id stringlengths 7 14 | test_class dict | test_case dict | focal_class dict | focal_method dict | repository dict |
|---|---|---|---|---|---|
128393918_125 | {
"fields": [
{
"declarator": "epsilon = 0.01",
"modifier": "private static",
"original_string": "private static double epsilon = 0.01;",
"type": "double",
"var_name": "epsilon"
},
{
"declarator": "N = 100000",
"modifier": "private static",
"original_string"... | {
"body": "@Category(Slow.class)\n @Test\n public void ofDirichletVertexHasCorrectProportions() {\n final DoubleTensor concentration = DoubleTensor.create(1, 2, 3, 4);\n final DirichletVertex dirichletVertex = new DirichletVertex(new ConstantDoubleVertex(concentration));\n final Categorical... | {
"fields": [
{
"declarator": "selectableValues",
"modifier": "private final",
"original_string": "private final Map<CATEGORY, DoubleVertex> selectableValues;",
"type": "Map<CATEGORY, DoubleVertex>",
"var_name": "selectableValues"
}
],
"file": "keanu-project/src/main/java/io/... | {
"body": "public static <CATEGORY> CategoricalVertex<CATEGORY> of(\n Map<CATEGORY, Double> selectableValues\n ) {\n return new CategoricalVertex<>(toDoubleVertices(selectableValues));\n }",
"class_method_signature": "CategoricalVertex.of(\n Map<CATEGORY, Double> selectableValues\n )",... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_172 | {
"fields": [
{
"declarator": "VERTEX_INCREMENT = 0.1",
"modifier": "private final",
"original_string": "private final double VERTEX_INCREMENT = 0.1;",
"type": "double",
"var_name": "VERTEX_INCREMENT"
},
{
"declarator": "random",
"modifier": "private",
"orig... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfVector() {\n DoubleVertex location = ConstantVertex.of(1.);\n DoubleVertex scale = ConstantVertex.of(1.5, 1.5);\n ParetoVertex paretoVertex = new ParetoVertex(new long[]{2}, location, scale);\n LogProbGraph logProbGraph ... | {
"fields": [
{
"declarator": "scale",
"modifier": "private final",
"original_string": "private final DoubleVertex scale;",
"type": "DoubleVertex",
"var_name": "scale"
},
{
"declarator": "location",
"modifier": "private final",
"original_string": "private fi... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex locationPlaceholder = new DoublePlaceholderVertex(location.getShape());\n final DoublePlaceholderVertex scale... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_308 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/algorithms/graphtraversal/TopologicalSortTest.java",
"identifier": "TopologicalSortTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test\n public void sortsSimpleDiamondGraph() {\n\n DoubleVertex A = new GaussianVertex(5.0, 1.0);\n DoubleVertex B = new GaussianVertex(A, 1.0);\n DoubleVertex C = new GaussianVertex(A, 1.0);\n DoubleVertex D = new GaussianVertex(B, C);\n\n List<? extends Vertex> sort... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/algorithms/graphtraversal/TopologicalSort.java",
"identifier": "TopologicalSort",
"interfaces": "",
"methods": [
{
"class_method_signature": "TopologicalSort.TopologicalSort()",
"constructor": true,
"full_signature"... | {
"body": "public static List<Vertex> sort(Collection<? extends Vertex> vertices) {\n return vertices\n .stream()\n .sorted(Comparator.comparing(Vertex::getId, Comparator.naturalOrder()))\n .collect(Collectors.toList());\n }",
"class_method_signature": "TopologicalSort.sor... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_164 | {
"fields": [
{
"declarator": "DELTA = 0.0001",
"modifier": "private static final",
"original_string": "private static final double DELTA = 0.0001;",
"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
"modifier": "private",
"original_string":... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfNegativeVector() {\n DoubleVertex sigma = ConstantVertex.of(1., 1.);\n HalfGaussianVertex halfGaussianVertex = new HalfGaussianVertex(sigma);\n LogProbGraph logProbGraph = halfGaussianVertex.logProbGraph();\n LogProbGrap... | {
"fields": [
{
"declarator": "MU_ZERO = 0.0",
"modifier": "private static final",
"original_string": "private static final double MU_ZERO = 0.0;",
"type": "double",
"var_name": "MU_ZERO"
},
{
"declarator": "LOG_TWO = Math.log(2)",
"modifier": "private static fina... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex muPlaceholder = new DoublePlaceholderVertex(getMu().getShape());\n final DoublePlaceholderVertex sigmaPlaceho... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_133 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/vertices/tensor/number/floating/dbl/nonprobabilistic/diff/LogProbGradientCalculatorTest.java",
"identifier": "LogProbGradientCalculatorTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void doesThrowOnLogProbDiffOnNonProbabilistic() {\n DoubleVertex A = ConstantVertex.of(5.0);\n DoubleVertex B = A.times(4);\n LogProbGradientCalculator calculator = new LogProbGradientCalculator(ImmutableList.of(B), Immutable... | {
"fields": [
{
"declarator": "logProbOfVertices",
"modifier": "private final",
"original_string": "private final Set<Vertex> logProbOfVertices;",
"type": "Set<Vertex>",
"var_name": "logProbOfVertices"
},
{
"declarator": "wrtVertices",
"modifier": "private final",... | {
"body": "public Map<VertexId, DoubleTensor> getJointLogProbGradientWrtLatents() {\n LogProbGradients totalLogProbGradients = new LogProbGradients();\n\n for (final Vertex<?, ?> ofVertex : logProbOfVertices) {\n LogProbGradients logProbGradientOfVertex = reverseModeLogProbGradientWrtLatents(... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_144 | {
"fields": [
{
"declarator": "DELTA = 0.0001",
"modifier": "private static final",
"original_string": "private static final double DELTA = 0.0001;",
"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
"modifier": "private",
"original_string":... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfScalar() {\n DoubleVertex mu = ConstantVertex.of(0.);\n DoubleVertex sigma = ConstantVertex.of(1.);\n GaussianVertex vertex = new GaussianVertex(mu, sigma);\n LogProbGraph logProbGraph = vertex.logProbGraph();\n\n ... | {
"fields": [
{
"declarator": "mu",
"modifier": "private final",
"original_string": "private final DoubleVertex mu;",
"type": "DoubleVertex",
"var_name": "mu"
},
{
"declarator": "sigma",
"modifier": "private final",
"original_string": "private final DoubleVe... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex muPlaceholder = new DoublePlaceholderVertex(mu.getShape());\n final DoublePlaceholderVertex sigmaPlaceholder ... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_328 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void youCanConstructManySequenceItems() throws IOException {\n VertexLabel x1Label = new VertexLabel(\"x1\");\n VertexLabel x2Label = new VertexLabel(\"x2\");\n VertexLabel outputLabel = new VertexLabel(\"OUTPUT\");\n String sequence1Label = \"Sequence_1\";\n ... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/templating/SequenceLoader.java",
"identifier": "SequenceLoader",
"interfaces": "",
"methods": [
{
"class_method_signature": "SequenceLoader.loadFromBayesNet(BayesianNetwork network)",
"constructor": false,
"full_sig... | {
"body": "public static Map<Integer, Sequence> loadMultipleSequencesFromBayesNet(BayesianNetwork network) {\n return loadSequences(network, true);\n }",
"class_method_signature": "SequenceLoader.loadMultipleSequencesFromBayesNet(BayesianNetwork network)",
"constructor": false,
"full_signature": "publ... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_282 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public DeterministicRule rule = new DeterministicRule();",
"type": "DeterministicRule",
"var_name": "rule"
}
],
"file": "keanu-project/src/test/java... | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void doesValidateTargetAcceptanceUpper() {\n NUTS.builder().targetAcceptanceProb(1.1);\n }",
"class_method_signature": "NUTSTest.doesValidateTargetAcceptanceUpper()",
"constructor": false,
"full_signature": "@Test(expected = Illeg... | {
"fields": [
{
"declarator": "random",
"modifier": "@Getter\n private final",
"original_string": "@Getter\n private final KeanuRandom random;",
"type": "KeanuRandom",
"var_name": "random"
},
{
"declarator": "targetAcceptanceProb",
"modifier": "@Getter\n ... | {
"body": "public static NUTSBuilder builder() {\n return new NUTSBuilder();\n }",
"class_method_signature": "NUTS.builder()",
"constructor": false,
"full_signature": "public static NUTSBuilder builder()",
"identifier": "builder",
"invocations": [],
"modifiers": "public static",
"parameters": ... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_113 | {
"fields": [
{
"declarator": "thrown = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException thrown = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "thrown"
},
{
"declarator": "printSt... | {
"body": "@Test\n public void whenPrintIsCalledAddsPrintVertexAsChild() {\n final UniformVertex A = new UniformVertex(0, 1);\n A.print();\n final Vertex printVertex = Iterables.getOnlyElement(A.getChildren());\n assertThat(printVertex, instanceOf(PrintVertex.class));\n }",
"class_... | {
"fields": [
{
"declarator": "printStream = System.out",
"modifier": "private static",
"original_string": "private static PrintStream printStream = System.out;",
"type": "PrintStream",
"var_name": "printStream"
},
{
"declarator": "PARENT = \"parent\"",
"modifier"... | {
"body": "public static <T> T print(T parentValue, String message, boolean printData) {\n final String dataOutput = printData ? parentValue.toString() + \"\\n\" : \"\";\n printStream.print(message + dataOutput);\n return parentValue;\n }",
"class_method_signature": "PrintVertex.print(T pare... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_84 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void doesntCreateTooManyBooleansFromPadding() {\n byte byte1 = (byte) Integer.parseInt(\"10101010\", 2);\n byte byte2 = (byte) Integer.parseInt(\"10000000\", 2);\n byte[] bytes = new byte[]{byte1, byte2};\n\n boolean[] bools = Py4jByteArrayConverter.toBooleanAr... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/Py4jByteArrayConverter.java",
"identifier": "Py4jByteArrayConverter",
"interfaces": "",
"methods": [
{
"class_method_signature": "Py4jByteArrayConverter.toByteArray(double[] doubleArray)",
"constructor": false,
... | {
"body": "public static boolean[] toBooleanArray(byte[] byteArray, int numberOfBooleansInArray) {\n BitSet bits = BitSet.valueOf(byteArray);\n boolean[] bools = new boolean[numberOfBooleansInArray];\n for (int i = bits.nextSetBit(0); i != -1; i = bits.nextSetBit(i + 1)) {\n int positi... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_105 | {
"fields": [
{
"declarator": "Amu",
"modifier": "",
"original_string": "DoubleVertex Amu;",
"type": "DoubleVertex",
"var_name": "Amu"
},
{
"declarator": "Asigma",
"modifier": "",
"original_string": "DoubleVertex Asigma;",
"type": "DoubleVertex",
... | {
