Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.nd4j.linalg.api.ndarray.BaseNDArray Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ndarray;
import org.nd4j.shade.guava.primitives.Ints;
import org.nd4j.shade.guava.primitives.Longs;
import com.google.flatbuffers.FlatBufferBuilder;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import net.ericaro.neoitertools.Generator;
import org.apache.commons.math3.util.FastMath;
import org.bytedeco.javacpp.BytePointer;
import org.nd4j.autodiff.samediff.serde.FlatBuffersMapper;
import org.nd4j.base.Preconditions;
import org.nd4j.graph.ByteOrder;
import org.nd4j.graph.FlatArray;
import org.nd4j.linalg.api.blas.BlasBufferUtil;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.buffer.*;
import org.nd4j.linalg.api.iter.FirstAxisIterator;
import org.nd4j.linalg.api.iter.NdIndexIterator;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ops.CustomOp;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.reduce.HashCode;
import org.nd4j.linalg.api.ops.impl.reduce.bool.All;
import org.nd4j.linalg.api.ops.impl.reduce.bool.Any;
import org.nd4j.linalg.api.ops.impl.reduce.floating.*;
import org.nd4j.linalg.api.ops.impl.reduce.same.*;
import org.nd4j.linalg.api.ops.impl.reduce3.EqualsWithEps;
import org.nd4j.linalg.api.ops.impl.reduce3.EuclideanDistance;
import org.nd4j.linalg.api.ops.impl.reduce3.ManhattanDistance;
import org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition;
import org.nd4j.linalg.api.ops.impl.reduce.same.Max;
import org.nd4j.linalg.api.ops.impl.reduce.same.Min;
import org.nd4j.linalg.api.ops.impl.broadcast.*;
import org.nd4j.linalg.api.ops.impl.controlflow.Where;
import org.nd4j.linalg.api.ops.impl.scalar.*;
import org.nd4j.linalg.api.ops.impl.scalar.comparison.*;
import org.nd4j.linalg.api.ops.impl.shape.Tile;
import org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation;
import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
import org.nd4j.linalg.api.ops.impl.transforms.custom.Assign;
import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform;
import org.nd4j.linalg.api.ops.impl.transforms.custom.EqualTo;
import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThan;
import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThan;
import org.nd4j.linalg.api.ops.impl.transforms.custom.NotEqualTo;
import org.nd4j.linalg.api.ops.impl.transforms.same.Negative;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.*;
import org.nd4j.linalg.api.ops.impl.transforms.comparison.*;
import org.nd4j.linalg.api.ops.performance.PerformanceTracker;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
import org.nd4j.linalg.exception.*;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.*;
import org.nd4j.linalg.indexing.conditions.Condition;
import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.memory.MemcpyDirection;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.string.NDArrayStrings;
import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.linalg.util.LinAlgExceptions;
import org.nd4j.linalg.util.NDArrayMath;
import org.nd4j.linalg.workspace.WorkspaceUtils;
import java.io.*;
import java.nio.IntBuffer;
import java.nio.LongBuffer;
import java.util.*;
import static org.nd4j.linalg.factory.Nd4j.*;
/**
* NDArray: (think numpy)
*
* A few things of note.
*
* An NDArray can have any number of dimensions.
*
* An NDArray is accessed via strides.
*
* Strides are how to index over
* a contiguous block of data.
*
* This block of data has 2 orders(as of right now):
* fortran and c
*
* @author Adam Gibson
*/
@Slf4j
public abstract class BaseNDArray implements INDArray, Iterable {
private static final long serialVersionUID = 3285982317165542614L;
protected transient volatile DataBuffer shapeInformation;
protected transient volatile DataBuffer data;
//protected transient DataBuffer shape;
//protected transient DataBuffer stride;
protected transient boolean compressed = false;
protected transient boolean released = false;
// this field holds jvm copy of shapeInfo
protected transient JvmShapeInfo jvmShapeInfo;
//Precalculate these arrays (like [3,2,1,0], [2,1,0], [1,0], [0] etc) for use in TAD, to avoid creating same int[]s over and over
private static final int[][] tadFinalPermuteDimensions;
static {
tadFinalPermuteDimensions = new int[32][0];
tadFinalPermuteDimensions[1] = new int[] {1, 0}; //Edge case for 1d tensors: selectively apply to column vectors
for (int i = 2; i < 32; i++) {
tadFinalPermuteDimensions[i] = new int[i];
for (int k = i - 1, j = 0; k >= 0; k--, j++)
tadFinalPermuteDimensions[i][j] = k;
}
val t =1;
}
public BaseNDArray() {
}
@Override
public boolean isCompressed() {
return compressed;
}
@Override
public void markAsCompressed(boolean reallyCompressed) {
this.compressed = reallyCompressed;
}
/**
*
* @param buffer
*/
public BaseNDArray(DataBuffer buffer) {
this.data = buffer;
if (buffer.length() >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Length of buffer can not be >= Integer.MAX_VALUE");
long[] shape = {1, (int) buffer.length()};
long[] stride = Nd4j.getStrides(shape);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride, 1, Nd4j.order(), buffer.dataType(), false));
init(shape, stride);
}
/**
*
* @param buffer
* @param shape
* @param stride
* @param offset
* @param ordering
*/
public BaseNDArray(DataBuffer buffer, int[] shape, int[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
this.data = offset > 0 ? Nd4j.createBuffer(buffer, offset, Shape.lengthOfBuffer(shape, stride)) : buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride),
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, buffer.dataType(), false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, long offset, char ordering) {
this(buffer, shape, stride, offset, Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering);
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, long offset, long ews, char ordering) {
Shape.assertValidOrder(ordering);
this.data = offset > 0 ? Nd4j.createBuffer(buffer, offset, Shape.lengthOfBuffer(shape, stride)) : buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride, ews, ordering, buffer.dataType(), false ));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, long offset, char ordering, DataType dataType) {
this(buffer, shape, stride, offset, Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, dataType);
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, long offset, long ews, char ordering, DataType dataType) {
this.data = offset > 0 ? Nd4j.createBuffer(buffer, offset, Shape.lengthOfBuffer(shape, stride)) : buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride, ews, ordering, dataType, false));
init(shape, stride);
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, char ordering, DataType type) {
this.data = buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, type, false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
}
public BaseNDArray(DataBuffer buffer, long[] shape, long[] stride, char ordering, DataType type, MemoryWorkspace workspace) {
this.data = buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, type, false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
}
public BaseNDArray(DataBuffer buffer, DataType dataType, long[] shape, long[] stride, long offset, char ordering) {
this.data = offset > 0 ? Nd4j.createBuffer(buffer, offset, Shape.lengthOfBuffer(shape, stride)) : buffer;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, dataType, false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
}
/**
* Initialize the ndarray as a matrix
* with the given data (indices preserved)
* @param data
*/
public BaseNDArray(double[][] data) {
this(data, Nd4j.order());
}
/**
*
* @param data
* @param ordering
*/
public BaseNDArray(double[][] data, char ordering) {
this(internalCreateBuffer(ordering == 'c' ? ArrayUtil.flatten(data) : ArrayUtil.flattenF(data)),
new int[] {data.length, data[0].length},
Nd4j.getStrides(new int[] {data.length, data[0].length}, ordering), 0, ordering);
int c = columns();
for (int r = 0; r < rows(); r++) {
Preconditions.checkState(data[r].length == c, "data[%s].length=%s must be equal to number of columns %s", r, data[r].length, c );
}
}
/**
* Create with the specified shape and buffer
*
* @param shape the shape
* @param buffer the buffer
*/
public BaseNDArray(int[] shape, DataBuffer buffer) {
this.data = buffer;
init(shape, Nd4j.getStrides(shape));
}
/**
* Create this ndarray with the given data and shape and 0 offset
*
* @param data the data to use
* @param shape the shape of the ndarray
*/
public BaseNDArray(float[] data, int[] shape, char ordering) {
this(data, shape, 0, ordering);
}
/**
* @param data the data to use
* @param shape the shape of the ndarray
* @param offset the desired offset
* @param ordering the ordering of the ndarray
*/
public BaseNDArray(float[] data, int[] shape, long offset, char ordering) {
this(data, shape, Nd4j.getStrides(shape, ordering), offset);
}
public BaseNDArray(double[] data, long[] shape, long offset, char ordering) {
this(data, shape, Nd4j.getStrides(shape, ordering), offset);
}
public BaseNDArray(float[] data, long[] shape, long offset, char ordering) {
this(data, shape, Nd4j.getStrides(shape, ordering), offset);
}
/**
* Construct an ndarray of the specified shape
* with an empty data array
*
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
* @param offset the desired offset
* @param ordering the ordering of the ndarray
*/
public BaseNDArray(int[] shape, int[] stride, long offset, char ordering) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape)), shape, stride, offset, ordering);
}
public BaseNDArray(long[] shape, long[] stride, long offset, char ordering) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape)), shape, stride, offset, ordering);
}
/**
* Construct an ndarray of the specified shape.
*
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
* @param offset the desired offset
* @param ordering the ordering of the ndarray
* @param initialize Whether to initialize the INDArray. If true: initialize. If false: don't.
*/
public BaseNDArray(int[] shape, int[] stride, long offset, char ordering, boolean initialize) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape), initialize), shape, stride, offset, ordering);
}
public BaseNDArray(long[] shape, long[] stride, long offset, char ordering, boolean initialize) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape), initialize), shape, stride, offset, ordering);
}
public BaseNDArray(DataType type, long[] shape, long[] stride, long offset, char ordering, boolean initialize) {
this(Nd4j.createBuffer(type, ArrayUtil.prodLong(shape), initialize), type, shape, stride, offset, ordering);
}
public BaseNDArray(DataType type, long[] shape, long[] stride, long offset, char ordering, boolean initialize, MemoryWorkspace workspace) {
this(Nd4j.createBuffer(type, ArrayUtil.prodLong(shape), initialize, workspace), type, shape, stride, offset, ordering);
}
/**
* Create the ndarray with
* the specified shape and stride and an offset of 0
*
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
* @param ordering the ordering of the ndarray
*/
public BaseNDArray(int[] shape, int[] stride, char ordering) {
this(shape, stride, 0, ordering);
}
/**
*
* @param shape
* @param offset
* @param ordering
*/
public BaseNDArray(int[] shape, long offset, char ordering) {
this(shape, Nd4j.getStrides(shape, ordering), offset, ordering);
}
public BaseNDArray(long[] shape, long offset, char ordering) {
this(shape, Nd4j.getStrides(shape, ordering), offset, ordering);
}
/**
* Create an ndarray
* with the given shape
* @param shape
*/
public BaseNDArray(int[] shape) {
this(shape, 0, Nd4j.order());
}
public BaseNDArray(long[] shape) {
this(shape, 0, Nd4j.order());
}
/**
* Creates a new n times m DoubleMatrix .
*
* @param newRows the number of rows (n ) of the new matrix.
* @param newColumns the number of columns (m ) of the new matrix.
*/
public BaseNDArray(int newRows, int newColumns, char ordering) {
Shape.assertValidOrder(ordering);
this.data = Nd4j.createBuffer((long) newRows * newColumns);
val shape = new long[] {newRows, newColumns};
val stride = Nd4j.getStrides(shape, ordering);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, Nd4j.dataType(), false));
init(shape, stride);
}
public BaseNDArray(long newRows, long newColumns, char ordering) {
Shape.assertValidOrder(ordering);
this.data = Nd4j.createBuffer((long) newRows * newColumns);
long[] shape = new long[] {newRows, newColumns};
long[] stride = Nd4j.getStrides(shape, ordering);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, Nd4j.dataType(), false));
init(shape, stride);
}
/**
* Create an ndarray from the specified slices.
* This will go through and merge all of the
* data from each slice in to one ndarray
* which will then take the specified shape
*
* @param slices the slices to merge
* @param shape the shape of the ndarray
*/
public BaseNDArray(List slices, int[] shape, char ordering) {
this(slices, shape, Nd4j.getStrides(shape, ordering), ordering);
}
public BaseNDArray(List slices, long[] shape, char ordering) {
this(slices, shape, Nd4j.getStrides(shape, ordering), ordering);
}
/**
* Create an ndarray from the specified slices.
* This will go through and merge all of the
* data from each slice in to one ndarray
* which will then take the specified shape
*
* @param slices the slices to merge
* @param shape the shape of the ndarray
*/
public BaseNDArray(List slices, int[] shape, int[] stride, char ordering) {
Shape.assertValidOrder(ordering);
DataBuffer ret = slices.get(0).data().dataType() == (DataType.FLOAT)
? Nd4j.createBuffer(new float[ArrayUtil.prod(shape)])
: Nd4j.createBuffer(new double[ArrayUtil.prod(shape)]);
this.data = ret;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride),
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, slices.get(0).dataType(), false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
if (slices.get(0).isScalar()) {
for (int i = 0; i < length(); i++) {
putScalar(i, slices.get(i).getDouble(0));
}
} else {
for (int i = 0; i < slices(); i++) {
putSlice(i, slices.get(i));
}
}
}
public BaseNDArray(List slices, long[] shape, long[] stride, char ordering) {
DataBuffer ret = Nd4j.createBuffer(slices.get(0).dataType(), Shape.lengthOf(shape), false); /*slices.get(0).data().dataType() == (DataType.FLOAT)
? Nd4j.createBuffer(new float[ArrayUtil.prod(shape)])
: Nd4j.createBuffer(new double[ArrayUtil.prod(shape)]);
*/
this.data = ret;
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, slices.get(0).dataType(), false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, ordering == 'f'));
if (slices.get(0).isScalar()) {
for (int i = 0; i < length(); i++) {
putScalar(i, slices.get(i).getDouble(0));
}
} else {
for (int i = 0; i < slices(); i++) {
putSlice(i, slices.get(i));
}
}
}
/**
*
* @param data
* @param shape
* @param stride
* @param ordering
*/
public BaseNDArray(float[] data, int[] shape, int[] stride, char ordering) {
this(data, shape, stride, 0, ordering);
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
* @param ordering
*/
public BaseNDArray(float[] data, int[] shape, int[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride),
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, DataType.FLOAT, data != null && data.length > 0 ? false : true));
if (data != null && data.length > 0) {
val perfD = PerformanceTracker.getInstance().helperStartTransaction();
this.data = internalCreateBuffer(data, offset);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfD, data.length * Nd4j.sizeOfDataType(DataType.FLOAT), MemcpyDirection.HOST_TO_HOST);
if (offset >= data.length)
throw new IllegalArgumentException("invalid offset: must be < data.length");
}
init(shape, stride);
}
public BaseNDArray(float[] data, long[] shape, long[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, DataType.FLOAT, data != null && data.length > 0 ? false : true));
if (data != null && data.length > 0) {
this.data = Nd4j.createTypedBuffer(data, DataType.FLOAT);
if (offset >= data.length)
throw new IllegalArgumentException("invalid offset: must be < data.length");
}
init(shape, stride);
}
public BaseNDArray(double[] data, long[] shape, long[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride,
Shape.elementWiseStride(shape, stride, ordering == 'f'), ordering, DataType.DOUBLE, data != null && data.length > 0 ? false : true));
if (data != null && data.length > 0) {
this.data = Nd4j.createBuffer(data, offset);
if (offset >= data.length)
throw new IllegalArgumentException("invalid offset: must be < data.length");
}
init(shape, stride);
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
*/
public BaseNDArray(DataBuffer data, int[] shape, int[] stride, long offset) {
this.data = Nd4j.createBuffer(data, offset, ArrayUtil.prodLong(shape));
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride),
Shape.elementWiseStride(shape, stride, Nd4j.order() == 'f'), Nd4j.order(), data.dataType(), false));
init(shape, stride);
// Shape.setElementWiseStride(this.shapeInfo(),Shape.elementWiseStride(shape, stride, Nd4j.order() == 'f'));
}
/**
*
* @param data
* @param shape
* @param strides
*/
public BaseNDArray(int[] data, int[] shape, int[] strides) {
this(internalCreateBuffer(data), shape, strides);
}
/**
*
* @param data
* @param shape
*/
public BaseNDArray(DataBuffer data, int[] shape) {
this(data, shape, Nd4j.getStrides(shape, Nd4j.order()), 0, Nd4j.order());
}
public BaseNDArray(DataBuffer data, long[] shape) {
this(data, shape, Nd4j.getStrides(shape, Nd4j.order()), 0, Nd4j.order());
}
/**
*
* @param buffer
* @param shape
* @param offset
*/
public BaseNDArray(DataBuffer buffer, int[] shape, long offset) {
this(Nd4j.createBuffer(buffer, offset, ArrayUtil.prodLong(shape)), shape, Nd4j.getStrides(shape), offset,
Nd4j.order());
}
/**
*
* @param buffer
* @param shape
* @param ordering
*/
public BaseNDArray(DataBuffer buffer, int[] shape, char ordering) {
this(buffer, shape, Nd4j.getStrides(shape, ordering), 0, ordering);
}
public BaseNDArray(DataBuffer buffer, long[] shape, char ordering) {
this(buffer, shape, Nd4j.getStrides(shape, ordering), 0, ordering);
}
/**
*
* @param data
* @param shape
* @param ordering
*/
public BaseNDArray(double[] data, int[] shape, char ordering) {
this(Nd4j.createBuffer(data), shape, ordering);
}
public BaseNDArray(double[] data, long[] shape, char ordering) {
this(Nd4j.createBuffer(data), shape, ordering);
}
public BaseNDArray(float[] data, long[] shape, char ordering) {
this(Nd4j.createBuffer(data), shape, ordering);
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
* @param ordering
*/
public BaseNDArray(double[] data, int[] shape, int[] stride, long offset, char ordering) {
this(internalCreateBuffer(data, offset), shape, stride, offset, ordering);
}
/**
*
* @param data
* @param order
*/
public BaseNDArray(float[] data, char order) {
this(internalCreateBuffer(data), order);
}
protected static DataBuffer internalCreateBuffer(float[] data) {
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
val buffer = Nd4j.createBuffer(data);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(buffer.dataType()), MemcpyDirection.HOST_TO_HOST);
return buffer;
}
protected static DataBuffer internalCreateBuffer(double[] data) {
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
val buffer = Nd4j.createBuffer(data);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(buffer.dataType()), MemcpyDirection.HOST_TO_HOST);
return buffer;
}
protected static DataBuffer internalCreateBuffer(int[] data) {
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
val buffer = Nd4j.createBuffer(data);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(buffer.dataType()), MemcpyDirection.HOST_TO_HOST);
return buffer;
}
protected static DataBuffer internalCreateBuffer(float[] data, long offset) {
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
val buffer = Nd4j.createBuffer(data, offset);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(buffer.dataType()), MemcpyDirection.HOST_TO_HOST);
return buffer;
}
protected static DataBuffer internalCreateBuffer(double[] data, long offset) {
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
val buffer = Nd4j.createBuffer(data, offset);
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, data.length * Nd4j.sizeOfDataType(buffer.dataType()), MemcpyDirection.HOST_TO_HOST);
return buffer;
}
/**
*
* @param floatBuffer
* @param order
*/
public BaseNDArray(DataBuffer floatBuffer, char order) {
this(floatBuffer, new int[] {(int) floatBuffer.length()},
Nd4j.getStrides(new int[] {(int) floatBuffer.length()}, order), 0, order);
Shape.assertValidOrder(order);
if (floatBuffer.length() >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Length of buffer can not be >= Integer.MAX_VALUE");
}
/**
*
* @param buffer
* @param shape
* @param strides
*/
public BaseNDArray(DataBuffer buffer, int[] shape, int[] strides) {
this(buffer, shape, strides, 0, Nd4j.order());
}
/**
* Create this ndarray with the given data and shape and 0 offset
*
* @param data the data to use
* @param shape the shape of the ndarray
*/
public BaseNDArray(float[] data, int[] shape) {
this(data, shape, 0);
}
/**
*
* @param data
* @param shape
* @param offset
*/
public BaseNDArray(float[] data, int[] shape, long offset) {
this(data, shape, offset, Nd4j.order());
}
/**
* Construct an ndarray of the specified shape
* with an empty data array
*
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
* @param offset the desired offset
*/
public BaseNDArray(int[] shape, int[] stride, long offset) {
this(new float[ArrayUtil.prod(shape)], shape, stride, offset, Nd4j.order());
}
public BaseNDArray(long[] shape, long[] stride, long offset) {
this(new float[ArrayUtil.prod(shape)], shape, stride, offset, Nd4j.order());
}
/**
* Create the ndarray with
* the specified shape and stride and an offset of 0
*
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
*/
public BaseNDArray(int[] shape, int[] stride) {
this(shape, stride, 0);
}
/**
*
* @param shape
* @param offset
*/
public BaseNDArray(int[] shape, long offset) {
this(shape, Nd4j.getStrides(shape), offset);
}
/**
*
* @param shape
* @param ordering
*/
public BaseNDArray(int[] shape, char ordering) {
this(shape, 0, ordering);
}
/**
* Creates a new n times m DoubleMatrix .
