dau H. A., Keogh E. (2016, December)
* <i>Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets</i>
* </p>
*
* @since 0.17
*/
public class StampTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 5919724985496947961L;
public StampTransformer(int window) {
super(window);
}
public StampTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/StampTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import org.apache.commons.math3.complex.Complex;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
int[] indices = generateRandomIndices(n);
for (int i = 0; i < n; i++) {
int index = indices[i];
computeDistanceProfileWithProductSums(input, index, input, skip, n, distanceProfile);
updateMatrixProfileFromDistanceProfile(distanceProfile, n, index, mp, callback);
}
updateMatrixProfileWithSqrt(mp);
return mp;
}
protected void computeDistanceProfileWithProductSums(double[] a, int i, double[] b, int skip, int nb, double[] distanceProfile) {
for (int j = 0; j < nb; j++) {
if (inExclusionZone(i, j, skip)) {
distanceProfile[j] = Double.POSITIVE_INFINITY;
} else {
double distance = 0;
for (int k = 0; k < window; k++) {
distance += (a[k + i] - b[k + j]) * (a[k + i] - b[k + j]);
}
distanceProfile[j] = distance;
}
}
}
protected void computeNormalizedDistanceProfileWithFft(double[] a, int i, double[] b, int skip, int nb, double[] distanceProfile) { | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/StampTransformer.java
import org.apache.commons.math3.complex.Complex;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
int[] indices = generateRandomIndices(n);
for (int i = 0; i < n; i++) {
int index = indices[i];
computeDistanceProfileWithProductSums(input, index, input, skip, n, distanceProfile);
updateMatrixProfileFromDistanceProfile(distanceProfile, n, index, mp, callback);
}
updateMatrixProfileWithSqrt(mp);
return mp;
}
protected void computeDistanceProfileWithProductSums(double[] a, int i, double[] b, int skip, int nb, double[] distanceProfile) {
for (int j = 0; j < nb; j++) {
if (inExclusionZone(i, j, skip)) {
distanceProfile[j] = Double.POSITIVE_INFINITY;
} else {
double distance = 0;
for (int k = 0; k < window; k++) {
distance += (a[k + i] - b[k + j]) * (a[k + i] - b[k + j]);
}
distanceProfile[j] = distance;
}
}
}
protected void computeNormalizedDistanceProfileWithFft(double[] a, int i, double[] b, int skip, int nb, double[] distanceProfile) { | BothWaySummaryStatistics first = new BothWaySummaryStatistics(); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/UniformScalingDistance.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | this.minScalingFactor = minScalingFactor;
if (maxScalingFactor < minScalingFactor) {
throw new NumberIsTooSmallException(maxScalingFactor, minScalingFactor, true);
}
this.maxScalingFactor = maxScalingFactor;
if (steps < 1) {
throw new NumberIsTooSmallException(steps, 1, true);
}
this.steps = steps;
this.distance = distance;
}
@Override
public double compute(double[] a, double[] b) {
return compute(a, b, Double.POSITIVE_INFINITY);
}
@Override
public double compute(double[] a, double[] b, double cutOffValue) {
double min = Double.POSITIVE_INFINITY;
double[] aux = new double[a.length];
if (steps == 1) {
double scalingFactor = (minScalingFactor + maxScalingFactor) / 2;
return computeDistance(a, b, aux, scalingFactor, cutOffValue);
}
double interval = (maxScalingFactor - minScalingFactor) / (steps - 1); | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/UniformScalingDistance.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
this.minScalingFactor = minScalingFactor;
if (maxScalingFactor < minScalingFactor) {
throw new NumberIsTooSmallException(maxScalingFactor, minScalingFactor, true);
}
this.maxScalingFactor = maxScalingFactor;
if (steps < 1) {
throw new NumberIsTooSmallException(steps, 1, true);
}
this.steps = steps;
this.distance = distance;
}
@Override
public double compute(double[] a, double[] b) {
return compute(a, b, Double.POSITIVE_INFINITY);
}
@Override
public double compute(double[] a, double[] b, double cutOffValue) {
double min = Double.POSITIVE_INFINITY;
double[] aux = new double[a.length];
if (steps == 1) {
double scalingFactor = (minScalingFactor + maxScalingFactor) / 2;
return computeDistance(a, b, aux, scalingFactor, cutOffValue);
}
double interval = (maxScalingFactor - minScalingFactor) / (steps - 1); | if (Precision.equals(interval, 0.0, TimeSeriesPrecision.EPSILON)) { |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/filter/MovingAverageFilterTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.filter;
public class MovingAverageFilterTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor1() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("0 != 5");
new MovingAverageFilter(2, true, new double[]{});
}
@Test
public void testConstructor2() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("0 != 2");
new MovingAverageFilter(2, false, new double[]{});
}
@Test
public void testFilterSymmetricWithoutWeights() throws Exception {
double[] v = {1, 1, 2, 2, 3, 3, 4, 4, 5};
double[] expected = {1, 1, 1.8, 2.2, 2.8, 3.2, 3.8, 4, 5};
Filter filter = new MovingAverageFilter(2);
double[] result = filter.filter(v);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/filter/MovingAverageFilterTest.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.filter;
public class MovingAverageFilterTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor1() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("0 != 5");
new MovingAverageFilter(2, true, new double[]{});
}
@Test
public void testConstructor2() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("0 != 2");
new MovingAverageFilter(2, false, new double[]{});
}
@Test
public void testFilterSymmetricWithoutWeights() throws Exception {
double[] v = {1, 1, 2, 2, 3, 3, 4, 4, 5};
double[] expected = {1, 1, 1.8, 2.2, 2.8, 3.2, 3.8, 4, 5};
Filter filter = new MovingAverageFilter(2);
double[] result = filter.filter(v);
| Assert.assertArrayEquals(expected, result, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa; | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa; | private final Normalizer normalizer; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer; | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer; | private final DistributionDivider distributionDivider; |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final DistributionDivider distributionDivider;
private final int[] alphabetSizes;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSizes the size of the alphabet used to discretise the values for every segment
*/
public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) { | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final DistributionDivider distributionDivider;
private final int[] alphabetSizes;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSizes the size of the alphabet used to discretise the values for every segment
*/
public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) { | this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), alphabetSizes, new NormalDistributionDivider()); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final DistributionDivider distributionDivider;
private final int[] alphabetSizes;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSizes the size of the alphabet used to discretise the values for every segment
*/
public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) { | // Path: src/main/java/ro/hasna/ts/math/distribution/DistributionDivider.java
// public interface DistributionDivider extends Serializable {
// /**
// * Get the breakpoints for dividing the distribution in equal areas of probability.
// * NOTE: the breakpoints are in ascending order.
// *
// * @param areas the number of areas
// * @return the list of breakpoints
// * @throws NumberIsTooSmallException if areas is smaller than 2
// */
// double[] getBreakpoints(int areas);
// }
//
// Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/Normalizer.java
// public interface Normalizer extends Serializable {
// /**
// * Normalize the sequence of values.
// * NOTE: The length of the output array should be equal to the input array.
// *
// * @param values the sequence of values
// * @return the normalized sequence
// */
// double[] normalize(double[] values);
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximation.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import ro.hasna.ts.math.distribution.DistributionDivider;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.Normalizer;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Indexable Symbolic Aggregate Approximation (iSAX) algorithm.
* <p>
* Reference:
* Camerra A., Palpanas T., Shieh J., Keogh E. (2010)
* <i>iSAX 2.0: Indexing and mining one billion time series</i>
* </p>
*
* @since 0.7
*/
public class IndexableSymbolicAggregateApproximation implements GenericTransformer<double[], SaxPair[]> {
private static final long serialVersionUID = -1652621695908903282L;
private final PiecewiseAggregateApproximation paa;
private final Normalizer normalizer;
private final DistributionDivider distributionDivider;
private final int[] alphabetSizes;
/**
* Creates a new instance of this class with a given number of segments and
* the size of the alphabet.
*
* @param segments the number of segments
* @param alphabetSizes the size of the alphabet used to discretise the values for every segment
*/
public IndexableSymbolicAggregateApproximation(int segments, int[] alphabetSizes) { | this(new PiecewiseAggregateApproximation(segments), new ZNormalizer(), alphabetSizes, new NormalDistributionDivider()); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/TesparDzCoding.java | // Path: src/main/java/ro/hasna/ts/math/type/TesparSymbol.java
// @Data
// public class TesparSymbol {
// private final int duration;
// private final int shape;
// private final double amplitude;
//
// /**
// * Create a TESPAR symbol.
// *
// * @param duration the number of samples between two successive real zeros (one epoch)
// * @param shape the number of local minimums (for a positive epoch) or
// * the number of local maximums (for a negative epoch)
// * @param amplitude the amplitude of the epoch
// */
// public TesparSymbol(int duration, int shape, double amplitude) {
// this.duration = duration;
// this.shape = shape;
// if (amplitude < 0) {
// throw new NotPositiveException(amplitude);
// }
// this.amplitude = amplitude;
// }
// }
| import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.type.TesparSymbol; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Time Encoding Signal Processing and Recognition (TESPAR) coding method.
* <p>
* Reference:
* King R.A., Phipps T.C. (1998)
* <i>Shannon, TESPAR And Approximation Strategies</i>
* </p>
*
* @since 0.7
*/
public class TesparDzCoding implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = 7734158131264856074L;
@Override
public int[] transform(double[] values) { | // Path: src/main/java/ro/hasna/ts/math/type/TesparSymbol.java
// @Data
// public class TesparSymbol {
// private final int duration;
// private final int shape;
// private final double amplitude;
//
// /**
// * Create a TESPAR symbol.
// *
// * @param duration the number of samples between two successive real zeros (one epoch)
// * @param shape the number of local minimums (for a positive epoch) or
// * the number of local maximums (for a negative epoch)
// * @param amplitude the amplitude of the epoch
// */
// public TesparSymbol(int duration, int shape, double amplitude) {
// this.duration = duration;
// this.shape = shape;
// if (amplitude < 0) {
// throw new NotPositiveException(amplitude);
// }
// this.amplitude = amplitude;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/TesparDzCoding.java
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.type.TesparSymbol;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Time Encoding Signal Processing and Recognition (TESPAR) coding method.