"body": "@Test\n public void doesGetAllDownstreamVerticesOfCollection() {\n TransitiveClosure transitiveClosure = TransitiveClosure.getDownstreamVerticesForCollection(Arrays.asList(A, B, cSigma), true);\n assertThat(transitiveClosure.getAllVertices(), containsInAnyOrder(A, B, aPlusB, cSigma, C));\n... | {
"fields": [
{
"declarator": "ADD_ALL = vertex -> true",
"modifier": "private static final",
"original_string": "private static final Predicate<Vertex> ADD_ALL = vertex -> true;",
"type": "Predicate<Vertex>",
"var_name": "ADD_ALL"
},
{
"declarator": "PROBABILISTIC_OR_O... | {
"body": "public static TransitiveClosure getDownstreamVerticesForCollection(List<Vertex> vertices, boolean includeNonProbabilistic) {\n\n Predicate<Vertex> shouldAdd = includeNonProbabilistic ? ADD_ALL : PROBABILISTIC_OR_OBSERVED_ONLY;\n\n Set<Vertex> downstreamVertices = getVertices(\n ver... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_92 | {
"fields": [
{
"declarator": "thrown = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException thrown = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "thrown"
},
{
"declarator": "network... | {
"body": "@Test\n public void testGetAverageVertexDegree() {\n assertThat(network.getAverageVertexDegree(), equalTo((1. + 1. + 2. + 2. + 2.) / 5));\n }",
"class_method_signature": "BayesianNetworkTest.testGetAverageVertexDegree()",
"constructor": false,
"full_signature": "@Test public void testGet... | {
"fields": [
{
"declarator": "vertices",
"modifier": "private final",
"original_string": "private final List<? extends Vertex> vertices;",
"type": "List<? extends Vertex>",
"var_name": "vertices"
},
{
"declarator": "vertexLabels",
"modifier": "private final",
... | {
"body": "public double getAverageVertexDegree() {\n return getVertices().stream().mapToDouble(Vertex::getDegree).average().getAsDouble();\n }",
"class_method_signature": "BayesianNetwork.getAverageVertexDegree()",
"constructor": false,
"full_signature": "public double getAverageVertexDegree()",
"i... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_152 | {
"fields": [
{
"declarator": "DELTA = 0.001",
"modifier": "private static final",
"original_string": "private static final double DELTA = 0.001;",
"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
"modifier": "private",
"original_string": "... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfScalar() {\n DoubleVertex mu = ConstantVertex.of(0.5);\n DoubleVertex beta = ConstantVertex.of(1.);\n LaplaceVertex laplaceVertex = new LaplaceVertex(mu, beta);\n LogProbGraph logProbGraph = laplaceVertex.logProbGraph();... | {
"fields": [
{
"declarator": "mu",
"modifier": "private final",
"original_string": "private final DoubleVertex mu;",
"type": "DoubleVertex",
"var_name": "mu"
},
{
"declarator": "beta",
"modifier": "private final",
"original_string": "private final DoubleVer... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex muPlaceholder = new DoublePlaceholderVertex(mu.getShape());\n final DoublePlaceholderVertex betaPlaceholder =... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_294 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public DeterministicRule rule = new DeterministicRule();",
"type": "DeterministicRule",
"var_name": "rule"
}
],
"file": "keanu-project/src/test/java... | {
"body": "@Category(Slow.class)\n @Test\n public void samplesContinuousTensorPrior() {\n\n long[] shape = new long[]{1, 1};\n DoubleVertex A = new GaussianVertex(shape, 20.0, 1.0);\n DoubleVertex B = new GaussianVertex(shape, 20.0, 1.0);\n\n A.setValue(20.0);\n B.setValue(20.... | {
"fields": [
{
"declarator": "DEFAULT_VARIABLE_SELECTOR = SINGLE_VARIABLE_SELECTOR",
"modifier": "private static final",
"original_string": "private static final MHStepVariableSelector DEFAULT_VARIABLE_SELECTOR = SINGLE_VARIABLE_SELECTOR;",
"type": "MHStepVariableSelector",
"var_nam... | {
"body": "@Override\n public NetworkSamples getPosteriorSamples(ProbabilisticModel model,\n List<? extends Variable> variablesToSampleFrom,\n int sampleCount) {\n return generatePosteriorSamples(model, variablesToSamp... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_129 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/vertices/tensor/number/floating/dbl/nonprobabilistic/diff/LogProbGradientCalculatorTest.java",
"identifier": "LogProbGradientCalculatorTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test\n public void canFindGradientOfSingleVariantGaussianWrtSingleVariateLatent() {\n\n GaussianVertex A = new GaussianVertex(0, 1);\n GaussianVertex B = new GaussianVertex(A, 1);\n B.setValue(0.5);\n\n LogProbGradientCalculator calculator = new LogProbGradientCalculator(Imm... | {
"fields": [
{
"declarator": "logProbOfVertices",
"modifier": "private final",
"original_string": "private final Set<Vertex> logProbOfVertices;",
"type": "Set<Vertex>",
"var_name": "logProbOfVertices"
},
{
"declarator": "wrtVertices",
"modifier": "private final",... | {
"body": "public Map<VertexId, DoubleTensor> getJointLogProbGradientWrtLatents() {\n LogProbGradients totalLogProbGradients = new LogProbGradients();\n\n for (final Vertex<?, ?> ofVertex : logProbOfVertices) {\n LogProbGradients logProbGradientOfVertex = reverseModeLogProbGradientWrtLatents(... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_345 | {
"fields": [
{
"declarator": "random",
"modifier": "private",
"original_string": "private KeanuRandom random;",
"type": "KeanuRandom",
"var_name": "random"
}
],
"file": "keanu-project/src/test/java/io/improbable/keanu/KeanuRandomTest.java",
"identifier": "KeanuRandomTest",... | {
"body": "@Test\n public void canSampleUniformScalar() {\n assertEquals(0, random.nextDouble(new long[0]).getRank());\n assertEquals(1, random.nextDouble(new long[]{1}).getRank());\n assertEquals(2, random.nextDouble(new long[]{1, 1}).getRank());\n assertEquals(2, random.nextDouble(new... | {
"fields": [
{
"declarator": "DEFAULT_RANDOM = new AtomicReference<>()",
"modifier": "private static final",
"original_string": "private static final AtomicReference<KeanuRandom> DEFAULT_RANDOM = new AtomicReference<>();",
"type": "AtomicReference<KeanuRandom>",
"var_name": "DEFAULT... | {
"body": "public DoubleTensor nextDouble(long[] shape) {\n final int length = getLengthAsInt(shape);\n if (length > 1) {\n return DoubleTensor.create(nextDoubleBuffer(length), shape);\n } else {\n return DoubleTensor.create(nextDouble(), shape);\n }\n }",
"class... | {
"created": null,
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128393918_200 | {
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128393918_51 | {
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} | {
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128393918_191 | {
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128393918_312 | {
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} | {
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} |
128393918_257 | {
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"declarator": "currentStateForVertex1 = DoubleTensor.create(4.2, 4.7)",
"modifier": "private static fina... | {
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128393918_168 | {
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{
"declarator": "lowerBound = 10.",
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128393918_187 | {
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... | {
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128393918_304 | {
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"constructor": false,
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128393918_241 | {
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... | {
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128393918_10 | {
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128393918_6 | {
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128393918_353 | {
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128393918_47 | {
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} | {
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128393918_216 | {
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128393918_147 | {
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128393918_281 | {
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128393918_110 | {
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{
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}
],
"file": "keanu-project/src/test/java... | {
"body": "@Test\n public void logProbGraphClampsProbTrueTo0() {\n DoubleVertex probTrue = ConstantVertex.of(-1.);\n BernoulliVertex bernoulliVertex = new BernoulliVertex(probTrue);\n LogProbGraph logProbGraph = bernoulliVertex.logProbGraph();\n LogProbGraphValueFeeder.feedValue(logProb... | {
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{
"declarator": "PROBTRUE_NAME = \"probTrue\"",
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... | {
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128393918_87 | {
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{
"declarator": "network... | {
"body": "@Test\n public void youCanObserveAProbabilisticVariableAfterCreatingTheNetwork() {\n assertThat(network.getObservedVertices(), is(empty()));\n input1.observe(true);\n assertThat(network.getObservedVertices(), contains(input1));\n input2.observe(true);\n assertThat(netw... | {
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},
{
"declarator": "vertexLabels",
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... | {
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"constructor": false,
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128393918_68 | {
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{
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},
{
"declarator": "rowTensors = new ArrayList<>()",
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"original_string... | {
"body": "@Test\n public void writeColumnOfTensorsToCsvWithCustomEmptyValue() throws IOException {\n File file = WriteCsv.asColumns(columnTensors).withEmptyValue(\"None\").toFile(File.createTempFile(\"test\", \".csv\"));\n\n CsvReader reader = ReadCsv.fromFile(file).expectHeader(false);\n Lis... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/csv/WriteCsv.java",
"identifier": "WriteCsv",
"interfaces": "",
"methods": [
{
"class_method_signature": "WriteCsv.asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructo... | {
"body": "public static ColumnWriter asColumns(List<? extends Vertex<? extends Tensor, ?>> vertices) {\n return new ColumnWriter(vertices);\n }",
"class_method_signature": "WriteCsv.asColumns(List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructor": false,
"full_signature": "public stati... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_239 | {
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{
"declarator": "expectedException = ExpectedException.none()",
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"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void youCanDiminishTheNamespace() {\n String innerNamespace = \"inner\";\n String outerNamespace = \"outer\";\n String name = \"foo\";\n VertexLabel foo = new VertexLabel(outerNamespace, innerNamespace, name);\n VertexLabel newFoo = foo.withoutOuterNames... | {
