Java Code Examples for org.nd4j.linalg.indexing.NDArrayIndex#all()
The following examples show how to use
org.nd4j.linalg.indexing.NDArrayIndex#all() .
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Example 1
| Source File: ShapeResolutionTestsC.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
@Ignore
public void testIndexPointAll() {
INDArray zeros = Nd4j.zeros(3, 3, 3);
INDArrayIndex x = NDArrayIndex.point(1);
INDArrayIndex y = NDArrayIndex.all();
INDArrayIndex z = NDArrayIndex.point(1);
INDArray value = Nd4j.ones(1, 3);
zeros.put(new INDArrayIndex[] {x, y, z}, value);
String f1 = "[[[0,00,0,00,0,00]\n" + " [0,00,0,00,0,00]\n" + " [0,00,0,00,0,00]]\n" + " [[0,00,1,00,0,00]\n"
+ " [0,00,1,00,0,00]\n" + " [0,00,1,00,0,00]]\n" + " [[0,00,0,00,0,00]\n"
+ " [0,00,0,00,0,00]\n" + " [0,00,0,00,0,00]]]";
String f2 = "[[[0.00,0.00,0.00]\n" + " [0.00,0.00,0.00]\n" + " [0.00,0.00,0.00]]\n" + " [[0.00,1.00,0.00]\n"
+ " [0.00,1.00,0.00]\n" + " [0.00,1.00,0.00]]\n" + " [[0.00,0.00,0.00]\n"
+ " [0.00,0.00,0.00]\n" + " [0.00,0.00,0.00]]]";
if (!zeros.toString().equals(f1) && !zeros.toString().equals(f2))
assertEquals(f2, zeros.toString());
}
Example 2
| Source File: BatchedInferenceObservable.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
private INDArray[] splitExamples(INDArray netOutput, int firstInputComponent, int lastInputComponent){
int numSplits = lastInputComponent - firstInputComponent + 1;
if(numSplits == 1){
return new INDArray[]{netOutput};
} else {
INDArray[] out = new INDArray[numSplits];
INDArrayIndex[] indices = new INDArrayIndex[netOutput.rank()];
for(int i=1; i<indices.length; i++ ){
indices[i] = NDArrayIndex.all();
}
int examplesSoFar = 0;
for( int inNum = 0; inNum < numSplits; inNum++ ){
val inSizeEx = inputs.get(firstInputComponent + inNum)[0].size(0);
indices[0] = NDArrayIndex.interval(examplesSoFar, examplesSoFar+inSizeEx);
out[inNum] = netOutput.get(indices);
examplesSoFar += inSizeEx;
}
return out;
}
}
Example 3
| Source File: CoverageModelEMWorkspace.java From gatk-protected with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
/**
* This method applies the Fourier filter on a given bias covariates matrix, applies the Fourier filter on it,
* partitions the result, and pushes it to compute block(s)
*
* @param biasCovariates any T x D bias covariates matrix
*/
@UpdatesRDD
private void updateFilteredBiasCovariates(@Nonnull final INDArray biasCovariates) {
final INDArray filteredBiasCovariates = Nd4j.create(biasCovariates.shape());
/* instantiate the Fourier filter */
final FourierLinearOperatorNDArray regularizerFourierLinearOperator = createRegularizerFourierLinearOperator();
/* FFT by resolving W_tl on l */
for (int li = 0; li < numLatents; li++) {
final INDArrayIndex[] slice = {NDArrayIndex.all(), NDArrayIndex.point(li)};
filteredBiasCovariates.get(slice).assign(
regularizerFourierLinearOperator.operate(biasCovariates.get(slice)));
}
/* sent the new W to workers */
switch (config.getBiasCovariatesComputeNodeCommunicationPolicy()) {
case BROADCAST_HASH_JOIN:
pushToWorkers(mapINDArrayToBlocks(filteredBiasCovariates),
(W, cb) -> cb.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock
