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fix build issues 

Signed-off-by: raver119 <raver119@gmail.com>
master
raver119 2019-09-13 11:55:13 +03:00
parent 6b557f2441
commit 979ef13c0b
4 changed files with 7 additions and 56 deletions
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55
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java
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@ -3098,10 +3098,10 @@ public class Nd4j {
} }
/** /**
* @deprecated use {@link Nd4j#randn(long, long...)} * @deprecated use {@link Nd4j#randn(long, long[])}
*/ */
@Deprecated @Deprecated
public static INDArray randn(int[] shape, long seed) { public static INDArray randn(long seed, int[] shape) {
return randn(seed, ArrayUtil.toLongArray(shape)); return randn(seed, ArrayUtil.toLongArray(shape));
} }
@ -3111,60 +3111,11 @@ public class Nd4j {
* @param shape the shape of the array * @param shape the shape of the array
* @return new array with random values * @return new array with random values
*/ */
public static INDArray randn(long seed, @NonNull long... shape) { public static INDArray randn(long seed, @NonNull long[] shape) {
INDArray ret = Nd4j.createUninitialized(shape, order()); INDArray ret = Nd4j.createUninitialized(shape, order());
return randn(ret, seed); return randn(ret, seed);
} }
/**
* Random normal N(0, 1)
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return new array with random values
*/
public static INDArray randn(long rows, long columns) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
return randn(ret);
}
/**
* Random normal N(0,1) with the specified shape and array order
*
* @param order the order of the output array
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
*/
/*public static INDArray randn(char order, long rows, long columns) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order);
return randn(ret);
}*/
/**
* Random normal using the specified seed
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return new array with random values
*/
/*public static INDArray randn(long rows, long columns, long seed) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
return randn(ret, seed);
}*/
/**
* Random normal using the given rng
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @param r the random generator to use
* @return new array with random values
*/
/*public static INDArray randn(long rows, long columns, @NonNull org.nd4j.linalg.api.rng.Random r) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
return randn(ret, r);
}*/
/** /**
* @deprecated use {@link Nd4j#randn(org.nd4j.linalg.api.rng.Random, long...)} * @deprecated use {@link Nd4j#randn(org.nd4j.linalg.api.rng.Random, long...)}
*/ */

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeLabelsTest.java
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@ -191,7 +191,7 @@ public class NormalizerStandardizeLabelsTest extends BaseNd4jTest {
// transform ndarray as X = aA + bB * X // transform ndarray as X = aA + bB * X
INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures); INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures);
while (i < nFeatures) { while (i < nFeatures) {
INDArray randomSlice = Nd4j.randn(new int[]{nSamples, 1}, randSeed); INDArray randomSlice = Nd4j.randn(randSeed, new long[]{nSamples, 1});
randomSlice.muli(aA.getScalar(0, i)); randomSlice.muli(aA.getScalar(0, i));
randomSlice.addi(bB.getScalar(0, i)); randomSlice.addi(bB.getScalar(0, i));
randomFeatures.putColumn(i, randomSlice); randomFeatures.putColumn(i, randomSlice);

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeTest.java
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@ -309,7 +309,7 @@ public class NormalizerStandardizeTest extends BaseNd4jTest {
INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures); INDArray randomFeatures = Nd4j.zeros(nSamples, nFeatures);
INDArray randomFeaturesTransform = Nd4j.zeros(nSamples, nFeatures); INDArray randomFeaturesTransform = Nd4j.zeros(nSamples, nFeatures);
while (i < nFeatures) { while (i < nFeatures) {
INDArray randomSlice = Nd4j.randn(new int[]{nSamples, 1}, randSeed); INDArray randomSlice = Nd4j.randn(randSeed, new long[]{nSamples, 1});
randomFeaturesTransform.putColumn(i, randomSlice); randomFeaturesTransform.putColumn(i, randomSlice);
randomSlice.muli(aA.getScalar(0, i)); randomSlice.muli(aA.getScalar(0, i));
randomSlice.addi(bB.getScalar(0, i)); randomSlice.addi(bB.getScalar(0, i));

4
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java
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@ -906,8 +906,8 @@ public class RandomTests extends BaseNd4jTest {
public void testSignatures1() { public void testSignatures1() {
for (int x = 0; x < 100; x++) { for (int x = 0; x < 100; x++) {
INDArray z1 = Nd4j.randn(new int[]{128, 1}, 5325235); INDArray z1 = Nd4j.randn(5325235, new long[]{128, 1});
INDArray z2 = Nd4j.randn(new int[]{128, 1}, 5325235); INDArray z2 = Nd4j.randn(5325235, new long[]{128, 1});
assertEquals(z1, z2); assertEquals(z1, z2);
} }