plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | Alzathar/b-tk.googlecode.backup-master | testSetupExample.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/examples_general/testSetupExample.m | 285 | utf_8 | c97034e753adbc170317aeff3beaae86 | function test_suite = testSetupExample
initTestSuite;
function fh = setup
fh = figure;
function teardown(fh)
delete(fh);
function testColormapColumns(fh)
assertEqual(size(get(fh, 'Colormap'), 2), 3);
function testPointer(fh)
assertEqual(get(fh, 'Pointer'), 'arrow');
|
github | Alzathar/b-tk.googlecode.backup-master | testBadSinTest.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/examples_general/testBadSinTest.m | 198 | utf_8 | 8ff60b6183c1c48b0eecb9aa9581dfc1 | function test_suite = testBadSinTest
initTestSuite;
function testSinPi
% Example of a failing test case. The test writer should have used
% assertAlmostEqual here.
assertEqual(sin(pi), 0);
|
github | Alzathar/b-tk.googlecode.backup-master | testCos.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/examples_general/testCos.m | 131 | utf_8 | 7e061a28319448a380aa41c5bd35c21b | function test_suite = testCos
initTestSuite;
function testTooManyInputs
assertExceptionThrown(@() cos(1, 2), 'MATLAB:maxrhs'); |
github | Alzathar/b-tk.googlecode.backup-master | test_that.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/+abc_tests/test_that.m | 161 | utf_8 | 97300fd8d3adec69102d836a63110ca5 | % Do-nothing test used in the examples for organizing tests inside packages.
%
% Steven L. Eddins
% Copyright 2010 The MathWorks, Inc.
function test_that
|
github | Alzathar/b-tk.googlecode.backup-master | test_this.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/+abc_tests/test_this.m | 161 | utf_8 | fa26021122fc1ebe7ff54a143d85c458 | % Do-nothing test used in the examples for organizing tests inside packages.
%
% Steven L. Eddins
% Copyright 2010 The MathWorks, Inc.
function test_this
|
github | Alzathar/b-tk.googlecode.backup-master | TestRunDisplay.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/TestRunDisplay.m | 10,030 | utf_8 | 24f4a694479ffe2dcf3e7255bd249bef | classdef TestRunDisplay < TestRunMonitor
%TestRunDisplay Print test suite execution results.
% TestRunDisplay is a subclass of TestRunMonitor. If a TestRunDisplay
% object is passed to the run method of a TestComponent, such as a
% TestSuite or a TestCase, it will print information to the Command
% Window... |
github | Alzathar/b-tk.googlecode.backup-master | TestSuite.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/TestSuite.m | 13,487 | utf_8 | 4a3e72035a673d0511e4d6386689a4a8 | %TestSuite Collection of TestComponent objects
% The TestSuite class defines a collection of TestComponent objects.
%
% TestSuite methods:
% TestSuite - Constructor
% add - Add test component to test suite
% print - Display test suite summary to ... |
github | Alzathar/b-tk.googlecode.backup-master | runtests.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/runtests.m | 5,001 | utf_8 | 3bb41e64615af58cf99651fbefef9971 | function out = runtests(varargin)
%runtests Run unit tests
% runtests runs all the test cases that can be found in the current directory
% and summarizes the results in the Command Window.
%
% Test cases can be found in the following places in the current directory:
%
% * An M-file function whose nam... |
github | Alzathar/b-tk.googlecode.backup-master | isTestCaseSubclass.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/+xunit/+utils/isTestCaseSubclass.m | 933 | utf_8 | 1a1ee7cece4bd3d76c9823737c53c81b | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_met... |
github | Alzathar/b-tk.googlecode.backup-master | arrayToString.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/+xunit/+utils/arrayToString.m | 3,068 | utf_8 | a26e49f162e838cae6baa458ce8a3272 | function s = arrayToString(A)
%arrayToString Convert array to string for display.
% S = arrayToString(A) converts the array A into a string suitable for
% including in assertion messages. Small arrays are converted using disp(A).