.CoverageModelICGCacheNode.F_W_tl, W.get(cb.getTargetSpaceBlock())));
break;
case RDD_JOIN:
joinWithWorkersAndMap(chopINDArrayToBlocks(filteredBiasCovariates),
p -> p._1.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock
.CoverageModelICGCacheNode.F_W_tl, p._2));
break;
}
}
Example 4
| Source File: ROCBinaryTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testROCBinary3d() {
INDArray prediction = Nd4j.rand(DataType.FLOAT, 2, 5, 10);
INDArray label = Nd4j.rand(DataType.FLOAT, 2, 5, 10);
List<INDArray> rowsP = new ArrayList<>();
List<INDArray> rowsL = new ArrayList<>();
NdIndexIterator iter = new NdIndexIterator(2, 10);
while (iter.hasNext()) {
long[] idx = iter.next();
INDArrayIndex[] idxs = new INDArrayIndex[]{NDArrayIndex.point(idx[0]), NDArrayIndex.all(), NDArrayIndex.point(idx[1])};
rowsP.add(prediction.get(idxs));
rowsL.add(label.get(idxs));
}
INDArray p2d = Nd4j.vstack(rowsP);
INDArray l2d = Nd4j.vstack(rowsL);
ROCBinary e3d = new ROCBinary();
ROCBinary e2d = new ROCBinary();
e3d.eval(label, prediction);
e2d.eval(l2d, p2d);
for (ROCBinary.Metric m : ROCBinary.Metric.values()) {
for( int i=0; i<5; i++ ) {
double d1 = e3d.scoreForMetric(m, i);
double d2 = e2d.scoreForMetric(m, i);
assertEquals(m.toString(), d2, d1, 1e-6);
}
}
}
Example 5
| Source File: EvaluationBinaryTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testEvaluationBinary4d() {
INDArray prediction = Nd4j.rand(DataType.FLOAT, 2, 3, 10, 10);
INDArray label = Nd4j.rand(DataType.FLOAT, 2, 3, 10, 10);
List<INDArray> rowsP = new ArrayList<>();
List<INDArray> rowsL = new ArrayList<>();
NdIndexIterator iter = new NdIndexIterator(2, 10, 10);
while (iter.hasNext()) {
long[] idx = iter.next();
INDArrayIndex[] idxs = new INDArrayIndex[]{NDArrayIndex.point(idx[0]), NDArrayIndex.all(), NDArrayIndex.point(idx[1]), NDArrayIndex.point(idx[2])};
rowsP.add(prediction.get(idxs));
rowsL.add(label.get(idxs));
}
INDArray p2d = Nd4j.vstack(rowsP);
INDArray l2d = Nd4j.vstack(rowsL);
EvaluationBinary e4d = new EvaluationBinary();
EvaluationBinary e2d = new EvaluationBinary();
e4d.eval(label, prediction);
e2d.eval(l2d, p2d);
for (EvaluationBinary.Metric m : EvaluationBinary.Metric.values()) {
for( int i=0; i<3; i++ ) {
double d1 = e4d.scoreForMetric(m, i);
double d2 = e2d.scoreForMetric(m, i);
assertEquals(m.toString(), d2, d1, 1e-6);
}
}
}
Example 6
| Source File: NDArrayIndexResolveTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testResolvePoint() {
INDArray arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
INDArrayIndex[] test = NDArrayIndex.resolve(arr.shape(), NDArrayIndex.point(1));
INDArrayIndex[] assertion = {NDArrayIndex.point(1), NDArrayIndex.all()};
assertArrayEquals(assertion, test);
INDArrayIndex[] allAssertion = {NDArrayIndex.all(), NDArrayIndex.all()};
assertArrayEquals(allAssertion, NDArrayIndex.resolve(arr.shape(), NDArrayIndex.all()));
INDArrayIndex[] allAndOne = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(1)};
assertArrayEquals(allAndOne, NDArrayIndex.resolve(arr.shape(), allAndOne));
}
Example 7
| Source File: IndexShapeTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSinglePoint() {
/*
Assumes all indexes are filled out.