* <p>
* Reference:
* King R.A., Phipps T.C. (1998)
* <i>Shannon, TESPAR And Approximation Strategies</i>
* </p>
*
* @since 0.7
*/
public class TesparDzCoding implements GenericTransformer<double[], int[]> {
private static final long serialVersionUID = 7734158131264856074L;
@Override
public int[] transform(double[] values) { | TesparSymbol[] symbols = getTesparSymbols(values); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/DiscreteHaarWaveletTransformTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class DiscreteHaarWaveletTransformTest {
private DiscreteHaarWaveletTransform waveletTransform;
@Before
public void setUp() throws Exception {
waveletTransform = new DiscreteHaarWaveletTransform();
}
@After
public void tearDown() throws Exception {
waveletTransform = null;
}
@Test
public void testTransform() throws Exception {
double[] v = {1, 2, 1, 0, -1, -2, -1, 0};
double[] result = waveletTransform.transform(v);
double[] expected = {0, 8, 2, -2, -1, 1, 1, -1};
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/DiscreteHaarWaveletTransformTest.java
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class DiscreteHaarWaveletTransformTest {
private DiscreteHaarWaveletTransform waveletTransform;
@Before
public void setUp() throws Exception {
waveletTransform = new DiscreteHaarWaveletTransform();
}
@After
public void tearDown() throws Exception {
waveletTransform = null;
}
@Test
public void testTransform() throws Exception {
double[] v = {1, 2, 1, 0, -1, -2, -1, 0};
double[] result = waveletTransform.transform(v);
double[] expected = {0, 8, 2, -2, -1, 1, 1, -1};
| Assert.assertArrayEquals(expected, result, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/exception/NumberIsNotDivisibleException.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
| import org.apache.commons.math3.exception.MathIllegalNumberException;
import org.apache.commons.math3.exception.util.Localizable;
import ro.hasna.ts.math.exception.util.LocalizableMessages; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
/**
* Exception to be thrown when a number is not divisible with a given factor.
*
* @since 0.3
*/
public class NumberIsNotDivisibleException extends MathIllegalNumberException {
private static final long serialVersionUID = -3573144648031073903L;
/**
* The factor for the number.
*/
private final Integer factor;
/**
* Construct the exception.
*
* @param wrong Value that is not divisible with the factor.
* @param factor The factor.
*/
public NumberIsNotDivisibleException(Number wrong,
Integer factor) { | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/exception/NumberIsNotDivisibleException.java
import org.apache.commons.math3.exception.MathIllegalNumberException;
import org.apache.commons.math3.exception.util.Localizable;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
/**
* Exception to be thrown when a number is not divisible with a given factor.
*
* @since 0.3
*/
public class NumberIsNotDivisibleException extends MathIllegalNumberException {
private static final long serialVersionUID = -3573144648031073903L;
/**
* The factor for the number.
*/
private final Integer factor;
/**
* Construct the exception.
*
* @param wrong Value that is not divisible with the factor.
* @param factor The factor.
*/
public NumberIsNotDivisibleException(Number wrong,
Integer factor) { | this(LocalizableMessages.NUMBER_NOT_DIVISIBLE_WITH, wrong, factor); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Aggregate Approximation (PAA) algorithm.
* <p>
* Reference:
* Keogh E., Chakrabarti K., Pazzani M., Mehrotra S. (2001)
* <i>Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases </i>
* </p>
*
* @since 0.5
*/
public class PiecewiseAggregateApproximation implements GenericTransformer<double[], double[]> {
private static final long serialVersionUID = -8199587096227874425L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
@Override
public double[] transform(double[] values) {
int len = values.length;
if (len < segments) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseAggregateApproximation.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Aggregate Approximation (PAA) algorithm.
* <p>
* Reference:
* Keogh E., Chakrabarti K., Pazzani M., Mehrotra S. (2001)
* <i>Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases </i>
* </p>
*
* @since 0.5
*/
public class PiecewiseAggregateApproximation implements GenericTransformer<double[], double[]> {
private static final long serialVersionUID = -8199587096227874425L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
@Override
public double[] transform(double[] values) {
int len = values.length;
if (len < segments) { | throw new ArrayLengthIsTooSmallException(len, segments, true); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class MatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new MatrixProfileTransformer(0);
}
@Test
public void transform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).transform(new double[5], new double[3]);
}
@Test
public void fullJoinTransform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).fullJoinTransform(new double[5], new double[3]);
}
@Test
public void transform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
| // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class MatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new MatrixProfileTransformer(0);
}
@Test
public void transform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).transform(new double[5], new double[3]);
}
@Test
public void fullJoinTransform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).fullJoinTransform(new double[5], new double[3]);
}
@Test
public void transform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
| MatrixProfile transform = transformer.transform(v, v2); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class MatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new MatrixProfileTransformer(0);
}
@Test
public void transform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).transform(new double[5], new double[3]);
}
@Test
public void fullJoinTransform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).fullJoinTransform(new double[5], new double[3]);
}
@Test
public void transform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {1.4142135623730951, 46.05431575867782, 3.1622776601683795, 3.3166247903554};
int[] expectedIp = {0, 0, 6, 7};
| // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class MatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new MatrixProfileTransformer(0);
}
@Test
public void transform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).transform(new double[5], new double[3]);
}
@Test
public void fullJoinTransform_withSmallInput() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new MatrixProfileTransformer(4).fullJoinTransform(new double[5], new double[3]);
}
@Test
public void transform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {1.4142135623730951, 46.05431575867782, 3.1622776601683795, 3.3166247903554};
int[] expectedIp = {0, 0, 6, 7};
| Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {1.4142135623730951, 46.05431575867782, 3.1622776601683795, 3.3166247903554};
int[] expectedIp = {0, 0, 6, 7};
Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON);
Assert.assertArrayEquals(expectedIp, transform.getIndexProfile());
}
@Test
public void transform_withNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {0.5257667760397134, 0.0970386144176177, 0.12392968870054807, 0.16907943342270723};
int[] expectedIp = {1, 1, 6, 7};
Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON);
Assert.assertArrayEquals(expectedIp, transform.getIndexProfile());
}
@Test
public void fullJoinTransform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
| // Path: src/main/java/ro/hasna/ts/math/type/FullMatrixProfile.java
// @Data
// public class FullMatrixProfile {
// private final MatrixProfile leftMatrixProfile;
// private final MatrixProfile rightMatrixProfile;
//
// public FullMatrixProfile(MatrixProfile leftMatrixProfile, MatrixProfile rightMatrixProfile) {
// this.leftMatrixProfile = leftMatrixProfile;
// this.rightMatrixProfile = rightMatrixProfile;
// }
//
// public FullMatrixProfile(int leftSize, int rightSize) {
// this(new MatrixProfile(leftSize), new MatrixProfile(rightSize));
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/MatrixProfileTransformerTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.FullMatrixProfile;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {1.4142135623730951, 46.05431575867782, 3.1622776601683795, 3.3166247903554};
int[] expectedIp = {0, 0, 6, 7};
Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON);
Assert.assertArrayEquals(expectedIp, transform.getIndexProfile());
}
@Test
public void transform_withNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
MatrixProfile transform = transformer.transform(v, v2);
double[] expectedMp = {0.5257667760397134, 0.0970386144176177, 0.12392968870054807, 0.16907943342270723};
int[] expectedIp = {1, 1, 6, 7};
Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON);
Assert.assertArrayEquals(expectedIp, transform.getIndexProfile());
}
@Test
public void fullJoinTransform_withoutNormalization() throws Exception {
MatrixProfileTransformer transformer = new MatrixProfileTransformer(4, 0, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 49, 25, 19};
double[] v2 = {1, 2, 2, 5, 50, 25, 18};
| FullMatrixProfile transform = transformer.fullJoinTransform(v, v2); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/StompTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java
// public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
// private static final long serialVersionUID = 4542559569313251930L;
// private final double epsilon;
// private final double radiusPercentage;
//
// /**
// * Creates a new instance of this class with
// *
// * @param epsilon the maximum absolute difference between two values that are considered equal
// * @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
// * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
// */
// public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
// this.epsilon = epsilon;
// if (radiusPercentage < 0 || radiusPercentage > 1) {
// throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
// }
//
// this.radiusPercentage = radiusPercentage;
// }
//
// @Override
// public double compute(double[] a, double[] b) {
// return compute(a, b, Double.POSITIVE_INFINITY);
// }
//
// @Override
// public double compute(double[] a, double[] b, double cutOffValue) {
// int n = a.length;
// int radius = (int) (n * radiusPercentage);
// double min = -1;
// if (cutOffValue < 1) {
// min = n * (1 - cutOffValue);
// }
//
// int lcs = computeLcs(a, b, n, radius, min);
// if (lcs == -1) {
// return Double.POSITIVE_INFINITY;
// }
//
// return 1 - lcs * 1.0 / n;
// }
//
// private int computeLcs(double[] a, double[] b, int n, int radius, double min) {
// min = min - n + 1;
//
// int w = 2 * radius + 1;
// int[] prev = new int[w];
// int[] current = new int[w];
// int start, end, x, y, k = 0;
// for (int i = 0; i < n; i++) {
// k = FastMath.max(0, radius - i);
// start = FastMath.max(0, i - radius);
// end = FastMath.min(n - 1, i + radius);
// for (int j = start; j <= end; j++, k++) {
// if (Precision.equals(a[i], b[j], epsilon)) {
// current[k] = prev[k] + 1;
// } else {
// if (k - 1 >= 0) x = current[k - 1];
// else x = 0;
//
// if (k + 1 < w) y = prev[k + 1];
// else y = 0;
//
// current[k] = FastMath.max(x, y);
// }
// }
//
// if (current[k - 1] - i <= min) {
// return -1;
// }
//
// System.arraycopy(current, 0, prev, 0, w);
// }
//
// return current[k - 1];
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.ml.distance.LongestCommonSubsequenceDistance;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.Locale;