% Large arrays are displayed similar to the way structure field values display
... |
github | Alzathar/b-tk.googlecode.backup-master | compareFloats.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/xunit/+xunit/+utils/compareFloats.m | 4,504 | utf_8 | 8e53c740ecedcf9e986479cd7c359a73 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is eithe... |
github | Alzathar/b-tk.googlecode.backup-master | testIsAlmostEqual.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/tests/testIsAlmostEqual.m | 1,280 | utf_8 | 1ba7369398f316f90d543c04b32e8765 | function test_suite = testIsAlmostEqual
%testIsAlmostEqual Unit tests for isAlmostEqual
% Steven L. Eddins
% Copyright 2008 The MathWorks, Inc.
initTestSuite;
function testExactlyEqual
A = [1 2; 3 4];
B = [1 2; 3 4];
assertTrue(mtest.utils.isAlmostEqual(A, B));
function testDefaultTolerance
assert... |
github | Alzathar/b-tk.googlecode.backup-master | testAssertAlmostEqual.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/tests/testAssertAlmostEqual.m | 1,102 | utf_8 | ba66ade4b38bb11cf47cf991b39c852e | function test_suite = testAssertAlmostEqual
%testAssertAlmostEqual Unit tests for assertAlmostEqual
% Steven L. Eddins
% Copyright 2008 The MathWorks, Inc.
initTestSuite;
function testEqual
assertAlmostEqual(1, 1);
function testEqualWithThreeInputs
assertAlmostEqual(1, 1.1, 0.2);
function testEqu... |
github | Alzathar/b-tk.googlecode.backup-master | testSubfunctions.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/tests/cwd_test/testSubfunctions.m | 229 | utf_8 | d40e59204ad52a0350ef36b9ea09a883 | function test_cases = testSubfunctions
%testSubfunctions Contains two passing subfunction tests
% Steven L. Eddins
% Copyright 2008 The MathWorks, Inc.
findSubfunctionTests;
function testSub1
function testSub2
|
github | Alzathar/b-tk.googlecode.backup-master | isTestCaseSubclass.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/+mtest/+utils/isTestCaseSubclass.m | 933 | utf_8 | 1a1ee7cece4bd3d76c9823737c53c81b | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_met... |
github | Alzathar/b-tk.googlecode.backup-master | compareFloats.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/+mtest/+utils/compareFloats.m | 3,598 | utf_8 | 6a545c961c56d5c60a5d1688e6a397b7 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is eithe... |
github | Alzathar/b-tk.googlecode.backup-master | parseFloatAssertInputs.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/obsolete/+mtest/+utils/parseFloatAssertInputs.m | 2,025 | utf_8 | 885cace1b16bb740872e6c94927a46e5 | function params = parseFloatAssertInputs(varargin)
%parseFloatAssertInputs Parse inputs for floating-point assertion functions.
% params = parseFloatAssertInputs(varargin) parses the input arguments for
% assertElementsAlmostEqual, assertVectorsAlmostEqual, and compareFcn. It
% returns a parameter struct cont... |
github | Alzathar/b-tk.googlecode.backup-master | isTestCaseSubclass.m | .m | b-tk.googlecode.backup-master/Utilities/octave_xunit/xunit/+xunit/+utils/isTestCaseSubclass.m | 933 | utf_8 | 1a1ee7cece4bd3d76c9823737c53c81b | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_met... |
github | Alzathar/b-tk.googlecode.backup-master | arrayToString.m | .m | b-tk.googlecode.backup-master/Utilities/octave_xunit/xunit/+xunit/+utils/arrayToString.m | 3,136 | utf_8 | 4443c02b5969a7dc69c097ffcc2c07a1 | function s = arrayToString(A)
%arrayToString Convert array to string for display.
% S = arrayToString(A) converts the array A into a string suitable for
% including in assertion messages. Small arrays are converted using disp(A).
