Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#diviRowVector()
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org.nd4j.linalg.api.ndarray.INDArray#diviRowVector() .
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Example 1
Source File: CudaBroadcastTests.java From nd4j with Apache License 2.0 | 6 votes |
@Test public void testPinnedDivRowVector() throws Exception { // simple way to stop test if we're not on CUDA backend here assertEquals("JcublasLevel1", Nd4j.getBlasWrapper().level1().getClass().getSimpleName()); INDArray array1 = Nd4j.zeros(15,15); array1.putRow(0, Nd4j.create(new float[]{2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f})); array1.putRow(1, Nd4j.create(new float[]{2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f})); INDArray array2 = Nd4j.create(new float[]{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}); array1.diviRowVector(array2); System.out.println("Array1: " + array1); System.out.println("Array2: " + array2); assertEquals(2.0f, array1.getRow(0).getFloat(0), 0.01); }
Example 2
Source File: CudaPairwiseTrainformsTests.java From nd4j with Apache License 2.0 | 6 votes |
@Test public void testPinnedDiviRowVector() throws Exception { // simple way to stop test if we're not on CUDA backend here assertEquals("JcublasLevel1", Nd4j.getBlasWrapper().level1().getClass().getSimpleName()); INDArray array1 = Nd4j.create(new float[]{1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f, 1.5f}); INDArray array2 = Nd4j.create(new float[]{2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f, 2.0f}); INDArray result = array1.diviRowVector(array2); System.out.println("Array1: " + array1); System.out.println("Array2: " + array2); System.out.println("Result: " + result); assertEquals(0.75f, array1.getRow(0).getFloat(0), 0.01); }
Example 3
Source File: StandardizeStrategy.java From nd4j with Apache License 2.0 | 6 votes |
/** * Normalize a data array * * @param array the data to normalize * @param stats statistics of the data population */ @Override public void preProcess(INDArray array, INDArray maskArray, DistributionStats stats) { if (array.rank() <= 2) { array.subiRowVector(stats.getMean()); array.diviRowVector(filteredStd(stats)); } // if array Rank is 3 (time series) samplesxfeaturesxtimesteps // if array Rank is 4 (images) samplesxchannelsxrowsxcols // both cases operations should be carried out in dimension 1 else { Nd4j.getExecutioner().execAndReturn(new BroadcastSubOp(array, stats.getMean(), array, 1)); Nd4j.getExecutioner().execAndReturn(new BroadcastDivOp(array, filteredStd(stats), array, 1)); } if (maskArray != null) { DataSetUtil.setMaskedValuesToZero(array, maskArray); } }
Example 4
Source File: MinMaxStrategy.java From nd4j with Apache License 2.0 | 6 votes |
/** * Normalize a data array * * @param array the data to normalize * @param stats statistics of the data population */ @Override public void preProcess(INDArray array, INDArray maskArray, MinMaxStats stats) { if (array.rank() <= 2) { array.subiRowVector(stats.getLower()); array.diviRowVector(stats.getRange()); } // if feature Rank is 3 (time series) samplesxfeaturesxtimesteps // if feature Rank is 4 (images) samplesxchannelsxrowsxcols // both cases operations should be carried out in dimension 1 else { Nd4j.getExecutioner().execAndReturn(new BroadcastSubOp(array, stats.getLower(), array, 1)); Nd4j.getExecutioner().execAndReturn(new BroadcastDivOp(array, stats.getRange(), array, 1)); } // Scale by target range array.muli(maxRange - minRange); // Add target range minimum values array.addi(minRange); if (maskArray != null) { DataSetUtil.setMaskedValuesToZero(array, maskArray); } }
Example 5
Source File: StandardizeStrategy.java From deeplearning4j with Apache License 2.0 | 6 votes |
/** * Normalize a data array * * @param array the data to normalize * @param stats statistics of the data population */ @Override public void preProcess(INDArray array, INDArray maskArray, DistributionStats stats) { if (array.rank() <= 2) { array.subiRowVector(stats.getMean().castTo(array.dataType())); array.diviRowVector(filteredStd(stats).castTo(array.dataType())); } // if array Rank is 3 (time series) samplesxfeaturesxtimesteps // if array Rank is 4 (images) samplesxchannelsxrowsxcols // both cases operations should be carried out in dimension 1 else { Nd4j.getExecutioner().execAndReturn(new BroadcastSubOp(array, stats.getMean().castTo(array.dataType()), array, 1)); Nd4j.getExecutioner().execAndReturn(new BroadcastDivOp(array, filteredStd(stats).castTo(array.dataType()), array, 1)); } if (maskArray != null) { DataSetUtil.setMaskedValuesToZero(array, maskArray); } }
Example 6
Source File: MinMaxStrategy.java From deeplearning4j with Apache License 2.0 | 6 votes |
