/dports/devel/spark/spark-2.1.1/mllib/src/test/java/org/apache/spark/mllib/regression/ |
H A D | JavaRidgeRegressionSuite.java | 54 int numExamples = 50; in runRidgeRegressionUsingConstructor() local 56 List<LabeledPoint> data = generateRidgeData(2 * numExamples, numFeatures, 10.0); in runRidgeRegressionUsingConstructor() 58 JavaRDD<LabeledPoint> testRDD = jsc.parallelize(data.subList(0, numExamples)); in runRidgeRegressionUsingConstructor() 59 List<LabeledPoint> validationData = data.subList(numExamples, 2 * numExamples); in runRidgeRegressionUsingConstructor() 78 int numExamples = 50; 80 List<LabeledPoint> data = generateRidgeData(2 * numExamples, numFeatures, 10.0); 82 JavaRDD<LabeledPoint> testRDD = jsc.parallelize(data.subList(0, numExamples)); 83 List<LabeledPoint> validationData = data.subList(numExamples, 2 * numExamples);
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/dports/biology/gatk/gatk-4.2.0.0/src/test/java/org/broadinstitute/hellbender/tools/walkers/readorientation/ |
H A D | LearnReadOrientationModelEngineUnitTest.java | 99 final int numExamples = numRefExamples + numAltExamples; in testSimpleCase() local 294 final int numExamples = numExamples1 + numExamples2; in testMergeHistograms() local 307 Assert.assertEquals((int) combinedRefAGA.getSumOfValues(), numExamples); in testMergeHistograms() 333 final int numExamples = 1000; in testMergeDesignMatrices() local 357 .filter(a -> a.getAltAllele() == Nucleotide.C).count(), 2*numExamples); in testMergeDesignMatrices() 359 .filter(a -> a.getAltAllele() == Nucleotide.C).count(), 2*numExamples); in testMergeDesignMatrices() 366 .filter(a -> a.getAltAllele() == Nucleotide.A).count(), numExamples); in testMergeDesignMatrices() 391 final List<AltSiteRecord> altDesignMatrix = new ArrayList<>(numExamples); in createDesignMatrixOfSingleContext() 396 IntStream.range(0, numExamples).forEach(i -> in createDesignMatrixOfSingleContext() 404 refSiteHistogram.increment(refDepth, numExamples); in createRefHistograms() [all …]
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H A D | ArtifactPriorUnitTest.java | 65 final int numExamples = 1000; in testRevComp() local 79 …artifactPriorCollectionBefore.set(new ArtifactPrior(referenceContext1, pi1, numExamples, numAltExa… in testRevComp() 80 …artifactPriorCollectionBefore.set(new ArtifactPrior(referenceContext2, pi2, numExamples, numAltExa… in testRevComp()
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/dports/devel/spark/spark-2.1.1/examples/src/main/python/mllib/ |
H A D | sampled_rdds.py | 43 numExamples = examples.count() variable 44 if numExamples == 0: 47 print('Loaded data with %d examples from file: %s' % (numExamples, datapath)) 50 expectedSampleSize = int(numExamples * fraction) 80 fracA = keyCountsA[k] / float(numExamples)
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H A D | random_rdd_generation.py | 36 numExamples = 10000 # number of examples to generate variable 40 normalRDD = RandomRDDs.normalRDD(sc, numExamples) 49 normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/ |
H A D | TrainModel.scala | 45 def test(model: String, dataPath: String, numExamples: Int = 60000, 52 numExamples = numExamples, benchmark = benchmark, dtype = dtype) 53 val Acc = Trainer.fit(batchSize = 128, numExamples, devs = devs, 73 numLayers: Int = 50, numExamples: Int = 60000, 94 val iter = new SyntheticDataIter(numClasses, batchSize, datumShape, List(), numExamples, 125 inst.numLayers, inst.numExamples, inst.benchmark, dtype) 150 Trainer.fit(batchSize = inst.batchSize, numExamples = inst.numExamples, devs = devs, 187 private val numExamples: Int = 60000
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/ |
H A D | TrainModel.scala | 45 def test(model: String, dataPath: String, numExamples: Int = 60000, 52 numExamples = numExamples, benchmark = benchmark, dtype = dtype) 53 val Acc = Trainer.fit(batchSize = 128, numExamples, devs = devs, 73 numLayers: Int = 50, numExamples: Int = 60000, 94 val iter = new SyntheticDataIter(numClasses, batchSize, datumShape, List(), numExamples, 125 inst.numLayers, inst.numExamples, inst.benchmark, dtype) 150 Trainer.fit(batchSize = inst.batchSize, numExamples = inst.numExamples, devs = devs, 187 private val numExamples: Int = 60000
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/dports/biology/gatk/gatk-4.2.0.0/src/main/java/org/broadinstitute/hellbender/tools/walkers/readorientation/ |
H A D | ArtifactPrior.java | 19 private final int numExamples; field in ArtifactPrior 22 …public ArtifactPrior(final String referenceContext, final double[] pi, final int numExamples, fina… in ArtifactPrior() argument 25 this.numExamples = numExamples; in ArtifactPrior() 52 return new ArtifactPrior(revCompRefContext, revCompPi, numExamples, numAltExamples); in getReverseComplement() 55 public int getNumExamples() { return numExamples; } in getNumExamples() 111 final int numExamples = Integer.parseInt(dataLine.get(ArtifactPriorTableColumn.N)); in createRecord() local 113 return new ArtifactPrior(referenceContext, pi, numExamples, numAltExamples); in createRecord()
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H A D | LearnReadOrientationModelEngine.java | 58 private final int numExamples; field in LearnReadOrientationModelEngine 125 this.numExamples = numAltExamples + numRefExamples; in LearnReadOrientationModelEngine() 171 return new ArtifactPrior(referenceContext, statePrior, numExamples, numAltExamples); in learnPriorForArtifactStates()
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/dports/devel/spark/spark-2.1.1/examples/src/main/scala/org/apache/spark/examples/mllib/ |
H A D | SampledRDDs.scala | 68 val numExamples = examples.count() constant 69 if (numExamples == 0) { 72 println(s"Loaded data with $numExamples examples from file: ${params.input}") 75 val expectedSampleSize = (numExamples * fraction).toInt 110 val origFrac = keyCounts(key) / numExamples.toDouble
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H A D | RandomRDDGeneration.scala | 39 val numExamples = 10000 // number of examples to generate constant 43 val normalRDD: RDD[Double] = RandomRDDs.normalRDD(sc, numExamples) 50 val normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
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H A D | DenseKMeans.scala | 88 val numExamples = examples.count() constant 90 println(s"numExamples = $numExamples.")