import java.util.Scanner; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* @since 0.17
*/
public class StompTransformerTest {
@Test
public void transform_withoutNormalization() {
StompTransformer transformer = new StompTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| // Path: src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java
// public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
// private static final long serialVersionUID = 4542559569313251930L;
// private final double epsilon;
// private final double radiusPercentage;
//
// /**
// * Creates a new instance of this class with
// *
// * @param epsilon the maximum absolute difference between two values that are considered equal
// * @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
// * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
// */
// public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
// this.epsilon = epsilon;
// if (radiusPercentage < 0 || radiusPercentage > 1) {
// throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
// }
//
// this.radiusPercentage = radiusPercentage;
// }
//
// @Override
// public double compute(double[] a, double[] b) {
// return compute(a, b, Double.POSITIVE_INFINITY);
// }
//
// @Override
// public double compute(double[] a, double[] b, double cutOffValue) {
// int n = a.length;
// int radius = (int) (n * radiusPercentage);
// double min = -1;
// if (cutOffValue < 1) {
// min = n * (1 - cutOffValue);
// }
//
// int lcs = computeLcs(a, b, n, radius, min);
// if (lcs == -1) {
// return Double.POSITIVE_INFINITY;
// }
//
// return 1 - lcs * 1.0 / n;
// }
//
// private int computeLcs(double[] a, double[] b, int n, int radius, double min) {
// min = min - n + 1;
//
// int w = 2 * radius + 1;
// int[] prev = new int[w];
// int[] current = new int[w];
// int start, end, x, y, k = 0;
// for (int i = 0; i < n; i++) {
// k = FastMath.max(0, radius - i);
// start = FastMath.max(0, i - radius);
// end = FastMath.min(n - 1, i + radius);
// for (int j = start; j <= end; j++, k++) {
// if (Precision.equals(a[i], b[j], epsilon)) {
// current[k] = prev[k] + 1;
// } else {
// if (k - 1 >= 0) x = current[k - 1];
// else x = 0;
//
// if (k + 1 < w) y = prev[k + 1];
// else y = 0;
//
// current[k] = FastMath.max(x, y);
// }
// }
//
// if (current[k - 1] - i <= min) {
// return -1;
// }
//
// System.arraycopy(current, 0, prev, 0, w);
// }
//
// return current[k - 1];
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/StompTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.ml.distance.LongestCommonSubsequenceDistance;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.Locale;
import java.util.Scanner;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* @since 0.17
*/
public class StompTransformerTest {
@Test
public void transform_withoutNormalization() {
StompTransformer transformer = new StompTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| MatrixProfile transform = transformer.transform(v); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/StompTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java
// public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
// private static final long serialVersionUID = 4542559569313251930L;
// private final double epsilon;
// private final double radiusPercentage;
//
// /**
// * Creates a new instance of this class with
// *
// * @param epsilon the maximum absolute difference between two values that are considered equal
// * @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
// * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
// */
// public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
// this.epsilon = epsilon;
// if (radiusPercentage < 0 || radiusPercentage > 1) {
// throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
// }
//
// this.radiusPercentage = radiusPercentage;
// }
//
// @Override
// public double compute(double[] a, double[] b) {
// return compute(a, b, Double.POSITIVE_INFINITY);
// }
//
// @Override
// public double compute(double[] a, double[] b, double cutOffValue) {
// int n = a.length;
// int radius = (int) (n * radiusPercentage);
// double min = -1;
// if (cutOffValue < 1) {
// min = n * (1 - cutOffValue);
// }
//
// int lcs = computeLcs(a, b, n, radius, min);
// if (lcs == -1) {
// return Double.POSITIVE_INFINITY;
// }
//
// return 1 - lcs * 1.0 / n;
// }
//
// private int computeLcs(double[] a, double[] b, int n, int radius, double min) {
// min = min - n + 1;
//
// int w = 2 * radius + 1;
// int[] prev = new int[w];
// int[] current = new int[w];
// int start, end, x, y, k = 0;
// for (int i = 0; i < n; i++) {
// k = FastMath.max(0, radius - i);
// start = FastMath.max(0, i - radius);
// end = FastMath.min(n - 1, i + radius);
// for (int j = start; j <= end; j++, k++) {
// if (Precision.equals(a[i], b[j], epsilon)) {
// current[k] = prev[k] + 1;
// } else {
// if (k - 1 >= 0) x = current[k - 1];
// else x = 0;
//
// if (k + 1 < w) y = prev[k + 1];
// else y = 0;
//
// current[k] = FastMath.max(x, y);
// }
// }
//
// if (current[k - 1] - i <= min) {
// return -1;
// }
//
// System.arraycopy(current, 0, prev, 0, w);
// }
//
// return current[k - 1];
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.ml.distance.LongestCommonSubsequenceDistance;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.Locale;
import java.util.Scanner; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* @since 0.17
*/
public class StompTransformerTest {
@Test
public void transform_withoutNormalization() {
StompTransformer transformer = new StompTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| // Path: src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java
// public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
// private static final long serialVersionUID = 4542559569313251930L;
// private final double epsilon;
// private final double radiusPercentage;
//
// /**
// * Creates a new instance of this class with
// *
// * @param epsilon the maximum absolute difference between two values that are considered equal
// * @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
// * @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
// */
// public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
// this.epsilon = epsilon;
// if (radiusPercentage < 0 || radiusPercentage > 1) {
// throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1);
// }
//
// this.radiusPercentage = radiusPercentage;
// }
//
// @Override
// public double compute(double[] a, double[] b) {
// return compute(a, b, Double.POSITIVE_INFINITY);
// }
//
// @Override
// public double compute(double[] a, double[] b, double cutOffValue) {
// int n = a.length;
// int radius = (int) (n * radiusPercentage);
// double min = -1;
// if (cutOffValue < 1) {
// min = n * (1 - cutOffValue);
// }
//
// int lcs = computeLcs(a, b, n, radius, min);
// if (lcs == -1) {
// return Double.POSITIVE_INFINITY;
// }
//
// return 1 - lcs * 1.0 / n;
// }
//
// private int computeLcs(double[] a, double[] b, int n, int radius, double min) {
// min = min - n + 1;
//
// int w = 2 * radius + 1;
// int[] prev = new int[w];
// int[] current = new int[w];
// int start, end, x, y, k = 0;
// for (int i = 0; i < n; i++) {
// k = FastMath.max(0, radius - i);
// start = FastMath.max(0, i - radius);
// end = FastMath.min(n - 1, i + radius);
// for (int j = start; j <= end; j++, k++) {
// if (Precision.equals(a[i], b[j], epsilon)) {
// current[k] = prev[k] + 1;
// } else {
// if (k - 1 >= 0) x = current[k - 1];
// else x = 0;
//
// if (k + 1 < w) y = prev[k + 1];
// else y = 0;
//
// current[k] = FastMath.max(x, y);
// }
// }
//
// if (current[k - 1] - i <= min) {
// return -1;
// }
//
// System.arraycopy(current, 0, prev, 0, w);
// }
//
// return current[k - 1];
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/StompTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.ml.distance.LongestCommonSubsequenceDistance;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.Locale;
import java.util.Scanner;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* @since 0.17
*/
public class StompTransformerTest {
@Test
public void transform_withoutNormalization() {
StompTransformer transformer = new StompTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/DiscreteFourierTransformTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import java.util.Arrays;
import java.util.stream.Collectors;
import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.transform.DftNormalization;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | }
@Test
public void testTransformSineWave() throws Exception {
double signalFrequency = 100;
double amplitude = 30;
double displacement = 17;
double samplingFrequency = signalFrequency * 8; //it should be at least double (Shannon Theorem)
int len = 1000;
double[] v = new double[len];
for (int i = 0; i < len; i++) {
v[i] = amplitude * Math.sin(2 * Math.PI * signalFrequency * i / samplingFrequency) + displacement;
}
double[] result = new DiscreteFourierTransform().transform(v);
double max = 0;
int pos = 0;
for (int i = 1; i < result.length; i++) {
if (max < result[i]) {
max = result[i];
pos = i;
}
}
int powerOfTwo = Integer.highestOneBit(len);
if (len != powerOfTwo) {
powerOfTwo = powerOfTwo << 1;
}
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/DiscreteFourierTransformTest.java
import java.util.Arrays;
import java.util.stream.Collectors;
import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.transform.DftNormalization;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
}
@Test
public void testTransformSineWave() throws Exception {
double signalFrequency = 100;
double amplitude = 30;
double displacement = 17;
double samplingFrequency = signalFrequency * 8; //it should be at least double (Shannon Theorem)
int len = 1000;
double[] v = new double[len];
for (int i = 0; i < len; i++) {
v[i] = amplitude * Math.sin(2 * Math.PI * signalFrequency * i / samplingFrequency) + displacement;
}
double[] result = new DiscreteFourierTransform().transform(v);
double max = 0;
int pos = 0;
for (int i = 1; i < result.length; i++) {
if (max < result[i]) {
max = result[i];
pos = i;
}
}
int powerOfTwo = Integer.highestOneBit(len);
if (len != powerOfTwo) {
powerOfTwo = powerOfTwo << 1;
}
| Assert.assertEquals(amplitude, max, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximation.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.ArrayList;
import java.util.List; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements Piecewise Approximation using a curve fitting function (ex: linear = PLA, quadratic = PQA etc.).
* An optimised implementation for the mean function is the class {@code PiecewiseAggregateApproximation}.
*
* @since 0.10
*/
public class PiecewiseCurveFitterApproximation implements GenericTransformer<double[], double[][]> {
private static final long serialVersionUID = -410430777798956046L;
private final int segments;
private final AbstractCurveFitter curveFitter;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @param curveFitter the curve fitting function
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseCurveFitterApproximation(int segments, AbstractCurveFitter curveFitter) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
this.curveFitter = curveFitter;
}
@Override
public double[][] transform(double[] values) {
int len = values.length;
if (len < segments) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximation.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.ArrayList;
import java.util.List;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements Piecewise Approximation using a curve fitting function (ex: linear = PLA, quadratic = PQA etc.).
