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/dports/devel/spark/spark-2.1.1/mllib/src/test/java/org/apache/spark/mllib/regression/
H A DJavaRidgeRegressionSuite.java54 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);
/dports/biology/gatk/gatk-4.2.0.0/src/test/java/org/broadinstitute/hellbender/tools/walkers/readorientation/
H A DLearnReadOrientationModelEngineUnitTest.java99 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 …]
H A DArtifactPriorUnitTest.java65 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()
/dports/devel/spark/spark-2.1.1/examples/src/main/python/mllib/
H A Dsampled_rdds.py43 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)
H A Drandom_rdd_generation.py36 numExamples = 10000 # number of examples to generate variable
40 normalRDD = RandomRDDs.normalRDD(sc, numExamples)
49 normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2)
/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/
H A DTrainModel.scala45 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
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/
H A DTrainModel.scala45 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
/dports/biology/gatk/gatk-4.2.0.0/src/main/java/org/broadinstitute/hellbender/tools/walkers/readorientation/
H A DArtifactPrior.java19 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()
H A DLearnReadOrientationModelEngine.java58 private final int numExamples; field in LearnReadOrientationModelEngine
125 this.numExamples = numAltExamples + numRefExamples; in LearnReadOrientationModelEngine()
171 return new ArtifactPrior(referenceContext, statePrior, numExamples, numAltExamples); in learnPriorForArtifactStates()
/dports/devel/spark/spark-2.1.1/examples/src/main/scala/org/apache/spark/examples/mllib/
H A DSampledRDDs.scala68 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
H A DRandomRDDGeneration.scala39 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)
H A DDenseKMeans.scala88 val numExamples = examples.count() constant
90 println(s"numExamples = $numExamples.")
H A DDecisionTreeRunner.scala202 val numExamples = examples.count() constant
207 val frac = classCounts(c) / numExamples.toDouble
/dports/science/colt/colt/src/cern/jet/stat/quantile/
H A DQuantile1Test.java27 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()
/dports/devel/spark/spark-2.1.1/mllib/src/test/scala/org/apache/spark/mllib/regression/
H A DRidgeRegressionSuite.scala47 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)
/dports/devel/spark/spark-2.1.1/mllib/src/main/scala/org/apache/spark/mllib/optimization/
H A DLBFGS.scala195 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)
H A DGradientDescent.scala209 val numExamples = data.count() constant
212 if (numExamples == 0) {
217 if (numExamples * miniBatchFraction < 1) {
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/spark/src/main/scala/org/apache/mxnet/spark/
H A DMXNet.scala168 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/spark/src/main/scala/org/apache/mxnet/spark/
H A DMXNet.scala168 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)
/dports/devel/spark/spark-2.1.1/mllib/src/main/scala/org/apache/spark/ml/tree/impl/
H A DDecisionTreeMetadata.scala44 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,
/dports/misc/mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/util/
H A DTrainer.scala47 def fit(batchSize: Int, numExamples: Int, devs: Array[Context],
86 if (kvStore == "dist_sync") numExamples / batchSize / kv.numWorkers
87 else numExamples / batchSize
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/util/
H A DTrainer.scala47 def fit(batchSize: Int, numExamples: Int, devs: Array[Context],
86 if (kvStore == "dist_sync") numExamples / batchSize / kv.numWorkers
87 else numExamples / batchSize
/dports/dns/dnscrypt-proxy2/dnscrypt-proxy-2.1.1/vendor/github.com/ashanbrown/forbidigo/forbidigo/
H A Dforbidigo.go108 numExamples := 0
122 numExamples++
127 isWholeFileExample = numExamples == 1 && numTestsAndBenchmarks == 0
/dports/devel/juce/JUCE-f37e9a1/extras/Projucer/Source/Application/
H A Djucer_Application.cpp602 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()
H A Djucer_Application.h219 int numExamples = 0; variable

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