Test simple general point case
*/
int[] assertion = {2, 1, 4, 5, 1};
INDArrayIndex[] indexes = new INDArrayIndex[] {NDArrayIndex.point(0), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all()};
int[] testShape = Indices.shape(shape, indexes);
assertArrayEquals(assertion, testShape);
int[] secondAssertion = {1, 2, 1, 5, 1};
INDArrayIndex[] otherCase = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.point(0)
};
assertArrayEquals(secondAssertion, Indices.shape(shape, otherCase));
int[] thridAssertion = {1, 2, 1, 4, 5, 1};
INDArrayIndex[] thirdCase = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0),
};
assertArrayEquals(thridAssertion, Indices.shape(shape, thirdCase));
}
Example 8
| Source File: IndexShapeTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testNewAxis() {
//normal prepend
int[] prependAssertion = {1, 1, 1, 1, 2, 1, 3, 4, 5, 1};
INDArrayIndex[] prependTest = {NDArrayIndex.newAxis(), NDArrayIndex.newAxis(), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(),
};
assertArrayEquals(prependAssertion, Indices.shape(shape, prependTest));
//test setting for particular indexes.
//when an all is encountered before a new axis,
//it is assumed that new axis must occur at the destination
//where the new axis was specified
int[] addToMiddle = {1, 1, 2, 1, 1, 1, 3, 4, 5, 1};
INDArrayIndex[] setInMiddleTest = {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.newAxis(), NDArrayIndex.newAxis(), NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(),};
assertArrayEquals(addToMiddle, Indices.shape(shape, setInMiddleTest));
//test prepending AND adding to middle
int[] prependAndAddToMiddleAssertion = {1, 1, 1, 1, 2, 1, 1, 1, 3, 4, 5, 1};
INDArrayIndex[] prependAndMiddle = {NDArrayIndex.newAxis(), NDArrayIndex.newAxis(), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.newAxis(), NDArrayIndex.newAxis(),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.all(),};
assertArrayEquals(prependAndAddToMiddleAssertion, Indices.shape(shape, prependAndMiddle));
}
Example 9
| Source File: EvaluationCalibrationTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testEvaluationCalibration3dMasking() {
INDArray prediction = Nd4j.rand(DataType.FLOAT, 2, 3, 10);
INDArray label = Nd4j.rand(DataType.FLOAT, 2, 3, 10);
List<INDArray> rowsP = new ArrayList<>();
List<INDArray> rowsL = new ArrayList<>();
//Check "DL4J-style" 2d per timestep masking [minibatch, seqLength] mask shape
INDArray mask2d = Nd4j.randomBernoulli(0.5, 2, 10);
NdIndexIterator iter = new NdIndexIterator(2, 10);
while (iter.hasNext()) {
long[] idx = iter.next();
if(mask2d.getDouble(idx[0], idx[1]) != 0.0) {
INDArrayIndex[] idxs = new INDArrayIndex[]{NDArrayIndex.point(idx[0]), NDArrayIndex.all(), NDArrayIndex.point(idx[1])};
rowsP.add(prediction.get(idxs));
rowsL.add(label.get(idxs));
}
}
INDArray p2d = Nd4j.vstack(rowsP);
INDArray l2d = Nd4j.vstack(rowsL);
EvaluationCalibration e3d_m2d = new EvaluationCalibration();
EvaluationCalibration e2d_m2d = new EvaluationCalibration();
e3d_m2d.eval(label, prediction, mask2d);
e2d_m2d.eval(l2d, p2d);
assertEquals(e3d_m2d, e2d_m2d);
}
Example 10
| Source File: OldConvolution.java From nd4j with Apache License 2.0 | 5 votes |
|
/**
* Rearrange matrix
* columns into blocks
* @param col the column
* transposed image to convert
* @param sy stride y
* @param sx stride x
