diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java index c73a56c61..04a61f9d0 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java @@ -1007,19 +1007,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return toTad; } - /** - * Get the vector along a particular dimension - * - * @param index the index of the vector to getScalar - * @param dimension the dimension to getScalar the vector from - * @return the vector along a particular dimension - */ - @Override - @Deprecated - public INDArray javaTensorAlongDimension(int index, int... dimension) { - return doTad(index, dimension); - } - private void setShapeInformation(Pair shapeInfo) { this.shapeInformation = shapeInfo.getFirst(); this.jvmShapeInfo = new JvmShapeInfo(shapeInfo.getSecond()); @@ -1110,14 +1097,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return ret2.permutei(finalPermuteDims); } - - - /** - * Returns the number of possible vectors for a given dimension - * - * @param dimension the dimension to calculate the number of vectors for - * @return the number of possible vectors along a dimension - */ @Override public long vectorsAlongDimension(int dimension) { if (dimension == 0 && isVector() || isRowVectorOrScalar()) @@ -1150,17 +1129,11 @@ public abstract class BaseNDArray implements INDArray, Iterable { return length / size(dimension); } - /** - * Get the vector along a particular dimension - * - * @param index the index of the vector to get - * @param dimension the dimension to get the vector from - * @return the vector along a particular dimension - */ @Override public INDArray vectorAlongDimension(int index, int dimension) { - if (dimension < 0) + if (dimension < 0) { dimension = jvmShapeInfo.getRank() + dimension; + } //return the whole thing if (dimension == jvmShapeInfo.getRank() - 1 && size(dimension) == 1 && rank() > 2 @@ -1168,12 +1141,7 @@ public abstract class BaseNDArray implements INDArray, Iterable { return this; } - INDArray ret = tensorAlongDimension(index, dimension); - //if (isMatrix() && ret.isVector() && dimension == 1 && !ret.isRowVector()) - // return ret.reshape(ArrayUtil.reverseCopy(ret.shape())); - //else if (isMatrix() && ret.isVector() && dimension == 0 && !ret.isColumnVector()) - // return ret.reshape(ArrayUtil.reverseCopy(ret.shape())); - return ret; + return tensorAlongDimension(index, dimension); } @Override @@ -1196,13 +1164,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride), 0, ordering(), this.dataType(), false)); } - - /** - * Cumulative sum along a dimension - * - * @param dimension the dimension to perform cumulative sum along - * @return the cumulative sum along the specified dimension - */ @Override public INDArray cumsumi(int dimension) { validateNumericalArray("cumsumi", true); @@ -1351,25 +1312,12 @@ public abstract class BaseNDArray implements INDArray, Iterable { return logEntropy(Integer.MAX_VALUE).getDouble(0); } - /** - * Cumulative sum along a dimension (in place) - * - * @param dimension the dimension to perform cumulative sum along - * @return the cumulative sum along the specified dimension - */ @Override public INDArray cumsum(int dimension) { validateNumericalArray("cumsum", true); return dup().cumsumi(dimension); } - /** - * Assign all of the elements in the given - * ndarray to this ndarray - * - * @param arr the elements to assign - * @return this - */ @Override public INDArray assign(final INDArray arr) { Preconditions.checkState((this.isScalar() && arr.isScalar()) || (this.isVector() && arr.isVector()) || Shape.shapeEqualWithSqueeze(this.shape(), arr.shape()), @@ -1378,7 +1326,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { Preconditions.checkArgument(this.length() == arr.length(), "Length of both arrays must be equal"); - //Nd4j.getExecutioner().exec(new org.nd4j.linalg.api.ops.impl.transforms.pairwise.Set(this, arr, this, length())); Nd4j.getExecutioner().exec(new org.nd4j.linalg.api.ops.impl.transforms.any.Assign(arr, this)); return this; } @@ -1413,7 +1360,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { @Override public INDArray putScalar(long i, float value) { return putScalar(i, (double) value); - } @Override @@ -1540,7 +1486,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return this; } - @Override public INDArray putScalar(int[] indexes, float value) { return putScalar(indexes, (double) value); @@ -1556,27 +1501,12 @@ public abstract class BaseNDArray implements INDArray, Iterable { return putScalar(indexes, (double) value); } - /** - * Returns an ndarray with 1 if the element is epsilon equals - * - * @param other the number to compare - * @return a copied ndarray with the given - * binary conditions - */ @Override public INDArray eps(Number other) { validateNumericalArray("eps", true); return Nd4j.getExecutioner().exec(new ScalarEps(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other)); } - /** - * epsilon equals than comparison: - * If the given number is less than the - * comparison number the item is 0 otherwise 1 - * - * @param other the number to compare - * @return - */ @Override public INDArray eps(INDArray other) { validateNumericalArray("eps", true); @@ -1613,7 +1543,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return Nd4j.getExecutioner().exec(new ScalarGreaterThanOrEqual(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other)); } - @Override public INDArray lt(INDArray other) { validateNumericalArray("less than (lt)", false); @@ -1675,9 +1604,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return Nd4j.getExecutioner().exec(new MatchConditionTransform(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), Conditions.isNan())); } - /** - * Negate each element. - */ @Override public INDArray neg() { validateNumericalArray("negative (neg)", true); @@ -1686,9 +1612,6 @@ public abstract class BaseNDArray implements INDArray, Iterable { return Nd4j.getExecutioner().exec(new Negative(this, Nd4j.createUninitialized(this.dataType(), this.shape(), this.ordering()))); } - /** - * Negate each element (in-place). - */ @Override public INDArray negi() { validateNumericalArray("negative (negi)", true); @@ -3909,28 +3832,12 @@ public abstract class BaseNDArray implements INDArray, Iterable { return assign(value ? 1 : 0); } - - /** - * Assign all elements from given ndarray that are matching given condition, - * ndarray to this ndarray - * - * @param arr the elements to assign - * @param condition - * @return this - */ @Override public INDArray assignIf(INDArray arr, Condition condition) { BooleanIndexing.assignIf(this, arr, condition); return this; } - /** - * Replaces all elements in this ndarray that are matching give condition, with corresponding elements from given array - * - * @param arr - * @param condition - * @return - */ @Override public INDArray replaceWhere(INDArray arr, Condition condition) { Nd4j.getCompressor().autoDecompress(this); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java index 5257be12e..0d7aca8e0 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java @@ -411,11 +411,6 @@ public abstract class BaseSparseNDArray implements ISparseNDArray { return null; } - @Override - public INDArray javaTensorAlongDimension(int index, int... dimension) { - return null; - } - @Override public INDArray cumsumi(int dimension) { return null; @@ -476,7 +471,6 @@ public abstract class BaseSparseNDArray implements ISparseNDArray { return null; } - @Override public INDArray isInfinite() { throw new UnsupportedOperationException(); @@ -551,6 +545,7 @@ public abstract class BaseSparseNDArray implements ISparseNDArray { public INDArray lte(Number other) { return null; } + @Override public INDArray lt(INDArray other) { return null; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java index 88cda5b4f..fe940f3ee 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java @@ -106,27 +106,31 @@ public interface INDArray extends Serializable, AutoCloseable { /** * Element wise stride + * @return the element wise stride */ int