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org.nd4j.linalg.checkutil.NDArrayCreationUtil Maven / Gradle / Ivy
package org.nd4j.linalg.checkutil;
import org.apache.commons.math3.util.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
*
* This class contains utility methods for generating NDArrays for use in unit tests
* The idea is to generate arrays with a specific shape, after various operations have been undertaken on them
* So output is after get, reshape, transpose, permute, tensorAlongDimension etc operations have been done returned to aid
* debugging: String contains information on how to reproduce the matrix (i.e., which function, and arguments)
* Each NDArray in the returned array has been obtained by applying an operation such as transpose, tensorAlongDimension,
* etc to an original array.
*/
public static List> getAllTestMatricesWithShape(char ordering,int rows, int cols, int seed) {
List> all = new ArrayList<>();
Nd4j.getRandom().setSeed(seed);
all.add(new Pair<>(Nd4j.linspace(1,rows * cols,rows * cols).reshape(ordering,rows, cols),"Nd4j..linspace(1,rows * cols,rows * cols).reshape(rows,cols)"));
all.add(getTransposedMatrixWithShape(ordering,rows,cols,seed));
all.addAll(getSubMatricesWithShape(ordering,rows,cols,seed));
all.addAll(getTensorAlongDimensionMatricesWithShape(ordering,rows, cols,seed));
all.add(getPermutedWithShape(ordering,rows,cols,seed));
all.add(getReshapedWithShape(ordering,rows, cols, seed));
return all;
}
/** Get an array of INDArrays (2d) all with the specified shape. Pair returned to aid
* debugging: String contains information on how to reproduce the matrix (i.e., which function, and arguments)
* Each NDArray in the returned array has been obtained by applying an operation such as transpose, tensorAlongDimension,
* etc to an original array.
*/
public static List> getAllTestMatricesWithShape(int rows, int cols, int seed) {
List> all = new ArrayList<>();
Nd4j.getRandom().setSeed(seed);
all.add(new Pair<>(Nd4j.linspace(1,rows * cols,rows * cols).reshape(rows, cols),"Nd4j..linspace(1,rows * cols,rows * cols).reshape(rows,cols)"));
all.add(getTransposedMatrixWithShape(rows,cols,seed));
all.addAll(getSubMatricesWithShape(rows,cols,seed));
all.addAll(getTensorAlongDimensionMatricesWithShape(rows, cols,seed));
all.add(getPermutedWithShape(rows,cols,seed));
all.add(getReshapedWithShape(rows, cols, seed));
return all;
}
public static Pair getTransposedMatrixWithShape(char ordering,int rows, int cols, int seed) {
Nd4j.getRandom().setSeed(seed);
INDArray out = Nd4j.linspace(1,rows * cols,rows * cols).reshape(ordering,cols,rows);
return new Pair<>(out.transpose(),"getTransposedMatrixWithShape(" + rows+"," + cols +"," + seed + ")");
}
public static Pair getTransposedMatrixWithShape(int rows, int cols, int seed) {
Nd4j.getRandom().setSeed(seed);
INDArray out = Nd4j.linspace(1,rows * cols,rows * cols).reshape(cols,rows);
return new Pair<>(out.transpose(),"getTransposedMatrixWithShape(" + rows + "," + cols + "," + seed + ")");
}
public static List> getSubMatricesWithShape(int rows, int cols, int seed) {
return getSubMatricesWithShape(Nd4j.order(),rows,cols,seed);
}
public static List> getSubMatricesWithShape(char ordering,int rows, int cols, int seed) {
//Create 3 identical matrices. Could do get() on single original array, but in-place modifications on one
//might mess up tests for another
Nd4j.getRandom().setSeed(seed);