*
* @param newRows the number of rows (n ) of the new matrix.
* @param newColumns the number of columns (m ) of the new matrix.
*/
public BaseNDArray(int newRows, int newColumns) {
this(newRows, newColumns, Nd4j.order());
}
public BaseNDArray(long newRows, long newColumns) {
this(newRows, newColumns, Nd4j.order());
}
/**
* Create an ndarray from the specified slices.
* This will go through and merge all of the
* data from each slice in to one ndarray
* which will then take the specified shape
*
* @param slices the slices to merge
* @param shape the shape of the ndarray
*/
public BaseNDArray(List slices, int[] shape) {
this(slices, shape, Nd4j.order());
}
public BaseNDArray(List slices, long[] shape) {
this(slices, shape, Nd4j.order());
}
/**
* Create an ndarray from the specified slices.
* This will go through and merge all of the
* data from each slice in to one ndarray
* which will then take the specified shape
*
* @param slices the slices to merge
* @param shape the shape of the ndarray
*/
public BaseNDArray(List slices, int[] shape, int[] stride) {
this(slices, shape, stride, Nd4j.order());
}
public BaseNDArray(List slices, long[] shape, long[] stride) {
this(slices, shape, stride, Nd4j.order());
}
/**
*
* @param data
* @param shape
* @param stride
*/
public BaseNDArray(float[] data, int[] shape, int[] stride) {
this(data, shape, stride, Nd4j.order());
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
*/
public BaseNDArray(float[] data, int[] shape, int[] stride, long offset) {
this(data, shape, stride, offset, Nd4j.order());
}
public BaseNDArray(double[] data, long[] shape, long[] stride, long offset) {
this(data, shape, stride, offset, Nd4j.order());
}
public BaseNDArray(float[] data, long[] shape, long[] stride, long offset) {
this(data, shape, stride, offset, Nd4j.order());
}
/**
*
* @param data
*/
public BaseNDArray(float[] data) {
this(Nd4j.createBuffer(data));
}
/**
* Initialize the ndarray
* with the given data
* @param data
*/
public BaseNDArray(float[][] data) {
this(data, Nd4j.order());
}
/**
*
* @param data
* @param ordering
*/
public BaseNDArray(float[][] data, char ordering) {
this(internalCreateBuffer(ordering == 'c' ? ArrayUtil.flatten(data) : ArrayUtil.flattenF(data)),
new int[] {data.length, data[0].length},
Nd4j.getStrides(new int[] {data.length, data[0].length}, ordering), 0, ordering);
int c = columns();
for (int r = 0; r < rows(); r++) {
Preconditions.checkState(data[r].length == c, "data[%s].length=%s must be equal to number of columns %s", r, data[r].length, c );
}
}
/**
* Constructor for stride and offset
*
* @param buffer
* @param shape
* @param offset
* @param ordering
*/
public BaseNDArray(DataBuffer buffer, int[] shape, long offset, char ordering) {
this(buffer, shape, Nd4j.getStrides(shape, ordering), offset, ordering);
}
public BaseNDArray(double[] data, int[] shape, int[] stride, long offset) {
this(data, shape, stride, offset, Nd4j.order());
}
/**
* Returns whether the ndarray is valid or not
* @return true if the ndarray is valid
* false otherwise
*/
@Deprecated
public boolean isValid() {
try {
linearIndex(length() - 1);
} catch (Exception e) {
return false;
}
return true;
}
protected INDArray create(DataBuffer data, int[] shape, long offset) {
return Nd4j.create(data, shape, offset);
}
@Override
public int elementWiseStride() {
return Shape.elementWiseStride(shapeInfoDataBuffer());
}
@Override
public long tensorsAlongDimension(int... dimension) {
if (dimension == null || dimension.length == 0)
throw new IllegalArgumentException("Invalid input: dimensions not specified (null or length 0)");
if (dimension.length >= rank() || dimension.length == 1 && dimension[0] == Integer.MAX_VALUE)
return 1;
for (int i = 0; i < dimension.length; i++)
if (dimension[i] < 0)
dimension[i] += rank();
long[] tensorShape = ArrayUtil.keep(shape(), dimension);
long len = ArrayUtil.prodLong(tensorShape);
if (len == 0)
throw new IllegalStateException("Illegal length found after removing index");
long length = length();
if (length / len >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Tensors along dimension can not be >= Integer.MAX_VALUE");
return length / len;
}
@Override
public INDArray tensorAlongDimension(long index, int... dimension) {
if (dimension == null || dimension.length == 0)
throw new IllegalArgumentException("Invalid input: dimensions not specified (null or length 0)");
Preconditions.checkArgument(!this.isEmpty(), "tensorAlongDimension(...) can't be used on empty tensors");
if (dimension.length >= rank() || dimension.length == 1 && dimension[0] == Integer.MAX_VALUE)
return this;
for (int i = 0; i < dimension.length; i++)
if (dimension[i] < 0)
dimension[i] += rank();
//dedup
if (dimension.length > 1)
dimension = Ints.toArray(new ArrayList<>(new TreeSet<>(Ints.asList(dimension))));
if (dimension.length > 1) {
Arrays.sort(dimension);
}
long tads = tensorsAlongDimension(dimension);
if (index >= tads)
throw new IllegalArgumentException("Illegal index " + index + " out of tads " + tads);
if (dimension.length == 1) {
if (dimension[0] == 0 && isColumnVector()) {
return this.transpose();
} else if (dimension[0] == 1 && isRowVector()) {
return this;
}
}
Pair tadInfo =
Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(this, dimension);
DataBuffer shapeInfo = tadInfo.getFirst();
val shape = Shape.shape(shapeInfo);
val stride = Shape.stride(shapeInfo).asLong();
long offset = offset() + tadInfo.getSecond().getLong(index);
val ews = shapeInfo.getLong(shapeInfo.getLong(0) * 2 + 2);
char tadOrder = (char) shapeInfo.getInt(shapeInfo.getLong(0) * 2 + 3);
val toTad = Nd4j.create(data(), shape, stride, offset, ews, tadOrder);
return toTad;
}
private void setShapeInformation(Pair shapeInfo) {
this.shapeInformation = shapeInfo.getFirst();
this.jvmShapeInfo = new JvmShapeInfo(shapeInfo.getSecond());
}
private INDArray doTad(int index, int... dimension) {
if (dimension == null || dimension.length == 0)
throw new IllegalArgumentException("Invalid input: dimensions not specified (null or length 0)");
if (dimension.length >= rank())
return this;
for (int i = 0; i < dimension.length; i++)
if (dimension[i] < 0)
dimension[i] += rank();
if (dimension.length > 1)
Arrays.sort(dimension);
long tads = tensorsAlongDimension(dimension);
if (index >= tads)
throw new IllegalArgumentException("Illegal index " + index + " out of tads " + tads);
if (dimension.length == 1) {
if (dimension[0] == 0 && isColumnVector()) {
return this.transpose();
} else if (dimension[0] == 1 && isRowVector()) {
return this;
}
}
long[] tensorShape = ArrayUtil.keep(shape(), dimension);
int[] reverseDimensions = ArrayUtil.reverseCopy(dimension);
int[] remove = ArrayUtil.removeIndex(ArrayUtil.range(0, rank()), dimension);
int[] newPermuteDims = Ints.concat(remove, reverseDimensions);
int[] finalPermuteDims = tadFinalPermuteDimensions[dimension.length];
INDArray permuted = permute(newPermuteDims);
long sliceIdx = NDArrayMath.sliceOffsetForTensor(index, permuted, tensorShape);
INDArray ret2 = permuted.slice(sliceIdx);
if (dimension.length == tensorShape.length && ArrayUtil.prodLong(tensorShape) == ret2.length()) {
if (dimension.length == 1 && ret2.isRowVector())
return ret2;
if (finalPermuteDims.length != ret2.rank()) {
finalPermuteDims = new int[ret2.rank()];
int count = 0;
for (int i = finalPermuteDims.length - 1; i >= 0; i--)
finalPermuteDims[count++] = i;
}
return ret2.permutei(finalPermuteDims);
}
int length = ArrayUtil.prod(tensorShape);
int tensorLength = ArrayUtil.prod(tensorShape);
long offset = index * tensorLength / NDArrayMath.lengthPerSlice(ret2);
if (sliceIdx == 0 && length == NDArrayMath.lengthPerSlice(ret2)) {
// FIXME: LONG
ret2 = ret2.slice((int) offset);
if (dimension.length == 1 && ret2.isRowVectorOrScalar())
return ret2;
return ret2.permutei(finalPermuteDims);
}
else if (length == NDArrayMath.lengthPerSlice(ret2)) {
offset -= ret2.slices() * (offset / ret2.slices());
// FIXME: LONG
ret2 = ret2.slice((int) offset);
if (dimension.length == 1 && ret2.isRowVectorOrScalar())
return ret2;
return ret2.permutei(finalPermuteDims);
}
while (ret2.length() > length) {
sliceIdx = NDArrayMath.sliceOffsetForTensor(index, ret2, tensorShape);
sliceIdx -= ret2.slices() * (sliceIdx / ret2.slices());
ret2 = ret2.slice(sliceIdx);
}
if (dimension.length == 1 && ret2.isRowVectorOrScalar())
return ret2;
return ret2.permutei(finalPermuteDims);
}
@Override
public long vectorsAlongDimension(int dimension) {
if (dimension == 0 && isVector() || isRowVectorOrScalar())
return 1;
if (size(dimension) == 1 && !isVector()) {
for (int i = dimension; i < rank(); i++) {
if (size(i) != 1)
return vectorsAlongDimension(i);
}
return length();
} else if (size(0) == 1 && !isVectorOrScalar()) {
int realDimension = rank() - getLeadingOnes();
long length = length();
if (length / size(realDimension) >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Vectors along dimension can not be >= Integer.MAX_VALUE");
return length / size(realDimension);
}
long length = length();
if (dimension >= jvmShapeInfo.rank) {
if (length / size(jvmShapeInfo.rank - 1) >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Vectors along dimension can not be >= Integer.MAX_VALUE");
return (int) (length / size(jvmShapeInfo.rank - 1));
}
if (length / size(dimension) >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Vectors along dimension can not be >= Integer.MAX_VALUE");
return length / size(dimension);
}
@Override
public INDArray vectorAlongDimension(int index, int dimension) {
if (dimension < 0) {
dimension = jvmShapeInfo.getRank() + dimension;
}
//return the whole thing
if (dimension == jvmShapeInfo.getRank() - 1 && size(dimension) == 1 && rank() > 2
|| rank() > 2 && dimension == 0 && size(dimension) == 1) {
return this;
}
return tensorAlongDimension(index, dimension);
}
@Override
public void setOrder(char order) {
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(shape(), stride(), elementWiseStride(), order, this.dataType(), isEmpty()));
}
@Override
public void setShapeAndStride(int[] shape, int[] stride) {
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride), 0, ordering(), this.dataType(), false));
}
@Override
public INDArray cumsumi(int dimension) {
validateNumericalArray("cumsumi", true);
if(isScalar() || isEmpty())
return this;
if (isVector()) {
double s = 0.0;
for (int i = 0; i < length(); i++) {
s += getDouble(i);
putScalar(i, s);
}
} else if (dimension == Integer.MAX_VALUE) {
INDArray flattened = ravel();
double prevVal = flattened.getDouble(0);
for (int i = 1; i < flattened.length(); i++) {
double d = prevVal + flattened.getDouble(i);
flattened.putScalar(i, d);
prevVal = d;
}
return flattened;
} else {
for (int i = 0; i < vectorsAlongDimension(dimension); i++) {
INDArray vec = vectorAlongDimension(i, dimension);
vec.cumsumi(0);
}
}
return this;
}
@Override
public Number normmaxNumber() {
return normmax(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number norm2Number() {
return norm2(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number norm1Number() {
return norm1(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number stdNumber() {
return std(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number prodNumber() {
if(isScalar())
return getNumber(0);
return prod(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number meanNumber() {
validateNumericalArray("meanNumber", false);
if(isScalar())
return getNumber(0);
return mean(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number ameanNumber() {
return amean(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number varNumber() {
return var(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number maxNumber() {
if(isScalar())
return getNumber(0);
return max(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number amaxNumber() {
return amax(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number minNumber() {
if(isScalar())
return getNumber(0);
return min(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number aminNumber() {
return amin(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number scan(Condition condition) {
MatchCondition op = new MatchCondition(this, condition);
return Nd4j.getExecutioner().exec(op).getDouble(0);
}
@Override
public Number sumNumber() {
validateNumericalArray("sum", false);
if(isScalar())
return getNumber(0);
val scalar = sum(Integer.MAX_VALUE);
Nd4j.getExecutioner().commit();
return scalar.getDouble(0);
}
@Override
public Number entropyNumber() {
return entropy(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number shannonEntropyNumber() {
return shannonEntropy(Integer.MAX_VALUE).getDouble(0);
}
@Override
public Number logEntropyNumber() {
return logEntropy(Integer.MAX_VALUE).getDouble(0);
}
@Override
public INDArray cumsum(int dimension) {
validateNumericalArray("cumsum", true);
return dup().cumsumi(dimension);
}
@Override
public INDArray assign(final INDArray arr) {
Preconditions.checkState((this.isScalar() && arr.isScalar()) || (this.isVector() && arr.isVector()) || Shape.shapeEqualWithSqueeze(this.shape(), arr.shape()),
"Cannot assign arrays: arrays must both be scalars, both vectors, or shapes must be equal other than size 1 dimensions. Attempting to do x.assign(y)" +
" with x.shape=%ndShape and y.shape=%ndShape", this, arr );
Preconditions.checkArgument(this.length() == arr.length(), "Length of both arrays must be equal");
Nd4j.getExecutioner().exec(new org.nd4j.linalg.api.ops.impl.transforms.any.Assign(arr, this));
return this;
}
@Override
public INDArray putScalar(long i, double value) {
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
if (i < 0)
i += rank();
// TODO: i'm not sure that rank == 1 has fair shortcut here
if (isScalar()) {
autoProcessScalarCall();
data.put(i, value);
return this;
} else if (rank() == 1) {
data.put(i * stride(0), value);
return this;
}
// we cant raise rank here, if original rank is 1
if (isRowVector() && rank() == 2) {
return putScalar(0, i, value);
} else if (isColumnVector() && rank() == 2) {