elementWiseStride(); /** - * Get a scalar - * at the given linear offset + * Get a double at the given linear offset unsafe, without checks. * @param offset the offset to get at - * @return this + * @return double value at offset */ - double getDoubleUnsafe(long offset); + double getDoubleUnsafe(long offset); //TODO: consider deleting. + /** + * Get string value at given index. + * @param index index to retreive + * @return string value at index. + */ String getString(long index); /** - * Insert a scalar - * at the given linear offset + * Insert a scalar at the given linear offset * @param offset the offset to insert at * @param value the value to insert * @return this */ - INDArray putScalarUnsafe(long offset, double value); + INDArray putScalarUnsafe(long offset, double value); //TODO: consider deleting. /** * Returns the number of possible vectors for a given dimension @@ -162,17 +166,6 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray tensorAlongDimension(long index, int... dimension); - /** - * Get the vector along a particular dimension - * - * @param index the index of the vector to getScalar - * @param dimension the dimension to getScalar the vector from - * @return the vector along a particular dimension - */ - @Deprecated - INDArray javaTensorAlongDimension(int index, int... dimension); - - /** * Returns the cumulative sum along a dimension. In-place method. * @@ -190,8 +183,7 @@ public interface INDArray extends Serializable, AutoCloseable { INDArray cumsum(int dimension); /** - * Assign all of the elements in the given - * ndarray to this ndarray + * Assign all of the elements in the given ndarray to this ndarray * * @param arr the elements to assign * @return this @@ -254,9 +246,19 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray putScalar(int[] i, double value); - + /** + * See {@link #putScalar(int[], double)} + */ INDArray putScalar(long[] i, double value); + + /** + * See {@link #putScalar(int[], double)} + */ INDArray putScalar(long[] i, float value); + + /** + * See {@link #putScalar(int[], double)} + */ INDArray putScalar(long[] i, int value); /** @@ -300,7 +302,6 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray lt(Number other); - /** * Put the specified float value at the specified indices in this array * @@ -327,8 +328,6 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray eps(Number other); - - /** * Returns the binary ndarray for "Equals" comparison. * @@ -367,10 +366,8 @@ public interface INDArray extends Serializable, AutoCloseable { * @param other the ndarray to compare. * @return the binary ndarray for "Less" comparison. */ - INDArray lt(INDArray other); - /** * Returns the binary ndarray for "Epsilon equals" comparison. * @@ -403,7 +400,6 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray eq(INDArray other); - /** * Returns the binary ndarray for "Greater Than" comparison. * @@ -424,8 +420,6 @@ public interface INDArray extends Serializable, AutoCloseable { */ INDArray isNaN(); - - /** * Returns the ndarray negative (cloned) * diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/NDArrayCreationUtil.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/NDArrayCreationUtil.java index 6039e4b1a..f3ac98565 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/NDArrayCreationUtil.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/NDArrayCreationUtil.java @@ -181,37 +181,37 @@ public class NDArrayCreationUtil { INDArray[] out = new INDArray[12]; INDArray temp01 = Nd4j.linspace(1, cols * rows * 4, cols * rows * 4, dataType).reshape(cols, rows, 4); - out[0] = temp01.javaTensorAlongDimension(0, 0, 1).reshape(rows, cols); + out[0] = temp01.tensorAlongDimension(0, 0, 1).reshape(rows, cols); long[] temp01Shape = new long[] {cols, rows, 4}; int len = ArrayUtil.prod(temp01Shape); temp01 = Nd4j.linspace(1, len, len, dataType).reshape(temp01Shape); - out[1] = temp01.javaTensorAlongDimension(2, 0, 1).reshape(rows, cols); + out[1] = temp01.tensorAlongDimension(2, 0, 