* An optimised implementation for the mean function is the class {@code PiecewiseAggregateApproximation}.
*
* @since 0.10
*/
public class PiecewiseCurveFitterApproximation implements GenericTransformer<double[], double[][]> {
private static final long serialVersionUID = -410430777798956046L;
private final int segments;
private final AbstractCurveFitter curveFitter;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @param curveFitter the curve fitting function
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseCurveFitterApproximation(int segments, AbstractCurveFitter curveFitter) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
this.curveFitter = curveFitter;
}
@Override
public double[][] transform(double[] values) {
int len = values.length;
if (len < segments) { | throw new ArrayLengthIsTooSmallException(len, segments, true); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximation.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.ArrayList;
import java.util.List; | List<WeightedObservedPoint> segment = new ArrayList<>(segmentSize);
for (int i = 0; i < segmentSize; i++) {
segment.add(null);
}
int n = 0;
for (int i = 0; i < len; i++) {
int index = i % segmentSize;
segment.set(index, new WeightedObservedPoint(1, index, values[i]));
if (index + 1 == segmentSize) {
reducedValues[n++] = curveFitter.fit(segment);
if (n == segments) break;
}
}
} else {
double segmentSize = len * 1.0 / segments;
int k = 0;
int segmentSizeReal = (int) FastMath.ceil(segmentSize) + 1;
int index = 0;
List<WeightedObservedPoint> segment = new ArrayList<>(segmentSizeReal);
for (int i = 0; i < segments - 1; i++) {
double x = (i + 1) * segmentSize - 1;
for (; k < x; k++) {
segment.add(new WeightedObservedPoint(1, index, values[k]));
index++;
}
double delta = x - (int) x; | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseCurveFitterApproximation.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
import java.util.ArrayList;
import java.util.List;
List<WeightedObservedPoint> segment = new ArrayList<>(segmentSize);
for (int i = 0; i < segmentSize; i++) {
segment.add(null);
}
int n = 0;
for (int i = 0; i < len; i++) {
int index = i % segmentSize;
segment.set(index, new WeightedObservedPoint(1, index, values[i]));
if (index + 1 == segmentSize) {
reducedValues[n++] = curveFitter.fit(segment);
if (n == segments) break;
}
}
} else {
double segmentSize = len * 1.0 / segments;
int k = 0;
int segmentSizeReal = (int) FastMath.ceil(segmentSize) + 1;
int index = 0;
List<WeightedObservedPoint> segment = new ArrayList<>(segmentSizeReal);
for (int i = 0; i < segments - 1; i++) {
double x = (i + 1) * segmentSize - 1;
for (; k < x; k++) {
segment.add(new WeightedObservedPoint(1, index, values[k]));
index++;
}
double delta = x - (int) x; | if (!Precision.equals(delta, 0, TimeSeriesPrecision.EPSILON)) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/AbstractMatrixProfileTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
| import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.transform.DftNormalization;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import java.io.Serializable;
import java.util.Random;
import java.util.concurrent.ThreadLocalRandom; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Useful methods used by full and self join matrix profile algorithms.
*
* @since 0.17
*/
public abstract class AbstractMatrixProfileTransformer implements Serializable {
private static final long serialVersionUID = -4758909899130005950L;
protected final int window;
protected final double exclusionZonePercentage;
protected final boolean useNormalization;
/**
* Creates a new instance of this class with normalization enabled and an exclusion zone of 25%.
*
* @param window the length of the window
* @throws NumberIsTooSmallException if window is lower than 4
*/
public AbstractMatrixProfileTransformer(int window) {
this(window, 0.25, true);
}
/**
* @param window the length of the window
* @param exclusionZonePercentage percentage of window that should be excluded at distance profile computing
* @param useNormalization flag to use Z-Normalization
* @throws NumberIsTooSmallException if window is lower than 1
*/
public AbstractMatrixProfileTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
this.useNormalization = useNormalization;
if (window < 1) {
throw new NumberIsTooSmallException(window, 1, true);
}
this.window = window;
this.exclusionZonePercentage = exclusionZonePercentage;
}
/**
* This method will update bStatistics with last sliding window statistics
*/ | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/AbstractMatrixProfileTransformer.java
import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.transform.DftNormalization;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import java.io.Serializable;
import java.util.Random;
import java.util.concurrent.ThreadLocalRandom;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Useful methods used by full and self join matrix profile algorithms.
*
* @since 0.17
*/
public abstract class AbstractMatrixProfileTransformer implements Serializable {
private static final long serialVersionUID = -4758909899130005950L;
protected final int window;
protected final double exclusionZonePercentage;
protected final boolean useNormalization;
/**
* Creates a new instance of this class with normalization enabled and an exclusion zone of 25%.
*
* @param window the length of the window
* @throws NumberIsTooSmallException if window is lower than 4
*/
public AbstractMatrixProfileTransformer(int window) {
this(window, 0.25, true);
}
/**
* @param window the length of the window
* @param exclusionZonePercentage percentage of window that should be excluded at distance profile computing
* @param useNormalization flag to use Z-Normalization
* @throws NumberIsTooSmallException if window is lower than 1
*/
public AbstractMatrixProfileTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
this.useNormalization = useNormalization;
if (window < 1) {
throw new NumberIsTooSmallException(window, 1, true);
}
this.window = window;
this.exclusionZonePercentage = exclusionZonePercentage;
}
/**
* This method will update bStatistics with last sliding window statistics
*/ | protected void computeFirstNormalizedDistanceProfile(double[] a, BothWaySummaryStatistics aStatistics, double[] b, BothWaySummaryStatistics bStatistics, int skip, int nb, double[] productSums, double[] distanceProfile) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
| import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.util.LocalizableMessages; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Longest Common Subsequence.
* <p>
* Reference:
* Vlachos Michail, George Kollios and Dimitrios Gunopulos (2002)
* <i>Discovering similar multidimensional trajectories</i>
* </p>
*
* @since 0.7
*/
public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 4542559569313251930L;
private final double epsilon;
private final double radiusPercentage;
/**
* Creates a new instance of this class with
*
* @param epsilon the maximum absolute difference between two values that are considered equal
* @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
* @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
*/
public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
this.epsilon = epsilon;
if (radiusPercentage < 0 || radiusPercentage > 1) { | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/LongestCommonSubsequenceDistance.java
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Longest Common Subsequence.
* <p>
* Reference:
* Vlachos Michail, George Kollios and Dimitrios Gunopulos (2002)
* <i>Discovering similar multidimensional trajectories</i>
* </p>
*
* @since 0.7
*/
public class LongestCommonSubsequenceDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = 4542559569313251930L;
private final double epsilon;
private final double radiusPercentage;
/**
* Creates a new instance of this class with
*
* @param epsilon the maximum absolute difference between two values that are considered equal
* @param radiusPercentage Sakoe-Chiba Band width used to constraint the searching window
* @throws OutOfRangeException if radiusPercentage is outside the interval [0, 1]
*/
public LongestCommonSubsequenceDistance(double epsilon, double radiusPercentage) {
this.epsilon = epsilon;
if (radiusPercentage < 0 || radiusPercentage > 1) { | throw new OutOfRangeException(LocalizableMessages.OUT_OF_RANGE_BOTH_INCLUSIVE, radiusPercentage, 0, 1); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/normalization/MinMaxNormalizerTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.normalization;
public class MinMaxNormalizerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooLargeException.class);
thrown.expectMessage("4 is larger than the maximum (3)");
new MinMaxNormalizer(4, 3);
}
@Test
public void testNormalizeDefaultConstructor() throws Exception {
MinMaxNormalizer normalizer = new MinMaxNormalizer();
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
double[] expected = {0.0, 0.25, 0.5, 0.75, 1.0};
double[] out = normalizer.normalize(v);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/normalization/MinMaxNormalizerTest.java
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.normalization;
public class MinMaxNormalizerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooLargeException.class);
thrown.expectMessage("4 is larger than the maximum (3)");
new MinMaxNormalizer(4, 3);
}
@Test
public void testNormalizeDefaultConstructor() throws Exception {
MinMaxNormalizer normalizer = new MinMaxNormalizer();
double[] v = {1.0, 2.0, 3.0, 4.0, 5.0};
double[] expected = {0.0, 0.25, 0.5, 0.75, 1.0};
double[] out = normalizer.normalize(v);
| Assert.assertArrayEquals(expected, out, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Linear Aggregate Approximation (PLAA) algorithm.
* <p>
* Reference:
* Hung N. Q. V., Anh D. T. (2008)
* <i>An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases</i>
* </p>
*
* @since 0.6
*/
public class PiecewiseLinearAggregateApproximation implements GenericTransformer<double[], MeanSlopePair[]> {
private static final long serialVersionUID = -4073250977010141095L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseLinearAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
/**
* Transform a given sequence of values using the algorithm PLAA.
*
* @param values the sequence of values
* @return the result of the transformation
*/
public MeanSlopePair[] transform(double[] values) {
int len = values.length;
if (len < segments) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximation.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Linear Aggregate Approximation (PLAA) algorithm.
* <p>
* Reference:
* Hung N. Q. V., Anh D. T. (2008)
* <i>An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases</i>
* </p>
*
* @since 0.6
*/
public class PiecewiseLinearAggregateApproximation implements GenericTransformer<double[], MeanSlopePair[]> {
private static final long serialVersionUID = -4073250977010141095L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseLinearAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
/**
* Transform a given sequence of values using the algorithm PLAA.
*
* @param values the sequence of values
* @return the result of the transformation
*/
public MeanSlopePair[] transform(double[] values) {
int len = values.length;
if (len < segments) { | throw new ArrayLengthIsTooSmallException(len, segments, true); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximation.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Linear Aggregate Approximation (PLAA) algorithm.
* <p>
* Reference:
* Hung N. Q. V., Anh D. T. (2008)
* <i>An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases</i>
* </p>
*
* @since 0.6
*/
public class PiecewiseLinearAggregateApproximation implements GenericTransformer<double[], MeanSlopePair[]> {
private static final long serialVersionUID = -4073250977010141095L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseLinearAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
/**
* Transform a given sequence of values using the algorithm PLAA.