return putScalar(i, 0, value);
}
long[] indexes = ordering() == 'c' ? Shape.ind2subC(this, i) : Shape.ind2sub(this, i);
return putScalar(indexes, value);
}
@Override
public INDArray putScalar(long i, float value) {
return putScalar(i, (double) value);
}
@Override
public INDArray putScalar(long i, int value) {
return putScalar(i, (double) value);
}
@Override
public INDArray putScalar(int[] indexes, double value) {
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
for (int i = 0; i < indexes.length; i++) {
if (indexes[i] < 0)
indexes[i] += this.size(i);
}
if (indexes.length == 1) {
return putScalar(indexes[0], value);
} else if (indexes.length == 2) {
return putScalar(indexes[0], indexes[1], value);
} else if (indexes.length == 3) {
return putScalar(indexes[0], indexes[1], indexes[2], value);
} else if (indexes.length == 4) {
return putScalar(indexes[0], indexes[1], indexes[2], indexes[3], value);
} else {
autoProcessScalarCall();
long offset = Shape.getOffset(jvmShapeInfo.javaShapeInformation, indexes);
data.put(offset, value);
}
return this;
}
@Override
public INDArray putScalar(long[] indexes, double value) {
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
for (int i = 0; i < indexes.length; i++) {
if (indexes[i] < 0)
indexes[i] += size(i);
}
if (indexes.length == 1) {
return putScalar(indexes[0], value);
} else if (indexes.length == 2) {
return putScalar(indexes[0], indexes[1], value);
} else if (indexes.length == 3) {
return putScalar(indexes[0], indexes[1], indexes[2], value);
} else if (indexes.length == 4) {
return putScalar(indexes[0], indexes[1], indexes[2], indexes[3], value);
} else {
autoProcessScalarCall();
long offset = Shape.getOffset(jvmShapeInfo.javaShapeInformation, indexes);
data.put(offset, value);
}
return this;
}
@Override
public INDArray putScalar(long[] indexes, float value) {
return putScalar(indexes, (double) value);
}
@Override
public INDArray putScalar(long row, long col, double value) {
Nd4j.getCompressor().autoDecompress(this);
autoProcessScalarCall();
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
if (rank() > 2)
throw new IllegalStateException("Cannot use putScalar(int,int,double) on a rank " + rank() + " INDArray");
long offset = Shape.getOffsetUnsafe(jvmShapeInfo.javaShapeInformation, row, col);
data.put(offset, value);
return this;
}
@Override
public INDArray putScalar(long dim0, long dim1, long dim2, double value) {
Nd4j.getCompressor().autoDecompress(this);
autoProcessScalarCall();
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
if (rank() != 3)
throw new IllegalStateException(
"Cannot use putScalar(int,int,int,double) on a rank " + rank() + " INDArray");
long offset = 0; // Shape.getOffsetUnsafe(javaShapeInformation, dim0, dim1, dim2);
long size_0 = jvmShapeInfo.javaShapeInformation[1];
long size_1 = jvmShapeInfo.javaShapeInformation[1 + 1];
long size_2 = jvmShapeInfo.javaShapeInformation[1 + 2];
if (size_0 != 1)
offset += dim0 * jvmShapeInfo.javaShapeInformation[1 + 0 + 3];
if (size_1 != 1)
offset += dim1 * jvmShapeInfo.javaShapeInformation[1 + 1 + 3];
if (size_2 != 1)
offset += dim2 * jvmShapeInfo.javaShapeInformation[1 + 2 + 3];
data.put(offset, value);
return this;
}
@Override
public INDArray putScalar(long dim0, long dim1, long dim2, long dim3, double value) {
Nd4j.getCompressor().autoDecompress(this);
autoProcessScalarCall();
Preconditions.checkArgument(dataType() != DataType.BOOL || value == 0.0 || value == 1.0, "Cannot put value %s into boolean array" +
" - only putScalar with values 0 or 1 is allowed on boolean arrays", value);
if (rank() != 4)
throw new IllegalStateException(
"Cannot use putScalar(int,int,int,int,double) on a rank " + rank() + " INDArray");
long offset = Shape.getOffsetUnsafe(jvmShapeInfo.javaShapeInformation, dim0, dim1, dim2, dim3);
data.put(offset, value);
return this;
}
@Override
public INDArray putScalar(int[] indexes, float value) {
return putScalar(indexes, (double) value);
}
@Override
public INDArray putScalar(int[] indexes, int value) {
return putScalar(indexes, (double) value);
}
@Override
public INDArray putScalar(long[] indexes, int value) {
return putScalar(indexes, (double) value);
}
@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));
}
@Override
public INDArray eps(INDArray other) {
validateNumericalArray("eps", true);
return Nd4j.getExecutioner().exec(new Eps(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())));
}
@Override
public INDArray lt(Number other) {
validateNumericalArray("less than (lt)", false);
return Nd4j.getExecutioner().exec(new ScalarLessThan(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other));
}
@Override
public INDArray lte(Number other) {
validateNumericalArray("less than or equals (lte)", false);
return Nd4j.getExecutioner().exec(new ScalarLessThanOrEqual(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other));
}
@Override
public INDArray eq(Number other) {
Preconditions.checkArgument(dataType() != DataType.BOOL || other.doubleValue() == 0.0 || other.doubleValue() == 1.0, "Scalar equality on boolean arrays can only be applied with values 0 or 1: got value %s",other);
return Nd4j.getExecutioner().exec(new ScalarEquals(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other));
}
@Override
public INDArray gt(Number other) {
validateNumericalArray("greater than (gt)", false);
return Nd4j.getExecutioner().exec(new ScalarGreaterThan(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other));
}
@Override
public INDArray gte(Number other) {
validateNumericalArray("greater than or equals (gte)", false);
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);
if (Shape.shapeEquals(this.shape(), other.shape())) {
return Nd4j.getExecutioner().exec(new LessThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
} else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return Nd4j.exec(new LessThan(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
} else
throw new IllegalArgumentException("Shapes must be broadcastable");
}
@Override
public INDArray neq(Number other) {
Preconditions.checkArgument(dataType() != DataType.BOOL || other.doubleValue() == 0.0 || other.doubleValue() == 1.0, "Scalar non-equality on boolean arrays can only be applied with values 0 or 1: got value %s",other);
Preconditions.checkState(!isEmpty(), "Cannot perform operation neq (not equal) on empty array");
return Nd4j.getExecutioner().exec(new ScalarNotEquals(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), other));
}
@Override
public INDArray neq(INDArray other) {
Preconditions.checkState(!isEmpty(), "Cannot perform operation neq (not equal) on empty array");
return Nd4j.getExecutioner().exec(new NotEqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
}
@Override
public INDArray eq(INDArray other) {
if (Shape.shapeEquals(this.shape(), other.shape())) {
return Nd4j.getExecutioner().exec(new EqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
} else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return Nd4j.exec(new EqualTo(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
} else
throw new IllegalArgumentException("Shapes must be broadcastable");
}
@Override
public INDArray gt(INDArray other) {
validateNumericalArray("greater than (gt)", false);
if (Shape.shapeEquals(this.shape(), other.shape())) {
return Nd4j.getExecutioner().exec(new GreaterThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
} else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return Nd4j.exec(new GreaterThan(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
} else
throw new IllegalArgumentException("Shapes must be broadcastable");
}
@Override
public INDArray isInfinite(){
validateNumericalArray("isInfinite", true);
if(isEmpty())
return Nd4j.empty(DataType.BOOL);
return Nd4j.getExecutioner().exec(new MatchConditionTransform(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), Conditions.isInfinite()));
}
@Override
public INDArray isNaN(){
validateNumericalArray("isNaN", true);
if(isEmpty())
return Nd4j.empty(DataType.BOOL);
return Nd4j.getExecutioner().exec(new MatchConditionTransform(this, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering()), Conditions.isNan()));
}
@Override
public INDArray neg() {
validateNumericalArray("negative (neg)", true);
if(isEmpty())
return this;
return Nd4j.getExecutioner().exec(new Negative(this, Nd4j.createUninitialized(this.dataType(), this.shape(), this.ordering())));
}
@Override
public INDArray negi() {
validateNumericalArray("negative (negi)", true);
if(isEmpty())
return this;
Nd4j.getExecutioner().exec(new Negative(this));
return this;
}
@Override
public INDArray rdiv(Number n, INDArray result) {
return rdivi(n, result);
}
@Override
public INDArray rdivi(Number n, INDArray result) {
validateNumericalArray("rdivi", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarReverseDivision(this, null, result, n));
return result;
}
@Override
public INDArray rsub(Number n, INDArray result) {
return rsubi(n, result);
}
@Override
public INDArray rsubi(Number n, INDArray result) {
validateNumericalArray("rsubi", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarReverseSubtraction(this, result, n));
return result;
}
@Override
public INDArray div(Number n, INDArray result) {
return divi(n, result);
}
@Override
public INDArray divi(Number n, INDArray result) {
validateNumericalArray("divi", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarDivision(this, null, result, n));
return result;
}
@Override
public INDArray mul(Number n, INDArray result) {
return muli(n, result);
}
@Override
public INDArray muli(Number n, INDArray result) {
validateNumericalArray("muli", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarMultiplication(this, null, result, n));
return result;
}
@Override
public INDArray sub(Number n, INDArray result) {
return subi(n, result);
}
@Override
public INDArray subi(Number n, INDArray result) {
validateNumericalArray("subi", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarSubtraction(this, null, result, n));
return result;
}
@Override
public INDArray add(Number n, INDArray result) {
return addi(n, result);
}
@Override
public INDArray addi(Number n, INDArray result) {
validateNumericalArray("addi", false);
if (Double.isNaN(n.doubleValue()))
n = Nd4j.EPS_THRESHOLD;
Nd4j.getExecutioner().exec(new ScalarAdd(this, null, result, n));
return result;
}
@Override
public INDArray getScalar(long row, long column) {
return getScalar(new long[] {row, column});
}
@Override
public INDArray dup() {
return dup(Nd4j.order());
}
@Override
public INDArray dup(char order) {
WorkspaceUtils.assertValidArray(this, "Cannot duplicate INDArray");
if (this.isCompressed() && this.ordering() == order) {
INDArray ret = Nd4j.createArrayFromShapeBuffer(data().dup(), this.shapeInfoDataBuffer());
ret.markAsCompressed(true);
return ret;
}
if(isEmpty())
return this;
Nd4j.getCompressor().autoDecompress(this);
// fixme: eventually it would be nice to have this in native code
if (isS()) {
val list = new ArrayList();
for (int e = 0; e < this.length(); e++)
list.add(this.getString(e));
return Nd4j.create(list, this.shape(), this.ordering());
}
val z = Nd4j.createUninitialized(this.dataType(), this.shape(), order);
z.assign(this);
return z;
}
/**
* Returns the elements at the specified indices
*
* @param indices the indices to getScalar
* @return the array with the specified elements
*/
@Override
public int getInt(int... indices) {
return (int) getDouble(indices);
}
@Override
public long getLong(long index) {
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkState(!isEmpty(), "Unable to get value from empty array");
if (index >= length()) {
throw new IllegalArgumentException("Unable to get linear index " + index + ": values is greater than length (" + length() + ")");
}
autoProcessScalarCall();
if (index == 0)
return data().getLong(index);
long[] dimensions = ordering() == 'c' ? Shape.ind2subC(this, index) : Shape.ind2sub(this, index);
Shape.assertShapeLessThan(dimensions, shape());
return getLong(dimensions);
}
@Override
public long getLong(long... indices) {
if(isScalar())
return data().getLong(0);
return Shape.getLong(this, indices);
}
/**
* Returns the elements at the specified indices
*
* @param indices the indices to get
* @return the array with the specified elements
*/
@Override
public double getDouble(int... indices) {
autoProcessScalarCall();
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkState(!isEmpty(), "Unable to get value from empty array");
for (int i = 0; i < indices.length; i++) {
if (indices[i] < 0)
indices[i] += rank();
}
if (indices.length == 1) {
if (rank() == 1)
return Shape.getDouble(this, indices[0]);
else if (isRowVector())
return Shape.getDouble(this, 0, indices[0]);
else if (isColumnVector())
return Shape.getDouble(this, indices[0], 0);
else if ((isScalar() || length() == 1) && indices[0] == 0)
return data().getDouble(0);
}
return Shape.getDouble(this, indices);
}
@Override
public double getDouble(long... indices) {
autoProcessScalarCall();
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkState(!isEmpty(), "Unable to get value from empty array");
for (int i = 0; i < indices.length; i++) {
if (indices[i] < 0)
indices[i] += rank();
}
if (indices.length == 1) {
if (rank() == 1)
return Shape.getDouble(this, indices[0]);
else if (isRowVector())
return Shape.getDouble(this, 0, indices[0]);
else if (isColumnVector())
return Shape.getDouble(this, indices[0], 0);
else if (isScalar() && indices[0] == 0)
return data().getDouble(0);
else
throw new IllegalStateException("Indexes length must be > 1 for non vectors and scalars");
}
return Shape.getDouble(this, indices);
}
/**
* Returns the elements at the specified indices
*
* @param indices the indices to get
* @return the array with the specified elements
*/
@Override
public float getFloat(int... indices) {
return (float) getDouble(indices);
}
@Override
public float getFloat(long... indices) {
return (float) getDouble(indices);
}
/**
* Test whether a matrix is scalar.