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{
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"var_name": "NAMESPACE_SEPARATOR"
},
{
"declarator": "namespace",
"modifier": ... | {
"body": "public VertexLabel withoutOuterNamespace() {\n List<String> reducedNamespace = namespace.subList(1, namespace.size());\n return new VertexLabel(reducedNamespace, this.name);\n }",
"class_method_signature": "VertexLabel.withoutOuterNamespace()",
"constructor": false,
"full_signature":... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_106 | {
"fields": [
{
"declarator": "innerNet",
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"var_name": "innerNet"
},
{
"declarator": "trueLocation",
"modifier": "private",
"original_string": "private U... | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void willRejectProxyWithParent() {\n DoubleProxyVertex proxy = new DoubleProxyVertex(new VertexLabel(\"Input1\"));\n DoubleVertex output = new GaussianVertex(proxy, 1.0);\n output.setLabel(\"Output1\");\n DoubleVertex ... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/network/ModelComposition.java",
"identifier": "ModelComposition",
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"methods": [
{
"class_method_signature": "ModelComposition.ModelComposition()",
"constructor": true,
"full_signature": "private Mo... | {
"body": "public static Map<VertexLabel, Vertex> composeModel(BayesianNetwork bayesianNetwork,\n Map<VertexLabel, Vertex> inputVertices,\n List<VertexLabel> desiredOutputs) {\n Map<VertexLabel, Vertex... | {
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128393918_91 | {
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"constructor": false,
"full_signature": "@Test public void testGetNumVertices()",
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{
"declarator": "vertexLabels",
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... | {
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"identifier": "getVertexCount",
"invocations": [
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"... | {
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} |
128393918_151 | {
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{
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},
{
"declarator": "thrown = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_st... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfVector() {\n DoubleVertex xMin = ConstantVertex.of(0., 0.);\n DoubleVertex xMax = ConstantVertex.of(20., 20.);\n DoubleVertex c = ConstantVertex.of(10., 10.);\n TriangularVertex triangularVertex = new TriangularVertex(xM... | {
"fields": [
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},
{
"declarator": "xMax",
"modifier": "private final",
"original_string": "private final Dou... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex xMinPlaceholder = new DoublePlaceholderVertex(xMin.getShape());\n final DoublePlaceholderVertex xMaxPlacehold... | {
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} |
128393918_297 | {
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"var_name": "rule"
}
],
"file": "keanu-project/src/test/java... | {
"body": "@Test\n public void samplesFromPriorWithObservedDeterministic() {\n\n BernoulliVertex A = new BernoulliVertex(0.5);\n BernoulliVertex B = new BernoulliVertex(0.5);\n BooleanVertex C = A.or(B);\n C.observe(false);\n\n BayesianNetwork net = new BayesianNetwork(A.getConne... | {
"fields": [
{
"declarator": "DEFAULT_VARIABLE_SELECTOR = SINGLE_VARIABLE_SELECTOR",
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"original_string": "private static final MHStepVariableSelector DEFAULT_VARIABLE_SELECTOR = SINGLE_VARIABLE_SELECTOR;",
"type": "MHStepVariableSelector",
"var_nam... | {
"body": "@Override\n public NetworkSamples getPosteriorSamples(ProbabilisticModel model,\n List<? extends Variable> variablesToSampleFrom,\n int sampleCount) {\n return generatePosteriorSamples(model, variablesToSamp... | {
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} |
128393918_29 | {
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{
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},
{
"declarator": "scalar2 = new long[0]",
"modifier": "private",
"original_str... | {
"body": "@Test\n public void checkIsNotBroadcastableWithShapeMismatch() {\n\n long[] a = new long[]{2, 1, 2, 2};\n long[] b = new long[]{4, 3, 2, 2};\n\n assertFalse(TensorShapeValidation.isBroadcastable(a, b));\n }",
"class_method_signature": "TensorShapeValidationTest.checkIsNotBroadc... | {
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{
"class_method_signature": "TensorShapeValidation.TensorShapeValidation()",
"constructor": true,
"full_signa... | {
"body": "public static boolean isBroadcastable(long[] left, long[] right) {\n try {\n TensorShape.getBroadcastResultShape(left, right);\n return true;\n } catch (IllegalArgumentException e) {\n return false;\n }\n }",
"class_method_signature": "TensorShapeV... | {
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} |
128393918_278 | {
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{
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{
"declarator": "gradientCalculator",
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"original_string": "private Kea... | {
"body": "@Test\n public void leapsForwardWithMinimalEnergyLoss() {\n\n LeapfrogState leap = start;\n for (int i = 0; i < 1000; i++) {\n leap = integrator.step(leap, gradientCalculator, 1e-3);\n }\n\n assertEquals(start.getEnergy(), leap.getEnergy(), 1e-6);\n }",
"class... | {
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{
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],
"file": "keanu-project/src/main/java/io/improbable/keanu/algorithms/mcmc/nuts/LeapfrogIntegrator.... | {
"body": "public LeapfrogState step(LeapfrogState fromState, final ProbabilisticModelWithGradient logProbGradientCalculator, final double timeStep) {\n\n final double halfTimeStep = timeStep / 2.0;\n\n Map<VariableReference, DoubleTensor> nextMomentum = stepMomentum(halfTimeStep, fromState.getMomentum(... | {
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} |
128393918_52 | {
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"superclass": ""
} | {
"body": "@Test\n public void canDiagFromMatrix() {\n GenericTensor<String> actual = GenericTensor.create(new String[]{\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"}).reshape(3, 3).diagPart();\n GenericTensor<String> expected = GenericTensor.create(\"1\", \"5\", \"9\");\n\n as... | {
"fields": [
{
"declarator": "factory = new GenericBuffer.GenericArrayWrapperFactory()",
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"type": "GenericBuffer... | {
"body": "public static <T> GenericTensor<T> create(T... data) {\n return create(data, new long[]{data.length});\n }",
"class_method_signature": "GenericTensor.create(T... data)",
"constructor": false,
"full_signature": "public static GenericTensor<T> create(T... data)",
"identifier": "create",
"... | {
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} |
128393918_203 | {
"fields": [
{
"declarator": "N = 100000",
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{
"declarator": "epsilon = 0.05",
"modifier": "private",
"original_string": "private double epsilon = 0.05;",
... | {
"body": "@Test\n public void logProbUpperBoundIsNegativeInfinity() {\n UniformIntVertex testUniformVertex = new UniformIntVertex(new long[]{1, N}, lowerBound, upperBound);\n assertEquals(testUniformVertex.logProb(IntegerTensor.scalar(upperBound)), Double.NEGATIVE_INFINITY, 1e-6);\n }",
"class_... | {
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{
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},
{
"declarator": "max",
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"t... | {
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"class_method_signature": "UniformIntVertex.logProb(IntegerTensor value)",
"constructor": false,
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} |
128393918_346 | {
"fields": [
{
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],
"file": "keanu-project/src/test/java/io/improbable/keanu/KeanuRandomTest.java",
"identifier": "KeanuRandomTest",... | {
"body": "@Test\n public void canSampleGammaScalar() {\n assertEquals(0, random.nextGamma(new long[0], DoubleTensor.scalar(2), DoubleTensor.scalar(2)).getRank());\n assertEquals(1, random.nextGamma(new long[]{1}, DoubleTensor.scalar(2), DoubleTensor.scalar(2)).getRank());\n assertEquals(2, ra... | {
"fields": [
{
"declarator": "DEFAULT_RANDOM = new AtomicReference<>()",
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"original_string": "private static final AtomicReference<KeanuRandom> DEFAULT_RANDOM = new AtomicReference<>();",
"type": "AtomicReference<KeanuRandom>",
"var_name": "DEFAULT... | {
"body": "public DoubleTensor nextGamma(long[] shape, DoubleTensor theta, DoubleTensor k) {\n return Gamma.withParameters(theta, k).sample(shape, this);\n }",
"class_method_signature": "KeanuRandom.nextGamma(long[] shape, DoubleTensor theta, DoubleTensor k)",
"constructor": false,
"full_signature": "... | {
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} |
128393918_192 | {
"fields": [
{
"declarator": "DELTA = 0.0001",
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"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
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"original_string":... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfVector() {\n DoubleVertex alpha = ConstantVertex.of(2., 2.);\n DoubleVertex beta = ConstantVertex.of(1., 1.);\n InverseGammaVertex tensorInverseGammaVertex = new InverseGammaVertex(alpha, beta);\n LogProbGraph logProbGra... | {
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{
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},
{
"declarator": "beta",
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} |
128393918_254 | {
"fields": [
{
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"type": "DeterministicRule",
"var_name": "deterministicRule"
}
],... | {
"body": "@Test\n public void increasingSequenceReturnsCorrectAutocorr() {\n double[] samples = {1, 2, 3, 4, 5, 6, 7, 8};\n double[] autocorr = Autocorrelation.calculate(samples);\n double[] expected = {1., 0.625, 0.27380952, -0.0297619, -0.26190476,\n -0.39880952, -0.41666667, -0.... | {
"fields": [
{
"declarator": "ffTransformer = new FastFourierTransformer(DftNormalization.STANDARD)",
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"type": "FastF... | {
"body": "public static double[] calculate(double[] samples) {\n double[] acovResult = autocovariance(samples);\n double variance = acovResult[0];\n double[] autocorr = Arrays.stream(acovResult).map(x -> x / variance).toArray();\n return autocorr;\n }",
"class_method_signature": "Aut... | {