* @param ph padding height
* @param pw padding width
* @param h height
* @param w width
* @return
*/
public static INDArray col2im(INDArray col, int sy, int sx, int ph, int pw, int h, int w) {
//number of images
long n = col.size(0);
//number of columns
long c = col.size(1);
//kernel height
long kh = col.size(2);
//kernel width
long kw = col.size(3);
//out height
long outH = col.size(4);
//out width
long outW = col.size(5);
INDArray img = Nd4j.create(n, c, h + 2 * ph + sy - 1, w + 2 * pw + sx - 1);
for (int i = 0; i < kh; i++) {
//iterate over the kernel rows
long iLim = i + sy * outH;
for (int j = 0; j < kw; j++) {
//iterate over the kernel columns
long jLim = j + sx * outW;
INDArrayIndex[] indices = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(i, sy, iLim), NDArrayIndex.interval(j, sx, jLim)};
INDArray get = img.get(indices);
INDArray colAdd = col.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(i),
NDArrayIndex.point(j), NDArrayIndex.all(), NDArrayIndex.all());
get.addi(colAdd);
img.put(indices, get);
}
}
//return the subset of the padded image relative to the height/width of the image and the padding width/height
return img.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(ph, ph + h),
NDArrayIndex.interval(pw, pw + w));
}
Example 11
| Source File: OldConvolution.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
/**
* Rearrange matrix
* columns into blocks
* @param col the column
* transposed image to convert
* @param sy stride y
* @param sx stride x
* @param ph padding height
* @param pw padding width
* @param h height
* @param w width
* @return
*/
public static INDArray col2im(INDArray col, int sy, int sx, int ph, int pw, int h, int w) {
//number of images
long n = col.size(0);
//number of columns
long c = col.size(1);
//kernel height
long kh = col.size(2);
//kernel width
long kw = col.size(3);
//out height
long outH = col.size(4);
//out width
long outW = col.size(5);
INDArray img = Nd4j.create(n, c, h + 2 * ph + sy - 1, w + 2 * pw + sx - 1);
for (int i = 0; i < kh; i++) {
//iterate over the kernel rows
long iLim = i + sy * outH;
for (int j = 0; j < kw; j++) {
//iterate over the kernel columns
long jLim = j + sx * outW;
INDArrayIndex[] indices = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(),
NDArrayIndex.interval(i, sy, iLim), NDArrayIndex.interval(j, sx, jLim)};
INDArray get = img.get(indices);
INDArray colAdd = col.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(i),
NDArrayIndex.point(j), NDArrayIndex.all(), NDArrayIndex.all());
get.addi(colAdd);
img.put(indices, get);
}
}
//return the subset of the padded image relative to the height/width of the image and the padding width/height
return img.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(ph, ph + h),
NDArrayIndex.interval(pw, pw + w));
}
Example 12
| Source File: IndexShapeTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInterval() {
int[] basicAssertion = {1, 1, 1, 1, 3, 1, 2, 1};
INDArrayIndex[] basicTest = {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 1),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(1, 2),
NDArrayIndex.interval(2, 4), NDArrayIndex.all()};
assertArrayEquals(basicAssertion, Indices.shape(shape, basicTest));
}
Example 13
| Source File: IndexShapeTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSinglePoint() {
/*
Assumes all indexes are filled out.