*/
@Override
public boolean isScalar() {
if (isEmpty())
return false;
if (jvmShapeInfo.rank == 0) {
return true;
} else if (jvmShapeInfo.rank > 2) {
return false;
} else if (jvmShapeInfo.rank == 1) {
return shape()[0] == 1;
} else if (jvmShapeInfo.rank == 2) {
return shape()[0] == 1 && shape()[1] == 1 || length() == 1;
}
else
return false;
}
@Override
public INDArray put(int[] indices, INDArray element) {
Nd4j.getCompressor().autoDecompress(this);
if (!element.isScalar())
throw new IllegalArgumentException("Unable to insert anything but a scalar");
if (isRowVector() && indices[0] == 0 && indices.length == 2) {
int ix = 0;
for (int i = 1; i < indices.length; i++)
ix += indices[i] * stride(i);
if (ix >= data.length())
throw new IllegalArgumentException("Illegal indices " + Arrays.toString(indices));
data.put(ix, element.getDouble(0));
} else {
int ix = 0;
for (int i = 0; i < indices.length; i++)
if (size(i) != 1)
ix += indices[i] * stride(i);
if (ix >= data.length())
throw new IllegalArgumentException("Illegal indices " + Arrays.toString(indices));
data.put(ix, element.getDouble(0));
}
return this;
}
@Override
public INDArray match(INDArray comp, Condition condition) {
return Nd4j.getExecutioner().exec(new MatchConditionTransform(this,comp,condition));
}
@Override
public INDArray match(Number comp, Condition condition) {
return Nd4j.getExecutioner().exec(new MatchConditionTransform(this,comp.doubleValue(), condition));
}
@Override
public INDArray getWhere(INDArray comp, Condition condition) {
return BooleanIndexing.chooseFrom(new INDArray[]{this,comp},condition);
}
@Override
public INDArray getWhere(Number comp, Condition condition) {
return BooleanIndexing.chooseFrom(new INDArray[]{this},Arrays.asList(comp.doubleValue()),Collections.emptyList(),condition);
}
@Override
public INDArray putWhere(INDArray comp, INDArray put, Condition condition) {
Nd4j.getCompressor().autoDecompress(this);
MatchConditionTransform matchCondition = new MatchConditionTransform(this,comp,condition);
Nd4j.getExecutioner().exec(matchCondition);
return putWhereWithMask(matchCondition.z(),put);
}
@Override
public INDArray putWhere(Number comp, INDArray put, Condition condition) {
return putWhere(Nd4j.scalar(comp),put,condition);
}
@Override
public INDArray putWhere(Number comp, Number put, Condition condition) {
return putWhere(Nd4j.scalar(comp),Nd4j.scalar(put),condition);
}
@Override
public INDArray putWhereWithMask(INDArray mask, INDArray put) {
INDArray output = dup();
Nd4j.getExecutioner().execAndReturn(new Where(new INDArray[]{mask,this,put},new INDArray[]{output}));
return output;
}
@Override
public INDArray putWhereWithMask(INDArray mask, Number put) {
return putWhereWithMask(mask,Nd4j.scalar(put));
}
@Override
public INDArray put(int i, int j, INDArray element) {
return put(new int[] {i, j}, element);
}
@Override
public INDArray put(int i, int j, Number element) {
return putScalar(new int[] {i, j}, element.doubleValue());
}
@Override
public INDArray putSlice(int slice, INDArray put) {
Nd4j.getCompressor().autoDecompress(this);
if (isScalar()) {
Preconditions.checkState(put.isScalar(), "Invalid dimension. Can only insert a scalar in to another scalar");
put(0, put.getScalar(0));
return this;
} else if (isVector()) {
Preconditions.checkState(put.isVectorOrScalar() && put.length() == length(),
"Invalid dimension on insertion. Can only insert scalars/vectors into other scalar/vectors");
if (put.isScalar())
putScalar(slice, put.getDouble(0));
else
for (int i = 0; i < length(); i++)
putScalar(i, put.getDouble(i));
return this;
}
assertSlice(put, slice);
INDArray view = slice(slice);
if (put.length() == 1) {
putScalar(slice, put.getDouble(0));
} else {
if(!(view.isVector() && put.isVector() && view.length() == put.length()) && !view.equalShapes(put)){
throw new IllegalStateException("Cannot put slice: array to be put (" + Arrays.toString(put.shape()) +
") and slice array (" + Arrays.toString(view.shape()) + ") have different shapes");
}
view.assign(put);
}
return this;
}
protected void assertSlice(INDArray put, long slice) {
Preconditions.checkArgument(slice < slices(), "Invalid slice specified: slice %s must be in range 0 (inclusive) to numSlices=%s (exclusive)", slice, slices());
long[] sliceShape = put.shape();
if (Shape.isRowVectorShape(sliceShape)) {
return;
} else {
long[] requiredShape = ArrayUtil.removeIndex(shape(), 0);
//no need to compare for scalar; primarily due to shapes either being [1] or length 0
if (put.isScalar())
return;
if (isVector() && put.isVector() && put.length() < length())
return;
//edge case for column vectors
if (Shape.isColumnVectorShape(sliceShape))
return;
if (!Shape.shapeEquals(sliceShape, requiredShape) && !Shape.isRowVectorShape(requiredShape)
&& !Shape.isRowVectorShape(sliceShape))
throw new IllegalStateException(String.format("Invalid shape size of %s . Should have been %s ",
Arrays.toString(sliceShape), Arrays.toString(requiredShape)));
}
}
/**
* Returns true if this ndarray is 2d
*
* @return true if the element is a matrix, false otherwise
*/
public boolean isMatrix() {
return rank() == 2;
}
protected INDArray newShape(long[] newShape, char ordering) {
return Nd4j.create(data(), newShape, stride(), 0, ordering);
}
protected INDArray create(DataBuffer data, int[] newShape, int[] newStrides, long offset, char ordering) {
return Nd4j.create(data, newShape, newStrides, offset, ordering);
}
protected INDArray create(DataBuffer data, long[] newShape, long[] newStrides, long offset, char ordering) {
return Nd4j.create(data, newShape, newStrides, offset, ordering);
}
protected INDArray create(DataBuffer data, int[] newShape, int[] newStrides, long offset) {
return Nd4j.create(data, newShape, newStrides, offset);
}
protected INDArray create(int[] shape) {
return Nd4j.create(shape, getStrides(shape, Nd4j.order()), 0);
}
protected INDArray create(int[] shape, int[] strides, long offset) {
return Nd4j.create(shape, strides, offset);
}
protected int[] getStrides(int[] shape, char ordering) {
return Nd4j.getStrides(shape, ordering);
}
@Override
public double squaredDistance(INDArray other) {
validateNumericalArray("squaredDistance", false);
double d2 = distance2(other);
return d2 * d2;
}
@Override
public double distance2(INDArray other) {
validateNumericalArray("distance2", false);
Nd4j.getCompressor().autoDecompress(this);
return Nd4j.getExecutioner().execAndReturn(new EuclideanDistance(this, other)).getFinalResult().doubleValue();
}
@Override
public double distance1(INDArray other) {
validateNumericalArray("distance1", false);
Nd4j.getCompressor().autoDecompress(this);
return Nd4j.getExecutioner().execAndReturn(new ManhattanDistance(this, other)).getFinalResult().doubleValue();
}
@Override
public INDArray get(INDArray indices) {
if(indices.rank() > 2) {
throw new ND4JIllegalArgumentException("Indices must be a vector or matrix.");
}
if(indices.rows() == rank()) {
INDArray ret = Nd4j.create(indices.dataType(), indices.columns());
for(int i = 0; i < indices.columns(); i++) {
int[] specifiedIndex = indices.getColumn(i).dup().data().asInt();
val v = getDouble(specifiedIndex);
ret.putScalar(i, v);
}
return ret;
}
else {
List arrList = new ArrayList<>();
if(indices.isMatrix() || indices.isColumnVector()
|| (indices.isScalar() && indices.rank() == 2)) { // we need this for compatibility with legacy code
for(int i = 0; i < indices.rows(); i++) {
if(i == 0) {
INDArray row = indices.getRow(i);
for(int j = 0; j < row.length(); j++) {
arrList.add(slice(row.getInt(j)));
}
}
else {
INDArray row = indices.slice(i);
for(int j = 0; j < row.length(); j++) {
INDArray put = arrList.get(j).slice(row.getInt(j));
put = put.reshape(Longs.concat(new long[]{1},put.shape()));
arrList.set(j,put);
}
}
}
}
else if(indices.isRowVector()) {
for(int i = 0; i < indices.length(); i++) {
INDArray add = slice(indices.getInt(i));
add = add.reshape(Longs.concat(new long[] {1,},add.shape()));
arrList.add(add);
}
}
return Nd4j.concat(0,arrList.toArray(new INDArray[arrList.size()]));
}
}
@Override
public INDArray put(INDArray indices, INDArray element) {
if(indices.rank() > 2) {
throw new ND4JIllegalArgumentException("Indices must be a vector or matrix.");
}
if(indices.rows() == rank()) {
NdIndexIterator ndIndexIterator = new NdIndexIterator(element.shape());
for(int i = 0; i < indices.columns(); i++) {
int[] specifiedIndex = indices.getColumn(i).dup().data().asInt();
putScalar(specifiedIndex,element.getDouble(ndIndexIterator.next()));
}
}
else {
List arrList = new ArrayList<>();
if(indices.isMatrix() || indices.isColumnVector()) {
for(int i = 0; i < indices.rows(); i++) {
INDArray row = indices.getRow(i);
for(int j = 0; j < row.length(); j++) {
INDArray slice = slice(row.getInt(j));
Nd4j.getExecutioner().execAndReturn(new Assign(new INDArray[]{slice,element},new INDArray[]{slice}));
arrList.add(slice(row.getInt(j)));
}
}
}
else if(indices.isRowVector()) {
for(int i = 0; i < indices.length(); i++) {
arrList.add(slice(indices.getInt(i)));
}
}
}
return this;
}
@Override
public INDArray put(INDArrayIndex[] indices, INDArray element) {
Nd4j.getCompressor().autoDecompress(this);
boolean isSpecifiedIndex = false;
for(INDArrayIndex idx : indices){
if(idx instanceof SpecifiedIndex){
isSpecifiedIndex = true;
break;
}
}
if(!isSpecifiedIndex){
return get(indices).assign(element);
} else {
//Can't get a view, so we'll do it in subsets instead
// This is inefficient, but it is correct...
int numSpecified = 0;
List specifiedIdxs = new ArrayList<>();
List specifiedIdxDims = new ArrayList<>();
INDArrayIndex[] destinationIndices = indices.clone(); //Shallow clone
INDArrayIndex[] sourceIndices = indices.clone();
for( int i=0; i can't use point(1) on [1,x,y]
sourceIndices[i] = NDArrayIndex.point(0);
}
}
int[] counts = new int[specifiedIdxs.size()];
int[] dims = new int[specifiedIdxDims.size()];
for( int i=0; i 0
|| (length() < data().length() && data.dataType() != DataType.INT)
|| data().originalDataBuffer() != null;
}
@Override
public boolean isSparse() {
return false;
}
@Override
public DataBuffer data() {
return data;
}
@Override
public void setData(DataBuffer data) {
this.data = data;
}
/**
* Number of slices: aka shape[0]
*
* @return the number of slices
* for this nd array
*/
@Override
public long slices() {
return size(0);
}
protected INDArray create(DataBuffer buffer) {
return Nd4j.create(buffer);
}
@Override
public INDArray cond(Condition condition) {
if(isEmpty())
return Nd4j.empty(DataType.BOOL);
INDArray ret = Nd4j.createUninitialized(DataType.BOOL, this.shape());
Nd4j.getExecutioner().exec(new MatchConditionTransform(this,ret, condition));
return ret;
}
protected void init(int[] shape, int[] stride) {
//null character
if (shapeInformation == null || jvmShapeInfo == null || ordering() == '\u0000') {
//Shape.setOrder(shapeInfo(), Nd4j.order());
val si = Nd4j.getShapeInfoProvider().createShapeInformation(ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride), 1, Nd4j.order(), this.dataType(), false);
setShapeInformation(si);
}
}
protected void init(long[] shape, long[] stride) {
//null character
if (shapeInformation == null || jvmShapeInfo == null || ordering() == '\u0000') {
val si = Nd4j.getShapeInfoProvider().createShapeInformation(shape,stride, 1, Nd4j.order(), this.dataType(), false);
setShapeInformation(si);
}
}
@Override
public INDArray getScalar(long i) {
if (i >= this.length())
throw new ND4JIllegalStateException("Index can't be greater then array length");
if (i < 0)
i += this.length();
long idx = this.isScalar() ? 0 : Shape.getOffset(jvmShapeInfo.javaShapeInformation, Shape.ind2subC(this.shape(), i));
val buffer = Nd4j.createBuffer( this.data(), this.data().originalOffset() + idx, 1);
val shape = Nd4j.getShapeInfoProvider().createShapeInformation(new long[0], new long[0],1,'c', dataType(), false);
return Nd4j.createArrayFromShapeBuffer(buffer, shape);
}
/**
* Do a row wise op (a,s,m,d)
* a : add
* s : subtract
* m : multiply
* d : divide
* h : reverse subtraction
* t : reverse division
*
* @param columnVector the column vector
* @param operation the operation
* @return
*/
protected INDArray doColumnWise(INDArray columnVector, char operation) {
Nd4j.getCompressor().autoDecompress(this);
if(columnVector.isScalar()) {
switch (operation) {
case 'a':
addi(columnVector.getDouble(0));
break;
case 'p':
assign(columnVector.getDouble(0));
break;
case 's':
subi(columnVector.getDouble(0));
break;
case 'm':
muli(columnVector.getDouble(0));
break;
case 'd':
divi(columnVector.getDouble(0));
break;
case 'h':
rsubi(columnVector.getDouble(0));
break;
case 't':
rdivi(columnVector.getDouble(0));
break;
}
return this;
}
else if(isScalar()) {
switch (operation) {
case 'a':
return columnVector.addi(getDouble(0));
case 'p':
return columnVector.assign(getDouble(0));
case 's':
return columnVector.subi(getDouble(0));
case 'm':
return columnVector.muli(getDouble(0));
case 'd':
return columnVector.divi(getDouble(0));
case 'h':
return columnVector.rsubi(getDouble(0));
case 't':
return columnVector.rdivi(getDouble(0));
}
}
//Input validation: require (a) columnVector to actually be a column vector, and (b) this.size(0) to match columnVector.size(0)
//Or, simply require it to be a rank 1 vector
if ((!columnVector.isColumnVector() && columnVector.rank() > 1) || this.size(0) != columnVector.size(0) || columnVector.length() <= 1) {
throw new IllegalStateException("Mismatched shapes (shape = " + Arrays.toString(shape())
+ ", column vector shape =" + Arrays.toString(columnVector.shape()) + ")");
}
if (columnVector.data().sameUnderlyingData(data()))
return doColumnWise(columnVector.dup(), operation);
if (equalShapes(columnVector)) {
switch (operation) {
case 'a':
addi(columnVector);
break;
case 'p':
assign(columnVector);
break;
case 's':
subi(columnVector);
break;
case 'm':
muli(columnVector);
break;
case 'd':
divi(columnVector);
break;
case 'h':
rsubi(columnVector);
break;
case 't':
rdivi(columnVector);
break;
}
return this;
}
if (rows() == 1 && columnVector.isScalar()) {
applyScalarOp(columnVector, operation);
} else {
// special optimization case, broadcast turns into ScalarOp Along Dimension
if (rank() == 2 && elementWiseStride() == 1 && ordering() == 'c' && columnVector.elementWiseStride() == 1) {
switch (operation) {
case 'a': {
ScalarAdd op = new ScalarAdd(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 'p': {
ScalarSet op = new ScalarSet(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 's': {
ScalarSubtraction op = new ScalarSubtraction(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 'm': {
ScalarMultiplication op =
new ScalarMultiplication(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 'd': {
ScalarDivision op = new ScalarDivision(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 'h': {
ScalarReverseSubtraction op =
new ScalarReverseSubtraction(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
case 't': {
ScalarReverseDivision op =
new ScalarReverseDivision(this, columnVector, this, 0.0);
op.setDimension(1);
Nd4j.getExecutioner().exec(op);
break;
}
}
} else {
applyBroadcastOp(columnVector, operation);
}
}
return this;
}
/**
* Do a row wise op (a,s,m,d)
* a : add
* s : subtract
* m : multiply
* d : divide
* h : reverse subtraction
* t : reverse division
*
* @param rowVector the row vector
* @param operation the operation
* @return
*/
protected INDArray doRowWise(INDArray rowVector, final char operation) {
Nd4j.getCompressor().autoDecompress(this);
if(rowVector.isScalar()) {
switch (operation) {
case 'a':
addi(rowVector.getDouble(0));
break;
case 'p':
assign(rowVector.getDouble(0));
break;
case 's':
subi(rowVector.getDouble(0));
break;
case 'm':
muli(rowVector.getDouble(0));
break;
case 'd':
divi(rowVector.getDouble(0));
break;
case 'h':
rsubi(rowVector.getDouble(0));
break;
case 't':
rdivi(rowVector.getDouble(0));
break;
}
return this;
}
else if(isScalar()) {
switch (operation) {
case 'a':
return rowVector.addi(getDouble(0));
case 'p':
return rowVector.assign(getDouble(0));
case 's':
return rowVector.subi(getDouble(0));
case 'm':
return rowVector.muli(getDouble(0));
case 'd':
return rowVector.divi(getDouble(0));
case 'h':
return rowVector.rsubi(getDouble(0));
case 't':
return rowVector.rdivi(getDouble(0));
}
}
//Input validation: require (a) rowVector to actually be a row vector, and (b) this.size(1) to match rowVector.size(1)
if (!rowVector.isRowVector() || this.rank() > 1 && rowVector.rank() > 1 && this.size(1) != rowVector.size(1) || rowVector.length() <= 1) {
throw new IllegalStateException("Mismatched shapes (shape = " + Arrays.toString(shape())
+ ", row vector shape =" + Arrays.toString(rowVector.shape()) + ")");
}
if (rowVector.data().sameUnderlyingData(data()))
return doRowWise(rowVector.dup(), operation);
if (isVector()) {
switch (operation) {
case 'a':
addi(rowVector);
break;
case 'p':
assign(rowVector);
break;
case 's':
subi(rowVector);
break;
case 'm':
muli(rowVector);
break;
case 'd':
divi(rowVector);
break;
case 'h':
rsubi(rowVector);
break;
case 't':
rdivi(rowVector);
break;
}
return this;
}
if (rank() == 2 && columns() == 1 && rowVector.isScalar()) {
applyScalarOp(rowVector, operation);
} else {
// special optimization case, broadcast turns into ScalarOp Along Dimension
if (rank() == 2 && elementWiseStride() == 1 && ordering() == 'f' && rowVector.elementWiseStride() == 1) {
switch (operation) {
case 'a': {
ScalarAdd op = new ScalarAdd(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 'p': {
ScalarSet op = new ScalarSet(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 's': {
ScalarSubtraction op = new ScalarSubtraction(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 'm': {
ScalarMultiplication op = new ScalarMultiplication(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 'd': {
ScalarDivision op = new ScalarDivision(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 'h': {
ScalarReverseSubtraction op =
new ScalarReverseSubtraction(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
case 't': {
ScalarReverseDivision op = new ScalarReverseDivision(this, rowVector, this, 0.0);
op.setDimension(0);
Nd4j.getExecutioner().exec(op);
break;
}
}
} else {
applyBroadcastOp(rowVector, operation);
}
}
return this;
}
private void applyBroadcastOp(INDArray vector, final char operation) {
Nd4j.getCompressor().autoDecompress(this);
int alongDimension = Shape.isRowVectorShape(vector.shape()) ? 1 : 0;
// FIXME: probably this is wrong, because strict equality is always false in current DataBuffer mechanics
if (this.data() == vector.data())
vector = vector.dup();
switch (operation) {
case 'a':
Nd4j.getExecutioner().exec(new BroadcastAddOp(this, vector, this, alongDimension));
return;
case 's':
Nd4j.getExecutioner().exec(new BroadcastSubOp(this, vector, this, alongDimension));
return;
case 'm':
Nd4j.getExecutioner().exec(new BroadcastMulOp(this, vector, this, alongDimension));
return;
case 'd':