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128393918_311 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/algorithms/graphtraversal/MarkovBlanketTest.java",
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"interfaces": "",
"superclass": ""
} | {
"body": "@Test\n public void findBlanketFromSimpleGraph() {\n\n DoubleVertex A = new GaussianVertex(5.0, 1.0);\n DoubleVertex B = new GaussianVertex(5.0, 1.0);\n DoubleVertex C = new GaussianVertex(A, B);\n\n Set<Vertex> blanket = MarkovBlanket.get(C);\n\n assertTrue(blanket.si... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/algorithms/graphtraversal/MarkovBlanket.java",
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"methods": [
{
"class_method_signature": "MarkovBlanket.MarkovBlanket()",
"constructor": true,
"full_signature": "priva... | {
"body": "public static Set<Vertex> get(Vertex<?, ?> aVertex) {\n\n LambdaSection parents = LambdaSection.getUpstreamLambdaSection(aVertex, false);\n LambdaSection children = LambdaSection.getDownstreamLambdaSection(aVertex, false);\n Set<Vertex> childrensParents = getUpstreamProbabilisticVertic... | {
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_184 | {
"fields": [
{
"declarator": "DELTA = 0.0001",
"modifier": "private static final",
"original_string": "private static final double DELTA = 0.0001;",
"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
"modifier": "private",
"original_string":... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfNegativeScalar() {\n\n DoubleVertex scale = ConstantVertex.of(1.);\n HalfCauchyVertex halfCauchyVertex = new HalfCauchyVertex(scale);\n LogProbGraph logProbGraph = halfCauchyVertex.logProbGraph();\n LogProbGraphValueFeed... | {
"fields": [
{
"declarator": "LOC_ZERO = 0.0",
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"type": "double",
"var_name": "LOC_ZERO"
},
{
"declarator": "LOG_TWO = Math.log(2)",
"modifier": "private static f... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex locationPlaceholder = new DoublePlaceholderVertex(getLocation().getShape());\n final DoublePlaceholderVertex ... | {
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"repo_id": 128393918,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_13 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
},
... | {
"body": "@Test\n public void theMeanSquaredErrorIsZeroWhenTheDataIsPerfectlyCorrelated() {\n BivariateDataStatisticsCalculator perfectStats = new BivariateDataStatisticsCalculator(X_DATA, X_DATA.times(2.).plus(42.));\n assertThat(perfectStats.meanSquaredError(), equalTo(0.));\n }",
"class_meth... | {
"fields": [
{
"declarator": "xData",
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"type": "DoubleTensor",
"var_name": "xData"
},
{
"declarator": "yData",
"modifier": "private final",
"original_string": "private final... | {
"body": "public double meanSquaredError() {\n DoubleTensor calculatedY = xData.times(estimatedGradient()).plusInPlace(estimatedIntercept());\n DoubleTensor residuals = yData.minus(calculatedY);\n long unbiasedMultiplier = size() - 2;\n return residuals.times(residuals).sumNumber() / unbi... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
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"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_242 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void isInNamespaceOfEqualValue() {\n VertexLabel label = new VertexLabel(\"outer\", \"inner\", \"foo\");\n assertTrue(label.isInNamespace(\"outer\", \"inner\"));\n }",
"class_method_signature": "VertexLabelTest.isInNamespaceOfEqualValue()",
"constructor": false,
"fu... | {
"fields": [
{
"declarator": "NAMESPACE_SEPARATOR = '.'",
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"type": "char",
"var_name": "NAMESPACE_SEPARATOR"
},
{
"declarator": "namespace",
"modifier": ... | {
"body": "public boolean isInNamespace(String... namespace) {\n\n if (namespace.length > this.namespace.size()) {\n return false;\n }\n\n for (int i = 0; i < namespace.length; i++) {\n if (!this.namespace.get(i).equals(namespace[i])) {\n return false;\n ... | {
"created": null,
"fork": null,
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"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_307 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/algorithms/graphtraversal/TopologicalSortTest.java",
"identifier": "TopologicalSortTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test\n public void sortsSimpleLinearGraph() {\n\n DoubleVertex A = new GaussianVertex(5.0, 1.0);\n DoubleVertex B = new GaussianVertex(A, 1.0);\n DoubleVertex C = new GaussianVertex(B, 1.0);\n\n List<? extends Vertex> sorted = TopologicalSort.sort(Arrays.asList(C, B, A));\n ... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/algorithms/graphtraversal/TopologicalSort.java",
"identifier": "TopologicalSort",
"interfaces": "",
"methods": [
{
"class_method_signature": "TopologicalSort.TopologicalSort()",
"constructor": true,
"full_signature"... | {
"body": "public static List<Vertex> sort(Collection<? extends Vertex> vertices) {\n return vertices\n .stream()\n .sorted(Comparator.comparing(Vertex::getId, Comparator.naturalOrder()))\n .collect(Collectors.toList());\n }",
"class_method_signature": "TopologicalSort.sor... | {
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"repo_id": 128393918,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_5 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/tensor/TensorShapeTest.java",
"identifier": "TensorShapeTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void doesThrowOnInvalidPositiveAbsoluteDimensionsFromRelative() {\n TensorShape.getAbsoluteDimensions(3, new int[]{3, 1, 2});\n }",
"class_method_signature": "TensorShapeTest.doesThrowOnInvalidPositiveAbsoluteDimensionsFromRelative()"... | {
"fields": [
{
"declarator": "shape",
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"type": "long[]",
"var_name": "shape"
}
],
"file": "keanu-project/src/main/java/io/improbable/keanu/tensor/TensorShape.java",
"identifier": "TensorShape",
"interfaces... | {
"body": "public static int[] getAbsoluteDimensions(int rank, int[] input) {\n int[] output = new int[input.length];\n for (int i = 0; i < output.length; i++) {\n output[i] = getAbsoluteDimension(input[i], rank);\n }\n return output;\n }",
"class_method_signature": "Tensor... | {
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"is_fork": false,
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"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_215 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
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"type": "DeterministicRule",
"var_name": "rule"
},
{
"declarator": "thrown = Expect... | {
"body": "@Test\n public void itThrowsIfTheLogProbShapeDoesntMatchTheNumberOfCategories() {\n thrown.expect(IllegalArgumentException.class);\n thrown.expectMessage(\"x shape must have far right dimension matching number of categories k 4\");\n\n IntegerTensor n = IntegerTensor.create(100);\n ... | {
"fields": [
{
"declarator": "p",
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"type": "DoubleVertex",
"var_name": "p"
},
{
"declarator": "n",
"modifier": "private final",
"original_string": "private final IntegerVertex n... | {
"body": "@Override\n public double logProb(IntegerTensor x) {\n return Multinomial.withParameters(n.getValue(), p.getValue(), validationEnabled).logProb(x).sumNumber();\n }",
"class_method_signature": "MultinomialVertex.logProb(IntegerTensor x)",
"constructor": false,
"full_signature": "@Override... | {
"created": null,
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"fork_count": 33,
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"stargazer_count": 146,
"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_44 | {
"fields": [
{
"declarator": "thrown = ExpectedException.none()",
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"original_string": "@Rule\n public ExpectedException thrown = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "thrown"
}
],
"file": "keanu-project/src/t... | {
"body": "@Test\n public void canBroadcastToShape() {\n BooleanTensor a = BooleanTensor.create(\n true, false, true\n ).reshape(3);\n\n BooleanTensor expectedByRow = BooleanTensor.create(\n true, false, true,\n true, false, true,\n true, false, true... | {
"fields": [
{
"declarator": "factory = BooleanBuffer.factory",
"modifier": "private static final",
"original_string": "private static final BooleanBuffer.BooleanArrayWrapperFactory factory = BooleanBuffer.factory;",
"type": "BooleanBuffer.BooleanArrayWrapperFactory",
"var_name": "f... | {
"body": "@Override\n protected JVMBooleanTensor create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape, long[] stride) {\n return new JVMBooleanTensor(buffer, shape, stride);\n }",
"class_method_signature": "JVMBooleanTensor.create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape... | {
"created": null,
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"is_fork": false,
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"stargazer_count": 146,
"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_350 | {
"fields": [
{
"declarator": "numberOfStudents = 100",
"modifier": "private final",
"original_string": "private final int numberOfStudents = 100;",
"type": "int",
"var_name": "numberOfStudents"
},
{
"declarator": "numberOfYesAnswers = 35",
"modifier": "private fi... | {
"body": "@Test\n public void doesWorkWithHigherDimensionDescription() {\n double approximateProbabilityOfCheating = CheatingStudents.runWithBernoulli(numberOfStudents, numberOfYesAnswers);\n assertEquals(0.2, approximateProbabilityOfCheating, 0.05);\n }",
"class_method_signature": "CheatingStu... | {
"fields": [],
"file": "keanu-examples/bayesianMethodsForHackers/src/main/java/com/examples/CheatingStudents.java",
"identifier": "CheatingStudents",
"interfaces": "",
"methods": [
{
"class_method_signature": "CheatingStudents.runWithBernoulli(int numberOfStudents, int numberOfYesAnswers)",
"... | {
"body": "public static double runWithBernoulli(int numberOfStudents, int numberOfYesAnswers) {\n\n int numberOfSamples = 2000;\n UniformVertex probabilityOfCheating = new UniformVertex(0.0, 1.0);\n BooleanVertex studentCheated = new BernoulliVertex(new long[]{numberOfStudents}, probabilityOfChe... | {
"created": null,
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"is_fork": false,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_327 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
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"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void itThrowsWhenManyAreStoredButOneIsRequested() throws IOException {\n expectedException.expect(SequenceConstructionException.class);\n expectedException.expectMessage(\"The provided BayesianNetwork contains more than one Sequence\");\n\n VertexLabel x1Label = new V... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/templating/SequenceLoader.java",
"identifier": "SequenceLoader",
"interfaces": "",