% Large arrays are displayed similar to the way structure field values display
... |
github | Alzathar/b-tk.googlecode.backup-master | compareFloats.m | .m | b-tk.googlecode.backup-master/Utilities/octave_xunit/xunit/+xunit/+utils/compareFloats.m | 4,492 | utf_8 | 3f5d60ae454a5eaf186e947dbb9e13a2 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is eithe... |
github | Alzathar/b-tk.googlecode.backup-master | testBTKBasicFilters.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/testBTKBasicFilters.m | 8,389 | utf_8 | ea6b8d71b1090f23094196cc5b64ebca | function test_suite = testBTKBasicFilters
initTestSuite;
end
function d = setup
d = TDDConfigure();
end
function testGetMarkersResiduals(d)
h = btkNewAcquisition(2,10);
res = btkGetMarkersResiduals(h);
assertEqual(size(res,1), 10);
assertEqual(size(res,2), 2);
res_ = zeros(10,2);
assertEqual(res,res_);
btkDeleteAcqui... |
github | Alzathar/b-tk.googlecode.backup-master | testBTKIO.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/testBTKIO.m | 4,392 | utf_8 | fe62b6a47b4a2b43d323ebca70fe0709 | function test_suite = testBTKIO
initTestSuite;
end
function d = setup
d = TDDConfigure();
end
function testReadC3DSample01Eb015pi(d)
[h, bo, sf] = btkReadAcquisition(strcat(d.in,'/C3DSamples/sample01/Eb015pi.c3d'));
assertEqual(bo, 'IEEE_LittleEndian');
assertEqual(sf, 'Integer');
btkDeleteAcquisition(h);
end
functi... |
github | Alzathar/b-tk.googlecode.backup-master | testC3DserverEmulation.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/testC3DserverEmulation.m | 10,459 | utf_8 | e75377a28737297377c26b6ca11872ae | function test_suite = testC3DserverEmulation
initTestSuite;
end
function d = setup
d = TDDConfigure();
end
function testDoubleConstructor(d)
btksrv_ = btkEmulateC3Dserver();
btksrv_.Open(strcat(d.in,'/C3DSamples/sample01/Eb015pi.c3d'),3);
btksrv = btkEmulateC3Dserver();
btksrv.Open(strcat(d.in,'/C3DSamples/sample09/P... |
github | Alzathar/b-tk.googlecode.backup-master | benchmarkC3DserverEmulation.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/benchmarkC3DserverEmulation.m | 7,052 | utf_8 | 51745958df6085b1c2ca3653e9ef838d | function benchmarkC3DserverEmulation
try
dataPath = TDDConfigure();
catch
error('Error when trying to execute the function ''TDDConfigure''. You certainly did not add the path for this function. You will find the function in the subfolder Testing/Matlab of the project build directory.')
end
d.filename = strcat(data... |
github | Alzathar/b-tk.googlecode.backup-master | testC3DserverEmulationVersus.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/testC3DserverEmulationVersus.m | 175,577 | utf_8 | 8f2954b96146b9997a7c1623bdf80396 | function test_suite = testC3DserverEmulationVersus
initTestSuite;
end
function d = setup
dataPath = TDDConfigure();
d.filename = strcat(dataPath.in,'/C3DSamples/sample01/Eb015pi.c3d');
d.filename2 = strcat(dataPath.in,'/C3DSamples/sample01/Eb015vr.c3d');
d.filename16bits = strcat(dataPath.in,'/C3DSamples/sample19/samp... |
github | Alzathar/b-tk.googlecode.backup-master | testBTKCommon.m | .m | b-tk.googlecode.backup-master/Testing/Matlab/testBTKCommon.m | 11,457 | utf_8 | 082681e4a0d35965d0bbb19acf08bcb0 | function test_suite = testBTKCommon
initTestSuite;
end
function d = setup
d = TDDConfigure();
end
function testNewAcquisition(d)
h = btkNewAcquisition(10,100,2,10);
assertEqual(btkGetPointNumber(h), 10);
assertEqual(btkGetPointFrameNumber(h), 100);
assertEqual(btkGetAnalogNumber(h), 2);
assertEqual(btkGetAnalogFrameN... |
github | aranyadan/caltech-lane-detection-master | ccvGetLaneDetectionStats.m | .m | caltech-lane-detection-master/matlab/ccvGetLaneDetectionStats.m | 5,767 | utf_8 | 3b1a1bbdb2c02cde9f338e584d4523e4 | function ccvGetLaneDetectionStats(detectionFiles, truthFiles)
% CCVGETLANEDETECTIONSTATS computes stats for the results compared to the
% ground truth
%
% INPUTS
% ------
% detectionFiles - a cell array of the detection files
% truthFiles - a cell array of the corresponding ground truth files
%
% OUTPUTS
% -------
%
... |
github | aranyadan/caltech-lane-detection-master | ccvLabel.m | .m | caltech-lane-detection-master/matlab/ccvLabel.m | 9,282 | utf_8 | a4cfee3bd06cea44bcb2ba59e53582b8 | function varargout = ccvLabel(f, varargin)
% CCVLABEL performs different tasks on the label structure, like creating
% new structure, adding frames, labels, ...etc.