Nd4j.getExecutioner().exec(new BroadcastDivOp(this, vector, this, alongDimension));
return;
case 'h':
Nd4j.getExecutioner().exec(new BroadcastRSubOp(this, vector, this, alongDimension));
return;
case 't':
Nd4j.getExecutioner().exec(new BroadcastRDivOp(this, vector, this, alongDimension));
return;
case 'p':
Nd4j.getExecutioner().exec(new BroadcastCopyOp(this, vector, this, alongDimension));
return;
default:
throw new UnsupportedOperationException("Unknown operation: " + operation);
}
}
private void applyScalarOp(INDArray vector, char operation) {
Nd4j.getCompressor().autoDecompress(this);
switch (operation) {
case 'a':
addi(vector.getDouble(0));
break;
case 's':
subi(vector.getDouble(0));
break;
case 'm':
muli(vector.getDouble(0));
break;
case 'd':
divi(vector.getDouble(0));
break;
case 'h':
rsubi(vector.getDouble(0));
break;
case 't':
rdivi(vector.getDouble(0));
break;
}
}
protected DataBuffer shapeOf() {
// if (shape == null)
// shape = Shape.shapeOf(shapeInfoDataBuffer());
// return shape;
return Shape.shapeOf(shapeInfoDataBuffer());
}
protected DataBuffer strideOf() {
// if (stride == null)
// stride = Shape.stride(shapeInfoDataBuffer());
// return stride;
return Shape.stride(shapeInfoDataBuffer());
}
@Override
public int stride(int dimension) {
int rank = jvmShapeInfo.rank;
Preconditions.checkArgument(dimension < rank, "Cannot get stride for dimension %s from rank %s array: " +
"dimension indices must be in range -rank <= dimension < rank", dimension, rank);
if (dimension < 0)
return (int) stride()[dimension + rank];
return (int) stride()[dimension];
}
@Override
public INDArray rdiviColumnVector(INDArray columnVector) {
validateNumericalArray("rdiviColumnVector", false);
return doColumnWise(columnVector, 't');
}
@Override
public INDArray rdivColumnVector(INDArray columnVector) {
validateNumericalArray("rdivColumnVector", false);
return dup().rdiviColumnVector(columnVector);
}
@Override
public INDArray rdiviRowVector(INDArray rowVector) {
validateNumericalArray("rdiviRowVector", false);
return doRowWise(rowVector, 't');
}
@Override
public INDArray rdivRowVector(INDArray rowVector) {
validateNumericalArray("rdivRowVector", false);
return dup().rdiviRowVector(rowVector);
}
@Override
public INDArray rsubiColumnVector(INDArray columnVector) {
validateNumericalArray("rsubiColumnVector", false);
return doColumnWise(columnVector, 'h');
}
@Override
public INDArray rsubColumnVector(INDArray columnVector) {
validateNumericalArray("rsubColumnVector", false);
return dup().rsubiColumnVector(columnVector);
}
@Override
public INDArray rsubiRowVector(INDArray rowVector) {
validateNumericalArray("rsubiRowVector", false);
return doRowWise(rowVector, 'h');
}
@Override
public INDArray rsubRowVector(INDArray rowVector) {
validateNumericalArray("rsubRowVector", false);
return dup().rsubiRowVector(rowVector);
}
@Override
public INDArray put(int i, INDArray element) {
Preconditions.checkArgument(element.isScalar(), "Element must be a scalar: element has shape %ndShape", element);
return putScalar(i, element.getDouble(0));
}
@Override
public INDArray diviColumnVector(INDArray columnVector) {
validateNumericalArray("diviColumnVector", false);
return doColumnWise(columnVector, 'd');
}
@Override
public INDArray divColumnVector(INDArray columnVector) {
validateNumericalArray("divColumnVector", false);
return dup().diviColumnVector(columnVector);
}
@Override
public INDArray diviRowVector(INDArray rowVector) {
validateNumericalArray("diviRowVector", false);
return doRowWise(rowVector, 'd');
}
@Override
public INDArray divRowVector(INDArray rowVector) {
validateNumericalArray("divRowVector", false);
return dup().diviRowVector(rowVector);
}
@Override
public INDArray muliColumnVector(INDArray columnVector) {
validateNumericalArray("muliColumnVector", false);
return doColumnWise(columnVector, 'm');
}
@Override
public INDArray mulColumnVector(INDArray columnVector) {
validateNumericalArray("mulColumnVector", false);
return dup().muliColumnVector(columnVector);
}
@Override
public INDArray muliRowVector(INDArray rowVector) {
validateNumericalArray("muliRowVector", false);
return doRowWise(rowVector, 'm');
}
@Override
public INDArray mulRowVector(INDArray rowVector) {
validateNumericalArray("mulRowVector", false);
return dup().muliRowVector(rowVector);
}
@Override
public INDArray subiColumnVector(INDArray columnVector) {
validateNumericalArray("subiColumnVector", false);
return doColumnWise(columnVector, 's');
}
@Override
public INDArray subColumnVector(INDArray columnVector) {
validateNumericalArray("subColumnVector", false);
return dup().subiColumnVector(columnVector);
}
@Override
public INDArray subiRowVector(INDArray rowVector) {
validateNumericalArray("subiRowVector", false);
return doRowWise(rowVector, 's');
}
@Override
public INDArray subRowVector(INDArray rowVector) {
validateNumericalArray("subRowVector", false);
return dup().subiRowVector(rowVector);
}
@Override
public INDArray addiColumnVector(INDArray columnVector) {
validateNumericalArray("addiColumnVector", false);
return doColumnWise(columnVector, 'a');
}
@Override
public INDArray putiColumnVector(INDArray columnVector) {
return doColumnWise(columnVector, 'p');
}
@Override
public INDArray addColumnVector(INDArray columnVector) {
validateNumericalArray("addColumnVector", false);
return dup().addiColumnVector(columnVector);
}
@Override
public INDArray addiRowVector(INDArray rowVector) {
validateNumericalArray("addiRowVector", false);
return doRowWise(rowVector, 'a');
}
@Override
public INDArray putiRowVector(INDArray rowVector) {
validateNumericalArray("putiRowVector", false);
return doRowWise(rowVector, 'p');
}
@Override
public INDArray addRowVector(INDArray rowVector) {
validateNumericalArray("addRowVector", false);
return dup().addiRowVector(rowVector);
}
@Override
public INDArray mmul(INDArray other, INDArray result, MMulTranspose mMulTranspose) {
return mMulTranspose.exec(this, other, result);
}
@Override
public INDArray mmul(INDArray other, MMulTranspose mMulTranspose) {
return mMulTranspose.exec(this, other, null);
}
@Override
public INDArray mmul(INDArray other) {
Preconditions.checkState(this.dataType() == other.dataType(), "Matrix multiplication: arrays must have same dtype: %s vs. %s", this.dataType(), other.dataType());
// FIXME: for 1D case, we probably want vector output here?
long[] shape = {rows(), other.rank() == 1 ? 1 : other.columns()};
INDArray result = createUninitialized(this.dataType(), shape, 'f');
if (result.isScalar())
return Nd4j.scalar(this.dataType(), Nd4j.getBlasWrapper().dot(this, other)).reshape(1, 1);
return mmuli(other, result);
}
protected INDArray create(int[] shape, char ordering) {
return Nd4j.create(shape, ordering);
}
@Override
public double[][] toDoubleMatrix() {
if(!isMatrix()) {
throw new ND4JIllegalStateException("Unable to create a 2d array from a non matrix! Shape: " + Shape.shapeToStringShort(this));
}
if (this.size(0) > Integer.MAX_VALUE || this.size(1) > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
double[][] ret = new double[rows()][columns()];
for(int i = 0; i < ret.length; i++) {
ret[i] = getRow(i).dup().data().asDouble();
}
return ret;
}
@Override
public double[] toDoubleVector() {
if(!isVectorOrScalar()) {
throw new ND4JIllegalStateException("Unable to create a 1d array from a non vector! Shape: " + Shape.shapeToStringShort(this));
}
return dup().data().asDouble();
}
@Override
public float[] toFloatVector() {
if(!isVectorOrScalar()) {
throw new ND4JIllegalStateException("Unable to create a 1d array from a non vector! Shape: " + Shape.shapeToStringShort(this));
}
return dup().data().asFloat();
}
@Override
public float[][] toFloatMatrix() {
if(!isMatrix()) {
throw new ND4JIllegalStateException("Unable to create a 2d array from a non matrix! Shape: " + Shape.shapeToStringShort(this));
}
if (this.rows() > Integer.MAX_VALUE || this.columns() > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
float[][] ret = new float[(int) rows()][ (int) columns()];
for(int i = 0; i < ret.length; i++) {
ret[i] = getRow(i).dup().data().asFloat();
}
return ret;
}
@Override
public int[] toIntVector() {
if (isEmpty())
return new int[0];
if(!isVectorOrScalar()) {
throw new ND4JIllegalStateException("Unable to create a 1d array from a non vector! Shape: " + Shape.shapeToStringShort(this));
}
if(isView() || elementWiseStride() != 1){
return dup().data().asInt();
}
return data().asInt();
}
@Override
public long[] toLongVector() {
if(!isVectorOrScalar()) {
throw new ND4JIllegalStateException("Unable to create a 1d array from a non vector! Shape: " + Shape.shapeToStringShort(this));
}
if(isView() || elementWiseStride() != 1){
return dup().data().asLong();
}
return data().asLong();
}
@Override
public long[][] toLongMatrix() {
if(!isMatrix()) {
throw new ND4JIllegalStateException("Unable to create a 2d array from a non matrix! Shape: " + Shape.shapeToStringShort(this));
}
if (this.rows() > Integer.MAX_VALUE || this.columns() > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
long[][] ret = new long[(int) rows()][(int) columns()];
for(int i = 0; i < ret.length; i++) {
ret[i] = getRow(i).dup().data().asLong();
}
return ret;
}
@Override
public int[][] toIntMatrix() {
if(!isMatrix()) {
throw new ND4JIllegalStateException("Unable to create a 2d array from a non matrix! Shape: " + Shape.shapeToStringShort(this));
}
if (this.rows() > Integer.MAX_VALUE || this.columns() > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
int[][] ret = new int[(int) rows()][(int) columns()];
for(int i = 0; i < ret.length; i++) {
ret[i] = getRow(i).dup().data().asInt();
}
return ret;
}
/**
* Perform an copy matrix multiplication
*
* @param other the other matrix to perform matrix multiply with
* @param result the result ndarray
* @return the result of the matrix multiplication
*/
@Override
public INDArray mmul(INDArray other, INDArray result) {
return mmuli(other, result);
}
@Override
public INDArray div(INDArray other) {
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return divi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
return divi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), this.shape(), this.ordering()));
}
}
@Override
public INDArray div(INDArray other, INDArray result) {
validateNumericalArray("div", true);
return divi(other, result);
}
@Override
public INDArray mul(INDArray other) {
validateNumericalArray("mul", false);
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return muli(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
val z = Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), this.shape(), this.ordering());
return muli(other, z);
}
}
@Override
public INDArray mul(INDArray other, INDArray result) {
return muli(other, result);
}
@Override
public INDArray sub(INDArray other) {
validateNumericalArray("sub", false);
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return subi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
return subi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), this.shape(), this.ordering()));
}
}
@Override
public INDArray sub(INDArray other, INDArray result) {
return subi(other, result);
}
@Override
public INDArray add(INDArray other) {
validateNumericalArray("add", false);
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return addi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
return addi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), this.shape(), this.ordering()));
}
}
@Override
public INDArray add(INDArray other, INDArray result) {
validateNumericalArray("add", false);
return addi(other, result);
}
@Override
public INDArray mmuli(INDArray other, MMulTranspose transpose) {
validateNumericalArray("mmuli", false);
return dup().mmuli(other, this,transpose);
}
@Override
public INDArray mmuli(INDArray other) {
validateNumericalArray("mmuli", false);
return dup().mmuli(other, this);
}
@Override
public INDArray mmuli(INDArray other, INDArray result, MMulTranspose transpose) {
return transpose.exec(this, other, result);
}
@Override
public INDArray mmuli(INDArray other, INDArray result) {
validateNumericalArray("mmuli", false);
LinAlgExceptions.assertMultiplies(this, other);
if(other.rank() == 1){
//GEMV edge case
Preconditions.checkState(result.length() == this.size(0) && this.size(1) == other.size(0),
"Invalid matrix multiplication: %ndShape x %ndShape with result shape %ndShape", this, other, result);
} else {
//Standard case
Preconditions.checkState(
result.rank() == 2 && result.size(0) == this.size(0) && result.size(1) == other.size(1),
"Invalid result array shape: expected shape [%s,%s], got shape %ndShape result array for %ndShape x %ndShape", this.size(0), other.size(1), result,
this, other);
}
if (other.isScalar()) {
return muli(other.getDouble(0), result);
}
if (isScalar()) {
return other.muli(getDouble(0), result);
}
/* check sizes and resize if necessary */
if (result == this || result == other) {
/* actually, blas cannot do multiplications in-place. Therefore, we will fake by
* allocating a temporary object on the side and copy the result later.
*/
INDArray temp = Nd4j.create(result.dataType(), result.shape(), Nd4j.getStrides(result.shape(), 'f'), 'f');
if (other.columns() == 1 || other.rank() == 1) {
Nd4j.getBlasWrapper().level2().gemv(BlasBufferUtil.getCharForTranspose(result),
BlasBufferUtil.getCharForTranspose(this), 1.0, this, other, 0.0, temp);
}
else {
Nd4j.getBlasWrapper().level3().gemm(BlasBufferUtil.getCharForTranspose(result),
BlasBufferUtil.getCharForTranspose(this), BlasBufferUtil.getCharForTranspose(temp), 1.0,
this, other, 0.0, temp);
}
result.assign(temp);
} else {
//We require that the result array is 'f' (fortran) order
// However, user might have called mmuli with a c order array for the result
// In which case, we need to allocate a temporary f order array, and later do an assign to the real result array
boolean requiresTemp = result.ordering() != 'f' || result.isView() || !Shape.hasDefaultStridesForShape(result);
INDArray gemmResultArr;
if (requiresTemp) {
//Can use createUninitialized due to beta==0.0 parameter in gemm
gemmResultArr = Nd4j.createUninitialized(result.dataType(), result.shape(), 'f');
} else {
gemmResultArr = result;
}
if (other.columns() == 1 || other.rank() == 1) {
Nd4j.getBlasWrapper().level2().gemv(
ordering(),
BlasBufferUtil.getCharForTranspose(other),
1.0,
this,
other,
0.0,
gemmResultArr);
} else {
//gemm doesn't support strides so vectors and views
//don't work
Nd4j.getBlasWrapper().level3().gemm(ordering(),
BlasBufferUtil.getCharForTranspose(other),
BlasBufferUtil.getCharForTranspose(gemmResultArr),
1.0,
this,
other,
0.0,
gemmResultArr);
}
if (requiresTemp) {
result.assign(gemmResultArr);
}
}
// 1D edge case: reshape back to vector
if (other.rank() == 1)
result = result.reshape(result.length());
return result;
}
private INDArray create(int[] shape, int[] stride) {
return Nd4j.create(shape, stride);
}
@Override
public INDArray divi(INDArray other) {
return divi(other, this);
}
@Override
public INDArray divi(INDArray other, INDArray result) {
validateNumericalArray("divi", false);
Shape.assertBroadcastable("divi", this, other, result);
Nd4j.exec(new DivOp(this, other, result));
return result;
}
@Override
public INDArray muli(INDArray other) {
return muli(other, this);
}
@Override
public INDArray muli(INDArray other, INDArray result) {
validateNumericalArray("muli", false);
Shape.assertBroadcastable("muli", this, other, result);
Nd4j.exec(new MulOp(this, other, result));
return result;
}
@Override
public INDArray subi(INDArray other) {
return subi(other, this);
}
/**
* in place subtraction of two matrices
*
* @param other the second ndarray to subtract
* @param result the result ndarray
* @return the result of the subtraction
*/
@Override
public INDArray subi(INDArray other, INDArray result) {
validateNumericalArray("subi", false);
Shape.assertBroadcastable("subi", this, other, result);
Nd4j.exec(new SubOp(this, other, result));
return result;
}
@Override
public INDArray addi(INDArray other) {
return addi(other, this);
}
@Override
public INDArray addi(INDArray other, INDArray result) {
validateNumericalArray("addi", false);
Shape.assertBroadcastable("addi", this, other, result);
Nd4j.exec(new AddOp(this, other, result));
return result;
}
@Override
public INDArray normmax(boolean keepDims, int... dimension) {
validateNumericalArray("normmax", false);
return Nd4j.getExecutioner().exec(new NormMax(this, keepDims, dimension));
}
@Override
public INDArray normmax(int... dimension) {
return normmax(false, dimension);
}
@Override
public INDArray rdiv(INDArray other) {
validateNumericalArray("rdiv", false);
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return rdivi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
return rdivi(other, this.ulike());
}
}
@Override
public INDArray rdivi(INDArray other) {
return rdivi(other, this);
}
@Override
public INDArray rdiv(INDArray other, INDArray result) {
validateNumericalArray("rdiv", false);
return dup().rdivi(other, result);
}
@Override
public INDArray rdivi(INDArray other, INDArray result) {
validateNumericalArray("rdivi", false);
Shape.assertBroadcastable("rdivi", this, other, result);
Nd4j.exec(new RDivOp(this, other, result));
return result;
}
@Override
public INDArray rsub(INDArray other, INDArray result) {
validateNumericalArray("rsub", false);
return rsubi(other, result);
}
@Override
public INDArray rsub(INDArray other) {
validateNumericalArray("rsub", false);
if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
return rsubi(other, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), other.dataType()), Shape.broadcastOutputShape(this.shape(), other.shape()), this.ordering()));
} else {
return rsubi(other, this.ulike());
}
}
@Override
public INDArray rsubi(INDArray other) {
return rsubi(other, this);
}
@Override
public INDArray rsubi(INDArray other, INDArray result) {
validateNumericalArray("rsubi", false);
Shape.assertBroadcastable("rsubi", this, other, result);
Nd4j.exec(new RSubOp(this, other, result));
return result;
}
@Override
public INDArray assign(Number value) {
Preconditions.checkState(dataType() != DataType.BOOL || value.doubleValue() == 0.0 || value.doubleValue() == 1.0, "Only values 0 or 1 are allowed for scalar " +
"assign on boolean arrays: got value %s on to assign to boolean array with shape %ndShape", value, this);
Nd4j.getExecutioner().exec(new ScalarSet(this, value));
return this;
}
@Override
public INDArray assign(boolean value) {
return assign(value ? 1 : 0);
}
@Override
public INDArray assignIf(INDArray arr, Condition condition) {
BooleanIndexing.assignIf(this, arr, condition);
return this;
}
@Override
public INDArray replaceWhere(INDArray arr, Condition condition) {
Nd4j.getCompressor().autoDecompress(this);
BooleanIndexing.replaceWhere(this, arr, condition);
return this;
}
@Override
@Deprecated //TODO: Investigate. Not deprecated in the base interface.