*
* @param values the sequence of values
* @return the result of the transformation
*/
public MeanSlopePair[] transform(double[] values) {
int len = values.length;
if (len < segments) {
throw new ArrayLengthIsTooSmallException(len, segments, true);
}
int modulo = len % segments;
if (modulo != 0) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsNotDivisibleException.java
// public class ArrayLengthIsNotDivisibleException extends NumberIsNotDivisibleException {
// private static final long serialVersionUID = 1652407890465175618L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Number wrong, Integer factor) {
// super(wrong, factor);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is not divisible with the factor.
// * @param factor The factor.
// */
// public ArrayLengthIsNotDivisibleException(Localizable specific, Number wrong, Integer factor) {
// super(specific, wrong, factor);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanSlopePair.java
// @Data
// public class MeanSlopePair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The slope of the segment.
// */
// private final double slope;
// }
// Path: src/main/java/ro/hasna/ts/math/representation/PiecewiseLinearAggregateApproximation.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import ro.hasna.ts.math.exception.ArrayLengthIsNotDivisibleException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanSlopePair;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
/**
* Implements the Piecewise Linear Aggregate Approximation (PLAA) algorithm.
* <p>
* Reference:
* Hung N. Q. V., Anh D. T. (2008)
* <i>An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases</i>
* </p>
*
* @since 0.6
*/
public class PiecewiseLinearAggregateApproximation implements GenericTransformer<double[], MeanSlopePair[]> {
private static final long serialVersionUID = -4073250977010141095L;
private final int segments;
/**
* Creates a new instance of this class.
*
* @param segments the number of segments
* @throws NumberIsTooSmallException if segments lower than 1
*/
public PiecewiseLinearAggregateApproximation(int segments) {
if (segments < 1) {
throw new NumberIsTooSmallException(segments, 1, true);
}
this.segments = segments;
}
/**
* Transform a given sequence of values using the algorithm PLAA.
*
* @param values the sequence of values
* @return the result of the transformation
*/
public MeanSlopePair[] transform(double[] values) {
int len = values.length;
if (len < segments) {
throw new ArrayLengthIsTooSmallException(len, segments, true);
}
int modulo = len % segments;
if (modulo != 0) { | throw new ArrayLengthIsNotDivisibleException(len, segments); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/distribution/AdaptiveDistributionDividerTest.java | // Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class AdaptiveDistributionDividerTest {
private AdaptiveDistributionDivider divider;
@Before
public void setUp() throws Exception {
double[][] trainingSet = {
{60.7, 96.05, 130.45, 151.3, 160.45, 162.55, 160.3, 138.8, 111.85, 79.6},
{59.5, 93.85, 128.9, 150.9, 160.55, 162.6, 160.45, 140.1, 112.6, 81.25},
{63.7, 99.4, 131.55, 150.9, 160.85, 163.8, 159.55, 135.2, 109.4, 78.65},
{62.6, 96.7, 129.85, 150.35, 161.05, 163.75, 160.5, 137.15, 110.95, 80.6},
{59.35, 94, 128.1, 150, 160.3, 163.7, 160.55, 139.15, 111, 83.05},
{58.35, 92.1, 127.3, 149.4, 160, 162.8, 160.95, 139.9, 112.6, 83.5}
};
| // Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/distribution/AdaptiveDistributionDividerTest.java
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class AdaptiveDistributionDividerTest {
private AdaptiveDistributionDivider divider;
@Before
public void setUp() throws Exception {
double[][] trainingSet = {
{60.7, 96.05, 130.45, 151.3, 160.45, 162.55, 160.3, 138.8, 111.85, 79.6},
{59.5, 93.85, 128.9, 150.9, 160.55, 162.6, 160.45, 140.1, 112.6, 81.25},
{63.7, 99.4, 131.55, 150.9, 160.85, 163.8, 159.55, 135.2, 109.4, 78.65},
{62.6, 96.7, 129.85, 150.35, 161.05, 163.75, 160.5, 137.15, 110.95, 80.6},
{59.35, 94, 128.1, 150, 160.3, 163.7, 160.55, 139.15, 111, 83.05},
{58.35, 92.1, 127.3, 149.4, 160, 162.8, 160.95, 139.9, 112.6, 83.5}
};
| ZNormalizer normalizer = new ZNormalizer(); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/distribution/AdaptiveDistributionDividerTest.java | // Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class AdaptiveDistributionDividerTest {
private AdaptiveDistributionDivider divider;
@Before
public void setUp() throws Exception {
double[][] trainingSet = {
{60.7, 96.05, 130.45, 151.3, 160.45, 162.55, 160.3, 138.8, 111.85, 79.6},
{59.5, 93.85, 128.9, 150.9, 160.55, 162.6, 160.45, 140.1, 112.6, 81.25},
{63.7, 99.4, 131.55, 150.9, 160.85, 163.8, 159.55, 135.2, 109.4, 78.65},
{62.6, 96.7, 129.85, 150.35, 161.05, 163.75, 160.5, 137.15, 110.95, 80.6},
{59.35, 94, 128.1, 150, 160.3, 163.7, 160.55, 139.15, 111, 83.05},
{58.35, 92.1, 127.3, 149.4, 160, 162.8, 160.95, 139.9, 112.6, 83.5}
};
ZNormalizer normalizer = new ZNormalizer();
for (int i = 0; i < trainingSet.length; i++) {
trainingSet[i] = normalizer.normalize(trainingSet[i]);
}
| // Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/distribution/AdaptiveDistributionDividerTest.java
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class AdaptiveDistributionDividerTest {
private AdaptiveDistributionDivider divider;
@Before
public void setUp() throws Exception {
double[][] trainingSet = {
{60.7, 96.05, 130.45, 151.3, 160.45, 162.55, 160.3, 138.8, 111.85, 79.6},
{59.5, 93.85, 128.9, 150.9, 160.55, 162.6, 160.45, 140.1, 112.6, 81.25},
{63.7, 99.4, 131.55, 150.9, 160.85, 163.8, 159.55, 135.2, 109.4, 78.65},
{62.6, 96.7, 129.85, 150.35, 161.05, 163.75, 160.5, 137.15, 110.95, 80.6},
{59.35, 94, 128.1, 150, 160.3, 163.7, 160.55, 139.15, 111, 83.05},
{58.35, 92.1, 127.3, 149.4, 160, 162.8, 160.95, 139.9, 112.6, 83.5}
};
ZNormalizer normalizer = new ZNormalizer();
for (int i = 0; i < trainingSet.length; i++) {
trainingSet[i] = normalizer.normalize(trainingSet[i]);
}
| divider = new AdaptiveDistributionDivider(trainingSet, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class SelfJoinAbstractMatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new SelfJoinAbstractMatrixProfileTransformer(3) {
private static final long serialVersionUID = 7691413957352355633L;
@Override | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformerTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class SelfJoinAbstractMatrixProfileTransformerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void constructor_withSmallWindow() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (4)");
new SelfJoinAbstractMatrixProfileTransformer(3) {
private static final long serialVersionUID = 7691413957352355633L;
@Override | protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformer.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/CancellationException.java
// public class CancellationException extends MathIllegalStateException {
// private static final long serialVersionUID = 8488577710717564366L;
//
// public CancellationException() {
// super(LocalizableMessages.OPERATION_WAS_CANCELLED);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.exception.CancellationException;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Useful methods for self-join matrix profile algorithms.
*
* @since 0.17
*/
public abstract class SelfJoinAbstractMatrixProfileTransformer extends AbstractMatrixProfileTransformer implements GenericTransformer<double[], MatrixProfile> {
private static final long serialVersionUID = 4273395812927663256L;
/**
* Creates a new instance of this class with normalization enabled and an exclusion zone of 25%.
*
* @param window the length of the window
* @throws NumberIsTooSmallException if window is lower than 4
*/
public SelfJoinAbstractMatrixProfileTransformer(int window) {
this(window, 0.25, true);
}
/**
* @param window the length of the window
* @param exclusionZonePercentage percentage of window that should be excluded at distance profile computing
* @param useNormalization flag to use Z-Normalization
* @throws NumberIsTooSmallException if window or window * exclusionZonePercentage is lower than 1
*/
public SelfJoinAbstractMatrixProfileTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
int skip = (int) (window * exclusionZonePercentage);
if (skip < 1) {
int minWindow = (int) Math.ceil(1 / exclusionZonePercentage);
throw new NumberIsTooSmallException(window, minWindow, true);
}
}
@Override
public MatrixProfile transform(double[] input) {
int len = input.length;
int skip = (int) (window * exclusionZonePercentage);
if (len < window + skip) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/CancellationException.java
// public class CancellationException extends MathIllegalStateException {
// private static final long serialVersionUID = 8488577710717564366L;
//
// public CancellationException() {
// super(LocalizableMessages.OPERATION_WAS_CANCELLED);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformer.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.exception.CancellationException;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Useful methods for self-join matrix profile algorithms.
*
* @since 0.17
*/
public abstract class SelfJoinAbstractMatrixProfileTransformer extends AbstractMatrixProfileTransformer implements GenericTransformer<double[], MatrixProfile> {
private static final long serialVersionUID = 4273395812927663256L;
/**
* Creates a new instance of this class with normalization enabled and an exclusion zone of 25%.