int[] shape = new int[]{2 * rows + 4,2 * cols + 4};
int len = ArrayUtil.prod(shape);
INDArray orig = Nd4j.linspace(1,len,len).reshape(ordering,shape);
INDArray first = orig.get(NDArrayIndex.interval(0, rows), NDArrayIndex.interval(0, cols));
Nd4j.getRandom().setSeed(seed);
orig = Nd4j.linspace(1,len,len).reshape(shape);
INDArray second = orig.get(NDArrayIndex.interval(3, rows + 3), NDArrayIndex.interval(3, cols + 3));
Nd4j.getRandom().setSeed(seed);
orig = Nd4j.linspace(1,len,len).reshape(ordering,shape);
INDArray third = orig.get(NDArrayIndex.interval(rows,2 * rows),NDArrayIndex.interval(cols,2 * cols));
String baseMsg = "getSubMatricesWithShape(" + rows + "," + cols +"," + seed + ")";
List> list = new ArrayList<>(3);
list.add(new Pair<>(first, baseMsg + ".get(0)"));
list.add(new Pair<>(second, baseMsg + ".get(1)"));
list.add(new Pair<>(third, baseMsg + ".get(2)"));
return list;
}
public static List> getTensorAlongDimensionMatricesWithShape(char ordering,int rows, int cols, int seed) {
Nd4j.getRandom().setSeed(seed);
//From 3d NDArray: do various tensors. One offset 0, one offset > 0
//[0,1], [0,2], [1,0], [1,2], [2,0], [2,1]
INDArray[] out = new INDArray[12];
INDArray temp01 = Nd4j.linspace(1,cols * rows * 4,cols * rows * 4).reshape(cols,rows,4);
out[0] = temp01.tensorAlongDimension(0, 0,1).reshape(rows,cols);
int[] temp01Shape = new int[]{cols, rows, 4};
int len = ArrayUtil.prod(temp01Shape);
temp01 = Nd4j.linspace(1,len,len).reshape(temp01Shape);
out[1] = temp01.tensorAlongDimension(2, 0, 1).reshape(rows,cols);
Nd4j.getRandom().setSeed(seed);
INDArray temp02 = Nd4j.linspace(1, len,len).reshape(new int[]{
cols, 4, rows});
out[2] = temp02.tensorAlongDimension(0, 0,2).reshape(rows,cols);
temp02 = Nd4j.linspace(1, len,len).reshape(cols, 4, rows);
out[3] = temp02.tensorAlongDimension(2, 0,2).reshape(rows,cols);
INDArray temp10 = Nd4j.linspace(1, len,len).reshape(rows, cols, 4);
out[4] = temp10.tensorAlongDimension(0, 1,0).reshape(rows,cols);
temp10 = Nd4j.linspace(1, len,len).reshape(rows, cols, 4);
out[5] = temp10.tensorAlongDimension(2, 1,0).reshape(rows,cols);
INDArray temp12 = Nd4j.linspace(1, len,len).reshape(4, cols, rows);
out[6] = temp12.tensorAlongDimension(0, 1,2).reshape(rows,cols);
temp12 = Nd4j.linspace(1, len,len).reshape(4,cols,rows);
out[7] = temp12.tensorAlongDimension(2, 1,2).reshape(rows,cols);
INDArray temp20 = Nd4j.linspace(1, len,len).reshape(rows, 4, cols);
out[8] = temp20.tensorAlongDimension(0, 2,0).reshape(rows,cols);
temp20 = Nd4j.linspace(1, len,len).reshape(rows, 4, cols);
out[9] = temp20.tensorAlongDimension(2, 2,0).reshape(rows,cols);
INDArray temp21 = Nd4j.linspace(1, len,len).reshape(4, rows, cols);
out[10] = temp21.tensorAlongDimension(0, 2,1).reshape(rows,cols);
temp21 = Nd4j.linspace(1, len,len).reshape(4, rows, cols);
out[11] = temp21.tensorAlongDimension(2, 2, 1).reshape(rows,cols);
String baseMsg = "getTensorAlongDimensionMatricesWithShape(" + rows +"," + cols + "," + seed + ")";
List> list = new ArrayList<>(12);
for( int i = 0 ; i < out.length; i++ )
list.add(new Pair<>(out[i],baseMsg + ".get(" + i + ")"));
return list;
}
public static List> getTensorAlongDimensionMatricesWithShape(int rows, int cols, int seed) {
return getTensorAlongDimensionMatricesWithShape(Nd4j.order(),rows,cols,seed);
}
public static Pair getPermutedWithShape(char ordering,int rows, int cols, int seed) {
Nd4j.getRandom().setSeed(seed);
int len = rows * cols;