public long linearIndex(long i) {
long idx = i;
for (int j = 0; j < jvmShapeInfo.rank - 1; j++) {
if (size((int) i) == 1)
continue;
idx += i * stride(j);
}
return Shape.offset(jvmShapeInfo.javaShapeInformation) + (idx);
}
/**
* Returns the specified slice of this matrix.
* In matlab, this would be equivalent to (given a 2 x 2 x 2):
* A(:,:,x) where x is the slice you want to return.
*
* The slice is always relative to the final dimension of the matrix.
*
* @param slice the slice to return
* @return the specified slice of this matrix
*/
@Override
public INDArray slice(long slice) {
Nd4j.getCompressor().autoDecompress(this);
long slices = slices();
if (slice >= slices)
throw new IllegalArgumentException("Illegal slice " + slice);
if (jvmShapeInfo.rank == 0 ) {
throw new IllegalArgumentException("Can't slice a 0-d NDArray");
}
if (slice < 0)
slice += rank();
INDArrayIndex[] indexes = new INDArrayIndex[rank()];
indexes[0] = NDArrayIndex.point(slice);
for (int i = 1; i < rank(); i++) {
indexes[i] = NDArrayIndex.all();
}
return get(indexes);
}
protected INDArray createScalarForIndex(long i, boolean applyOffset) {
if(isVector())
return getScalar(i);
return Nd4j.create(data(), new long[] {1, 1}, new long[] {1, 1}, i);
}
protected INDArray createScalar(double d) {
return Nd4j.scalar(d);
}
@Override
public int getTrailingOnes() {
int numLeadingOnes = 0;
for (int i = rank() - 1; i > 0; i--) {
if (size(i) == 1)
numLeadingOnes++;
}
return numLeadingOnes;
}
@Override
public int getLeadingOnes() {
int numLeadingOnes = 0;
for (int i = 0; i < rank(); i++) {
if (size(i) == 1)
numLeadingOnes++;
}
return numLeadingOnes;
}
/**
* Returns the slice of this from the specified dimension
*
* @param slice the dimension to return from
* @param dimension the dimension of the slice to return
* @return the slice of this matrix from the specified dimension
* and dimension
*/
@Override
public INDArray slice(long slice, int dimension) {
Nd4j.getCompressor().autoDecompress(this);
long slices = size(dimension);
if (slice >= slices)
throw new IllegalArgumentException("Illegal slice " + slice);
if (jvmShapeInfo.rank == 0) {
if (slice == 0)
return createScalarForIndex(slice, true);
else
throw new IllegalArgumentException("Can't slice a 0-d NDArray");
}
if (slice < 0)
slice += rank();
INDArrayIndex[] indexes = new INDArrayIndex[rank()];
indexes[dimension] = NDArrayIndex.point(slice);
for (int i = 0; i < rank(); i++) {
if (i != dimension)
indexes[i] = NDArrayIndex.all();
}
return get(indexes);
}
/**
* Fetch a particular number on a multi dimensional scale.
*
* @param indexes the indexes to get a number from
* @return the number at the specified indices
*/
@Override
public INDArray getScalar(int[] indexes) {
if (indexes.length > rank())
throw new ND4JIllegalStateException("Indexes can't be longer then array rank");
for (int i = 0; i < indexes.length; i++) {
if (indexes[i] < 0)
indexes[i] += this.size(i);
}
long idx = Shape.getOffset(jvmShapeInfo.javaShapeInformation, indexes);
val buffer = Nd4j.createBuffer(this.data(), idx, 1);
val shape = Nd4j.getShapeInfoProvider().createShapeInformation(new long[0], new long[0],1, 'c', this.dataType(), false);
return Nd4j.createArrayFromShapeBuffer(buffer, shape);
}
@Override
public INDArray getScalar(long... indexes) {
if (indexes.length > rank())
throw new ND4JIllegalStateException("Indexes can't be longer then array rank");
for (int i = 0; i < indexes.length; i++) {
if (indexes[i] < 0)
indexes[i] += this.size(i);
}
long idx = Shape.getOffset(jvmShapeInfo.javaShapeInformation, indexes);
val buffer = Nd4j.createBuffer(this.data(), idx, 1);
val shape = Nd4j.getShapeInfoProvider().createShapeInformation(new long[0], new long[0],1,'c', this.dataType(), false);
return Nd4j.createArrayFromShapeBuffer(buffer, shape);
}
@Override
public INDArray rdiv(Number n) {
//return dup().rdivi(n);
return rdivi(n, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), n), this.shape(), this.ordering()));
}
@Override
public INDArray rdivi(Number n) {
return rdivi(n, this);
}
@Override
public INDArray rsub(Number n) {
validateNumericalArray("rsub", false);
return rsubi(n, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), n),this.shape(), this.ordering()));
}
@Override
public INDArray rsubi(Number n) {
return rsubi(n, this);
}
@Override
public INDArray div(Number n) {
validateNumericalArray("div", false);
return divi(n, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), n),this.shape(), this.ordering()));
}
@Override
public INDArray divi(Number n) {
return divi(n, this);
}
@Override
public INDArray mul(Number n) {
validateNumericalArray("mul", false);
return muli(n, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), n), this.shape(), this.ordering()));
}
@Override
public INDArray muli(Number n) {
return muli(n, this);
}
@Override
public INDArray sub(Number n) {
validateNumericalArray("sub", false);
return subi(n, Nd4j.createUninitialized(this.dataType(), this.shape(), this.ordering()));
}
@Override
public INDArray subi(Number n) {
return subi(n, this);
}
@Override
public INDArray add(Number n) {
validateNumericalArray("add", false);
return addi(n, Nd4j.createUninitialized(Shape.pickPairwiseDataType(this.dataType(), n),this.shape(), this.ordering()));
}
@Override
public INDArray addi(Number n) {
return addi(n, this);
}
@Override
public INDArray repmat(int[] shape) {
Nd4j.getCompressor().autoDecompress(this);
long rows = rows() * shape[0];
long cols = columns() * shape[1];
INDArray ret = reshape(1, length()).repeat(0, shape[0]).reshape(rows, columns()).repeat(0, shape[1]);
return ret.reshape(rows, cols);
}
@Override
public INDArray repeat(int dimension, long... repeats) {
Nd4j.getCompressor().autoDecompress(this);
CustomOp op = DynamicCustomOp.builder("repeat")
.addInputs(this)
.addIntegerArguments(ArrayUtil.toInts(repeats)) //TODO int cast
.build();
op.addIArgument(dimension); //Native op: last iarg is dimension
LongShapeDescriptor l = op.calculateOutputShape().get(0);
INDArray out = Nd4j.create(l);
op.addOutputArgument(out);
Nd4j.exec(op);
return out;
}
@Override
public INDArray putRow(long row, INDArray toPut) {
if (isRowVector() && toPut.isVector()) {
return assign(toPut);
}
return put(new INDArrayIndex[] {NDArrayIndex.point(row), NDArrayIndex.all()}, toPut);
}
@Override
public INDArray putColumn(int column, INDArray toPut) {
Nd4j.getCompressor().autoDecompress(this);
if (isColumnVector() && toPut.isVector()) {
return assign(toPut);
}
return put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(column)}, toPut);
}
@Override
public Number getNumber(long i){
switch (dataType()){
case DOUBLE:
case FLOAT:
case HALF:
return getDouble(i);
case LONG:
case INT:
case SHORT:
case UBYTE:
case BYTE:
case BOOL:
return getLong(i);
case UTF8:
case COMPRESSED:
case UNKNOWN:
default:
throw new UnsupportedOperationException("Cannot get number from array of datatype: " + dataType());
}
}
@Override
public Number getNumber(long... idx){
switch (dataType()){
case DOUBLE:
case FLOAT:
case HALF:
return getDouble(idx);
case LONG:
case INT:
case SHORT:
case UBYTE:
case BYTE:
case BOOL:
return getLong(idx);
case UTF8:
case COMPRESSED:
case UNKNOWN:
default:
throw new UnsupportedOperationException("Cannot get number from array of datatype: " + dataType());
}
}
@Override
public double getDouble(long i) {
Nd4j.getCompressor().autoDecompress(this);
Preconditions.checkState(!isEmpty(), "Unable to get value from empty array");
if (i >= length()) {
throw new IllegalArgumentException("Unable to get linear index " + i + ": values is greater than length (" + length() + ")");
}
autoProcessScalarCall();
if (i == 0)
return data().getDouble(i);
long[] dimensions = ordering() == 'c' ? Shape.ind2subC(this, i) : Shape.ind2sub(this, i);
Shape.assertShapeLessThan(dimensions, shape());
return getDouble(dimensions);
}
@Override
public double getDouble(long i, long j) {
return getDouble(new long[] {i, j});
}
@Override
public float getFloat(long i) {
return (float) getDouble(i);
}
@Override
public float getFloat(long i, long j) {
return (float) getDouble(i, j);
}
/**
* Return transposed copy of this matrix.
*/
@Override
public INDArray transpose() {
Preconditions.checkState(rank() >= 2, "Can't transpose array with rank < 2: array shape %ndShape", this);
return permute(ArrayUtil.reverseCopy(ArrayUtil.range(0, rank())));
}
/**
*
* Return transposed version of this matrix.
*
* PLEASE NOTE: This method is NOT in place, it will return transposed copy instead.
*/
@Override
public INDArray transposei() {
Preconditions.checkState(rank() >= 2, "Can't transpose array with rank < 2: array shape %ndShape", this);
return permutei(ArrayUtil.reverseCopy(ArrayUtil.range(0, rank())));
}
protected INDArray create(DataBuffer data, int[] shape, int[] strides) {
return Nd4j.create(data, shape, strides, 0, ordering());
}
@Override
public INDArray reshape(char order, int... newShape) {
// FIXME: int cast
return reshape(order, ArrayUtil.toLongArray(newShape));
}
@Override
public INDArray reshape(char order, long... newShape) {
return reshape(order, false, newShape);
}
@Override
public INDArray reshape(char order, boolean enforceView, long... newShape){
Nd4j.getCompressor().autoDecompress(this);
// special case for empty reshape
if (this.length() == 1 && (newShape == null || newShape.length == 0) && this.elementWiseStride() == 1) {
return Nd4j.create(this.data(), new int[0], new int[0], 0);
}
if (newShape == null || newShape.length < 1)
throw new ND4JIllegalStateException(
"Can't reshape(long...) without shape arguments. Got empty shape instead.");
// TODO: maybe toFlatten() makes more sense here?
// reshape(-1) special case
if (newShape.length == 1 && newShape[0] == -1)
newShape[0] = this.length();
int numberNegativesOnes = 0;
long[] shape = ArrayUtil.copy(newShape);
for (int i = 0; i < shape.length; i++) {
if (shape[i] < 0) {
if (numberNegativesOnes >= 1)
throw new IllegalArgumentException("Only one dimension can be negative ones. Got shape "
+ Arrays.toString(newShape));
numberNegativesOnes++;
int shapeLength = 1;
for (int j = 0; j < shape.length; j++)
if (shape[j] >= 1)
shapeLength *= shape[j];
long realShape = Math.abs(length() / shapeLength);
long[] thisNewShape = new long[shape.length];
for (int j = 0; j < shape.length; j++) {
if (i != j) {
thisNewShape[j] = shape[j];
} else
thisNewShape[j] = realShape;
}
shape = thisNewShape;
break;
}
}
long prod = ArrayUtil.prodLong(shape);
if (prod != this.length()){
throw new ND4JIllegalStateException("New shape length doesn't match original length: [" + prod + "] vs [" + this.length() + "]. Original shape: "+Arrays.toString(this.shape())+" New Shape: "+Arrays.toString(newShape));
}
INDArray reshapeAttempt = Shape.newShapeNoCopy(this, shape, order == 'f');
if (reshapeAttempt != null) {
// kinda strange get/set usage
// reshapeAttempt.setOrder(Shape.getOrder(reshapeAttempt));
return reshapeAttempt;
}
if(enforceView){
throw new ND4JIllegalStateException("Unable to reshape array as view, called with enforceView=true. " +
"Use enforceView=false to return a copy instead, or call reshape on a non-strided array. Array shape info: " + this.shapeInfoToString().replaceAll("\n",""));
}
if (order != ordering()) {
INDArray ret = Nd4j.createUninitialized(this.dataType(), shape, order);
ret.setData(dup(order).data());
return ret;
} else {
INDArray ret = this.dup(order);
return Nd4j.create(ret.data(), shape);
}
}
@Override
public double getDoubleUnsafe(long offset) {
return data().getDouble(offset);
}
@Override
public INDArray putScalarUnsafe(long offset, double value) {
autoProcessScalarCall();
data().put(offset, value);
return this;
}
@Override
public INDArray reshape(char order, int rows, int columns) {
return reshape(order, new long[] {rows, columns});
}
/**
* Reshape the ndarray in to the specified dimensions,
* possible errors being thrown for invalid shapes
*
* Note here that one dimension can be -1.
* The dimension that is -1 will be inferred from the shape and
* the length of the ndarray
*
* @param shape the shape of the ndarray.