%
% INPUTS
% ------
% f - the input function to perform
% varargin - the rest of the inputs (potentially zero)
%
% OUTPUTS
% -------
% varargout... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | iAmWillShepherd/MOOC-MachineLearning-master | porterStemmer.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submit.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | iAmWillShepherd/MOOC-MachineLearning-master | submitWithConfiguration.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | iAmWillShepherd/MOOC-MachineLearning-master | savejson.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | iAmWillShepherd/MOOC-MachineLearning-master | loadubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | iAmWillShepherd/MOOC-MachineLearning-master | saveubjson.m | .m | MOOC-MachineLearning-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Korogodin/gnss-qcno-estimator-master | qcno_alg.m | .m | gnss-qcno-estimator-master/qcno_alg.m | 1,106 | utf_8 | c2335d6239a47f11380e811c343fef90 | function main()
clc
close all
S2 = 30:1:100;
A2 = 1:200;
Q = nan(length(A2), length(S2));
Err = nan(length(A2), length(S2));
for i = 1:length(A2)
for j = 1:length(S2)
Q(i, j) = qcno(A2(i), S2(j));
Err(i, j) = Q(i, j) - 10*log10(A2(i) /S2(j) / 2 / 0.001);
end
end
fprintf('Max Err = %f; p2p E... |
github | Korogodin/gnss-qcno-estimator-master | fig_main.m | .m | gnss-qcno-estimator-master/matlab/fig_main.m | 14,579 | utf_8 | 91f4c0160a99bd80843079c7629c000d | function varargout = fig_main(varargin)
% FIG_MAIN M-file for fig_main.fig
% FIG_MAIN, by itself, creates a new FIG_MAIN or raises the existing
% singleton*.
%
% H = FIG_MAIN returns the handle to a new FIG_MAIN or the handle to
% the existing singleton*.
%
% FIG_MAIN('CALLBACK',hObject,eventDa... |
github | Korogodin/gnss-qcno-estimator-master | out_popup_scenarios_Callback.m | .m | gnss-qcno-estimator-master/matlab/out_form_func/out_popup_scenarios_Callback.m | 1,928 | utf_8 | f03a33d1d38c2b05e5db36cc29155954 | %/**
%Выбор сценария из списка
%@param hObject - указатель на popup-menu на форме
%*/
function out_popup_scenarios_Callback(hObject, eventdata, handles)
globals;
first_init_globals;
select_string = get(hObject, 'Value');
all_strings = get(hObject, 'String');
select_scen = all_strings(select_string, 1); % // Scenario... |
github | Korogodin/gnss-qcno-estimator-master | ro.m | .m | gnss-qcno-estimator-master/matlab/common_func/ro.m | 432 | utf_8 | 5f4169b3babc3ab3d7190e4502d03ada | %/**
%Вычисление значения автокорреляционной функции ДК
%@param x - рассогласование по задержке в тех же единицах, в которых задана глобальная переменная TauChip (длительность одного чипа ДК)
%*/
function f=ro(x)
global TauChip
f = (abs(x) < TauChip).*(1 - abs(x)/TauChip);
end |
github | Korogodin/gnss-qcno-estimator-master | qcno_change.m | .m | gnss-qcno-estimator-master/matlab/common_func/qcno_change.m | 443 | utf_8 | e52896085cb836b9cb96507c3c320339 | %/**
%Расчет амплитуды квадратур для статистических эквивалентов
%@param qcno_dB - отношение qcno = Ps/No в дБГц
%@param stdn_IQ - СКО шума квадратур
%@param Tc - интервал когерентного накопления
%*/
function [A_IQ qcno] = qcno_change(qcno_dB, stdn_IQ, Tc)
qcno = 10.^(qcno_dB/10);
A_IQ = stdn_IQ .* sqrt(2 * qcno * T... |