INDArray arr = Nd4j.linspace(1,len,len).reshape(cols,rows);
return new Pair<>(arr.permute(1, 0),"getPermutedWithShape(" + rows + "," + cols +"," + seed +")");
}
public static Pair getPermutedWithShape(int rows, int cols, int seed) {
return getPermutedWithShape(Nd4j.order(), rows, cols, seed);
}
public static Pair getReshapedWithShape(char ordering,int rows, int cols, int seed) {
Nd4j.getRandom().setSeed(seed);
int[] origShape = new int[3];
if(rows % 2 == 0) {
origShape[0] = rows / 2;
origShape[1] = cols;
origShape[2] = 2;
} else if(cols % 2 == 0) {
origShape[0] = rows;
origShape[1] = cols / 2;
origShape[2] = 2;
} else {
origShape[0] = 1;
origShape[1] = rows;
origShape[2] = cols;
}
int len = ArrayUtil.prod(origShape);
INDArray orig = Nd4j.linspace(1, len,len).reshape(ordering,origShape);
return new Pair<>(orig.reshape(ordering,rows, cols),"getReshapedWithShape(" + rows + "," + cols + "," + seed + ")");
}
public static Pair getReshapedWithShape(int rows, int cols, int seed) {
return getReshapedWithShape(Nd4j.order(),rows,cols,seed);
}
public static List> getAll3dTestArraysWithShape(int seed, int... shape) {
if(shape.length != 3) throw new IllegalArgumentException("Shape is not length 3");
List> list = new ArrayList<>();
String baseMsg = "getAll3dTestArraysWithShape("+seed+","+ Arrays.toString(shape)+").get(";
int len = ArrayUtil.prod(shape);
//Basic 3d in C and F orders:
Nd4j.getRandom().setSeed(seed);
INDArray stdC = Nd4j.linspace(1,len,len).reshape('c',shape);
INDArray stdF = Nd4j.linspace(1, len,len).reshape('f',shape);
list.add(new Pair<>(stdC, baseMsg + "0)/Nd4j.linspace(1,len,len)(" + Arrays.toString(shape) + ",'c')"));
list.add(new Pair<>(stdF,baseMsg+"1)/Nd4j.linspace(1,len,len(" + Arrays.toString(shape) + ",'f')"));
//Various sub arrays:
list.addAll(get3dSubArraysWithShape(seed, shape));
//TAD
list.addAll(get3dTensorAlongDimensionWithShape(seed, shape));
//Permuted
list.addAll(get3dPermutedWithShape(seed, shape));
//Reshaped
list.addAll(get3dReshapedWithShape(seed, shape));
return list;
}
public static List> get3dSubArraysWithShape(int seed, int... shape) {
List> list = new ArrayList<>();
String baseMsg = "get3dSubArraysWithShape("+seed+","+Arrays.toString(shape)+")";
//Create and return various sub arrays:
Nd4j.getRandom().setSeed(seed);
int[] newShape1 = Arrays.copyOf(shape, shape.length);
newShape1[0] += 5;
int len = ArrayUtil.prod(newShape1);
INDArray temp1 = Nd4j.linspace(1,len,len).reshape(newShape1);
INDArray subset1 = temp1.get(NDArrayIndex.interval(2, shape[0] + 2), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset1, baseMsg + ".get(0)"));
int[] newShape2 = Arrays.copyOf(shape,shape.length);
newShape2[1] += 5;
int len2 = ArrayUtil.prod(newShape2);
INDArray temp2 = Nd4j.linspace(1,len2,len2).reshape(newShape2);
INDArray subset2 = temp2.get(NDArrayIndex.all(), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.all());
list.add(new Pair<>(subset2, baseMsg + ".get(1)"));
int[] newShape3 = Arrays.copyOf(shape,shape.length);
newShape3[2] += 5;
int len3 = ArrayUtil.prod(newShape3);
INDArray temp3 = Nd4j.linspace(1,len3,len3).reshape(newShape3);
INDArray subset3 = temp3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(4, shape[2] + 4));
list.add(new Pair<>(subset3,baseMsg+".get(2)"));
int[] newShape4 = Arrays.copyOf(shape,shape.length);
newShape4[0] += 5;
newShape4[1] += 5;
newShape4[2] += 5;
int len4 = ArrayUtil.prod(newShape4);