Test simple general point case
*/
int[] assertion = {2, 1, 4, 5, 1};
INDArrayIndex[] indexes = new INDArrayIndex[] {NDArrayIndex.point(0), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all()};
int[] testShape = Indices.shape(shape, indexes);
assertArrayEquals(assertion, testShape);
int[] secondAssertion = {1, 2, 1, 5, 1};
INDArrayIndex[] otherCase = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.point(0)
};
assertArrayEquals(secondAssertion, Indices.shape(shape, otherCase));
int[] thridAssertion = {1, 2, 1, 4, 5, 1};
INDArrayIndex[] thirdCase = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(0), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.point(0),
};
assertArrayEquals(thridAssertion, Indices.shape(shape, thirdCase));
}
Example 14
| Source File: IndexShapeTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInterval() {
int[] basicAssertion = {1, 1, 1, 1, 3, 1, 2, 1};
INDArrayIndex[] basicTest = {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 1),
NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(1, 2),
NDArrayIndex.interval(2, 4), NDArrayIndex.all()};
assertArrayEquals(basicAssertion, Indices.shape(shape, basicTest));
}
Example 15
| Source File: NDArrayIndexResolveTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testResolvePoint() {
INDArray arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
INDArrayIndex[] test = NDArrayIndex.resolve(arr.shape(), NDArrayIndex.point(1));
INDArrayIndex[] assertion = {NDArrayIndex.point(1), NDArrayIndex.all()};
assertArrayEquals(assertion, test);
INDArrayIndex[] allAssertion = {NDArrayIndex.all(), NDArrayIndex.all()};
assertArrayEquals(allAssertion, NDArrayIndex.resolve(arr.shape(), NDArrayIndex.all()));
INDArrayIndex[] allAndOne = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(1)};
assertArrayEquals(allAndOne, NDArrayIndex.resolve(arr.shape(), allAndOne));
}
Example 16
| Source File: NDArrayRecordBatch.java From DataVec with Apache License 2.0 | 5 votes |
|
private static INDArray getExample(int idx, INDArray from){
INDArrayIndex[] idxs = new INDArrayIndex[from.rank()];
idxs[0] = NDArrayIndex.interval(idx, idx, true); //Use interval to avoid collapsing point dimension
for( int i=1; i<from.rank(); i++){
idxs[i] = NDArrayIndex.all();
}
return from.get(idxs);
}
Example 17
| Source File: IndexingTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGet() {
System.out.println("Testing sub-array put and get with a 3D array ...");
INDArray arr = Nd4j.linspace(0, 124, 125).reshape(5, 5, 5);
/*
* Extract elements with the following indices:
*
* (2,1,1) (2,1,2) (2,1,3)
* (2,2,1) (2,2,2) (2,2,3)
* (2,3,1) (2,3,2) (2,3,3)
*/
int slice = 2;
int iStart = 1;
int jStart = 1;
int iEnd = 4;
int jEnd = 4;
// Method A: Element-wise.
INDArray subArr_A = Nd4j.create(new int[] {3, 3});
for (int i = iStart; i < iEnd; i++) {
for (int j = jStart; j < jEnd; j++) {
double val = arr.getDouble(slice, i, j);
int[] sub = new int[] {i - iStart, j - jStart};
subArr_A.putScalar(sub, val);
}
}
// Method B: Using NDArray get and put with index classes.
INDArray subArr_B = Nd4j.create(new int[] {3, 3});
INDArrayIndex ndi_Slice = NDArrayIndex.point(slice);
INDArrayIndex ndi_J = NDArrayIndex.interval(jStart, jEnd);
INDArrayIndex ndi_I = NDArrayIndex.interval(iStart, iEnd);
INDArrayIndex[] whereToGet = new INDArrayIndex[] {ndi_Slice, ndi_I, ndi_J};
INDArray whatToPut = arr.get(whereToGet);
assertEquals(subArr_A, whatToPut);
System.out.println(whatToPut);
INDArrayIndex[] whereToPut = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all()};
subArr_B.put(whereToPut, whatToPut);
assertEquals(subArr_A, subArr_B);
System.out.println("... done");
}
Example 18
| Source File: IndexingTestsC.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGet() {
System.out.println("Testing sub-array put and get with a 3D array ...");
INDArray arr = Nd4j.linspace(0, 124, 125).reshape(5, 5, 5);
/*
* Extract elements with the following indices:
*
* (2,1,1) (2,1,2) (2,1,3)
* (2,2,1) (2,2,2) (2,2,3)
* (2,3,1) (2,3,2) (2,3,3)
*/
int slice = 2;
int iStart = 1;
int jStart = 1;
int iEnd = 4;
int jEnd = 4;
// Method A: Element-wise.
INDArray subArr_A = Nd4j.create(new int[] {3, 3});
for (int i = iStart; i < iEnd; i++) {
for (int j = jStart; j < jEnd; j++) {
double val = arr.getDouble(slice, i, j);
int[] sub = new int[] {i - iStart, j - jStart};
subArr_A.putScalar(sub, val);
}
}
// Method B: Using NDArray get and put with index classes.