*
* @param window the length of the window
* @throws NumberIsTooSmallException if window is lower than 4
*/
public SelfJoinAbstractMatrixProfileTransformer(int window) {
this(window, 0.25, true);
}
/**
* @param window the length of the window
* @param exclusionZonePercentage percentage of window that should be excluded at distance profile computing
* @param useNormalization flag to use Z-Normalization
* @throws NumberIsTooSmallException if window or window * exclusionZonePercentage is lower than 1
*/
public SelfJoinAbstractMatrixProfileTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
int skip = (int) (window * exclusionZonePercentage);
if (skip < 1) {
int minWindow = (int) Math.ceil(1 / exclusionZonePercentage);
throw new NumberIsTooSmallException(window, minWindow, true);
}
}
@Override
public MatrixProfile transform(double[] input) {
int len = input.length;
int skip = (int) (window * exclusionZonePercentage);
if (len < window + skip) { | throw new ArrayLengthIsTooSmallException(len, window + skip, true); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformer.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/CancellationException.java
// public class CancellationException extends MathIllegalStateException {
// private static final long serialVersionUID = 8488577710717564366L;
//
// public CancellationException() {
// super(LocalizableMessages.OPERATION_WAS_CANCELLED);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.exception.CancellationException;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | for (int i = 0; i < profile.length; i++) {
profile[i] = FastMath.sqrt(profile[i]);
}
}
protected void updateMatrixProfileFromDistanceProfile(double[] distanceProfile, int i, int skip, int n, MatrixProfile mp, Predicate<MatrixProfile> callback) {
for (int j = i + skip; j < n; j++) {
// update horizontal line from upper triangle
if (mp.getProfile()[j] > distanceProfile[j]) {
mp.getProfile()[j] = distanceProfile[j];
mp.getIndexProfile()[j] = i;
}
// update vertical line i from matrix profile
if (mp.getProfile()[i] > distanceProfile[j]) {
mp.getProfile()[i] = distanceProfile[j];
mp.getIndexProfile()[i] = j;
}
}
executeCallback(callback, mp);
}
protected void executeCallback(Predicate<MatrixProfile> callback, MatrixProfile mp) {
if (callback == null) {
return;
}
MatrixProfile clone = mp.clone();
updateMatrixProfileWithSqrt(clone);
if (!callback.test(clone)) { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/exception/CancellationException.java
// public class CancellationException extends MathIllegalStateException {
// private static final long serialVersionUID = 8488577710717564366L;
//
// public CancellationException() {
// super(LocalizableMessages.OPERATION_WAS_CANCELLED);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/SelfJoinAbstractMatrixProfileTransformer.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.FastMath;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.exception.CancellationException;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
for (int i = 0; i < profile.length; i++) {
profile[i] = FastMath.sqrt(profile[i]);
}
}
protected void updateMatrixProfileFromDistanceProfile(double[] distanceProfile, int i, int skip, int n, MatrixProfile mp, Predicate<MatrixProfile> callback) {
for (int j = i + skip; j < n; j++) {
// update horizontal line from upper triangle
if (mp.getProfile()[j] > distanceProfile[j]) {
mp.getProfile()[j] = distanceProfile[j];
mp.getIndexProfile()[j] = i;
}
// update vertical line i from matrix profile
if (mp.getProfile()[i] > distanceProfile[j]) {
mp.getProfile()[i] = distanceProfile[j];
mp.getIndexProfile()[i] = j;
}
}
executeCallback(callback, mp);
}
protected void executeCallback(Predicate<MatrixProfile> callback, MatrixProfile mp) {
if (callback == null) {
return;
}
MatrixProfile clone = mp.clone();
updateMatrixProfileWithSqrt(clone);
if (!callback.test(clone)) { | throw new CancellationException(); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/DiscreteChebyshevTransformTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | @Test
public void testTransform() throws Exception {
double[] v = {1, 2, 3};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer).transform(new double[]{1, 2, 3, 2}, TransformType.FORWARD);
}
@Test
public void testTransform2() throws Exception {
double[] v = {1, 2, 3, 4};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer).transform(new double[]{1, 2, 3, 4, 3, 2, 0, 0}, TransformType.FORWARD);
}
@Test
public void testTransformSmallVector() throws Exception {
double[] v = {1, 2};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer, Mockito.never()).transform(Mockito.<double[]>any(), Mockito.any());
}
@Test
public void testTransformConcrete() throws Exception {
double[] v = {98, 100}; // 99 * x^2 - x
double[] expected = {99, -1};
double[] result = new DiscreteChebyshevTransform().transform(v);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/DiscreteChebyshevTransformTest.java
import org.apache.commons.math3.complex.Complex;
import org.apache.commons.math3.transform.FastFourierTransformer;
import org.apache.commons.math3.transform.TransformType;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.Mockito;
import org.mockito.junit.MockitoJUnitRunner;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
@Test
public void testTransform() throws Exception {
double[] v = {1, 2, 3};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer).transform(new double[]{1, 2, 3, 2}, TransformType.FORWARD);
}
@Test
public void testTransform2() throws Exception {
double[] v = {1, 2, 3, 4};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer).transform(new double[]{1, 2, 3, 4, 3, 2, 0, 0}, TransformType.FORWARD);
}
@Test
public void testTransformSmallVector() throws Exception {
double[] v = {1, 2};
discreteChebyshevTransform.transform(v);
Mockito.verify(fastFourierTransformer, Mockito.never()).transform(Mockito.<double[]>any(), Mockito.any());
}
@Test
public void testTransformConcrete() throws Exception {
double[] v = {98, 100}; // 99 * x^2 - x
double[] expected = {99, -1};
double[] result = new DiscreteChebyshevTransform().transform(v);
| Assert.assertArrayEquals(expected, result, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/exception/CancellationException.java | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
| import org.apache.commons.math3.exception.MathIllegalStateException;
import ro.hasna.ts.math.exception.util.LocalizableMessages; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
/**
* Exception to be thrown when an async task is canceled.
*
* @since 0.17
*/
public class CancellationException extends MathIllegalStateException {
private static final long serialVersionUID = 8488577710717564366L;
public CancellationException() { | // Path: src/main/java/ro/hasna/ts/math/exception/util/LocalizableMessages.java
// public class LocalizableMessages {
// public static final Localizable NUMBER_NOT_DIVISIBLE_WITH = new DummyLocalizable("{0} is not divisible with {1}");
// public static final Localizable OUT_OF_RANGE_BOTH_EXCLUSIVE = new DummyLocalizable("{0} out of ({1}, {2}) range");
// public static final Localizable OUT_OF_RANGE_BOTH_INCLUSIVE = new DummyLocalizable("{0} out of [{1}, {2}] range");
// public static final Localizable OPERATION_WAS_CANCELLED = new DummyLocalizable("The operation was cancelled.");
//
// private LocalizableMessages() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/exception/CancellationException.java
import org.apache.commons.math3.exception.MathIllegalStateException;
import ro.hasna.ts.math.exception.util.LocalizableMessages;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.exception;
/**
* Exception to be thrown when an async task is canceled.
*
* @since 0.17
*/
public class CancellationException extends MathIllegalStateException {
private static final long serialVersionUID = 8488577710717564366L;
public CancellationException() { | super(LocalizableMessages.OPERATION_WAS_CANCELLED); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/distribution/NormalDistributionDividerTest.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class NormalDistributionDividerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private NormalDistributionDivider divider;
@Before
public void setUp() throws Exception {
divider = new NormalDistributionDivider();
}
@After
public void tearDown() throws Exception {
divider = null;
}
@Test
public void testGetBreakpointsWithException() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("1 is smaller than the minimum (2)");
divider.getBreakpoints(1);
}
@Test
public void testGetBreakpoints1() throws Exception {
double[] expected = {-0.4307272992954576, 0.4307272992954576};
double[] v = divider.getBreakpoints(3);
| // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/distribution/NormalDistributionDividerTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.*;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.distribution;
public class NormalDistributionDividerTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
private NormalDistributionDivider divider;
@Before
public void setUp() throws Exception {
divider = new NormalDistributionDivider();
}
@After
public void tearDown() throws Exception {
divider = null;
}
@Test
public void testGetBreakpointsWithException() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("1 is smaller than the minimum (2)");
divider.getBreakpoints(1);
}
@Test
public void testGetBreakpoints1() throws Exception {
double[] expected = {-0.4307272992954576, 0.4307272992954576};
double[] v = divider.getBreakpoints(3);
| Assert.assertArrayEquals(expected, v, TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/ml/distance/RealSequenceEditDistance.java | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Edit Distance on Real Sequence.
* <p>
* Reference:
* Wagner Robert A. and Michael J. Fischer (1974)
* <i>The string-to-string correction problem</i>
* Chen Lei and Raymond Ng (2004)
* <i>On the marriage of lp-norms and edit distance</i>
* Chen Lei (2005)
* <i>Similarity search over time series and trajectory data</i>
* </p>
*
* @since 0.10
*/
public class RealSequenceEditDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = -373272813771443967L;
private final double epsilon;
private final double radiusPercentage;
public RealSequenceEditDistance() { | // Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/main/java/ro/hasna/ts/math/ml/distance/RealSequenceEditDistance.java
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance;
/**
* Calculates the distance between two vectors using Edit Distance on Real Sequence.
* <p>
* Reference:
* Wagner Robert A. and Michael J. Fischer (1974)
* <i>The string-to-string correction problem</i>
* Chen Lei and Raymond Ng (2004)
* <i>On the marriage of lp-norms and edit distance</i>
* Chen Lei (2005)
* <i>Similarity search over time series and trajectory data</i>
* </p>
*
* @since 0.10
*/
public class RealSequenceEditDistance implements GenericDistanceMeasure<double[]> {
private static final long serialVersionUID = -373272813771443967L;
private final double epsilon;
private final double radiusPercentage;
public RealSequenceEditDistance() { | this(TimeSeriesPrecision.EPSILON, 1.0); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception { | thrown.expect(ArrayLengthIsTooSmallException.class); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (8)");
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(4);
double[] v = {1, 2, 3};
apca.transform(v);
}
@Test
public void testTransform() throws Exception {
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(3);
double[] v = {1, 1, 4, 5, 1, 0, 1, 2, 1};
| // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (8)");
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(4);
double[] v = {1, 2, 3};
apca.transform(v);
}
@Test
public void testTransform() throws Exception {
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(3);
double[] v = {1, 1, 4, 5, 1, 0, 1, 2, 1};
| MeanLastPair[] result = apca.transform(v); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (8)");
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(4);
double[] v = {1, 2, 3};
apca.transform(v);
}
@Test
public void testTransform() throws Exception {
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(3);
double[] v = {1, 1, 4, 5, 1, 0, 1, 2, 1};
MeanLastPair[] result = apca.transform(v);
| // Path: src/main/java/ro/hasna/ts/math/exception/ArrayLengthIsTooSmallException.java
// public class ArrayLengthIsTooSmallException extends NumberIsTooSmallException {
// private static final long serialVersionUID = -2633584088507009304L;
//
// /**
// * Construct the exception.