INDArray temp4 = Nd4j.linspace(1, len4,len4).reshape(newShape4);
INDArray subset4 = temp4.get(NDArrayIndex.interval(4,shape[0] + 4), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.interval(2,shape[2] + 2));
list.add(new Pair<>(subset4, baseMsg + ".get(3)"));
return list;
}
public static List> get3dTensorAlongDimensionWithShape(int seed, int... shape) {
List> list = new ArrayList<>();
String baseMsg = "get3dTensorAlongDimensionWithShape("+seed+","+Arrays.toString(shape)+")";
//Create some 4d arrays and get subsets using 3d TAD on them
//This is not an exhaustive list of possible 3d arrays from 4d via TAD
Nd4j.getRandom().setSeed(seed);
// int[] shape4d1 = {shape[2],shape[1],shape[0],3};
int[] shape4d1 = {shape[0],shape[1],shape[2],3};
int lenshape4d1 = ArrayUtil.prod(shape4d1);
INDArray orig1a = Nd4j.linspace(1, lenshape4d1, lenshape4d1).reshape(shape4d1);
INDArray tad1a = orig1a.tensorAlongDimension(0, 0, 1, 2);
INDArray orig1b = Nd4j.linspace(1, lenshape4d1, lenshape4d1).reshape(shape4d1);
INDArray tad1b = orig1b.tensorAlongDimension(1, 0, 1, 2);
list.add(new Pair<>(tad1a,baseMsg+".get(0)"));
list.add(new Pair<>(tad1b,baseMsg+".get(1)"));
int[] shape4d2 = {3, shape[0], shape[1], shape[2]};
int lenshape4d2 = ArrayUtil.prod(shape4d2);
INDArray orig2 = Nd4j.linspace(1,lenshape4d2,lenshape4d2).reshape(shape4d2);
INDArray tad2 = orig2.tensorAlongDimension(1,1,2,3);
list.add(new Pair<>(tad2,baseMsg+".get(2)"));
int[] shape4d3 = {shape[0],shape[1],3,shape[2]};
int lenshape4d3 = ArrayUtil.prod(shape4d3);
INDArray orig3 = Nd4j.linspace(1, lenshape4d3,lenshape4d3).reshape(shape4d3);
INDArray tad3 = orig3.tensorAlongDimension(1,1,3,0);
list.add(new Pair<>(tad3,baseMsg +".get(3)"));
int[] shape4d4 = {shape[0],3,shape[1],shape[2]};
int lenshape4d4 = ArrayUtil.prod(shape4d4);
INDArray orig4 = Nd4j.linspace(1, lenshape4d4,lenshape4d4).reshape(shape4d4);
INDArray tad4 = orig4.tensorAlongDimension(1,2,0,3);
list.add(new Pair<>(tad4,baseMsg+".get(4)"));
return list;
}
public static List> get3dPermutedWithShape(int seed, int... shape) {
Nd4j.getRandom().setSeed(seed);
int[] createdShape = {shape[1],shape[2],shape[0]};
int lencreatedShape = ArrayUtil.prod(createdShape);
INDArray arr = Nd4j.linspace(1,lencreatedShape,lencreatedShape).reshape(createdShape);
INDArray permuted = arr.permute(2, 0, 1);
return Collections.singletonList(new Pair<>(permuted, "get3dPermutedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> get3dReshapedWithShape(int seed, int... shape) {
Nd4j.getRandom().setSeed(seed);
int[] shape2d = {shape[0] * shape[2],shape[1]};
int lenshape2d = ArrayUtil.prod(shape2d);
INDArray array2d = Nd4j.linspace(1, lenshape2d,lenshape2d).reshape(shape2d);
INDArray array3d = array2d.reshape(shape);
return Collections.singletonList(new Pair<>(array3d, "get3dReshapedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> getAll4dTestArraysWithShape(int seed, int... shape) {
if(shape.length != 4) throw new IllegalArgumentException("Shape is not length 4");
List> list = new ArrayList<>();
String baseMsg = "getAll4dTestArraysWithShape("+seed+"," + Arrays.toString(shape) + ").get(";
//Basic 4d in C and F orders:
Nd4j.getRandom().setSeed(seed);
int len = ArrayUtil.prod(shape);
INDArray stdC = Nd4j.linspace(1, len,len).reshape('c',shape);
INDArray stdF = Nd4j.linspace(1,len,len).reshape('f',shape);
list.add(new Pair<>(stdC,baseMsg + "0)/Nd4j.rand(" + Arrays.toString(shape) + ",'c')"));
