parent
6b557f2441
commit
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@ -3098,10 +3098,10 @@ public class Nd4j {
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}
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/**
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* @deprecated use {@link Nd4j#randn(long, long...)}
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* @deprecated use {@link Nd4j#randn(long, long[])}
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*/
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@Deprecated
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public static INDArray randn(int[] shape, long seed) {
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public static INDArray randn(long seed, int[] shape) {
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return randn(seed, ArrayUtil.toLongArray(shape));
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}
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@ -3111,60 +3111,11 @@ public class Nd4j {
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* @param shape the shape of the array
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* @return new array with random values
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*/
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public static INDArray randn(long seed, @NonNull long... shape) {
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public static INDArray randn(long seed, @NonNull long[] shape) {
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INDArray ret = Nd4j.createUninitialized(shape, order());
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return randn(ret, seed);
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}
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/**
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* Random normal N(0, 1)
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*
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* @param rows the number of rows in the matrix
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* @param columns the number of columns in the matrix
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* @return new array with random values
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*/
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public static INDArray randn(long rows, long columns) {
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INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
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return randn(ret);
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}
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/**
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* Random normal N(0,1) with the specified shape and array order
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*
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* @param order the order of the output array
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* @param rows the number of rows in the matrix
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* @param columns the number of columns in the matrix
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*/
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/*public static INDArray randn(char order, long rows, long columns) {
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INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order);
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return randn(ret);
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}*/
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/**
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* Random normal using the specified seed
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*
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* @param rows the number of rows in the matrix
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* @param columns the number of columns in the matrix
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* @return new array with random values
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*/
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/*public static INDArray randn(long rows, long columns, long seed) {
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INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
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return randn(ret, seed);
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}*/
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/**
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* Random normal using the given rng
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*
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* @param rows the number of rows in the matrix
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* @param columns the number of columns in the matrix
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* @param r the random generator to use
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* @return new array with random values
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*/
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/*public static INDArray randn(long rows, long columns, @NonNull org.nd4j.linalg.api.rng.Random r) {
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INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
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return randn(ret, r);
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}*/
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/**
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* @deprecated use {@link Nd4j#randn(org.nd4j.linalg.api.rng.Random, long...)}
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*/
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@ -191,7 +191,7 @@ public class NormalizerStandardizeLabelsTest extends BaseNd4jTest {
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// transform ndarray as X = aA + bB * X
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INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures);
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while (i < nFeatures) {
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INDArray randomSlice = Nd4j.randn(new int[]{nSamples, 1}, randSeed);
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INDArray randomSlice = Nd4j.randn(randSeed, new long[]{nSamples, 1});
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randomSlice.muli(aA.getScalar(0, i));
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randomSlice.addi(bB.getScalar(0, i));
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randomFeatures.putColumn(i, randomSlice);
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@ -309,7 +309,7 @@ public class NormalizerStandardizeTest extends BaseNd4jTest {
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INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures);
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INDArray randomFeaturesTransform = Nd4j.zeros(nSamples, nFeatures);
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while (i < nFeatures) {
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INDArray randomSlice = Nd4j.randn(new int[]{nSamples, 1}, randSeed);
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INDArray randomSlice = Nd4j.randn(randSeed, new long[]{nSamples, 1});
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randomFeaturesTransform.putColumn(i, randomSlice);
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randomSlice.muli(aA.getScalar(0, i));
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randomSlice.addi(bB.getScalar(0, i));
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@ -906,8 +906,8 @@ public class RandomTests extends BaseNd4jTest {
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public void testSignatures1() {
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for (int x = 0; x < 100; x++) {
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INDArray z1 = Nd4j.randn(new int[]{128, 1}, 5325235);
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INDArray z2 = Nd4j.randn(new int[]{128, 1}, 5325235);
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INDArray z1 = Nd4j.randn(5325235, new long[]{128, 1});
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INDArray z2 = Nd4j.randn(5325235, new long[]{128, 1});
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assertEquals(z1, z2);
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}
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