// *
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Number wrong, Number min, boolean boundIsAllowed) {
// super(wrong, min, boundIsAllowed);
// }
//
// /**
// * Construct the exception with a specific context.
// *
// * @param specific Specific context pattern.
// * @param wrong Value that is smaller than the minimum.
// * @param min Minimum.
// * @param boundIsAllowed Whether {@code min} is included in the allowed range.
// */
// public ArrayLengthIsTooSmallException(Localizable specific, Number wrong, Number min, boolean boundIsAllowed) {
// super(specific, wrong, min, boundIsAllowed);
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MeanLastPair.java
// @Data
// public class MeanLastPair {
// /**
// * The mean value for the segment.
// */
// private final double mean;
// /**
// * The position from the last element of the segment.
// */
// private final int last;
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/AdaptivePiecewiseConstantApproximationTest.java
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.exception.ArrayLengthIsTooSmallException;
import ro.hasna.ts.math.type.MeanLastPair;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class AdaptivePiecewiseConstantApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructor() throws Exception {
thrown.expect(NumberIsTooSmallException.class);
thrown.expectMessage("0 is smaller than the minimum (1)");
new AdaptivePiecewiseConstantApproximation(0);
}
@Test
public void testTransformMoreSegmentsThanValues() throws Exception {
thrown.expect(ArrayLengthIsTooSmallException.class);
thrown.expectMessage("3 is smaller than the minimum (8)");
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(4);
double[] v = {1, 2, 3};
apca.transform(v);
}
@Test
public void testTransform() throws Exception {
AdaptivePiecewiseConstantApproximation apca = new AdaptivePiecewiseConstantApproximation(3);
double[] v = {1, 1, 4, 5, 1, 0, 1, 2, 1};
MeanLastPair[] result = apca.transform(v);
| Assert.assertEquals(1, result[0].getMean(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class IndexableSymbolicAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructorException() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("5 != 4");
new IndexableSymbolicAggregateApproximation(4, new int[5]);
}
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
} | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation;
public class IndexableSymbolicAggregateApproximationTest {
@Rule
public ExpectedException thrown = ExpectedException.none();
@Test
public void testConstructorException() throws Exception {
thrown.expect(DimensionMismatchException.class);
thrown.expectMessage("5 != 4");
new IndexableSymbolicAggregateApproximation(4, new int[5]);
}
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
} | SaxPair[] expected = new SaxPair[9]; |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random; |
new IndexableSymbolicAggregateApproximation(4, new int[5]);
}
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
}
SaxPair[] expected = new SaxPair[9];
expected[0] = new SaxPair(0, 3);
expected[1] = new SaxPair(0, 3);
expected[2] = new SaxPair(1, 3);
expected[3] = new SaxPair(2, 3);
expected[4] = new SaxPair(2, 3);
expected[5] = new SaxPair(2, 3);
expected[6] = new SaxPair(1, 3);
expected[7] = new SaxPair(0, 3);
expected[8] = new SaxPair(0, 3);
IndexableSymbolicAggregateApproximation isax = new IndexableSymbolicAggregateApproximation(
new PiecewiseAggregateApproximation(9), | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random;
new IndexableSymbolicAggregateApproximation(4, new int[5]);
}
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
}
SaxPair[] expected = new SaxPair[9];
expected[0] = new SaxPair(0, 3);
expected[1] = new SaxPair(0, 3);
expected[2] = new SaxPair(1, 3);
expected[3] = new SaxPair(2, 3);
expected[4] = new SaxPair(2, 3);
expected[5] = new SaxPair(2, 3);
expected[6] = new SaxPair(1, 3);
expected[7] = new SaxPair(0, 3);
expected[8] = new SaxPair(0, 3);
IndexableSymbolicAggregateApproximation isax = new IndexableSymbolicAggregateApproximation(
new PiecewiseAggregateApproximation(9), | new ZNormalizer(), |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
| import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random; | }
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
}
SaxPair[] expected = new SaxPair[9];
expected[0] = new SaxPair(0, 3);
expected[1] = new SaxPair(0, 3);
expected[2] = new SaxPair(1, 3);
expected[3] = new SaxPair(2, 3);
expected[4] = new SaxPair(2, 3);
expected[5] = new SaxPair(2, 3);
expected[6] = new SaxPair(1, 3);
expected[7] = new SaxPair(0, 3);
expected[8] = new SaxPair(0, 3);
IndexableSymbolicAggregateApproximation isax = new IndexableSymbolicAggregateApproximation(
new PiecewiseAggregateApproximation(9),
new ZNormalizer(),
alphabetSizes, | // Path: src/main/java/ro/hasna/ts/math/distribution/NormalDistributionDivider.java
// public class NormalDistributionDivider implements DistributionDivider {
// private static final long serialVersionUID = -909800668897655203L;
//
// @Override
// public double[] getBreakpoints(int areas) {
// if (areas < 2) {
// throw new NumberIsTooSmallException(areas, 2, true);
// }
//
// NormalDistribution normalDistribution = new NormalDistribution();
// int len = areas - 1;
// double[] result = new double[len];
// double searchArea = 1.0 / areas;
// for (int i = 0; i < len; i++) {
// result[i] = normalDistribution.inverseCumulativeProbability(searchArea * (i + 1));
// }
//
// return result;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/normalization/ZNormalizer.java
// public class ZNormalizer implements Normalizer {
// private static final long serialVersionUID = 6446811014325682141L;
// private final Mean mean;
// private final StandardDeviation standardDeviation;
//
// public ZNormalizer() {
// this(new Mean(), new StandardDeviation(false));
// }
//
// /**
// * Creates a new instance of this class with the given mean and standard deviation algorithms.
// *
// * @param mean the mean
// * @param standardDeviation the standard deviation
// */
// public ZNormalizer(final Mean mean, final StandardDeviation standardDeviation) {
// this.mean = mean;
// this.standardDeviation = standardDeviation;
// }
//
// /**
// * {@inheritDoc}
// */
// @Override
// public double[] normalize(double[] values) {
// double m = mean.evaluate(values);
// double sd = standardDeviation.evaluate(values, m);
//
// int length = values.length;
// double[] normalizedValues = new double[length];
// for (int i = 0; i < length; i++) {
// normalizedValues[i] = (values[i] - m) / sd;
// }
// return normalizedValues;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/SaxPair.java
// @Data
// public class SaxPair {
// /**
// * The symbol value for the segment.
// */
// private final int symbol;
// /**
// * The size of the alphabet used by the symbol.
// */
// private final int alphabetSize;
// }
// Path: src/test/java/ro/hasna/ts/math/representation/IndexableSymbolicAggregateApproximationTest.java
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.junit.Assert;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import ro.hasna.ts.math.distribution.NormalDistributionDivider;
import ro.hasna.ts.math.normalization.ZNormalizer;
import ro.hasna.ts.math.type.SaxPair;
import java.util.Random;
}
@Test
public void testTransformToSaxPairArray1() throws Exception {
double[] list = new double[64];
for (int i = 0; i < 32; i++) {
list[i] = i;
}
for (int i = 32; i < 64; i++) {
list[i] = 64 - i;
}
int[] alphabetSizes = new int[9];
for (int i = 0; i < 9; i++) {
alphabetSizes[i] = 3;
}
SaxPair[] expected = new SaxPair[9];
expected[0] = new SaxPair(0, 3);
expected[1] = new SaxPair(0, 3);
expected[2] = new SaxPair(1, 3);
expected[3] = new SaxPair(2, 3);
expected[4] = new SaxPair(2, 3);
expected[5] = new SaxPair(2, 3);
expected[6] = new SaxPair(1, 3);
expected[7] = new SaxPair(0, 3);
expected[8] = new SaxPair(0, 3);
IndexableSymbolicAggregateApproximation isax = new IndexableSymbolicAggregateApproximation(
new PiecewiseAggregateApproximation(9),
new ZNormalizer(),
alphabetSizes, | new NormalDistributionDivider()); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/StompTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STOMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Zimmerman Z., Senobari N. S., Yeh C. C. M., Funning G., Mueen A., Keogh E. (2016)
* <i>Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins</i>
* </p>
*
* @since 0.17
*/
public class StompTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 2910211807207716085L;
public StompTransformer(int window) {
super(window);
}
public StompTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/StompTransformer.java
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STOMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Zimmerman Z., Senobari N. S., Yeh C. C. M., Funning G., Mueen A., Keogh E. (2016)
* <i>Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins</i>
* </p>
*
* @since 0.17
*/
public class StompTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 2910211807207716085L;
public StompTransformer(int window) {
super(window);
}
public StompTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/StompTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STOMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Zimmerman Z., Senobari N. S., Yeh C. C. M., Funning G., Mueen A., Keogh E. (2016)
* <i>Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins</i>
* </p>
*
* @since 0.17
*/
public class StompTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 2910211807207716085L;
public StompTransformer(int window) {
super(window);
}
public StompTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override
protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) {
int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
double[] productSums = new double[n]; | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/StompTransformer.java
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the STOMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Zimmerman Z., Senobari N. S., Yeh C. C. M., Funning G., Mueen A., Keogh E. (2016)
* <i>Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins</i>
* </p>
*
* @since 0.17
*/
public class StompTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = 2910211807207716085L;
public StompTransformer(int window) {
super(window);
}
public StompTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override
protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) {
int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
double[] productSums = new double[n]; | BothWaySummaryStatistics first = new BothWaySummaryStatistics(); |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/ScrimpTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the SCRIMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Yeh C. C. M., Zimmerman Z., Kamgar K., Keogh E. (2018, November)
* <i>Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds</i>
* </p>
*
* @since 0.17
*/
public class ScrimpTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = -7360923839319598742L;
public ScrimpTransformer(int window) {
super(window);
}
public ScrimpTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/ScrimpTransformer.java