list.add(new Pair<>(stdF,baseMsg + "1)/Nd4j.rand(" + Arrays.toString(shape) + ",'f')"));
//Various sub arrays:
list.addAll(get4dSubArraysWithShape(seed, shape));
//TAD
list.addAll(get4dTensorAlongDimensionWithShape(seed, shape));
//Permuted
list.addAll(get4dPermutedWithShape(seed, shape));
//Reshaped
list.addAll(get4dReshapedWithShape(seed, shape));
return list;
}
public static List> get4dSubArraysWithShape(int seed, int... shape){
List> list = new ArrayList<>();
String baseMsg = "get4dSubArraysWithShape("+seed+","+Arrays.toString(shape)+")";
//Create and return various sub arrays:
Nd4j.getRandom().setSeed(seed);
int[] newShape1 = Arrays.copyOf(shape, shape.length);
newShape1[0] += 5;
int len = ArrayUtil.prod(newShape1);
INDArray temp1 = Nd4j.linspace(1, len,len).reshape(newShape1);
INDArray subset1 = temp1.get(NDArrayIndex.interval(2, shape[0] + 2), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset1, baseMsg + ".get(0)"));
int[] newShape2 = Arrays.copyOf(shape,shape.length);
newShape2[1] += 5;
int len2 = ArrayUtil.prod(newShape2);
INDArray temp2 = Nd4j.linspace(1, len2,len2).reshape(newShape2);
INDArray subset2 = temp2.get(NDArrayIndex.all(), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset2,baseMsg+".get(1)"));
int[] newShape3 = Arrays.copyOf(shape, shape.length);
newShape3[2] += 5;
int len3 = ArrayUtil.prod(newShape3);
INDArray temp3 = Nd4j.linspace(1, len3,len3).reshape(newShape3);
INDArray subset3 = temp3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(4, shape[2] + 4), NDArrayIndex.all());
list.add(new Pair<>(subset3,baseMsg+".get(2)"));
int[] newShape4 = Arrays.copyOf(shape, shape.length);
newShape4[3] += 5;
int len4 = ArrayUtil.prod(newShape4);
INDArray temp4 = Nd4j.linspace(1, len4,len4).reshape(newShape4);
INDArray subset4 = temp4.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(3, shape[3] + 3));
list.add(new Pair<>(subset4,baseMsg+".get(3)"));
int[] newShape5 = Arrays.copyOf(shape, shape.length);
newShape5[0] += 5;
newShape5[1] += 5;
newShape5[2] += 5;
newShape5[3] += 5;
int len5 = ArrayUtil.prod(newShape5);
INDArray temp5 = Nd4j.linspace(1,len5,len5).reshape(newShape5);
INDArray subset5 = temp5.get(NDArrayIndex.interval(4, shape[0] + 4), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.interval(2, shape[2] + 2),
NDArrayIndex.interval(1,shape[3] + 1));
list.add(new Pair<>(subset5, baseMsg + ".get(4)"));
return list;
}
public static List> get4dTensorAlongDimensionWithShape(int seed, int... shape){
List> list = new ArrayList<>();
String baseMsg = "get4dTensorAlongDimensionWithShape("+seed+","+Arrays.toString(shape)+")";
//Create some 5d arrays and get subsets using 4d TAD on them
//This is not an exhausive list of possible 4d arrays from 5d via TAD
Nd4j.getRandom().setSeed(seed);
int[] shape4d1 = {3,shape[0],shape[1],shape[2],shape[3]};
int len = ArrayUtil.prod(shape4d1);
INDArray orig1a = Nd4j.linspace(1,len,len).reshape(shape4d1);
INDArray tad1a = orig1a.tensorAlongDimension(0, 1, 2, 3, 4);
INDArray orig1b = Nd4j.linspace(1,len,len).reshape(shape4d1);
INDArray tad1b = orig1b.tensorAlongDimension(2, 1, 2, 3, 4);
list.add(new Pair<>(tad1a,baseMsg + ".get(0)"));
list.add(new Pair<>(tad1b,baseMsg +".get(1)"));
int[] shape4d2 = {3, shape[0], shape[1], shape[2], shape[3]};
int len2 = ArrayUtil.prod(shape4d2);
INDArray orig2 = Nd4j.linspace(1,len2,len2).reshape(shape4d2);
INDArray tad2 = orig2.tensorAlongDimension(1,3,4,2,1);