/** * Normalize a data array * * @param array the data to normalize * @param stats statistics of the data population */ @Override public void preProcess(INDArray array, INDArray maskArray, MinMaxStats stats) { if (array.rank() <= 2) { array.subiRowVector(stats.getLower().castTo(array.dataType())); array.diviRowVector(stats.getRange().castTo(array.dataType())); } // if feature Rank is 3 (time series) samplesxfeaturesxtimesteps // if feature Rank is 4 (images) samplesxchannelsxrowsxcols // both cases operations should be carried out in dimension 1 else { Nd4j.getExecutioner().execAndReturn(new BroadcastSubOp(array, stats.getLower().castTo(array.dataType()), array, 1)); Nd4j.getExecutioner().execAndReturn(new BroadcastDivOp(array, stats.getRange().castTo(array.dataType()), array, 1)); } // Scale by target range array.muli(maxRange - minRange); // Add target range minimum values array.addi(minRange); if (maskArray != null) { DataSetUtil.setMaskedValuesToZero(array, maskArray); } }
Example 7
Source File: Nd4jMatrix.java From jstarcraft-ai with Apache License 2.0 | 5 votes |
@Override public MathMatrix divideRowVector(MathVector vector) { if (vector instanceof Nd4jVector) { Nd4jEnvironmentThread thread = EnvironmentThread.getThread(Nd4jEnvironmentThread.class); try (MemoryWorkspace workspace = thread.getSpace()) { INDArray thisArray = this.getArray(); INDArray thatArray = Nd4jVector.class.cast(vector).getArray(); thisArray.diviRowVector(thatArray); return this; } } else { return MathMatrix.super.divideRowVector(vector); } }
Example 8
Source File: Transforms.java From nd4j with Apache License 2.0 | 5 votes |
/** * Normalize data to zero mean and unit variance * substract by the mean and divide by the standard deviation * * @param toNormalize the ndarray to normalize * @return the normalized ndarray */ public static INDArray normalizeZeroMeanAndUnitVariance(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); INDArray columnStds = toNormalize.std(0); toNormalize.subiRowVector(columnMeans); //padding for non zero columnStds.addi(Nd4j.EPS_THRESHOLD); toNormalize.diviRowVector(columnStds); return toNormalize; }
Example 9
Source File: FeatureUtil.java From nd4j with Apache License 2.0 | 5 votes |
public static void normalizeMatrix(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); toNormalize.subiRowVector(columnMeans); INDArray std = toNormalize.std(0); std.addi(Nd4j.scalar(1e-12)); toNormalize.diviRowVector(std); }
Example 10
Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testClipByNorm0(){ //Expected: if array.norm2(0) is less than 1.0, not modified //Otherwise: array.tad(x,1) = array.tad(x,1) * 1.0 / array.tad(x,1).norm2() Nd4j.getRandom().setSeed(12345); INDArray arr = Nd4j.rand(5,4); INDArray norm2_0 = arr.norm2(0); arr.diviRowVector(norm2_0); INDArray initNorm2 = Nd4j.create(new double[]{2.2, 2.1, 2.0, 1.9}, new int[]{4}); //Initial norm2s along dimension 0 arr.muliRowVector(initNorm2); norm2_0 = arr.norm2(0); assertEquals(initNorm2, norm2_0); INDArray out = Nd4j.create(arr.shape()); INDArray norm2_0b = out.norm2(0); INDArray expNorm = Nd4j.create(new double[]{2.0, 2.0, 2.0, 1.9}, new int[]{1, 4}); //Post clip norm2s along dimension 0 INDArray exp = arr.divRowVector(norm2_0b).muliRowVector(expNorm); OpTestCase op = new OpTestCase(//Clip to norm2 of 2.0, along dimension 0 new ClipByNorm(arr, out, 2.0, 0)) .expectedOutput(0, exp); assertNull(OpValidation.validate(op)); }
Example 11
Source File: Transforms.java From deeplearning4j with Apache License 2.0 | 5 votes |
/** * Normalize data to zero mean and unit variance * substract by the mean and divide by the standard deviation * * @param toNormalize the ndarray to normalize * @return the normalized ndarray */ public static INDArray normalizeZeroMeanAndUnitVariance(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); INDArray columnStds = toNormalize.std(0); toNormalize.subiRowVector(columnMeans); //padding for non zero columnStds.addi(Nd4j.EPS_THRESHOLD); toNormalize.diviRowVector(columnStds); return toNormalize; }
Example 12
Source File: FeatureUtil.java From deeplearning4j with Apache License 2.0 | 5 votes |
public static void normalizeMatrix(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); toNormalize.subiRowVector(columnMeans); INDArray std = toNormalize.std(0); std.addi(Nd4j.scalar(1e-12)); toNormalize.diviRowVector(std); }
Example 13
Source File: BasicClassifier.java From audiveris with GNU Affero General Public License v3.0 | 4 votes |
/** * Apply the known norms on the provided (raw) features. * * @param features raw features, to be normalized in situ */ private void normalize (INDArray features) { features.subiRowVector(norms.means); features.diviRowVector(norms.stds); }