github | Korogodin/gnss-qcno-estimator-master | refresh_scen_list.m | .m | gnss-qcno-estimator-master/matlab/common_func/refresh_scen_list.m | 620 | utf_8 | 9b2bfdee1b9388317985b906908c5165 | %/**
%Добавление в popup-список доступных сценариев
%@param hObject - указатель на popup-menu на форме
%*/
function erro = refresh_scen_list(hObject)
globals;
scen_list = what([pwd '/matlab/scenarios']); % // Смотрим структуру каталога сценариев
if size(scen_list.mat, 1) > 0 % // Если в нем есть mat-файлы
set(hO... |
github | Korogodin/gnss-qcno-estimator-master | read_file_and_plot.m | .m | gnss-qcno-estimator-master/matlab/common_func/read_file_and_plot.m | 1,213 | utf_8 | 7789f88869508b256de727ba1f1553b0 | %/**
% Чтение из file_out и отрисовка полученных данных
%@param handles - всея структура
%*/
function read_file_and_plot( handles )
globals;
fid = fopen('file_out.csv', 'r');
% K = fscanf(fid, '%u', 1);
A_IQ2_est = (fscanf(fid, '%u', K))';
sqrtA_IQ2_est = sqrt(A_IQ2_est);
A_IQ_est = (fscanf(fid, '%d', K))';
plot_A_... |
github | Korogodin/gnss-qcno-estimator-master | plot_R2.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_R2.m | 490 | utf_8 | 236e1f9313c23f3d72dd207d05ca1fcd | %/**
% Plot R2 vector
% @param handles - указатель на всея структуру
%*/
function plot_R2(handles)
globals;
if isstruct(handles)
hA = handles.axes_R2;
else
hF = figure;
hA = gca;
end
maxR2 = 2^32 - 1;
if (2*max(R2) >= maxR2)
redline = ones(1,K)*maxR2;
else
redline = nan(1,K);
end
plot(hA, 1:K, R2... |
github | Korogodin/gnss-qcno-estimator-master | plot_R4_acum.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_R4_acum.m | 471 | utf_8 | b721e6b3f8e91d56e4467a1ed5952a00 | %/**
% Plot R4_acum vector
% @param handles - указатель на всея структуру
%*/
function plot_R4_acum(handles)
globals;
if isstruct(handles)
hA = handles.axes_R4_acum;
else
hF = figure;
hA = gca;
end
maxR4_acum = 2^64 - 1;
if (2*max(R4_acum) >= maxR4_acum)
redline = ones(1,K)*maxR4_acum;
else
redli... |
github | Korogodin/gnss-qcno-estimator-master | plot_sum_counter.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_sum_counter.m | 411 | utf_8 | 0917a67bde4b25e05a9309a47b936b93 | %/**
% Plot sum_counter vector
% @param handles - указатель на всея структуру
%*/
function plot_sum_counter(handles)
globals;
if isstruct(handles)
hA = handles.axes_sum_counter;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, sum_counter);
ylabel(hA, 'sum\_counter');
grid(hA, 'on');
if isstruct(handles)
... |
github | Korogodin/gnss-qcno-estimator-master | plot_IQ.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_IQ.m | 395 | utf_8 | 0551ddfcfcc95090b278fa3704e98895 | %/**
% Plot I, Q vectors
% @param handles - указатель на всея структуру
%*/
function plot_IQ(handles)
globals;
if isstruct(handles)
hA = handles.axes_IQ;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, I, 1:K, Q);
ylabel(hA, 'I, Q');
set(hA, 'XGrid','on','YGrid','on')
if isstruct(handles)
set(hA, 'XTi... |
github | Korogodin/gnss-qcno-estimator-master | plot_qcno.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_qcno.m | 567 | utf_8 | 0e790f5c56728f0cb736f7f743277f2e | %/**
% Plot qcno real vector
% @param handles - указатель на всея структуру
%*/
function plot_qcno(handles)
globals;
if isstruct(handles)
hA = handles.axes_qcno;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, qcno_est, 1:K, 10*log10(A_IQ_eff.^2/ 2 ./ stdn_IQ.^2 / Tc),...