"methods": [
{
"class_method_signature": "SequenceLoader.loadFromBayesNet(BayesianNetwork network)",
"constructor": false,
"full_sig... | {
"body": "public static Sequence loadFromBayesNet(BayesianNetwork network) {\n Collection<Sequence> sequences = loadSequences(network, false).values();\n return sequences.stream().findFirst().get();\n }",
"class_method_signature": "SequenceLoader.loadFromBayesNet(BayesianNetwork network)",
"cons... | {
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"fork": null,
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"is_fork": false,
"language": "Java",
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"repo_id": 128393918,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_33 | {
"fields": [
{
"declarator": "thrown = ExpectedException.none()",
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"original_string": "@Rule\n public ExpectedException thrown = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "thrown"
}
],
"file": "keanu-project/src/t... | {
"body": "@Test\n public void youCanCreateARankOneTensor() {\n BooleanTensor booleanVector = BooleanTensor.create(new boolean[]{true, false, false, true, true}, new long[]{5});\n assertTrue(booleanVector.getValue(3));\n assertEquals(1, booleanVector.getRank());\n }",
"class_method_signat... | {
"fields": [
{
"declarator": "factory = BooleanBuffer.factory",
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"original_string": "private static final BooleanBuffer.BooleanArrayWrapperFactory factory = BooleanBuffer.factory;",
"type": "BooleanBuffer.BooleanArrayWrapperFactory",
"var_name": "f... | {
"body": "@Override\n protected JVMBooleanTensor create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape, long[] stride) {\n return new JVMBooleanTensor(buffer, shape, stride);\n }",
"class_method_signature": "JVMBooleanTensor.create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape... | {
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"is_fork": false,
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"repo_id": 128393918,
"size": 15461,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_262 | {
"fields": [
{
"declarator": "vertex1 = mock(Vertex.class)",
"modifier": "private",
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"type": "Vertex",
"var_name": "vertex1"
},
{
"declarator": "vertex2 = mock(Vertex.class)",
"modifier": "private"... | {
"body": "@Test\n public void youCanTrackTheAcceptanceRateForDifferentSetsOfVertices() {\n Proposal proposal = new Proposal();\n proposal.setProposal(vertex1, 1.);\n notifier.notifyProposalCreated(proposal);\n notifier.notifyProposalRejected();\n assertThat(acceptanceRateTracker... | {
"fields": [
{
"declarator": "numApplied = Maps.newHashMap()",
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"type": "Map<VariableReference, Counter>",
"var_name": "numApplied"
},
{
"declarator": "n... | {
"body": "public double getAcceptanceRate(VariableReference variableReference) {\n if (!numApplied.keySet().contains(variableReference)) {\n throw new IllegalStateException(\"No proposals have been registered for \" + variableReference);\n }\n return 1. - (double) numRejected.getOrDef... | {
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"size": 15461,
"stargazer_count": 146,
"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_235 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
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"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
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"var_name": "expectedException"
}
... | {
"body": "@Test\n public void youCanSetTheNamespace() {\n String namespace = \"namespace\";\n String name = \"foo\";\n VertexLabel foo = new VertexLabel(name);\n VertexLabel newFoo = foo.withExtraNamespace(namespace);\n assertThat(newFoo, equalTo(new VertexLabel(namespace, name)... | {
"fields": [
{
"declarator": "NAMESPACE_SEPARATOR = '.'",
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"type": "char",
"var_name": "NAMESPACE_SEPARATOR"
},
{
"declarator": "namespace",
"modifier": ... | {
"body": "public VertexLabel withExtraNamespace(String topLevelNamespace) {\n List<String> newNamespace = ImmutableList.<String>builder().add(topLevelNamespace).addAll(namespace).build();\n return new VertexLabel(newNamespace, this.name);\n }",
"class_method_signature": "VertexLabel.withExtraNames... | {
"created": null,
"fork": null,
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"language": "Java",
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"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_64 | {
"fields": [
{
"declarator": "csv = \"a,b,c\\nhel,lo,world\"",
"modifier": "",
"original_string": "String csv = \"a,b,c\\nhel,lo,world\";",
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"var_name": "csv"
}
],
"file": "keanu-project/src/test/java/io/improbable/keanu/util/csv/ReadCsvTest.java",
"identi... | {
"body": "@Test\n public void canReadFromFile() throws IOException {\n Path temp = Files.createTempFile(\"simple_data\", \".csv\");\n Files.write(temp, csv.getBytes());\n temp.toFile().deleteOnExit();\n\n CsvReader csvReader = ReadCsv.fromFile(temp);\n canRead(csvReader);\n }... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/csv/ReadCsv.java",
"identifier": "ReadCsv",
"interfaces": "",
"methods": [
{
"class_method_signature": "ReadCsv.ReadCsv()",
"constructor": true,
"full_signature": "private ReadCsv()",
"identifier": "Read... | {
"body": "public static CsvReader fromFile(File file) {\n return fromFile(file.toPath());\n }",
"class_method_signature": "ReadCsv.fromFile(File file)",
"constructor": false,
"full_signature": "public static CsvReader fromFile(File file)",
"identifier": "fromFile",
"invocations": [
"fromFile"... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_72 | {
"fields": [
{
"declarator": "samples",
"modifier": "private",
"original_string": "private NetworkSamples samples;",
"type": "NetworkSamples",
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},
{
"declarator": "rowTensors = new ArrayList<>()",
"modifier": "private",
"original_string... | {
"body": "@Test\n public void writeRowOfTensorsToCsvWithCustomEmptyValue() throws IOException {\n File file = WriteCsv.asRows(rowTensors).withEmptyValue(\"/\").toFile(File.createTempFile(\"test\", \".csv\"));\n\n CsvReader reader = ReadCsv.fromFile(file).expectHeader(false);\n List<List<Strin... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/csv/WriteCsv.java",
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"methods": [
{
"class_method_signature": "WriteCsv.asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructo... | {
"body": "public static RowWriter asRows(List<? extends Vertex<? extends Tensor, ?>> vertices) {\n return new RowWriter(vertices);\n }",
"class_method_signature": "WriteCsv.asRows(List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructor": false,
"full_signature": "public static RowWriter ... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
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"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_223 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public DeterministicRule rule = new DeterministicRule();",
"type": "DeterministicRule",
"var_name": "rule"
},
{
"declarator": "thrown = Expect... | {
"body": "@Test\n public void whenKEqualsTwoItsBinomial() {\n IntegerTensor n = IntegerTensor.scalar(10);\n DoubleTensor p = DoubleTensor.create(0.2, 0.8).reshape(1, 2);\n DiscreteDistribution multinomial = Multinomial.withParameters(n, p);\n DiscreteDistribution binomial = Binomial.wi... | {
"fields": [
{
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},
{
"declarator": "n",
"modifier": "private final",
"original_string": "private final IntegerVertex n... | {
"body": "@Override\n public double logProb(IntegerTensor x) {\n return Multinomial.withParameters(n.getValue(), p.getValue(), validationEnabled).logProb(x).sumNumber();\n }",
"class_method_signature": "MultinomialVertex.logProb(IntegerTensor x)",
"constructor": false,
"full_signature": "@Override... | {
"created": null,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_331 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
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}
... | {
"body": "@Test\n public void bayesNetConstructionFailsWhenThereAreNoSequenceItems() {\n expectedException.expect(RuntimeException.class);\n expectedException.expectMessage(\"Bayesian Network construction failed because the Sequence contains no SequenceItems\");\n Sequence sequence = new Sequ... | {
"fields": [
{
"declarator": "containedItems",
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"original_string": "private final ArrayList<SequenceItem> containedItems;",
"type": "ArrayList<SequenceItem>",
"var_name": "containedItems"
},
{
"declarator": "uniqueIdentifier",
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"body": "public BayesianNetwork toBayesianNetwork() {\n if (containedItems.size() == 0) {\n throw new RuntimeException(\"Bayesian Network construction failed because the Sequence contains no SequenceItems\");\n }\n Optional<Vertex<?, ?>> seedVertex = containedItems\n .get(... | {
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} |
128393918_274 | {
"fields": [
{
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},
{
"declarator": "BReference",
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"body": "@Test\n public void canZeros() {\n\n Map<VariableReference, DoubleTensor> result = VariableValues.zeros(left);\n\n DoubleTensor expectedA = DoubleTensor.create(0, 0);\n DoubleTensor expectedB = DoubleTensor.create(0, 0);\n\n assertEquals(expectedA, result.get(AReference));\n ... | {
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{
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"constructor": true,
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"constructor": false,
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} |
128393918_25 | {
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{
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},
{
"declarator": "scalar2 = new long[0]",
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"original_str... | {
"body": "@Test\n public void checkSquareMatrixAcceptsSquareMatrices() {\n TensorShapeValidation.checkShapeIsSquareMatrix(new long[]{3, 3});\n }",
"class_method_signature": "TensorShapeValidationTest.checkSquareMatrixAcceptsSquareMatrices()",
"constructor": false,
"full_signature": "@Test public v... | {
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"identifier": "TensorShapeValidation",
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"methods": [
{
"class_method_signature": "TensorShapeValidation.TensorShapeValidation()",
"constructor": true,
"full_signa... | {
"body": "public static void checkShapeIsSquareMatrix(long[] shape) {\n if (shape.length < 2) {\n throw new IllegalArgumentException(\"Input tensor must be a matrix\");\n }\n\n if (shape[shape.length - 1] != shape[shape.length - 2]) {\n throw new IllegalArgumentException(\"... | {