1).reshape(rows, cols); Nd4j.getRandom().setSeed(seed); INDArray temp02 = Nd4j.linspace(1, len, len, dataType).reshape(new long[] {cols, 4, rows}); - out[2] = temp02.javaTensorAlongDimension(0, 0, 2).reshape(rows, cols); + out[2] = temp02.tensorAlongDimension(0, 0, 2).reshape(rows, cols); temp02 = Nd4j.linspace(1, len, len, dataType).reshape(cols, 4, rows); - out[3] = temp02.javaTensorAlongDimension(2, 0, 2).reshape(rows, cols); + out[3] = temp02.tensorAlongDimension(2, 0, 2).reshape(rows, cols); INDArray temp10 = Nd4j.linspace(1, len, len, dataType).reshape(rows, cols, 4); - out[4] = temp10.javaTensorAlongDimension(0, 1, 0).reshape(rows, cols); + out[4] = temp10.tensorAlongDimension(0, 1, 0).reshape(rows, cols); temp10 = Nd4j.linspace(1, len, len, dataType).reshape(rows, cols, 4); - out[5] = temp10.javaTensorAlongDimension(2, 1, 0).reshape(rows, cols); + out[5] = temp10.tensorAlongDimension(2, 1, 0).reshape(rows, cols); INDArray temp12 = Nd4j.linspace(1, len, len, dataType).reshape(4, cols, rows); - out[6] = temp12.javaTensorAlongDimension(0, 1, 2).reshape(rows, cols); + out[6] = temp12.tensorAlongDimension(0, 1, 2).reshape(rows, cols); temp12 = Nd4j.linspace(1, len, len, dataType).reshape(4, cols, rows); - out[7] = temp12.javaTensorAlongDimension(2, 1, 2).reshape(rows, cols); + out[7] = temp12.tensorAlongDimension(2, 1, 2).reshape(rows, cols); INDArray temp20 = Nd4j.linspace(1, len, len, dataType).reshape(rows, 4, cols); - out[8] = temp20.javaTensorAlongDimension(0, 2, 0).reshape(rows, cols); + out[8] = temp20.tensorAlongDimension(0, 2, 0).reshape(rows, cols); temp20 = Nd4j.linspace(1, len, len, dataType).reshape(rows, 4, cols); - out[9] = temp20.javaTensorAlongDimension(2, 2, 0).reshape(rows, cols); + out[9] = temp20.tensorAlongDimension(2, 2, 0).reshape(rows, cols); INDArray temp21 = Nd4j.linspace(1, len, len, dataType).reshape(4, rows, cols); - out[10] = temp21.javaTensorAlongDimension(0, 2, 1).reshape(rows, cols); + out[10] = temp21.tensorAlongDimension(0, 2, 1).reshape(rows, cols); temp21 = Nd4j.linspace(1, len, len, dataType).reshape(4, rows, cols); - out[11] = temp21.javaTensorAlongDimension(2, 2, 1).reshape(rows, cols); + out[11] = temp21.tensorAlongDimension(2, 2, 1).reshape(rows, cols); String baseMsg = "getTensorAlongDimensionMatricesWithShape(" + rows + "," + cols + "," + seed + ")"; List> list = new ArrayList<>(12); @@ -361,9 +361,9 @@ public class NDArrayCreationUtil { val shape4d1 = new long[]{shape[0], shape[1], shape[2], 3}; int lenshape4d1 = ArrayUtil.prod(shape4d1); INDArray orig1a = Nd4j.linspace(1, lenshape4d1, lenshape4d1, dataType).reshape(shape4d1); - INDArray tad1a = orig1a.javaTensorAlongDimension(0, 0, 1, 2); + INDArray tad1a = orig1a.tensorAlongDimension(0, 0, 1, 2); INDArray orig1b = Nd4j.linspace(1, lenshape4d1, lenshape4d1, dataType).reshape(shape4d1); - INDArray tad1b = orig1b.javaTensorAlongDimension(1, 0, 1, 2); + INDArray tad1b = orig1b.tensorAlongDimension(1, 0, 1, 2); list.add(new Pair<>(tad1a, baseMsg + ".get(0)")); list.add(new Pair<>(tad1b, baseMsg + ".get(1)")); @@ -371,19 +371,19 @@ public class NDArrayCreationUtil { long[] shape4d2 = {3, shape[0], shape[1], shape[2]}; int lenshape4d2 = ArrayUtil.prod(shape4d2); INDArray orig2 = Nd4j.linspace(1, lenshape4d2, lenshape4d2, dataType).reshape(shape4d2); - INDArray tad2 = orig2.javaTensorAlongDimension(1, 1, 2, 3); + INDArray tad2 = orig2.tensorAlongDimension(1, 1, 2, 3); list.add(new Pair<>(tad2, baseMsg + ".get(2)")); long[] shape4d3 = {shape[0], shape[1], 3, shape[2]}; int lenshape4d3 = ArrayUtil.prod(shape4d3); INDArray orig3 = Nd4j.linspace(1, lenshape4d3, lenshape4d3, dataType).reshape(shape4d3); - INDArray tad3 = orig3.javaTensorAlongDimension(1, 1, 3, 0); + INDArray tad3 = orig3.tensorAlongDimension(1, 1, 3, 0); list.add(new Pair<>(tad3, baseMsg + ".get(3)")); long[] shape4d4 = {shape[0], 3, shape[1], shape[2]}; int lenshape4d4 = ArrayUtil.prod(shape4d4); INDArray orig4 = Nd4j.linspace(1, lenshape4d4, lenshape4d4, dataType).reshape(shape4d4); - INDArray