list.add(new Pair<>(tad2,baseMsg+".get(2)"));
int[] shape4d3 = {shape[0],shape[1],3,shape[2],shape[3]};
int len3 = ArrayUtil.prod(shape4d3);
INDArray orig3 = Nd4j.linspace(1,len3,len3).reshape(shape4d3);
INDArray tad3 = orig3.tensorAlongDimension(1,4,1,3,0);
list.add(new Pair<>(tad3,baseMsg+".get(3)"));
int[] shape4d4 = {shape[0],shape[1],shape[2],shape[3],3};
int len4 = ArrayUtil.prod(shape4d4);
INDArray orig4 = Nd4j.linspace(1,len4,len4).reshape(shape4d4);
INDArray tad4 = orig4.tensorAlongDimension(1,2,0,3,1);
list.add(new Pair<>(tad4,baseMsg+".get(4)"));
return list;
}
public static List> get4dPermutedWithShape(int seed, int... shape) {
Nd4j.getRandom().setSeed(seed);
int[] createdShape = {shape[1],shape[3],shape[2],shape[0]};
INDArray arr = Nd4j.rand(createdShape);
INDArray permuted = arr.permute(3, 0, 2, 1);
return Collections.singletonList(new Pair<>(permuted, "get4dPermutedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> get4dReshapedWithShape(int seed, int... shape){
Nd4j.getRandom().setSeed(seed);
int[] shape2d = {shape[0]*shape[2],shape[1]*shape[3]};
INDArray array2d = Nd4j.rand(shape2d);
INDArray array3d = array2d.reshape(shape);
return Collections.singletonList(new Pair<>(array3d, "get4dReshapedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> getAll5dTestArraysWithShape(int seed, int... shape){
if(shape.length != 5) throw new IllegalArgumentException("Shape is not length 5");
List> list = new ArrayList<>();
String baseMsg = "getAll5dTestArraysWithShape("+seed+","+Arrays.toString(shape)+").get(";
//Basic 5d in C and F orders:
Nd4j.getRandom().setSeed(seed);
INDArray stdC = Nd4j.rand(shape,'c');
INDArray stdF = Nd4j.rand(shape, 'f');
list.add(new Pair<>(stdC,baseMsg+"0)/Nd4j.rand("+Arrays.toString(shape)+",'c')"));
list.add(new Pair<>(stdF,baseMsg+"1)/Nd4j.rand("+Arrays.toString(shape)+",'f')"));
//Various sub arrays:
list.addAll(get5dSubArraysWithShape(seed, shape));
//TAD
list.addAll(get5dTensorAlongDimensionWithShape(seed, shape));
//Permuted
list.addAll(get5dPermutedWithShape(seed, shape));
//Reshaped
list.addAll(get5dReshapedWithShape(seed, shape));
return list;
}
public static List> get5dSubArraysWithShape(int seed, int... shape){
List> list = new ArrayList<>();
String baseMsg = "get5dSubArraysWithShape("+seed+","+Arrays.toString(shape)+")";
//Create and return various sub arrays:
Nd4j.getRandom().setSeed(seed);
int[] newShape1 = Arrays.copyOf(shape, shape.length);
newShape1[0] += 5;
INDArray temp1 = Nd4j.rand(newShape1);
INDArray subset1 = temp1.get(NDArrayIndex.interval(2, shape[0] + 2), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset1, baseMsg + ".get(0)"));
int[] newShape2 = Arrays.copyOf(shape,shape.length);
newShape2[1] += 5;
INDArray temp2 = Nd4j.rand(newShape2);
INDArray subset2 = temp2.get(NDArrayIndex.all(), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset2,baseMsg+".get(1)"));
int[] newShape3 = Arrays.copyOf(shape, shape.length);
newShape3[2] += 5;
INDArray temp3 = Nd4j.rand(newShape3);
INDArray subset3 = temp3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(4, shape[2] + 4), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset3,baseMsg+".get(2)"));
int[] newShape4 = Arrays.copyOf(shape, shape.length);
newShape4[3] += 5;
INDArray temp4 = Nd4j.rand(newShape4);
INDArray subset4 = temp4.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(3, shape[3] + 3), NDArrayIndex.all());