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} |
128393918_126 | {
"fields": [
{
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{
"declarator": "N = 100000",
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"original_string"... | {
"body": "@Category(Slow.class)\n @Test\n public void ofDirichletVertexUsesIntegerRangeByDefault() {\n final DoubleTensor concentration = DoubleTensor.create(1, 2, 3, 4, 5);\n final DirichletVertex dirichletVertex = new DirichletVertex(new ConstantDoubleVertex(concentration));\n final Cate... | {
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{
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}
],
"file": "keanu-project/src/main/java/io/... | {
"body": "public static <CATEGORY> CategoricalVertex<CATEGORY> of(\n Map<CATEGORY, Double> selectableValues\n ) {\n return new CategoricalVertex<>(toDoubleVertices(selectableValues));\n }",
"class_method_signature": "CategoricalVertex.of(\n Map<CATEGORY, Double> selectableValues\n )",... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_258 | {
"fields": [
{
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{
"declarator": "currentStateForVertex1 = DoubleTensor.create(4.2, 4.7)",
"modifier": "private static fina... | {
"body": "@Test\n public void getProposalThrowsIfProposalIsMissingASigmaForAVariable() {\n CauchyVertex notInProposalDistribution = new CauchyVertex(1., 1.);\n thrown.expect(IllegalStateException.class);\n thrown.expectMessage(\"A sigma was not specified for variable \" + notInProposalDistrib... | {
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{
"declarator": "proposalNotifier",
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"body": "@Override\n public Proposal getProposal(Set<? extends Variable> variables, KeanuRandom random) {\n Proposal proposal = new Proposal();\n for (Variable variable : variables) {\n if (!(variable.getValue() instanceof DoubleTensor)) {\n throw new IllegalStateException... | {
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"url": "https://github.com/improbable-research/keanu"
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128393918_171 | {
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{
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{
"declarator": "random",
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"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfScalar() {\n DoubleVertex location = ConstantVertex.of(1.);\n DoubleVertex scale = ConstantVertex.of(1.5);\n ParetoVertex paretoVertex = new ParetoVertex(location, scale);\n LogProbGraph logProbGraph = paretoVertex.logPr... | {
"fields": [
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{
"declarator": "location",
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"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex locationPlaceholder = new DoublePlaceholderVertex(location.getShape());\n final DoublePlaceholderVertex scale... | {
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} |
128393918_188 | {
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{
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{
"declarator": "TEST_VALUES_OF_V = new int[]{\n 1, 2, 3\n }",
... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfVector() {\n IntegerVertex v = ConstantVertex.of(1, 1);\n StudentTVertex studentT = new StudentTVertex(v);\n LogProbGraph logProbGraph = studentT.logProbGraph();\n\n LogProbGraphValueFeeder.feedValue(logProbGraph, v, v.g... | {
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{
"declarator": "V_NAME = \"v\"",
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_167 | {
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{
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},
{
"declarator": "lowerBound = 10.",
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"class_m... | {
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{
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{
"declarator": "xMax",
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"class_method_signature": "UniformVertex.logProb(DoubleTensor value)",
"constructor": false,
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} |
128393918_48 | {
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"superclass": ""
} | {
"body": "@Test\n public void canSetRandomAccessValue() {\n\n GenericTensor<Something> somethingTensor = GenericTensor.create(\n new Something[]{\n Something.A, Something.B, Something.B,\n Something.C, Something.D, Something.B,\n Something.D, Somethin... | {
"fields": [
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"type": "GenericBuffer... | {
"body": "public static <T> GenericTensor<T> create(T... data) {\n return create(data, new long[]{data.length});\n }",
"class_method_signature": "GenericTensor.create(T... data)",
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"full_signature": "public static GenericTensor<T> create(T... data)",
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"... | {
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128393918_219 | {
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{
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128393918_130 | {
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} | {
"body": "@Test\n public void canFindGradientOfSingleVariantGaussianWrtMultivariateLatent() {\n\n GaussianVertex A = new GaussianVertex(new long[]{3, 2}, 0, 1);\n GaussianVertex B = new GaussianVertex(A, 1);\n DoubleTensor bValue = DoubleTensor.create(new double[]{0.1, 0.2, 0.3, -0.2, -0.5, 0... | {
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{
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{
"declarator": "wrtVertices",
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"body": "public Map<VertexId, DoubleTensor> getJointLogProbGradientWrtLatents() {\n LogProbGradients totalLogProbGradients = new LogProbGradients();\n\n for (final Vertex<?, ?> ofVertex : logProbOfVertices) {\n LogProbGradients logProbGradientOfVertex = reverseModeLogProbGradientWrtLatents(... | {
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128393918_9 | {
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... | {
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"constructor": false,
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{
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128393918_131 | {
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} | {
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{
"declarator": "wrtVertices",
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"body": "public Map<VertexId, DoubleTensor> getJointLogProbGradientWrtLatents() {\n LogProbGradients totalLogProbGradients = new LogProbGradients();\n\n for (final Vertex<?, ?> ofVertex : logProbOfVertices) {\n LogProbGradients logProbGradientOfVertex = reverseModeLogProbGradientWrtLatents(... | {
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} |
128393918_8 | {
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} | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void throwsIfFlatIndexUnderflowsShape() {\n long[] shapeIndices = TensorShape.getShapeIndices(new long[]{5, 4}, new long[]{4, 1}, -1);\n }",
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"constructo... | {
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{
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],
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"body": "public static long[] getShapeIndices(long[] shape, long[] stride, long flatIndex) {\n Preconditions.checkArgument(flatIndex >= 0, \"Flat index must be >= 0 and less than the length of the shape\");\n long[] shapeIndices = new long[stride.length];\n long remainder = flatIndex;\n ... | {
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} |
128393918_49 | {
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} | {
"body": "@Test\n public void canPermute() {\n GenericTensor<Something> somethingTensor = GenericTensor.create(\n Something.A, Something.B, Something.B,\n Something.C, Something.D, Something.B,\n Something.D, Something.A, Something.C\n ).reshape(3, 3);\n\n Gen... | {
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{
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"type": "GenericBuffer... | {
"body": "public static <T> GenericTensor<T> create(T... data) {\n return create(data, new long[]{data.length});\n }",
"class_method_signature": "GenericTensor.create(T... data)",
"constructor": false,
"full_signature": "public static GenericTensor<T> create(T... data)",
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"... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_218 | {
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{
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"body": "@Test\n public void itThrowsIfTheLogProbStateContainsNegativeNumbers() {\n thrown.expect(IllegalArgumentException.class);\n thrown.expectMessage(\"x must be >= 0 and <= n\");\n\n IntegerTensor n = IntegerTensor.scalar(10);\n DoubleTensor p = DoubleTensor.create(0.2, 0.8);\n ... | {
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{
"declarator": "n",
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"original_string": "private final IntegerVertex n... | {
"body": "@Override\n public double logProb(IntegerTensor x) {\n return Multinomial.withParameters(n.getValue(), p.getValue(), validationEnabled).logProb(x).sumNumber();\n }",
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} |
128393918_189 | {
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{
"declarator": "DELTA = 0.0001",
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"original_string": "private final Dou... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex thetaPlaceholder = new DoublePlaceholderVertex(theta.getShape());\n final DoublePlaceholderVertex kPlaceholde... | {
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} |
128393918_166 | {
"fields": [
{
"declarator": "N = 100000",
"modifier": "private",
"original_string": "private int N = 100000;",
"type": "int",
"var_name": "N"
},
{
"declarator": "lowerBound = 10.",
"modifier": "private",
"original_string": "private Double lowerBound = 10.;... | {
"body": "@Test\n public void logProbGraphUpperBoundIsNegativeInfinity() {\n DoubleVertex xMin = ConstantVertex.of(lowerBound);\n DoubleVertex xMax = ConstantVertex.of(upperBound);\n UniformVertex uniformVertex = new UniformVertex(xMin, xMax);\n LogProbGraph logProbGraph = uniformVerte... | {
"fields": [
{
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"var_name": "xMin"
},
{
"declarator": "xMax",
"modifier": "private final",
"original_string": "private final Dou... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex xMinPlaceholder = new DoublePlaceholderVertex(xMin.getShape());\n final DoublePlaceholderVertex xMaxPlacehold... | {
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"is_fork": false,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_170 | {