tad4 = orig4.javaTensorAlongDimension(1, 2, 0, 3); + INDArray tad4 = orig4.tensorAlongDimension(1, 2, 0, 3); list.add(new Pair<>(tad4, baseMsg + ".get(4)")); return list; @@ -513,9 +513,9 @@ public class NDArrayCreationUtil { int[] shape4d1 = {3, shape[0], shape[1], shape[2], shape[3]}; int len = ArrayUtil.prod(shape4d1); INDArray orig1a = Nd4j.linspace(1, len, len, dataType).reshape(ArrayUtil.toLongArray(shape4d1)); - INDArray tad1a = orig1a.javaTensorAlongDimension(0, 1, 2, 3, 4); + INDArray tad1a = orig1a.tensorAlongDimension(0, 1, 2, 3, 4); INDArray orig1b = Nd4j.linspace(1, len, len, dataType).reshape(ArrayUtil.toLongArray(shape4d1)); - INDArray tad1b = orig1b.javaTensorAlongDimension(2, 1, 2, 3, 4); + 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)")); @@ -523,19 +523,19 @@ public class NDArrayCreationUtil { int[] shape4d2 = {3, shape[0], shape[1], shape[2], shape[3]}; int len2 = ArrayUtil.prod(shape4d2); INDArray orig2 = Nd4j.linspace(1, len2, len2, dataType).reshape(ArrayUtil.toLongArray(shape4d2)); - INDArray tad2 = orig2.javaTensorAlongDimension(1, 3, 4, 2, 1); + 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, dataType).reshape(ArrayUtil.toLongArray(shape4d3)); - INDArray tad3 = orig3.javaTensorAlongDimension(1, 4, 1, 3, 0); + 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, dataType).reshape(ArrayUtil.toLongArray(shape4d4)); - INDArray tad4 = orig4.javaTensorAlongDimension(1, 2, 0, 3, 1); + INDArray tad4 = orig4.tensorAlongDimension(1, 2, 0, 3, 1); list.add(new Pair<>(tad4, baseMsg + ".get(4)")); return list; @@ -655,26 +655,26 @@ public class NDArrayCreationUtil { Nd4j.getRandom().setSeed(seed); int[] shape4d1 = {3, shape[0], shape[1], shape[2], shape[3], shape[4]}; INDArray orig1a = Nd4j.rand(dataType, shape4d1); - INDArray tad1a = orig1a.javaTensorAlongDimension(0, 1, 2, 3, 4, 5); + INDArray tad1a = orig1a.tensorAlongDimension(0, 1, 2, 3, 4, 5); INDArray orig1b = Nd4j.rand(dataType, shape4d1); - INDArray tad1b = orig1b.javaTensorAlongDimension(2, 1, 2, 3, 4, 5); + 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(dataType, shape4d2); - INDArray tad2 = orig2.javaTensorAlongDimension(1, 3, 5, 4, 2, 1); + 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(dataType, shape4d3); - INDArray tad3 = orig3.javaTensorAlongDimension(1, 4, 1, 3, 2, 0); + 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(dataType, shape4d4); - INDArray tad4 = orig4.javaTensorAlongDimension(1, 5, 2, 0, 3, 1); + INDArray tad4 = orig4.tensorAlongDimension(1, 5, 2, 0, 3, 1); list.add(new Pair<>(tad4, baseMsg + ".get(4)")); return list; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java index 2ae7a32c4..a56d39567 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java @@ -374,7 +374,7 @@ public class DataSet implements org.nd4j.linalg.dataset.api.DataSet { long nTensors = labels.tensorsAlongDimension(1); for (int i = 0; i < nTensors; i++) { INDArray row = labels.tensorAlongDimension(i, 1); - INDArray javaRow = labels.javaTensorAlongDimension(i, 1); + INDArray javaRow = labels.tensorAlongDimension(i, 1); int maxIdx = Nd4j.getBlasWrapper().iamax(row); int maxIdxJava = Nd4j.getBlasWrapper().iamax(javaRow); if (maxIdx < 0) diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java index 915d6f650..22b911468 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java @@ -1334,7 +1334,7 @@ public class Nd4jTestsC extends BaseNd4jTest { INDArray zC = Nd4j.create(shape, 'c'); zC.setData(Nd4j.linspace(1, 24, 24, DataType.DOUBLE).data()); for (int tad = 0; tad < zC.tensorsAlongDimension(dim); tad++) { - INDArray javaTad = zC.javaTensorAlongDimension(tad, dim); + INDArray javaTad = zC.tensorAlongDimension(tad, dim); System.out.println("Tad " + tad + " is " + zC.tensorAlongDimension(tad, dim)); } diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/tad/TestTensorAlongDimension.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/tad/TestTensorAlongDimension.java index 755fd4d20..cede0543a 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/tad/TestTensorAlongDimension.