1:K, qcno_ist, 1:K, 10... |
github | Korogodin/gnss-qcno-estimator-master | plot_acum_counter.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_acum_counter.m | 416 | utf_8 | 2fe216ef3e70836ba107f08a22022151 | %/**
% Plot acum_counter vector
% @param handles - указатель на всея структуру
%*/
function plot_acum_counter(handles)
globals;
if isstruct(handles)
hA = handles.axes_acum_counter;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, acum_counter);
ylabel(hA, 'acum\_counter');
grid(hA, 'on');
if isstruct(handl... |
github | Korogodin/gnss-qcno-estimator-master | plot_stdn_IQ.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_stdn_IQ.m | 664 | utf_8 | bc377ab17d2aab07585640f04e31b576 | %/**
% Plot stdn vectors
% @param handles - указатель на всея структуру
%*/
function plot_stdn_IQ(handles)
globals;
if isstruct(handles)
hA = handles.axes_stdn_IQ;
else
hF = figure;
hA = gca;
end
U2_SHIFT = 0;
if isnan(stdn2_IQ_est_shifted(1))
plot(hA, 1:K, stdn2_IQ_est, 1, NaN, 1:K, ... |
github | Korogodin/gnss-qcno-estimator-master | plot_R4.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_R4.m | 491 | utf_8 | b628a64f533c85898052d26978d360c7 | %/**
% Plot R4 vector
% @param handles - указатель на всея структуру
%*/
function plot_R4(handles)
globals;
if isstruct(handles)
hA = handles.axes_R4;
else
hF = figure;
hA = gca;
end
maxR4 = 2^64 - 1;
if (2*max(R4) >= maxR4)
redline = ones(1,K)*maxR4;
else
redline = nan(1,K);
end
plot(hA, 1:K, R4... |
github | Korogodin/gnss-qcno-estimator-master | plot_A_IQ.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_A_IQ.m | 511 | utf_8 | 2483a9febafdc9979b87abb543f67812 | %/**
% Plot A_IQ, A_IQ_eff and other apm-vectors
% @param handles - указатель на всея структуру
%*/
function plot_A_IQ(handles)
globals;
if isstruct(handles)
hA = handles.axes_A_IQ;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, A_IQ_est, 1:K, A_IQ_eff, 1:K, A_IQ, 1:K, sqrtA_IQ2_est);
ylabel(hA, '... |
github | Korogodin/gnss-qcno-estimator-master | plot_R2_acum.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_R2_acum.m | 537 | utf_8 | a107bd705f2c6957a8d37dfb209012e1 | %/**
% Plot R2_acum vector
% @param handles - указатель на всея структуру
%*/
function plot_R2_acum(handles)
globals;
if isstruct(handles)
hA = handles.axes_R2_acum;
else
hF = figure;
hA = gca;
end
maxR2_acum = 2^32 - 1;
if (2*max(R2_acum) >= maxR2_acum)
redline = ones(1,K)*maxR2_acum;
else
redli... |
github | Korogodin/gnss-qcno-estimator-master | plot_allow_stdn2_est.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_allow_stdn2_est.m | 521 | utf_8 | 158835addbfd9d55ca4336f6f98a94c0 | %/**
% Plot allow_stdn2_est vector
% @param handles - указатель на всея структуру
%*/
function plot_allow_stdn2_est(handles)
globals;
if isstruct(handles)
hA = handles.axes_allow_stdn2_est;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, allow_stdn2_est);
ylabel(hA, 'allow \sigma^2_{est}');
grid(hA, 'on');... |
github | Korogodin/gnss-qcno-estimator-master | plot_InSync.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_InSync.m | 413 | utf_8 | 26dc943ba6c3a8d2f57f73dfa1f47814 | %/**
% Plot InSync vector
% @param handles - указатель на всея структуру
%*/
function plot_InSync(handles)
globals;
if isstruct(handles)
hA = handles.axes_InSync;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, InSync);
set(hA, 'XTickLabel', []);
set(hA, 'YTick', [0 1]);
set(hA, 'YTickLabel', {'Off', 'On'})... |
github | Korogodin/gnss-qcno-estimator-master | plot_fail_counter.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_fail_counter.m | 416 | utf_8 | 6398a7feec3741fc4b9f08282872f45b | %/**
% Plot fail_counter vector
% @param handles - указатель на всея структуру
%*/
function plot_fail_counter(handles)
globals;
if isstruct(handles)
hA = handles.axes_fail_counter;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, fail_counter);
ylabel(hA, 'fail\_counter');
grid(hA, 'on');
if isstruct(handl... |
github | Korogodin/gnss-qcno-estimator-master | plot_EpsW.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_EpsW.m | 401 | utf_8 | 484947bdee1089b61ef3de972cc1e5a1 | %/**
% Plot EpsW vector
% @param handles - указатель на всея структуру
%*/
function plot_EpsW(handles)
globals;
if isstruct(handles)
hA = handles.axes_EpsW;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, EpsW/2/pi);
ylabel(hA, 'EpsW, Hz');
set(hA,'XGrid','on','YGrid','on')
if isstruct(handles)
set(hA... |
github | Korogodin/gnss-qcno-estimator-master | plot_SyncFirstLast.m | .m | gnss-qcno-estimator-master/matlab/common_func/plot_func/plot_SyncFirstLast.m | 455 | utf_8 | dda555e611ad346ec33eed5814492bde | %/**
% Plot SyncFirst and SyncLast vectors
% @param handles - указатель на всея структуру
%*/
function plot_SyncFirstLast(handles)
globals;
if isstruct(handles)
hA = handles.axes_SyncFirstLast;
else
hF = figure;
hA = gca;
end
plot(hA, 1:K, SyncFirst, 1:K, SyncLast);
xlabel(hA, 'k');
set(hA, 'YTick', [0 1]... |
github | dangut/pcl-master | plot_camera_poses.m | .m | pcl-master/gpu/kinfu/tools/plot_camera_poses.m | 3,407 | utf_8 | d210c150da98c3f4667f2c1e8d4eb6d2 | % Copyright (c) 2014-, Open Perception, Inc.