"fields": [
{
"declarator": "DELTA = 0.0001",
"modifier": "private static final",
"original_string": "private static final double DELTA = 0.0001;",
"type": "double",
"var_name": "DELTA"
},
{
"declarator": "random",
"modifier": "private",
"original_string":... | {
"body": "@Test\n public void logProbGraphMatchesKnownLogDensityOfVector() {\n DoubleVertex mu = ConstantVertex.of(0., 0.);\n DoubleVertex s = ConstantVertex.of(1., 1.);\n LogisticVertex logisticVertex = new LogisticVertex(mu, s);\n LogProbGraph logProbGraph = logisticVertex.logProbGra... | {
"fields": [
{
"declarator": "mu",
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"type": "DoubleVertex",
"var_name": "mu"
},
{
"declarator": "s",
"modifier": "private final",
"original_string": "private final DoubleVertex... | {
"body": "@Override\n public LogProbGraph logProbGraph() {\n final DoublePlaceholderVertex xPlaceholder = new DoublePlaceholderVertex(this.getShape());\n final DoublePlaceholderVertex muPlaceholder = new DoublePlaceholderVertex(mu.getShape());\n final DoublePlaceholderVertex sPlaceholder = ne... | {
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"repo_id": 128393918,
"size": 15461,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_259 | {
"fields": [
{
"declarator": "proposal",
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"type": "Proposal",
"var_name": "proposal"
},
{
"declarator": "currentStateForVertex1 = DoubleTensor.create(4.2, 4.7)",
"modifier": "private static fina... | {
"body": "@Test\n public void logProbThrowsIfProposalIsMissingASigmaForAVariable() {\n CauchyVertex notInProposalDistribution = new CauchyVertex(1., 1.);\n thrown.expect(IllegalStateException.class);\n thrown.expectMessage(\"A sigma was not specified for variable \" + notInProposalDistributio... | {
"fields": [
{
"declarator": "sigmas",
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"type": "Map<? extends Variable, DoubleTensor>",
"var_name": "sigmas"
},
{
"declarator": "proposalNotifier",
"mod... | {
"body": "@Override\n public <T> double logProb(Probabilistic<T> variable, T ofValue, T givenValue) {\n if (!(ofValue instanceof DoubleTensor)) {\n throw new ClassCastException(\"Only DoubleTensor values are supported - not \" + ofValue.getClass().getSimpleName());\n }\n if (!sigma... | {
"created": null,
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"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_127 | {
"fields": [
{
"declarator": "epsilon = 0.01",
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"original_string": "private static double epsilon = 0.01;",
"type": "double",
"var_name": "epsilon"
},
{
"declarator": "N = 100000",
"modifier": "private static",
"original_string"... | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void ofDirichletWrongAmountOfCategoriesFails() {\n final DirichletVertex dirichletVertex = new DirichletVertex(1, 2, 3, 4, 5);\n CategoricalVertex.of(dirichletVertex, Arrays.asList(TestEnum.A, TestEnum.B, TestEnum.C, TestEnum.D));\n... | {
"fields": [
{
"declarator": "selectableValues",
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"type": "Map<CATEGORY, DoubleVertex>",
"var_name": "selectableValues"
}
],
"file": "keanu-project/src/main/java/io/... | {
"body": "public static <CATEGORY> CategoricalVertex<CATEGORY> of(\n Map<CATEGORY, Double> selectableValues\n ) {\n return new CategoricalVertex<>(toDoubleVertices(selectableValues));\n }",
"class_method_signature": "CategoricalVertex.of(\n Map<CATEGORY, Double> selectableValues\n )",... | {
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"is_fork": false,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_330 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
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"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test(expected = SequenceConstructionException.class)\n public void itThrowsIfYouAskForTheLastItemButThereIsNone() {\n new Sequence(10, this.hashCode(), null).getLastItem();\n }",
"class_method_signature": "SequenceTest.itThrowsIfYouAskForTheLastItemButThereIsNone()",
"constructor": false,... | {
"fields": [
{
"declarator": "containedItems",
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"type": "ArrayList<SequenceItem>",
"var_name": "containedItems"
},
{
"declarator": "uniqueIdentifier",
"modifie... | {
"body": "public SequenceItem getLastItem() {\n if (containedItems.isEmpty()) {\n throw new SequenceConstructionException(\"Sequence is empty!\");\n }\n return this.asList().get(this.size() - 1);\n }",
"class_method_signature": "Sequence.getLastItem()",
"constructor": false,
... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_275 | {
"fields": [
{
"declarator": "AReference",
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"type": "VariableReference",
"var_name": "AReference"
},
{
"declarator": "BReference",
"modifier": "private",
"original_string": "p... | {
"body": "@Test\n public void canOnes() {\n\n Map<VariableReference, DoubleTensor> result = VariableValues.ones(left);\n\n DoubleTensor expectedA = DoubleTensor.create(1, 1);\n DoubleTensor expectedB = DoubleTensor.create(1, 1);\n\n assertEquals(expectedA, result.get(AReference));\n ... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/algorithms/mcmc/nuts/VariableValues.java",
"identifier": "VariableValues",
"interfaces": "",
"methods": [
{
"class_method_signature": "VariableValues.VariableValues()",
"constructor": true,
"full_signature": "privat... | {
"body": "public static Map<VariableReference, DoubleTensor> ones(Map<VariableReference, DoubleTensor> shapeLike) {\n return withShape(1.0, shapeLike);\n }",
"class_method_signature": "VariableValues.ones(Map<VariableReference, DoubleTensor> shapeLike)",
"constructor": false,
"full_signature": "publi... | {
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_24 | {
"fields": [
{
"declarator": "scalar1 = new long[0]",
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"var_name": "scalar1"
},
{
"declarator": "scalar2 = new long[0]",
"modifier": "private",
"original_str... | {
"body": "@Test\n public void acceptsNonScalarToScalarParentShape() {\n TensorShapeValidation.checkTensorsMatchNonLengthOneShapeOrAreLengthOne(new long[]{2, 4}, scalar2, scalar1);\n }",
"class_method_signature": "TensorShapeValidationTest.acceptsNonScalarToScalarParentShape()",
"constructor": false,... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/tensor/TensorShapeValidation.java",
"identifier": "TensorShapeValidation",
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"methods": [
{
"class_method_signature": "TensorShapeValidation.TensorShapeValidation()",
"constructor": true,
"full_signa... | {
"body": "public static void checkTensorsMatchNonLengthOneShapeOrAreLengthOne(long[] proposalShape, long[]... shapes) {\n\n Set<TensorShape> nonLengthOneShapes = getNonLengthOneShapes(shapes);\n\n if (!nonLengthOneShapes.isEmpty()) {\n\n boolean moreThanOneNonLengthOneShape = nonLengthOneSha... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_73 | {
"fields": [
{
"declarator": "samples",
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"type": "NetworkSamples",
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},
{
"declarator": "rowTensors = new ArrayList<>()",
"modifier": "private",
"original_string... | {
"body": "@Test\n public void writeRowOfTensorsToCsvWithHeader() throws IOException {\n File file = WriteCsv.asRows(rowTensors).withDefaultHeader().toFile(File.createTempFile(\"test\", \".csv\"));\n\n CsvReader reader = ReadCsv.fromFile(file).expectHeader(true);\n List<List<String>> lines = r... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/csv/WriteCsv.java",
"identifier": "WriteCsv",
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"methods": [
{
"class_method_signature": "WriteCsv.asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructo... | {
"body": "public static RowWriter asRows(List<? extends Vertex<? extends Tensor, ?>> vertices) {\n return new RowWriter(vertices);\n }",
"class_method_signature": "WriteCsv.asRows(List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructor": false,
"full_signature": "public static RowWriter ... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_222 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
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"original_string": "@Rule\n public DeterministicRule rule = new DeterministicRule();",
"type": "DeterministicRule",
"var_name": "rule"
},
{
"declarator": "thrown = Expect... | {
"body": "@Test\n public void itWorksWithMatrixOfNAndMatrixOfProbabilities() {\n\n int n1 = 1;\n int n2 = 10;\n int n3 = 100;\n int n4 = 1000;\n\n IntegerTensor n = IntegerTensor.create(new int[]{\n n1, n2,\n n3, n4\n }, 2, 2);\n\n double[] p1... | {
"fields": [
{
"declarator": "p",
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"var_name": "p"
},
{
"declarator": "n",
"modifier": "private final",
"original_string": "private final IntegerVertex n... | {
"body": "@Override\n public double logProb(IntegerTensor x) {\n return Multinomial.withParameters(n.getValue(), p.getValue(), validationEnabled).logProb(x).sumNumber();\n }",
"class_method_signature": "MultinomialVertex.logProb(IntegerTensor x)",
"constructor": false,
"full_signature": "@Override... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_234 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void itReturnsEmptyIfYouAskForTheOuterNamespaceButThereIsntOne() {\n VertexLabel labelWithoutNamespace = new VertexLabel(\"foo\");\n assertThat(labelWithoutNamespace.getOuterNamespace().isPresent(), is(false));\n }",
"class_method_signature": "VertexLabelTest.itReturnsE... | {
"fields": [
{
"declarator": "NAMESPACE_SEPARATOR = '.'",
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"original_string": "private static final char NAMESPACE_SEPARATOR = '.';",
"type": "char",
"var_name": "NAMESPACE_SEPARATOR"
},
{
"declarator": "namespace",
"modifier": ... | {
"body": "public Optional<String> getOuterNamespace() {\n return namespace.stream().findFirst();\n }",
"class_method_signature": "VertexLabel.getOuterNamespace()",
"constructor": false,
"full_signature": "public Optional<String> getOuterNamespace()",
"identifier": "getOuterNamespace",
"invocation... | {
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} |
128393918_65 | {
"fields": [
{
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"type": "NetworkSamples",
"var_name": "samples"
},
{
"declarator": "rowTensors = new ArrayList<>()",
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"original_string... | {
"body": "@Test\n public void writeSamplesToCsv() throws IOException {\n File file = WriteCsv.asSamples(samples, rowTensors).toFile(File.createTempFile(\"test\", \".csv\"));\n\n CsvReader reader = ReadCsv.fromFile(file).expectHeader(false);\n List<List<String>> lines = reader.readLines();\n\n... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/util/csv/WriteCsv.java",
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"methods": [
{