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/tad/TestTensorAlongDimension.java @@ -57,7 +57,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < n; i++) { StopWatch javaTiming = new StopWatch(); javaTiming.start(); - row.javaTensorAlongDimension(0, 0); + row.tensorAlongDimension(0, 0); javaTiming.stop(); StopWatch cTiming = new StopWatch(); cTiming.start(); @@ -98,7 +98,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { assertEquals(cols, arr.tensorsAlongDimension(0)); for (int i = 0; i < cols; i++) { INDArray tad = arr.tensorAlongDimension(i, 0); - INDArray javaTad = arr.javaTensorAlongDimension(i, 0); + INDArray javaTad = arr.tensorAlongDimension(i, 0); assertEquals(javaTad, tad); assertArrayEquals(new int[] {rows}, tad.shape()); //assertEquals(testValues.javaTensorAlongDimension(i, 0), tad); @@ -120,7 +120,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { list = NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, new int[]{rows, cols, dim2}, DataType.DOUBLE); for (Pair p : list) { INDArray arr = p.getFirst().assign(testValues); - INDArray javaTad = arr.javaTensorAlongDimension(0, 0); + INDArray javaTad = arr.tensorAlongDimension(0, 0); INDArray tadTest = arr.tensorAlongDimension(0, 0); assertEquals(javaTad, tadTest); //Along dimension 0: expect row vector with length 'rows' @@ -165,7 +165,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { //Along dimension 0,1: expect matrix with shape [rows,cols] assertEquals(dim2, arr.tensorsAlongDimension(0, 1)); for (int i = 0; i < dim2; i++) { - INDArray javaTad = arr.javaTensorAlongDimension(i, 0, 1); + INDArray javaTad = arr.tensorAlongDimension(i, 0, 1); INDArray tad = arr.tensorAlongDimension(i, 0, 1); int javaEleStride = javaTad.elementWiseStride(); int testTad = tad.elementWiseStride(); @@ -178,11 +178,11 @@ public class TestTensorAlongDimension extends BaseNd4jTest { //Along dimension 0,2: expect matrix with shape [rows,dim2] assertEquals(cols, arr.tensorsAlongDimension(0, 2)); for (int i = 0; i < cols; i++) { - INDArray javaTad = arr.javaTensorAlongDimension(i, 0, 2); + INDArray javaTad = arr.tensorAlongDimension(i, 0, 2); INDArray tad = arr.tensorAlongDimension(i, 0, 2); assertEquals(javaTad, tad); assertArrayEquals(new long[] {rows, dim2}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 0, 2), tad); + assertEquals(testValues.tensorAlongDimension(i, 0, 2), tad); } //Along dimension 1,2: expect matrix with shape [cols,dim2] @@ -190,7 +190,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < rows; i++) { INDArray tad = arr.tensorAlongDimension(i, 1, 2); assertArrayEquals(new long[] {cols, dim2}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 1, 2), tad); + assertEquals(testValues.tensorAlongDimension(i, 1, 2), tad); } } @@ -207,7 +207,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < dim2 * dim3; i++) { INDArray tad = arr.tensorAlongDimension(i, 0, 1); assertArrayEquals(new long[] {rows, cols}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 0, 1), tad); + assertEquals(testValues.tensorAlongDimension(i, 0, 1), tad); } //Along dimension 0,2: expect matrix with shape [rows,dim2] @@ -215,7 +215,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < cols * dim3; i++) { INDArray tad = arr.tensorAlongDimension(i, 0, 2); assertArrayEquals(new long[] {rows, dim2}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 0, 2), tad); + assertEquals(testValues.tensorAlongDimension(i, 0, 2), tad); } //Along dimension 0,3: expect matrix with shape [rows,dim3] @@ -223,7 +223,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < cols * dim2; i++) { INDArray tad = arr.tensorAlongDimension(i, 0, 3); assertArrayEquals(new long[] {rows, dim3}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 0, 3), tad); + assertEquals(testValues.tensorAlongDimension(i, 0, 3), tad); } @@ -232,7 +232,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < rows * dim3; i++) { INDArray tad = arr.tensorAlongDimension(i, 1, 2); assertArrayEquals(new long[] {cols, dim2}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 1, 2), tad); + assertEquals(testValues.tensorAlongDimension(i, 1, 2), tad); } //Along dimension 1,3: expect matrix with shape [cols,dim3] @@ -240,7 +240,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < rows * dim2; i++) { INDArray tad = arr.tensorAlongDimension(i, 1, 3); assertArrayEquals(new long[] {cols, dim3}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 1, 3), tad); + assertEquals(testValues.tensorAlongDimension(i, 1, 3), tad); } //Along dimension 2,3: expect matrix with shape [dim2,dim3] @@ -248,7 +248,7 @@ public class TestTensorAlongDimension extends BaseNd4jTest { for (int i = 0; i < rows * cols; i++) { INDArray tad = arr.tensorAlongDimension(i, 2, 3); assertArrayEquals(new long[] {dim2, dim3}, tad.shape()); - assertEquals(testValues.javaTensorAlongDimension(i, 2, 3), tad); + assertEquals(testValues.tensorAlongDimension(i, 2, 3), tad); } } } diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTests.java index 49391b74c..52ede954a 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTests.java @@ -614,7 +614,7 @@ public class OpExecutionerTests extends BaseNd4jTest { assertEquals(arr5s.getDouble(i), 16, 1e-1); System.out.println("6d"); INDArray arr6 = Nd4j.ones(1, 1, 4, 4, 4, 4); - INDArray arr6Tad = arr6.javaTensorAlongDimension(0, 2, 3); + INDArray arr6Tad = arr6.tensorAlongDimension(0, 2, 3); INDArray arr6s = arr6.sum(2, 3); for (int i = 0; i < arr6s.length(); i++) assertEquals(arr6s.getDouble(i), 16, 1e-1); diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/TADTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/TADTests.java index f4d2bf80c..6f47d00da 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/TADTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/TADTests.java @@ -79,7 +79,7 @@ public class TADTests extends BaseNd4jTest { int[] shape = new int[] {e, x}; Arrays.sort(shape); - INDArray assertion = array.javaTensorAlongDimension(0, shape); + INDArray assertion = array.tensorAlongDimension(0, shape); INDArray test = array.tensorAlongDimension(0, shape); assertEquals(assertion, test); @@ -101,7 +101,7 @@ public class TADTests extends BaseNd4jTest { Arrays.sort(shape); log.info("About to do shape: " + Arrays.toString(shape) + " for array of shape " + array.shapeInfoToString()); - INDArray assertion = array.javaTensorAlongDimension(0, shape); + INDArray assertion = array.tensorAlongDimension(0, shape); INDArray test = array.tensorAlongDimension(0, shape); assertEquals(assertion, test); //assertEquals(assertion.shapeInfoDataBuffer(), test.shapeInfoDataBuffer()); @@ -121,8 +121,8 @@ public class TADTests extends BaseNd4jTest { public void testMysteriousCrash() { INDArray arrayF = Nd4j.create(new int[] {1, 1, 4, 4}, 'f'); INDArray arrayC = Nd4j.create(new int[] {1, 1, 4, 4}, 'c'); - INDArray javaCTad = arrayC.javaTensorAlongDimension(0, 2, 3); - INDArray javaFTad = arrayF.javaTensorAlongDimension(0, 2, 3); + INDArray javaCTad = arrayC.tensorAlongDimension(0, 2, 3); + INDArray javaFTad = arrayF.tensorAlongDimension(0, 2, 3); Pair tadBuffersF = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(arrayF, 2, 3); Pair tadBuffersC = diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/ConcatTestsC.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/ConcatTestsC.java index 596bf16a7..806cf4d08 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/ConcatTestsC.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/ConcatTestsC.java @@ -185,7 +185,7 @@ public class ConcatTestsC extends BaseNd4jTest { //ConcatV2, dim 1 second = Nd4j.linspace(24, 32, 8, Nd4j.dataType()).reshape('c', 2, 1, 4); for (int i = 0; i < second.tensorsAlongDimension(1); i++) { - INDArray secondTad = second.javaTensorAlongDimension(i, 1); + INDArray secondTad = second.tensorAlongDimension(i, 1); System.out.println(second.tensorAlongDimension(i, 1)); }