INDArray subArr_B = Nd4j.create(new int[] {3, 3});
INDArrayIndex ndi_Slice = NDArrayIndex.point(slice);
INDArrayIndex ndi_J = NDArrayIndex.interval(jStart, jEnd);
INDArrayIndex ndi_I = NDArrayIndex.interval(iStart, iEnd);
INDArrayIndex[] whereToGet = new INDArrayIndex[] {ndi_Slice, ndi_I, ndi_J};
INDArray whatToPut = arr.get(whereToGet);
System.out.println(whatToPut);
INDArrayIndex[] whereToPut = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all()};
subArr_B.put(whereToPut, whatToPut);
assertEquals(subArr_A, subArr_B);
System.out.println("... done");
}
Example 19
| Source File: IndexingTestsC.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGet() {
// System.out.println("Testing sub-array put and get with a 3D array ...");
INDArray arr = Nd4j.linspace(0, 124, 125).reshape(5, 5, 5);
/*
* Extract elements with the following indices:
*
* (2,1,1) (2,1,2) (2,1,3)
* (2,2,1) (2,2,2) (2,2,3)
* (2,3,1) (2,3,2) (2,3,3)
*/
int slice = 2;
int iStart = 1;
int jStart = 1;
int iEnd = 4;
int jEnd = 4;
// Method A: Element-wise.
INDArray subArr_A = Nd4j.create(new int[] {3, 3});
for (int i = iStart; i < iEnd; i++) {
for (int j = jStart; j < jEnd; j++) {
double val = arr.getDouble(slice, i, j);
int[] sub = new int[] {i - iStart, j - jStart};
subArr_A.putScalar(sub, val);
}
}
// Method B: Using NDArray get and put with index classes.
INDArray subArr_B = Nd4j.create(new int[] {3, 3});
INDArrayIndex ndi_Slice = NDArrayIndex.point(slice);
INDArrayIndex ndi_J = NDArrayIndex.interval(jStart, jEnd);
INDArrayIndex ndi_I = NDArrayIndex.interval(iStart, iEnd);
INDArrayIndex[] whereToGet = new INDArrayIndex[] {ndi_Slice, ndi_I, ndi_J};
INDArray whatToPut = arr.get(whereToGet);
// System.out.println(whatToPut);
INDArrayIndex[] whereToPut = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all()};
subArr_B.put(whereToPut, whatToPut);
assertEquals(subArr_A, subArr_B);
// System.out.println("... done");
}
Example 20
| Source File: IndexingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGet() {
// System.out.println("Testing sub-array put and get with a 3D array ...");
INDArray arr = Nd4j.linspace(0, 124, 125).reshape(5, 5, 5);
/*
* Extract elements with the following indices:
*
* (2,1,1) (2,1,2) (2,1,3)
* (2,2,1) (2,2,2) (2,2,3)
* (2,3,1) (2,3,2) (2,3,3)
*/
int slice = 2;
int iStart = 1;
int jStart = 1;
int iEnd = 4;
int jEnd = 4;
// Method A: Element-wise.
INDArray subArr_A = Nd4j.create(new int[] {3, 3});
for (int i = iStart; i < iEnd; i++) {
for (int j = jStart; j < jEnd; j++) {
double val = arr.getDouble(slice, i, j);
int[] sub = new int[] {i - iStart, j - jStart};
subArr_A.putScalar(sub, val);
}
}
// Method B: Using NDArray get and put with index classes.
INDArray subArr_B = Nd4j.create(new int[] {3, 3});
INDArrayIndex ndi_Slice = NDArrayIndex.point(slice);
INDArrayIndex ndi_J = NDArrayIndex.interval(jStart, jEnd);
INDArrayIndex ndi_I = NDArrayIndex.interval(iStart, iEnd);
INDArrayIndex[] whereToGet = new INDArrayIndex[] {ndi_Slice, ndi_I, ndi_J};
INDArray whatToPut = arr.get(whereToGet);
assertEquals(subArr_A, whatToPut);
// System.out.println(whatToPut);
INDArrayIndex[] whereToPut = new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all()};
subArr_B.put(whereToPut, whatToPut);
assertEquals(subArr_A, subArr_B);
// System.out.println("... done");
}