"class_method_signature": "WriteCsv.asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices)",
"constructo... | {
"body": "public static SampleWriter asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices) {\n return new SampleWriter(samples, vertices);\n }",
"class_method_signature": "WriteCsv.asSamples(NetworkSamples samples, List<? extends Vertex<? extends Tensor, ?>> vertices)",
... | {
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} |
128393918_326 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void youCanConstructASequenceItem() throws IOException {\n VertexLabel xLabel = new VertexLabel(\"x\");\n Sequence sequence = constructSimpleSequence(null, xLabel);\n BayesianNetwork network = sequence.toBayesianNetwork();\n\n ByteArrayOutputStream writer = new... | {
"fields": [],
"file": "keanu-project/src/main/java/io/improbable/keanu/templating/SequenceLoader.java",
"identifier": "SequenceLoader",
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"methods": [
{
"class_method_signature": "SequenceLoader.loadFromBayesNet(BayesianNetwork network)",
"constructor": false,
"full_sig... | {
"body": "public static Sequence loadFromBayesNet(BayesianNetwork network) {\n Collection<Sequence> sequences = loadSequences(network, false).values();\n return sequences.stream().findFirst().get();\n }",
"class_method_signature": "SequenceLoader.loadFromBayesNet(BayesianNetwork network)",
"cons... | {
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} |
128393918_32 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/tensor/jvm/JVMTensorTest.java",
"identifier": "JVMTensorTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test\n public void canToStringScalar() {\n DoubleTensor scalar = JVMDoubleTensorFactory.INSTANCE.scalar(9);\n String actual = scalar.toString();\n\n assertThat(actual, equalTo(\n \"{\\n\" +\n \"shape = []\\n\" +\n \"data = \\n9.0\\n}\"));\n ... | {
"fields": [
{
"declarator": "buffer",
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"type": "B",
"var_name": "buffer"
},
{
"declarator": "shape",
"modifier": "protected",
"original_string": "protected long[] shape;",
"type": "lon... | {
"body": "@Override\n public String toString() {\n\n return \"{\\n\" +\n \"shape = \" + Arrays.toString(shape) +\n \"\\ndata = \\n\" + arrayToString(buffer.asArray(), shape, stride) +\n \"\\n}\";\n }",
"class_method_signature": "JVMTensor.toString()",
"constructor": ... | {
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"is_fork": false,
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_263 | {
"fields": [
{
"declarator": "vertex1 = mock(Vertex.class)",
"modifier": "private",
"original_string": "private Vertex vertex1 = mock(Vertex.class);",
"type": "Vertex",
"var_name": "vertex1"
},
{
"declarator": "vertex2 = mock(Vertex.class)",
"modifier": "private"... | {
"body": "@Test\n public void youCanTrackTheAcceptanceRateOfASetOfMultipleVertices() {\n Proposal proposal = new Proposal();\n proposal.setProposal(vertex1, 1.);\n proposal.setProposal(vertex2, 2.);\n notifier.notifyProposalCreated(proposal);\n notifier.notifyProposalRejected();... | {
"fields": [
{
"declarator": "numApplied = Maps.newHashMap()",
"modifier": "private",
"original_string": "private Map<VariableReference, Counter> numApplied = Maps.newHashMap();",
"type": "Map<VariableReference, Counter>",
"var_name": "numApplied"
},
{
"declarator": "n... | {
"body": "public double getAcceptanceRate(VariableReference variableReference) {\n if (!numApplied.keySet().contains(variableReference)) {\n throw new IllegalStateException(\"No proposals have been registered for \" + variableReference);\n }\n return 1. - (double) numRejected.getOrDef... | {
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"stars": null,
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_214 | {
"fields": [
{
"declarator": "rule = new DeterministicRule()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public DeterministicRule rule = new DeterministicRule();",
"type": "DeterministicRule",
"var_name": "rule"
},
{
"declarator": "thrown = Expect... | {
"body": "@Test\n public void doesAllowSampleWithShapeThatIsBroadcastableWithVertexShape() {\n IntegerTensor n = IntegerTensor.create(100, 200, 300, 400);\n DoubleTensor p = DoubleTensor.create(new double[]{\n 0.1, 0.9,\n 0.2, 0.8,\n 0.3, 0.7,\n 0.4, 0.6\n... | {
"fields": [
{
"declarator": "p",
"modifier": "private final",
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"type": "DoubleVertex",
"var_name": "p"
},
{
"declarator": "n",
"modifier": "private final",
"original_string": "private final IntegerVertex n... | {
"body": "@Override\n public IntegerTensor sampleWithShape(long[] shape, KeanuRandom random) {\n return Multinomial.withParameters(n.getValue(), p.getValue(), validationEnabled).sample(shape, random);\n }",
"class_method_signature": "MultinomialVertex.sampleWithShape(long[] shape, KeanuRandom random)"... | {
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"url": "https://github.com/improbable-research/keanu"
} |
128393918_45 | {
"fields": [
{
"declarator": "thrown = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException thrown = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "thrown"
}
],
"file": "keanu-project/src/t... | {
"body": "@Test\n public void canDiagFromVector() {\n BooleanTensor expected = BooleanTensor.create(new boolean[]{true, false, false, false, true, false, false, false, false}, 3, 3);\n BooleanTensor actual = BooleanTensor.create(true, true, false).diag();\n\n Assert.assertEquals(expected, act... | {
"fields": [
{
"declarator": "factory = BooleanBuffer.factory",
"modifier": "private static final",
"original_string": "private static final BooleanBuffer.BooleanArrayWrapperFactory factory = BooleanBuffer.factory;",
"type": "BooleanBuffer.BooleanArrayWrapperFactory",
"var_name": "f... | {
"body": "@Override\n protected JVMBooleanTensor create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape, long[] stride) {\n return new JVMBooleanTensor(buffer, shape, stride);\n }",
"class_method_signature": "JVMBooleanTensor.create(BooleanBuffer.PrimitiveBooleanWrapper buffer, long[] shape... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_351 | {
"fields": [
{
"declarator": "numberOfStudents = 100",
"modifier": "private final",
"original_string": "private final int numberOfStudents = 100;",
"type": "int",
"var_name": "numberOfStudents"
},
{
"declarator": "numberOfYesAnswers = 35",
"modifier": "private fi... | {
"body": "@Test\n public void doesWorkWithBinomial() {\n double approximateProbabilityOfCheating = CheatingStudents.runUsingBinomial(numberOfStudents, numberOfYesAnswers);\n assertEquals(0.2, approximateProbabilityOfCheating, 0.05);\n }",
"class_method_signature": "CheatingStudentsTest.doesWork... | {
"fields": [],
"file": "keanu-examples/bayesianMethodsForHackers/src/main/java/com/examples/CheatingStudents.java",
"identifier": "CheatingStudents",
"interfaces": "",
"methods": [
{
"class_method_signature": "CheatingStudents.runWithBernoulli(int numberOfStudents, int numberOfYesAnswers)",
"... | {
"body": "public static double runUsingBinomial(int numberOfStudents, int numberOfYesAnswers) {\n int numberOfSamples = 100;\n\n UniformVertex probabilityOfCheating = new UniformVertex(0.0, 1.0);\n DoubleVertex pYesAnswer = probabilityOfCheating.times(0.5).plus(0.25);\n BinomialVertex ans... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_4 | {
"fields": [],
"file": "keanu-project/src/test/java/io/improbable/keanu/tensor/TensorShapeTest.java",
"identifier": "TensorShapeTest",
"interfaces": "",
"superclass": ""
} | {
"body": "@Test(expected = IllegalArgumentException.class)\n public void doesThrowOnInvalidNegativeAbsoluteDimensionsFromRelative() {\n TensorShape.getAbsoluteDimensions(3, new int[]{-4, 1, 2});\n }",
"class_method_signature": "TensorShapeTest.doesThrowOnInvalidNegativeAbsoluteDimensionsFromRelative()... | {
"fields": [
{
"declarator": "shape",
"modifier": "private",
"original_string": "private long[] shape;",
"type": "long[]",
"var_name": "shape"
}
],
"file": "keanu-project/src/main/java/io/improbable/keanu/tensor/TensorShape.java",
"identifier": "TensorShape",
"interfaces... | {
"body": "public static int[] getAbsoluteDimensions(int rank, int[] input) {\n int[] output = new int[input.length];\n for (int i = 0; i < output.length; i++) {\n output[i] = getAbsoluteDimension(input[i], rank);\n }\n return output;\n }",
"class_method_signature": "Tensor... | {
"created": null,
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"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_12 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
},
... | {
"body": "@Test\n public void youCanCalculateTheMeanSquaredError() {\n assertThat(stats.meanSquaredError(), greaterThan(0.));\n }",
"class_method_signature": "BivariateDataStatisticsCalculatorTest.youCanCalculateTheMeanSquaredError()",
"constructor": false,
"full_signature": "@Test public void you... | {
"fields": [
{
"declarator": "xData",
"modifier": "private final",
"original_string": "private final DoubleTensor xData;",
"type": "DoubleTensor",
"var_name": "xData"
},
{
"declarator": "yData",
"modifier": "private final",
"original_string": "private final... | {
"body": "public double meanSquaredError() {\n DoubleTensor calculatedY = xData.times(estimatedGradient()).plusInPlace(estimatedIntercept());\n DoubleTensor residuals = yData.minus(calculatedY);\n long unbiasedMultiplier = size() - 2;\n return residuals.times(residuals).sumNumber() / unbi... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
128393918_243 | {
"fields": [
{
"declarator": "expectedException = ExpectedException.none()",
"modifier": "@Rule\n public",
"original_string": "@Rule\n public ExpectedException expectedException = ExpectedException.none();",
"type": "ExpectedException",
"var_name": "expectedException"
}
... | {
"body": "@Test\n public void isInSuperNamespace() {\n VertexLabel label = new VertexLabel(\"outer\", \"inner\", \"foo\");\n assertTrue(label.isInNamespace(\"outer\"));\n }",
"class_method_signature": "VertexLabelTest.isInSuperNamespace()",
"constructor": false,
"full_signature": "@Test pub... | {
"fields": [
{
"declarator": "NAMESPACE_SEPARATOR = '.'",
"modifier": "private static final",
"original_string": "private static final char NAMESPACE_SEPARATOR = '.';",
"type": "char",
"var_name": "NAMESPACE_SEPARATOR"
},
{
"declarator": "namespace",
"modifier": ... | {
"body": "public boolean isInNamespace(String... namespace) {\n\n if (namespace.length > this.namespace.size()) {\n return false;\n }\n\n for (int i = 0; i < namespace.length; i++) {\n if (!this.namespace.get(i).equals(namespace[i])) {\n return false;\n ... | {
"created": null,
"fork": null,
"fork_count": 33,
"is_fork": false,
"language": "Java",
"license": "licensed",
"repo_id": 128393918,
"size": 15461,
"stargazer_count": 146,
"stars": null,
"updates": null,
"url": "https://github.com/improbable-research/keanu"
} |
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