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H A D | DecisionTreeRunner.scala | 202 val numExamples = examples.count() constant 207 val frac = classCounts(c) / numExamples.toDouble
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/dports/science/colt/colt/src/cern/jet/stat/quantile/ |
H A D | Quantile1Test.java | 27 int numExamples = 0; in main() local 29 numExamples = Integer.parseInt(argv[0]); in main() 36 System.out.println("Got numExamples=" + numExamples); in main() 79 for (int i = 1; i <= numExamples; i++) { in main()
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/dports/devel/spark/spark-2.1.1/mllib/src/test/scala/org/apache/spark/mllib/regression/ |
H A D | RidgeRegressionSuite.scala | 47 val numExamples = 50 constant 55 val data = LinearDataGenerator.generateLinearInput(3.0, w, 2 * numExamples, 42, 10.0) 56 val testData = data.take(numExamples) 57 val validationData = data.takeRight(numExamples)
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/dports/devel/spark/spark-2.1.1/mllib/src/main/scala/org/apache/spark/mllib/optimization/ |
H A D | LBFGS.scala | 195 val numExamples = data.count() constant 198 new CostFun(data, gradient, updater, regParam, numExamples) 235 numExamples: Long) extends DiffFunction[BDV[Double]] { 264 val loss = lossSum / numExamples + regVal 286 axpy(1.0 / numExamples, gradientSum, gradientTotal)
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H A D | GradientDescent.scala | 209 val numExamples = data.count() constant 212 if (numExamples == 0) { 217 if (numExamples * miniBatchFraction < 1) {
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/spark/src/main/scala/org/apache/mxnet/spark/ |
H A D | MXNet.scala | 168 numExamples: Int, 180 epochSize = numExamples / params.batchSize / kv.numWorkers) 216 var numExamples = 0 variable 219 numExamples += dataBatch.label.head.shape(0) 221 logger.debug("Number of samples: {}", numExamples) 230 val model = setFeedForwardModel(optimizer, numExamples, kv, dataIter)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/spark/src/main/scala/org/apache/mxnet/spark/ |
H A D | MXNet.scala | 168 numExamples: Int, 180 epochSize = numExamples / params.batchSize / kv.numWorkers) 216 var numExamples = 0 variable 219 numExamples += dataBatch.label.head.shape(0) 221 logger.debug("Number of samples: {}", numExamples) 230 val model = setFeedForwardModel(optimizer, numExamples, kv, dataIter)
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/dports/devel/spark/spark-2.1.1/mllib/src/main/scala/org/apache/spark/ml/tree/impl/ |
H A D | DecisionTreeMetadata.scala | 44 val numExamples: Long, constant 116 val numExamples = input.count() constant 122 val maxPossibleBins = math.min(strategy.maxBins, numExamples).toInt 207 new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,
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/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/util/ |
H A D | Trainer.scala | 47 def fit(batchSize: Int, numExamples: Int, devs: Array[Context], 86 if (kvStore == "dist_sync") numExamples / batchSize / kv.numWorkers 87 else numExamples / batchSize
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/util/ |
H A D | Trainer.scala | 47 def fit(batchSize: Int, numExamples: Int, devs: Array[Context], 86 if (kvStore == "dist_sync") numExamples / batchSize / kv.numWorkers 87 else numExamples / batchSize
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/dports/dns/dnscrypt-proxy2/dnscrypt-proxy-2.1.1/vendor/github.com/ashanbrown/forbidigo/forbidigo/ |
H A D | forbidigo.go | 108 numExamples := 0 122 numExamples++ 127 isWholeFileExample = numExamples == 1 && numTestsAndBenchmarks == 0
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/dports/devel/juce/JUCE-f37e9a1/extras/Projucer/Source/Application/ |
H A D | jucer_Application.cpp | 602 numExamples = 0; in createExamplesPopupMenu() 608 m.addItem (examplesBaseID + numExamples, f.getFileNameWithoutExtension()); in createExamplesPopupMenu() 609 ++numExamples; in createExamplesPopupMenu() 615 if (numExamples == 0) in createExamplesPopupMenu() 940 else if (menuItemID >= examplesBaseID && menuItemID < (examplesBaseID + numExamples)) in handleMainMenuCommand()
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H A D | jucer_Application.h | 219 int numExamples = 0; variable
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