* @return the new reshaped nd array
*/
@Override
public INDArray reshape(int[] shape) {
return reshape(Nd4j.order(), shape);
}
@Override
public INDArray reshape(long... shape) {
return reshape(Nd4j.order(), shape);
}
@Override
public INDArray prod(boolean keepDims, int... dimension) {
validateNumericalArray("prod", false);
return Nd4j.getExecutioner().exec(new Prod(this, keepDims, dimension));
}
@Override
public INDArray prod(int... dimension) {
return prod(false, dimension);
}
@Override
public INDArray mean(boolean keepDims, int... dimension) {
validateNumericalArray("mean", false);
return Nd4j.getExecutioner().exec(new Mean(this, keepDims, dimension));
}
@Override
public INDArray mean(int... dimension) {
return mean(false, dimension);
}
@Override
public INDArray amean(int... dimension) {
validateNumericalArray("amean", false);
return Nd4j.getExecutioner().exec(new AMean(this, dimension));
}
@Override
public INDArray mean(@NonNull INDArray result, boolean keepDims, int... dimension) {
validateNumericalArray("mean", false);
return Nd4j.getExecutioner().exec(new Mean(this, result, keepDims, dimension));
}
@Override
public INDArray mean(@NonNull INDArray result, int... dimension) {
return mean(result, false, dimension);
}
@Override
public INDArray var(int... dimension) {
validateNumericalArray("var", false);
return Nd4j.getExecutioner().exec(new Variance(this, dimension));
}
@Override
public INDArray var(boolean biasCorrected, int... dimension) {
validateNumericalArray("var", false);
return Nd4j.getExecutioner().exec(new Variance(this, biasCorrected, dimension));
}
@Override
public INDArray max(boolean keepDims, int... dimension) {
validateNumericalArray("max", false);
return Nd4j.getExecutioner().exec(new Max(this, keepDims, dimension));
}
@Override
public INDArray max(int... dimension) {
return max(false, dimension);
}
@Override
public INDArray amax(int... dimension) {
validateNumericalArray("amax", false);
return Nd4j.getExecutioner().exec(new AMax(this, dimension));
}
@Override
public INDArray min(boolean keepDims, int... dimension) {
validateNumericalArray("min", false);
return Nd4j.getExecutioner().exec(new Min(this, keepDims, dimension));
}
@Override
public INDArray min(int... dimension) {
return min(false, dimension);
}
@Override
public INDArray amin(int... dimension) {
validateNumericalArray("amin", false);
return Nd4j.getExecutioner().exec(new AMin(this, dimension));
}
@Override
public INDArray sum(int... dimension) {
validateNumericalArray("sum", true);
return Nd4j.getExecutioner().exec(new Sum(this, dimension));
}
@Override
public INDArray sum(boolean keepDim, int... dimension) {
validateNumericalArray("sum", true);
return Nd4j.getExecutioner().exec(new Sum(this, null, keepDim, dimension));
}
@Override
public INDArray entropy(int... dimension) {
validateNumericalArray("entropy", false);
return Nd4j.getExecutioner().exec(new Entropy(this, dimension));
}
@Override
public INDArray shannonEntropy(int... dimension) {
validateNumericalArray("shannonEntropy", false);
return Nd4j.getExecutioner().exec(new ShannonEntropy(this, dimension));
}
@Override
public INDArray logEntropy(int... dimension) {
validateNumericalArray("logEntropy", false);
return Nd4j.getExecutioner().exec(new LogEntropy(this, dimension));
}
@Override
public INDArray sum(@NonNull INDArray result, boolean keepDims, int... dimension) {
validateNumericalArray("sum", true);
return Nd4j.getExecutioner().exec(new Sum(this, result, keepDims, dimension));
}
@Override
public INDArray sum(@NonNull INDArray result, int... dimension) {
return sum(result, false, dimension);
}
@Override
public INDArray norm1(int... dimension) {
return norm1(false, dimension);
}
@Override
public INDArray norm1(boolean keepDims, int... dimension) {
validateNumericalArray("norm1", false);
return Nd4j.getExecutioner().exec(new Norm1(this, keepDims, dimension));
}
@Override
public INDArray std(int... dimension) {
return std(true, dimension);
}
@Override
public INDArray std(boolean biasCorrected, int... dimension) {
return std(biasCorrected, false, dimension);
}
@Override
public INDArray std(boolean biasCorrected, boolean keepDims, int... dimension) {
validateNumericalArray("std", false);
return Nd4j.getExecutioner().exec(new StandardDeviation(this, biasCorrected, keepDims, dimension));
}
@Override
public Number stdNumber(boolean biasCorrected) {
validateNumericalArray("stdNumber", false);
return Nd4j.getExecutioner().exec(new StandardDeviation(this, biasCorrected)).getDouble(0);
}
@Override
public INDArray norm2(boolean keepDims, int... dimension) {
validateNumericalArray("norm2", false);
return Nd4j.getExecutioner().exec(new Norm2(this, keepDims, dimension));
}
@Override
public INDArray norm2(int... dimension) {
return norm2(false, dimension);
}
/**
* Number of columns (shape[1]), throws an exception when
* called when not 2d
*
* @return the number of columns in the array (only 2d)
*/
@Override
public int columns() {
// FIXME: int cast
if (isMatrix())
return (int) size(1);
else if (Shape.isColumnVectorShape(shape())) {
return 1;
} else if (Shape.isRowVectorShape(shape())) {
return (int) length();
}
throw new IllegalStateException("Rank is [" + rank() + "]; columns() call is not valid");
}
/**
* Returns the number of rows
* in the array (only 2d) throws an exception when
* called when not 2d
*
* @return the number of rows in the matrix
*/
@Override
public int rows() {
// FIXME:
if (isMatrix())
return (int) size(0);
else if (Shape.isRowVectorShape(shape())) {
return 1;
} else if (Shape.isColumnVectorShape(shape())) {
return (int) length();
}
throw new IllegalStateException("Rank is " + rank() + " rows() call is not valid");
}
/**
* Flattens the array for linear indexing
*
* @return the flattened version of this array
*/
@Override
public INDArray ravel(char ordering) {
Nd4j.getCompressor().autoDecompress(this);
if(ordering == this.ordering() && Shape.hasDefaultStridesForShape(this)){
return reshape(ordering, length());
}
return dup(ordering).reshape(ordering, length());
}
/**
* Flattens the array for linear indexing
*
* @return the flattened version of this array
*/
@Override
public INDArray ravel() {
return reshape(length());
}
@Override
public void sliceVectors(List list) {
if (isVector())
list.add(this);
else {
for (int i = 0; i < slices(); i++) {
slice(i).sliceVectors(list);
}
}
}
/**
* Reshape the matrix. Number of elements must not change.
*
* @param newRows
* @param newColumns
*/
@Override
public INDArray reshape(long newRows, long newColumns) {
return reshape(new long[] {newRows, newColumns});
}
/**
* Get the specified column
*
* @param c
*/
@Override
public INDArray getColumn(long c) {
Nd4j.getCompressor().autoDecompress(this);
if (isColumnVector() && c == 0)
return this;
else if (isColumnVector() && c > 0)
throw new IllegalArgumentException("Illegal index for column");
Preconditions.checkArgument(this.rank() == 2, "getColumn() can be called on 2D arrays only");
return tensorAlongDimension(c, 0);
}
@Override
public INDArray getColumn(long c, boolean keepDim) {
INDArray col = getColumn(c);
if(!keepDim)
return col;
return col.reshape(col.length(), 1);
}
@Override
public INDArray getRows(int[] rindices) {
Nd4j.getCompressor().autoDecompress(this);
if (!isMatrix() && !isVector())
throw new IllegalArgumentException("Unable to get columns from a non matrix or vector");
if (isVector())
return Nd4j.pullRows(this, 1, rindices);
else {
INDArray ret = Nd4j.createUninitialized(this.dataType(), new long[] {rindices.length, columns()});
for (int i = 0; i < rindices.length; i++)
ret.putRow(i, getRow(rindices[i]));
return ret;
}
}
@Override
public INDArray get(INDArrayIndex... indexes) {
Nd4j.getCompressor().autoDecompress(this);
int numPoint = 0;
int numInterval = 0;
int numAll = 0;
int numNewAxis = 0;
int numSpecified = 0;
for(INDArrayIndex i : indexes){
if(i instanceof PointIndex){
numPoint++;
} else if(i instanceof NDArrayIndexAll){
numAll++;
} else if(i instanceof IntervalIndex){
numInterval++;
} else if(i instanceof NewAxis){
numNewAxis++;
} else if(i instanceof SpecifiedIndex){
numSpecified++;
} else {
throw new IllegalStateException("Unknown index: " + i);
}
}
// Padding remaining dimensions with all() index if too few indices provided
if (indexes.length - numNewAxis < this.rank()) {
val newIndexes = new INDArrayIndex[this.rank() + numNewAxis];
for (int e = 0; e < indexes.length; e++)
newIndexes[e] = indexes[e];
for (int e = indexes.length; e < newIndexes.length; e++) {
numAll++;
newIndexes[e] = NDArrayIndex.all();
}
indexes = newIndexes;
}
Preconditions.checkState((numPoint + numInterval + numAll + numSpecified) == rank(), "Illegal set of indices for array: need at least" +
" %s point/interval/all/specified indices for rank %s array (%ndShape), got indices %s", rank(), rank(), this, indexes);
int outRank = rank() + numNewAxis - numPoint;
Preconditions.checkState(outRank >= 0, "Illegal set of indices for array: %ndShape, %s", this, indexes);
//To work out sub-array, we need to work out 3 things: offset, shape and strides. We calculate all of these
long[] outShape = new long[outRank];
long[] outStrides = new long[outRank];
long offset = offset(); //Start with existing offset if view
int outIdx = 0; //Axis number counter for output array
int inIdx = 0; //Axis number counter for input array
for( int i=0; i= size(inIdx)) {
throw new IllegalStateException("Indices are out of range: Cannot get interval index " + indexes[i] +
" on array with size(" + inIdx + ")=" + size(inIdx) + ". Array shape: " + Arrays.toString(shape()) +
", indices: " + Arrays.toString(indexes));
}
long stride = ii.stride();
long length = (endInc - start)/stride + 1;
offset += ii.offset() * stride(inIdx);
outShape[outIdx] = length;
outStrides[outIdx] = ii.stride() * stride(inIdx);
inIdx++;
outIdx++;
} else if(indexes[i] instanceof NewAxis) {
//New axis: appends a 1 in shape. Axis not present in input, but is present in output
outShape[outIdx] = 1;
if (outIdx > 0) { //Stride doesn't matter for 1 size axis anyway...
outStrides[outIdx] = outStrides[outIdx - 1];
} else {
outStrides[outIdx] = 1;
}
outIdx++;
} else if(indexes[i] instanceof SpecifiedIndex){
//Specified index: axis present in both input and output
SpecifiedIndex si = (SpecifiedIndex)indexes[i];
outShape[outIdx++] = si.length();
inIdx++;
//Don't care about strides for specified index, as result won't be a view
} else {
throw new IllegalStateException("Unknown index type: " + i); //Should never happen
}
}
//Note: If we have specified indices, we can't return a view. Instead, we copy the specified sub-arrays from
// the input array to the output array.
//How? Create the output array, then do loop over the specified indices only, and copy sub-arrays for all other axes
if (numSpecified > 0) {
INDArray out = Nd4j.create(dataType(), outShape);
//Need to copy subsets here
long[] specifiedSizes = new long[numSpecified];
SpecifiedIndex[] si = new SpecifiedIndex[numSpecified];
int j=0;
for( int i=0; i replace with loop + point
// ii. new axis indices -> ignore/exclude (don't appear in input)
// iii. interval indices -> replace with all
//(2) Get from output: requested indices, except for:
// i. point indices -> ignore/exclude (don't appear in output)
// ii. new axis indices -> replace with point(0)
INDArrayIndex[] pointIdxsIn = new INDArrayIndex[indexes.length - numNewAxis]; //Indices for source (this array)
int[] specifiedAxisIn = new int[numSpecified];
int specCount = 0;
j = 0;
for( int i=0; i 0)
throw new IllegalArgumentException("Illegal index for row: requested row " + r + " but this.size(0)=" + this.size(0));
Preconditions.checkArgument(rank() == 2, "getRow() can be called on 2D arrays only");
Preconditions.checkArgument(r < rows(), "Row index must be smaller than total number of rows");
return tensorAlongDimension(r, 1);
}
@Override
public INDArray getRow(long r, boolean keepDim) {
INDArray row = getRow(r);
if(!keepDim)
return row;
return row.reshape(1, row.length());
}
/**
* This method allows you to compare INDArray against other INDArray, with variable eps
*
* @param o
* @param eps
* @return
*/
public boolean equalsWithEps(Object o, double eps) {
Nd4j.getCompressor().autoDecompress(this);
if (o == null)
return false;
if (!(o instanceof INDArray))
return false;
INDArray n = (INDArray) o;
Nd4j.getCompressor().autoDecompress(n);
if (n == this)
return true;
if (n.isSparse()) {
return n.equals(this);
}
if (this.rank() != n.rank())
return false;
if (this.length() != n.length())
return false;
if (this.isEmpty() != n.isEmpty())
return false;
if (this.isEmpty() && n.isEmpty())
return Shape.shapeEquals(this.shape(), n.shape());
if (this.dataType() != n.dataType())
return false;
// meh
if (this.dataType() == DataType.UTF8 && n.dataType() == DataType.UTF8) {
for (long e = 0; e < this.length(); e++) {
val str1 = this.getString(e);
val str2 = n.getString(e);
if (!str1.equals(str2))
return false;
}
return true;
}
//epsilon equals
if (isScalar() && n.isScalar()) {
if (data.dataType() == DataType.FLOAT) {
double val = getDouble(0);
double val2 = n.getDouble(0);
if (Double.isNaN(val) != Double.isNaN(val2))
return false;
return Math.abs(val - val2) < eps;
} else {
double val = getDouble(0);
double val2 = n.getDouble(0);
if (Double.isNaN(val) != Double.isNaN(val2))
return false;
return Math.abs(val - val2) < eps;
}
} else if (isVector() && n.isVector()) {
EqualsWithEps op = new EqualsWithEps(this, n, eps);
Nd4j.getExecutioner().exec(op);
double diff = op.z().getDouble(0);
return diff < 0.5;
}
if (!Arrays.equals(this.shape(), n.shape()))
return false;
if (!Shape.shapeEquals(shape(), n.shape())) {
return false;
}
if (slices() != n.slices())
return false;
if (n.ordering() == ordering()) {
EqualsWithEps op = new EqualsWithEps(this, n, eps);
Nd4j.getExecutioner().exec(op);
double diff = op.z().getDouble(0);
return diff < 0.5;
} else {
EqualsWithEps op = new EqualsWithEps(this, n, eps);
Nd4j.getExecutioner().exec(op);
double diff = op.z().getDouble(0);
return diff < 0.5;
}
}
@Override
public boolean equalShapes(@NonNull INDArray other){
if(isEmpty() != other.isEmpty())
return false;
if(rank() != other.rank())
return false;
for( int i=0; i= rank())
throw new IllegalArgumentException("Invalid size: cannot get size of dimension " + dimension + " for rank "
+ rank() + " NDArray (array shape: " + Arrays.toString(this.shape()) + ")");
return jvmShapeInfo.shape[dimension];
}
@Override
public int rank() {
return jvmShapeInfo.rank;
}
/**
* Returns the total number of elements in the ndarray
*
* @return the number of elements in the ndarray
*/
@Override
public long length() {
if (isEmpty())
return 0;
return jvmShapeInfo.length;
}
@Override
public INDArray broadcast(INDArray result) {
Nd4j.getCompressor().autoDecompress(this);
val shape = result.shape();
if (Shape.shapeEquals(shape, shape()))
return this;
// if we're on scalar, we can just create new array
if (this.isScalar())
return Nd4j.createUninitialized(this.dataType(), shape).assign(this.getDouble(0));
boolean compatible = true;
int count = shape.length - 1;
int thisCount = jvmShapeInfo.rank - 1;
for (int i = shape.length - 1; i > 0; i--) {
if (count < 0 || thisCount < 0)
break;
if (shape[count] != shape()[thisCount] && shape[count] != 1 && shape()[thisCount] != 1) {
compatible = false;
break;
}
count--;
thisCount--;
}
if (!compatible)
throw new IllegalArgumentException("Incompatible broadcast from " + Arrays.toString(shape()) + " to "
+ Arrays.toString(shape));
long[] retShape = new long[shape.length];
List broadCastDimensions = new ArrayList<>();
List nonBroadCastDimensions = new ArrayList<>();
for (int i = 0; i < retShape.length; i++) {
if (shape().length == 1) {
if (i == 0) {
if (i < shape().length)
retShape[i] = Math.max(1, shape[i]);
else
retShape[i] = shape[i];
} else {
if (i < shape().length)
retShape[i] = Math.max(shape[i], size(i));
else
retShape[i] = shape[i];
}
} else {
if (i < rank() && size(i) == 1)
broadCastDimensions.add(i);
else
nonBroadCastDimensions.add(i);
if (i < shape().length)
retShape[i] = Math.max(shape[i], size(i));
else
retShape[i] = shape[i];
}
}
if (isRowVector()) {
//number of times to repeat each value
for (int i = 0; i < result.slices(); i++) {
result.putSlice(i, this);
}
} else if (isColumnVector()) {
for (int i = 0; i < result.columns(); i++) {
result.putColumn(i, this);
}
}
else {
// FIXME: int cast
int[] repeat = new int[shape.length];
for(int i = 0; i < shape.length; i++) {
if(i < rank()) {
if(size(i) == 1)
repeat[i] = (int) shape[i];
else {
repeat[i] = 1;
}
}
else {
repeat[i] = (int) shape[i];
}
}
if (this.isView()) {
Nd4j.getExecutioner().execAndReturn(new Tile(new INDArray[]{this.dup(this.ordering())},new INDArray[]{result},repeat));
} else
Nd4j.getExecutioner().execAndReturn(new Tile(new INDArray[]{this},new INDArray[]{result},repeat));
}
return result;
}
/**
* Broadcasts this ndarray to be the specified shape
*
* @param shape the new shape of this ndarray
* @return the broadcasted ndarray
*/
@Override
public INDArray broadcast(long... shape) {
return broadcast(Nd4j.createUninitialized(this.dataType(), shape, this.ordering()));
}
@Override
public INDArray dimShuffle(Object[] rearrange, int[] newOrder, boolean[] broadCastable) {
// FIXME: int cast
return dimShuffle(rearrange, ArrayUtil.toLongArray(newOrder), broadCastable);
}
/**
* Dimshuffle: an extension of permute that adds the ability
* to broadcast various dimensions.