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions
% are met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of ... |
github | AndyZe/Lyapunov-Nonlinear-Control-master | launch.m | .m | Lyapunov-Nonlinear-Control-master/launch.m | 30,387 | utf_8 | ea0d09de5a6da3f443c56499ff66398a | function varargout = launch(varargin)
% LAUNCH MATLAB code for launch.fig
% LAUNCH, by itself, creates a new LAUNCH or raises the existing
% singleton*.
%
% H = LAUNCH returns the handle to a new LAUNCH or the handle to
% the existing singleton*.
%
% LAUNCH('CALLBACK',hObject,eventData,handles... |
github | svendaehne/TU_DK-master | mspocGitAugCrossLead.m | .m | TU_DK-master/code/sofie/mspocGitAugCrossLead.m | 2,919 | utf_8 | fcc0caf29da12151f6da2419e0a01630 | function [gamma,kappa_y,kappa_t,kappa_y0,kappa_t0, CrossEcut, corr_TEkf] = mspocGitAugCrossLead(Xe,Cxxe, Y_tr,G,Ne,Kf,Gamma,mspoc_params)
corr_TEkf = zeros(length(Gamma),length(mspoc_params.kappaY),length(mspoc_params.kappaT),Kf);
val_idx=0;
for kf=1:Kf
fprintf('Starting fold %d; \n',kf);
val_idx = val_id... |
github | svendaehne/TU_DK-master | optimize_mspoc_regularizersLead.m | .m | TU_DK-master/code/sofie/optimize_mspoc_regularizersLead.m | 5,726 | utf_8 | 9a043e762137b3830e9298cbf9672600 | function [best_kappa_tau, best_kappa_y, out] = ...
optimize_mspoc_regularizersLead(X, Y, mspoc_params, varargin)
opt = propertylist2struct(varargin{:});
opt = set_defaults(opt ...
,'n_xvalidation_folds', 5 ...
,'kappa_tau_list', 10.^(-4:1) ...
,'kappa_y_list', 10.^(-4:1) ...
,'Cxxe', [] ...
);
... |
github | svendaehne/TU_DK-master | mspocGitAugLead.m | .m | TU_DK-master/code/sofie/mspocGitAugLead.m | 13,268 | utf_8 | a3986d5c71662a068ca2cd28e5dde288 | function [Wx, Wy, Wtau, Ax, Ay, out] = mspocGitAugLead(X, Y,G,gamma,varargin)
% multimodal (temporal) (kernel) Source Power Co-modulation Analysis (mSPoC)
%
% Finds spatial filters wx and wy and a temporal filter wt such that the
% filtered power of the projected x-signal maximally covaries with the
% projected y-signa... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-GunnAndThomas/ocp-interfaces/Matlab/CompileOctave.m | 3,054 | utf_8 | f9fcf45322378be9e5335223c43e4678 | %-----------------------------------------------------------------------%
% file: GunnAndThomas_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-GunnAndThomas/ocp-interfaces/Matlab/CompileMex.m | 2,348 | utf_8 | 36acc72eb568b0965ea7626e7a62aed9 | %-----------------------------------------------------------------------%
% file: GunnAndThomas_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
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