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the SCRIMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Yeh C. C. M., Zimmerman Z., Kamgar K., Keogh E. (2018, November)
* <i>Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds</i>
* </p>
*
* @since 0.17
*/
public class ScrimpTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = -7360923839319598742L;
public ScrimpTransformer(int window) {
super(window);
}
public ScrimpTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override | protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) { |
octavian-h/time-series-math | src/main/java/ro/hasna/ts/math/representation/mp/ScrimpTransformer.java | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
| import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the SCRIMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Yeh C. C. M., Zimmerman Z., Kamgar K., Keogh E. (2018, November)
* <i>Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds</i>
* </p>
*
* @since 0.17
*/
public class ScrimpTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = -7360923839319598742L;
public ScrimpTransformer(int window) {
super(window);
}
public ScrimpTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override
protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) {
int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
double[] productSums = new double[n]; | // Path: src/main/java/ro/hasna/ts/math/stat/BothWaySummaryStatistics.java
// public class BothWaySummaryStatistics implements StatisticalSummary, Serializable, Cloneable {
// private static final long serialVersionUID = -8419769227216721345L;
// private long n;
// private double sum;
// private double sumSquares;
// private Max max;
// private Min min;
// private boolean removeMade;
//
// public BothWaySummaryStatistics() {
// n = 0;
// sum = 0;
// sumSquares = 0;
// max = new Max();
// min = new Min();
// removeMade = false;
// }
//
// public void addValue(double d) {
// sum += d;
// sumSquares += d * d;
// n++;
// if (!removeMade) {
// max.increment(d);
// min.increment(d);
// }
// }
//
// public void removeValue(double d) {
// if (n == 0) {
// throw new InsufficientDataException();
// }
//
// sum -= d;
// sumSquares -= d * d;
// n--;
// removeMade = true;
// max.clear();
// min.clear();
// }
//
// @Override
// public double getMean() {
// if (n == 0) return 0;
// return sum / n;
// }
//
// @Override
// public double getVariance() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// double mean = getMean();
// return sumSquares / n - mean * mean;
// }
//
// @Override
// public double getStandardDeviation() {
// if (n == 0) return Double.NaN;
// if (n == 1) return 0;
// return FastMath.sqrt(getVariance());
// }
//
// /**
// * @return max value or Double.NaN if removeValue was called
// */
// @Override
// public double getMax() {
// return max.getResult();
// }
//
// /**
// * @return min value or Double.NaN if removeValue was called
// */
// @Override
// public double getMin() {
// return min.getResult();
// }
//
// @Override
// public long getN() {
// return n;
// }
//
// @Override
// public double getSum() {
// return sum;
// }
//
// @Override
// public BothWaySummaryStatistics clone() {
// BothWaySummaryStatistics copy = new BothWaySummaryStatistics();
// copy.n = n;
// copy.sum = sum;
// copy.sumSquares = sumSquares;
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
// Path: src/main/java/ro/hasna/ts/math/representation/mp/ScrimpTransformer.java
import ro.hasna.ts.math.stat.BothWaySummaryStatistics;
import ro.hasna.ts.math.type.MatrixProfile;
import java.util.function.Predicate;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
/**
* Implements the SCRIMP algorithm to compute the self join matrix profile.
* <p>
* Reference:
* Zhu Y., Yeh C. C. M., Zimmerman Z., Kamgar K., Keogh E. (2018, November)
* <i>Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds</i>
* </p>
*
* @since 0.17
*/
public class ScrimpTransformer extends SelfJoinAbstractMatrixProfileTransformer {
private static final long serialVersionUID = -7360923839319598742L;
public ScrimpTransformer(int window) {
super(window);
}
public ScrimpTransformer(int window, double exclusionZonePercentage, boolean useNormalization) {
super(window, exclusionZonePercentage, useNormalization);
}
@Override
protected MatrixProfile computeNormalizedMatrixProfile(double[] input, int skip, Predicate<MatrixProfile> callback) {
int n = input.length - window + 1;
MatrixProfile mp = new MatrixProfile(n);
double[] distanceProfile = new double[n];
double[] productSums = new double[n]; | BothWaySummaryStatistics first = new BothWaySummaryStatistics(); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/ScrimpTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class ScrimpTransformerTest {
@Test
public void transform_withoutNormalization() {
ScrimpTransformer transformer = new ScrimpTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/ScrimpTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class ScrimpTransformerTest {
@Test
public void transform_withoutNormalization() {
ScrimpTransformer transformer = new ScrimpTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
| MatrixProfile transform = transformer.transform(v); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/representation/mp/ScrimpTransformerTest.java | // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class ScrimpTransformerTest {
@Test
public void transform_withoutNormalization() {
ScrimpTransformer transformer = new ScrimpTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| // Path: src/main/java/ro/hasna/ts/math/type/MatrixProfile.java
// @Data
// public class MatrixProfile implements Cloneable {
// private final double[] profile;
// private final int[] indexProfile;
//
// public MatrixProfile(double[] profile, int[] indexProfile) {
// if (profile.length != indexProfile.length) {
// throw new DimensionMismatchException(profile.length, indexProfile.length);
// }
//
// this.profile = profile;
// this.indexProfile = indexProfile;
// }
//
// public MatrixProfile(int size) {
// profile = new double[size];
// indexProfile = new int[size];
// for (int j = 0; j < size; j++) {
// profile[j] = Double.POSITIVE_INFINITY;
// indexProfile[j] = -1;
// }
// }
//
// @Override
// public MatrixProfile clone() {
// int size = profile.length;
// MatrixProfile copy = new MatrixProfile(size);
// for (int i = 0; i < size; i++) {
// copy.profile[i] = profile[i];
// copy.indexProfile[i] = indexProfile[i];
// }
// return copy;
// }
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/representation/mp/ScrimpTransformerTest.java
import org.junit.Assert;
import org.junit.Test;
import ro.hasna.ts.math.type.MatrixProfile;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.representation.mp;
public class ScrimpTransformerTest {
@Test
public void transform_withoutNormalization() {
ScrimpTransformer transformer = new ScrimpTransformer(4, 0.25, false);
double[] v = {1, 2, 3, 4, 120, 71, 2, 2, 3, 5, 19};
MatrixProfile transform = transformer.transform(v);
double[] expectedMp = {1.4142135623730951, 101.00495037373169, 125.93252161375949, 135.41787178950938, 84.63450832845902, 69.03622237637282, 1.4142135623730951, 14.177446878757825};
int[] expectedIp = {6, 7, 1, 7, 5, 6, 0, 6};
| Assert.assertArrayEquals(expectedMp, transform.getProfile(), TimeSeriesPrecision.EPSILON); |
octavian-h/time-series-math | src/test/java/ro/hasna/ts/math/ml/distance/util/DistanceTester.java | // Path: src/main/java/ro/hasna/ts/math/ml/distance/GenericDistanceMeasure.java
// public interface GenericDistanceMeasure<T> extends Serializable {
// /**
// * Compute the distance between two n-dimensional vectors.
// * <p>
// * The two vectors are required to have the same dimension.
// *
// * @param a the first vector
// * @param b the second vector
// * @return the distance between the two vectors
// */
// double compute(T a, T b);
//
// /**
// * Compute the distance between two n-dimensional vectors.
// * <p>
// * The two vectors are required to have the same dimension.
// *
// * @param a the first vector
// * @param b the second vector
// * @param cutOffValue if the distance being calculated is above this value
// * stop computing the remaining distance
// * @return the distance between the two vectors
// */
// double compute(T a, T b, double cutOffValue);
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
| import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.junit.Assert;
import ro.hasna.ts.math.ml.distance.GenericDistanceMeasure;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.util.TimeSeriesPrecision; | /*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance.util;
public class DistanceTester {
private GenericDistanceMeasure<double[]> distance;
private int vectorLength = 128;
private double offset;
private double cutOffValue;
private double expectedResult;
/**
* Private constructor.
*/
public DistanceTester() {
}
| // Path: src/main/java/ro/hasna/ts/math/ml/distance/GenericDistanceMeasure.java
// public interface GenericDistanceMeasure<T> extends Serializable {
// /**
// * Compute the distance between two n-dimensional vectors.
// * <p>
// * The two vectors are required to have the same dimension.
// *
// * @param a the first vector
// * @param b the second vector
// * @return the distance between the two vectors
// */
// double compute(T a, T b);
//
// /**
// * Compute the distance between two n-dimensional vectors.
// * <p>
// * The two vectors are required to have the same dimension.
// *
// * @param a the first vector
// * @param b the second vector
// * @param cutOffValue if the distance being calculated is above this value
// * stop computing the remaining distance
// * @return the distance between the two vectors
// */
// double compute(T a, T b, double cutOffValue);
// }
//
// Path: src/main/java/ro/hasna/ts/math/representation/GenericTransformer.java
// public interface GenericTransformer<I, O> extends Serializable {
// /**
// * Transform the input vector from type I into type O.
// *
// * @param input the input vector
// * @return the output vector
// */
// O transform(I input);
// }
//
// Path: src/main/java/ro/hasna/ts/math/util/TimeSeriesPrecision.java
// public class TimeSeriesPrecision {
// public static final double EPSILON = FastMath.pow(2, -30);
//
// /**
// * Private constructor.
// */
// private TimeSeriesPrecision() {
// }
// }
// Path: src/test/java/ro/hasna/ts/math/ml/distance/util/DistanceTester.java
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.junit.Assert;
import ro.hasna.ts.math.ml.distance.GenericDistanceMeasure;
import ro.hasna.ts.math.representation.GenericTransformer;
import ro.hasna.ts.math.util.TimeSeriesPrecision;
/*
* Copyright 2015 Octavian Hasna
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package ro.hasna.ts.math.ml.distance.util;
public class DistanceTester {
private GenericDistanceMeasure<double[]> distance;
private int vectorLength = 128;
private double offset;
private double cutOffValue;
private double expectedResult;
/**
* Private constructor.
*/
public DistanceTester() {
}
| public <T> DistanceTester withGenericDistanceMeasure(final GenericDistanceMeasure<T[]> distance, final GenericTransformer<double[], T[]> transformer) { |
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