list.add(new Pair<>(subset4,baseMsg+".get(3)"));
int[] newShape5 = Arrays.copyOf(shape, shape.length);
newShape5[4] += 5;
INDArray temp5 = Nd4j.rand(newShape5);
INDArray subset5 = temp5.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(3, shape[4] + 3));
list.add(new Pair<>(subset5,baseMsg+".get(4)"));
int[] newShape6 = Arrays.copyOf(shape, shape.length);
newShape6[0] += 5;
newShape6[1] += 5;
newShape6[2] += 5;
newShape6[3] += 5;
newShape6[4] += 5;
INDArray temp6 = Nd4j.rand(newShape6);
INDArray subset6 = temp6.get(NDArrayIndex.interval(4, shape[0] + 4), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.interval(2, shape[2] + 2),
NDArrayIndex.interval(1,shape[3]+1), NDArrayIndex.interval(2,shape[4]+2));
list.add(new Pair<>(subset6, baseMsg + ".get(5)"));
return list;
}
public static List> get5dTensorAlongDimensionWithShape(int seed, int... shape){
List> list = new ArrayList<>();
String baseMsg = "get5dTensorAlongDimensionWithShape("+seed+","+Arrays.toString(shape)+")";
//Create some 6d arrays and get subsets using 5d TAD on them
//This is not an exhausive list of possible 5d arrays from 6d via TAD
Nd4j.getRandom().setSeed(seed);
int[] shape4d1 = {3,shape[0],shape[1],shape[2],shape[3],shape[4]};
INDArray orig1a = Nd4j.rand(shape4d1);
INDArray tad1a = orig1a.tensorAlongDimension(0, 1, 2, 3, 4, 5);
INDArray orig1b = Nd4j.rand(shape4d1);
INDArray tad1b = orig1b.tensorAlongDimension(2, 1, 2, 3, 4, 5);
list.add(new Pair<>(tad1a,baseMsg+".get(0)"));
list.add(new Pair<>(tad1b,baseMsg+".get(1)"));
int[] shape4d2 = {3, shape[0], shape[1], shape[2], shape[3], shape[4]};
INDArray orig2 = Nd4j.rand(shape4d2);
INDArray tad2 = orig2.tensorAlongDimension(1,3,5,4,2,1);
list.add(new Pair<>(tad2,baseMsg+".get(2)"));
int[] shape4d3 = {shape[0],shape[1],shape[2],shape[3],shape[4], 2};
INDArray orig3 = Nd4j.rand(shape4d3);
INDArray tad3 = orig3.tensorAlongDimension(1,4,1,3,2,0);
list.add(new Pair<>(tad3,baseMsg+".get(3)"));
int[] shape4d4 = {shape[0],shape[1],shape[2],shape[3],3,shape[4]};
INDArray orig4 = Nd4j.rand(shape4d4);
INDArray tad4 = orig4.tensorAlongDimension(1,5,2,0,3,1);
list.add(new Pair<>(tad4,baseMsg+".get(4)"));
return list;
}
public static List> get5dPermutedWithShape(int seed, int... shape){
Nd4j.getRandom().setSeed(seed);
int[] createdShape = {shape[1],shape[4],shape[3],shape[2],shape[0]};
INDArray arr = Nd4j.rand(createdShape);
INDArray permuted = arr.permute(4, 0, 3, 2, 1);
return Collections.singletonList(new Pair<>(permuted, "get5dPermutedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> get5dReshapedWithShape(int seed, int... shape){
Nd4j.getRandom().setSeed(seed);
int[] shape2d = {shape[0]*shape[2],shape[4],shape[1]*shape[3]};
INDArray array3d = Nd4j.rand(shape2d);
INDArray array5d = array3d.reshape(shape);
return Collections.singletonList(new Pair<>(array5d, "get5dReshapedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> getAll6dTestArraysWithShape(int seed, int... shape){
if(shape.length != 6) throw new IllegalArgumentException("Shape is not length 6");
List> list = new ArrayList<>();
String baseMsg = "getAll6dTestArraysWithShape("+seed+","+Arrays.toString(shape)+").get(";
//Basic 5d in C and F orders:
Nd4j.getRandom().setSeed(seed);
INDArray stdC = Nd4j.rand(shape,'c');
INDArray stdF = Nd4j.rand(shape, 'f');
list.add(new Pair<>(stdC,baseMsg+"0)/Nd4j.rand("+Arrays.toString(shape)+",'c')"));
list.add(new Pair<>(stdF,baseMsg+"1)/Nd4j.rand("+Arrays.toString(shape)+",'f')"));
//Various sub arrays:
list.addAll(get6dSubArraysWithShape(seed, shape));
//Permuted
list.addAll(get6dPermutedWithShape(seed, shape));
//Reshaped
list.addAll(get6dReshapedWithShape(seed, shape));
return list;
}
public static List> get6dSubArraysWithShape(int seed, int... shape){
List> list = new ArrayList<>();
String baseMsg = "get6dSubArraysWithShape("+seed+","+Arrays.toString(shape)+")";
//Create and return various sub arrays:
Nd4j.getRandom().setSeed(seed);
int[] newShape1 = Arrays.copyOf(shape, shape.length);
newShape1[0] += 5;
INDArray temp1 = Nd4j.rand(newShape1);
INDArray subset1 = temp1.get(NDArrayIndex.interval(2, shape[0] + 2), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset1, baseMsg + ".get(0)"));
int[] newShape2 = Arrays.copyOf(shape,shape.length);
newShape2[1] += 5;
INDArray temp2 = Nd4j.rand(newShape2);
INDArray subset2 = temp2.get(NDArrayIndex.all(), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset2,baseMsg+".get(1)"));
int[] newShape3 = Arrays.copyOf(shape, shape.length);
newShape3[2] += 5;
INDArray temp3 = Nd4j.rand(newShape3);
INDArray subset3 = temp3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(4, shape[2] + 4), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset3,baseMsg+".get(2)"));
int[] newShape4 = Arrays.copyOf(shape, shape.length);
newShape4[3] += 5;
INDArray temp4 = Nd4j.rand(newShape4);
INDArray subset4 = temp4.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(3, shape[3] + 3), NDArrayIndex.all(), NDArrayIndex.all());
list.add(new Pair<>(subset4,baseMsg+".get(3)"));
int[] newShape5 = Arrays.copyOf(shape, shape.length);
newShape5[4] += 5;
INDArray temp5 = Nd4j.rand(newShape5);
INDArray subset5 = temp5.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(3, shape[4] + 3), NDArrayIndex.all());
list.add(new Pair<>(subset5,baseMsg+".get(4)"));
int[] newShape6 = Arrays.copyOf(shape, shape.length);
newShape6[5] += 5;
INDArray temp6 = Nd4j.rand(newShape6);
INDArray subset6 = temp6.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(1, shape[5] + 1));
list.add(new Pair<>(subset6,baseMsg+".get(5)"));
int[] newShape7 = Arrays.copyOf(shape, shape.length);
newShape7[0] += 5;
newShape7[1] += 5;
newShape7[2] += 5;
newShape7[3] += 5;
newShape7[4] += 5;
newShape7[5] += 5;
INDArray temp7 = Nd4j.rand(newShape7);
INDArray subset7 = temp7.get(NDArrayIndex.interval(4, shape[0] + 4), NDArrayIndex.interval(3, shape[1] + 3), NDArrayIndex.interval(2, shape[2] + 2),
NDArrayIndex.interval(1,shape[3]+1), NDArrayIndex.interval(2,shape[4]+2), NDArrayIndex.interval(3,shape[5]+3));
list.add(new Pair<>(subset7, baseMsg + ".get(6)"));
return list;
}
public static List> get6dPermutedWithShape(int seed, int... shape){
Nd4j.getRandom().setSeed(seed);
int[] createdShape = {shape[1],shape[4],shape[5],shape[3],shape[2],shape[0]};
INDArray arr = Nd4j.rand(createdShape);
INDArray permuted = arr.permute(5, 0, 4, 3, 1, 2);
return Collections.singletonList(new Pair<>(permuted, "get6dPermutedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
public static List> get6dReshapedWithShape(int seed, int... shape){
Nd4j.getRandom().setSeed(seed);
int[] shape3d = {shape[0]*shape[2],shape[4]*shape[5],shape[1]*shape[3]};
INDArray array3d = Nd4j.rand(shape3d);
INDArray array6d = array3d.reshape(shape);
return Collections.singletonList(new Pair<>(array6d, "get6dReshapedWithShape(" + seed + "," +
Arrays.toString(shape) + ").get(0)"));
}
}