*
* See theano for more examples.
* This will only accept integers and xs.
*
* An x indicates a dimension should be broadcasted rather than permuted.
*
* @param rearrange the dimensions to swap to
* @return the newly permuted array
*/
@Override
public INDArray dimShuffle(Object[] rearrange, long[] newOrder, boolean[] broadCastable) {
Nd4j.getCompressor().autoDecompress(this);
if (broadCastable.length != jvmShapeInfo.rank)
throw new IllegalArgumentException(
"The broadcastable dimensions must be the same length as the current shape");
boolean broadcast = false;
Set set = new HashSet<>();
for (int i = 0; i < rearrange.length; i++) {
set.add(rearrange[i]);
if (rearrange[i] instanceof Integer) {
Integer j = (Integer) rearrange[i];
if (j >= broadCastable.length)
throw new IllegalArgumentException(
"Illegal dimension, dimension must be < broadcastable.length (aka the real dimensions");
} else if (rearrange[i] instanceof Character) {
Character c = (Character) rearrange[i];
if (c != 'x')
throw new IllegalArgumentException("Illegal input: Must be x");
broadcast = true;
} else
throw new IllegalArgumentException("Only characters and integers allowed");
}
//just do permute
if (!broadcast) {
int[] ret = new int[rearrange.length];
for (int i = 0; i < ret.length; i++)
ret[i] = (Integer) rearrange[i];
return permute(ret);
} else {
List drop = new ArrayList<>();
for (int i = 0; i < broadCastable.length; i++) {
if (!set.contains(i)) {
if (broadCastable[i])
drop.add(i);
else
throw new IllegalArgumentException(
"We can't drop the given dimension because its not broadcastable");
}
}
//list of dimensions to keep
int[] shuffle = new int[broadCastable.length];
int count = 0;
for (int i = 0; i < rearrange.length; i++) {
if (rearrange[i] instanceof Integer) {
shuffle[count++] = (Integer) rearrange[i];
}
}
List augment = new ArrayList<>();
for (int i = 0; i < rearrange.length; i++) {
if (rearrange[i] instanceof Character)
augment.add(i);
}
Integer[] augmentDims = augment.toArray(new Integer[1]);
count = 0;
int dropIdx = 0;
int[] newShape = new int[shuffle.length + drop.size()];
for (int i = 0; i < newShape.length; i++) {
if (i < shuffle.length) {
newShape[count++] = shuffle[i];
} else
newShape[count++] = drop.get(dropIdx++);
}
INDArray ret; //TODO is this correct? This was old behaviour before adding permute input check
if(newShape.length == this.rank()){
ret = permute(newShape);
} else {
ret = dup();
}
List newDims = new ArrayList<>();
long[] shape = Arrays.copyOfRange(ret.shape(), 0, shuffle.length);
for (int i = 0; i < shape.length; i++) {
newDims.add(shape[i]);
}
for (int i = 0; i < augmentDims.length; i++) {
newDims.add(augmentDims[i], 1L);
}
long[] toReshape = ArrayUtil.toArrayLong(newDims);
ret = ret.reshape(toReshape);
return ret;
}
}
/**
* See: http://www.mathworks.com/help/matlab/ref/permute.html
*
* @param rearrange the dimensions to swap to
* @return the newly permuted array
*/
@Override
public INDArray permute(int... rearrange) {
Preconditions.checkArgument(rearrange.length == rank(), "Incorrect number of arguments for permute function:" +
" got arguments %s for rank %s array. Number of arguments must equal array rank", rearrange, rank());
Nd4j.getCompressor().autoDecompress(this);
boolean alreadyInOrder = true;
//IntBuffer shapeInfo = shapeInfo();
int rank = jvmShapeInfo.rank;
for (int i = 0; i < rank; i++) {
if (rearrange[i] != i) {
alreadyInOrder = false;
break;
}
}
if (alreadyInOrder)
return this;
checkArrangeArray(rearrange);
int[] newShape = doPermuteSwap(shapeOf(), rearrange);
int[] newStride = doPermuteSwap(strideOf(), rearrange);
char newOrder = Shape.getOrder(newShape, newStride, 1);
INDArray value = create(data(), newShape, newStride, offset(), newOrder);
return value;
}
/**
* An in-place version of permute. The array shape information (shape, strides)
* is modified by this operation (but not the data itself)
* See: http://www.mathworks.com/help/matlab/ref/permute.html
*
* @param rearrange the dimensions to swap to
* @return the current array
*/
@Override
public INDArray permutei(int... rearrange) {
Preconditions.checkArgument(rearrange.length == rank(), "Incorrect number of arguments for permute function:" +
" got arguments %s for rank %s array. Number of arguments must equal array rank", rearrange, rank());
boolean alreadyInOrder = true;
val shapeInfo = shapeInfo();
int rank = jvmShapeInfo.rank;
for (int i = 0; i < rank; i++) {
if (rearrange[i] != i) {
alreadyInOrder = false;
break;
}
}
if (alreadyInOrder)
return this;
checkArrangeArray(rearrange);
val newShape = doPermuteSwap(Shape.shapeOf(shapeInfo), rearrange);
val newStride = doPermuteSwap(Shape.stride(shapeInfo), rearrange);
char newOrder = Shape.getOrder(newShape, newStride, 1);
//Set the shape information of this array: shape, stride, order.
//Shape info buffer: [rank, [shape], [stride], offset, elementwiseStride, order]
/*for( int i=0; i 0) {
//for the backend to work - no ews for permutei
//^^ not true anymore? Not sure here. Marking this for raver
setShapeInformation(Nd4j.getShapeInfoProvider().createShapeInformation(newShape, newStride, 0, newOrder, dataType(), isEmpty()));
}
//this.shape = null;
//this.stride = null;
return this;
}
protected long[] doPermuteSwap(LongBuffer shape, int[] rearrange) {
val ret = new long[rearrange.length];
for (int i = 0; i < rearrange.length; i++) {
ret[i] = shape.get(rearrange[i]);
}
return ret;
}
protected int[] doPermuteSwap(IntBuffer shape, int[] rearrange) {
int[] ret = new int[rearrange.length];
for (int i = 0; i < rearrange.length; i++) {
ret[i] = shape.get(rearrange[i]);
}
return ret;
}
protected int[] doPermuteSwap(DataBuffer shape, int[] rearrange) {
int[] ret = new int[rearrange.length];
for (int i = 0; i < rearrange.length; i++) {
ret[i] = shape.getInt(rearrange[i]);
}
return ret;
}
protected void checkArrangeArray(int[] arr) {
Preconditions.checkArgument(arr.length == jvmShapeInfo.rank, "Invalid rearrangement: number of arrangement (%s) != rank (%s)",
arr.length, jvmShapeInfo.rank);
for (int i = 0; i < arr.length; i++) {
if (arr[i] >= arr.length)
throw new IllegalArgumentException("The specified dimensions can't be swapped. Given element " + i
+ " was >= number of dimensions");
if (arr[i] < 0)
throw new IllegalArgumentException("Invalid dimension: " + i + " : negative value");
}
for (int i = 0; i < arr.length; i++) {
for (int j = 0; j < arr.length; j++) {
if (i != j && arr[i] == arr[j])
throw new IllegalArgumentException("Permute array must have unique elements");
}
}
}
protected void autoProcessScalarCall() {
/* if (Nd4j.getExecutioner().getProfilingMode() != OpExecutioner.ProfilingMode.DISABLED && Nd4j.getExecutioner().getProfilingMode() != OpExecutioner.ProfilingMode.SCOPE_PANIC)
OpProfiler.getInstance().processScalarCall();*/
}
/**
* Checks whether the matrix is a vector.
*/
@Override
public boolean isVector() {
if (jvmShapeInfo.rank == 1)
return true;
return isRowVector() || isColumnVector();
}
@Override
public boolean isVectorOrScalar() {
return isVector() || isScalar();
}
@Override
public boolean isSquare() {
return isMatrix() && rows() == columns();
}
/**
* Checks whether the matrix is a row vector.
*/
@Override
public boolean isRowVector() {
return (rank() == 2 && rows() == 1) && length() > 1 || rank() == 1 && length() > 1;
}
/**
* Checks whether the matrix is a column vector.
*/
@Override
public boolean isColumnVector() {
return rank() == 2 && columns() == 1 && length() > 1;
}
@Override
public boolean isColumnVectorOrScalar() {
return isColumnVector() || isScalar();
}
@Override
public boolean isRowVectorOrScalar() {
return isRowVector() || isScalar();
}
/**
* Generate string representation of the matrix.
* Printing will switch to scientific notation on a per element basis
* - when abs value is greater than or equal to 10000
* - when abs value is less than or equal to 0.0001 and not zero
*
* If the number of elements in the array is greater than 1000 (by default) only the first and last three elements
* in a dimension are included. This can be changed globally using {@link NDArrayStrings#setMaxPrintElements(long)}
*
*
*/
@Override
public String toString() {
return toString(new NDArrayStrings());
}
@Override
public String toString(@NonNull NDArrayStrings options){
if (!isCompressed() && !preventUnpack)
return options.format(this);
else if (isCompressed() && compressDebug)
return "COMPRESSED ARRAY. SYSTEM PROPERTY compressdebug is true. This is to prevent auto decompression from being triggered.";
else if (preventUnpack)
return "Array string unpacking is disabled.";
return options.format(this);
}
@Override
public String toString(long maxElements, boolean forceSummarize, int precision){
return toString(new NDArrayStrings(maxElements, forceSummarize, precision));
}
@Override
public String toStringFull(){
return toString(Long.MAX_VALUE, false, -1 * dataType().precision());
}
/**
* Returns a scalar (individual element)
* of a scalar ndarray
*
* @return the individual item in this ndarray
*/
@Override
public Object element() {
if (!isScalar())
throw new IllegalStateException("Unable to retrieve element from non scalar matrix");
if (data.dataType() == DataType.FLOAT)
return data.getFloat(0);
return data.getDouble(0);
}
@Override
public INDArray remainder(INDArray denominator) {
if (Shape.areShapesBroadcastable(this.shape(), denominator.shape())) {
return remainder(denominator, Nd4j.createUninitialized(this.dataType(), Shape.broadcastOutputShape(this.shape(), denominator.shape())));
} else
return remainder(denominator, this.ulike());
}
@Override
public INDArray remainder(INDArray denominator, INDArray result) {
validateNumericalArray("remainder", false);
Preconditions.checkArgument(Shape.areShapesBroadcastable(this.shape(), denominator.shape()),"Shapes must be broadcastable");
val op = new RemainderOp(this, denominator, result);
Nd4j.getExecutioner().exec(op);
return result;
}
@Override
public INDArray remainder(Number denominator) {
return remainder(denominator, Nd4j.createUninitialized(this.dataType(), this.shape()));
}
@Override
public INDArray remainder(Number denominator, INDArray result) {
validateNumericalArray("remainder", false);
ScalarRemainder op = new ScalarRemainder(this, null, result, denominator);
Nd4j.getExecutioner().exec(op);
return result;
}
@Override
public INDArray remainderi(INDArray denominator) {
validateNumericalArray("remainderi", false);
RemainderOp op = new RemainderOp(this, denominator, this);
Nd4j.getExecutioner().exec(op);
return this;
}
@Override
public INDArray remainderi(Number denominator) {
validateNumericalArray("remainderi", false);
ScalarRemainder op = new ScalarRemainder(this, null, this, denominator);
Nd4j.getExecutioner().exec(op);
return this;
}
@Override
public INDArray fmod(INDArray denominator) {
validateNumericalArray("fmod", false);
if (Shape.areShapesBroadcastable(this.shape(), denominator.shape())) {
return fmod(denominator, Nd4j.createUninitialized(Nd4j.defaultFloatingPointType(), Shape.broadcastOutputShape(this.shape(), denominator.shape())));
} else
return fmod(denominator, this.ulike());
}
@Override
public INDArray fmod(INDArray denominator, INDArray result) {
validateNumericalArray("fmod", false);
if (Shape.areShapesBroadcastable(this.shape(), denominator.shape())) {
val outShape = Shape.broadcastOutputShape(this.shape(), denominator.shape());
Preconditions.checkArgument(Shape.shapeEquals(outShape, result.shape()), "Result shape doesn't match expectations: " + Arrays.toString(result.shape()));
Nd4j.exec(new FloorModOp(new INDArray[]{this, denominator}, new INDArray[]{result}));
return result;
} else {
FModOp op = new FModOp(this, denominator, result);
Nd4j.getExecutioner().exec(op);
return result;
}
}
@Override
public INDArray fmod(Number denominator) {
return fmod(denominator, Nd4j.createUninitialized(this.dataType(), this.shape()));
}
@Override
public INDArray fmod(Number denominator, INDArray result) {
validateNumericalArray("fmod", false);
ScalarFMod op = new ScalarFMod(this, null, result, denominator);
Nd4j.getExecutioner().exec(op);
return result;
}
@Override
public INDArray fmodi(INDArray denominator) {
validateNumericalArray("fmodi", false);
FModOp op = new FModOp(this, denominator, this);
Nd4j.getExecutioner().exec(op);
return this;
}
@Override
public INDArray fmodi(Number denominator) {
validateNumericalArray("fmodi", false);
ScalarFMod op = new ScalarFMod(this, null, this, denominator);
Nd4j.getExecutioner().exec(op);
return this;
}
@Override
public Iterator iterator() {
return new FirstAxisIterator(this);
}
/**
* Returns the start of where the ndarray is for the original data buffer
*
* @return
*/
@Override
public long originalOffset() {
if (data().originalOffset() >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Original offset of buffer can not be >= Integer.MAX_VALUE");
return data().originalOffset();
}
private void readObject(ObjectInputStream s) {
try {
s.defaultReadObject();
read(s);
} catch (Exception e) {
throw new RuntimeException(e);
}
}
private void writeObject(ObjectOutputStream out) throws IOException {
out.defaultWriteObject();
write(out);
}
//Custom serialization for Java serialization
protected void write(ObjectOutputStream out) throws IOException {
if (this.isView()) {
//As per Nd4j.write, duplicate before writing to the output stream
//BaseDataBuffer.write(...) doesn't know about strides etc, so dup (or equiv. strategy) is necessary here
//Furthermore, because we only want to save the *actual* data for a view (not the full data), the shape info
// (mainly strides, offset, element-wise stride) may be different in the duped array vs. the view array
INDArray copy = this.dup();
copy.shapeInfoDataBuffer().write(out);
copy.data().write(out);
} else {
shapeInformation.write(out);
data().write(out);
}
}
//Custom deserialization for Java serialization
protected void read(ObjectInputStream s) {
val headerShape = BaseDataBuffer.readHeader(s);
shapeInformation = Nd4j.createBuffer(new int[Shape.shapeInfoLength(rank())]);
shapeInformation.read(s, headerShape.getLeft(), headerShape.getMiddle(), headerShape.getRight());
setShapeInformation(Pair.create(shapeInformation, shapeInformation.asLong()));
val headerData = BaseDataBuffer.readHeader(s);
data = Nd4j.createBuffer(headerData.getRight(), headerData.getMiddle(), false);
data().read(s, headerData.getLeft(), headerData.getMiddle(), headerData.getRight());
}
/**
* This method returns index of highest value along specified dimension(s)
*
* @param dimension
* @return
*/
@Override
public INDArray argMax(int... dimension) {
return Nd4j.argMax(this, dimension);
}
/**
* This method returns True, if this INDArray instance is attached to some Workspace. False otherwise.
*
* @return
*/
@Override
public boolean isAttached() {
if (isEmpty())
return false;
Preconditions.checkArgument(!(data == null && !isEmpty()), "Array has no buffer!");
return data.isAttached() ||
(data.underlyingDataBuffer() != null && data.underlyingDataBuffer().isAttached()) ||
(data.originalDataBuffer() != null && data.originalDataBuffer().isAttached());
}
/**
* This method checks, if given attached INDArray is still in scope of its parent Workspace
*
* PLEASE NOTE: if this INDArray isn't attached to any Workspace, this method will return true
*
* @return
*/
@Override
public boolean isInScope() {
if (!isAttached())
return true;
return data.isInScope();
}
/**
* This metod detaches INDArray from Workspace, returning copy. Basically it's dup() into new memory chunk.
*
* PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
*
* @return
*/
@Override
public INDArray detach() {
if (!isAttached())
return this;
WorkspaceUtils.assertValidArray(this, "Cannot detach INDArray");
Nd4j.getExecutioner().commit();
/*
two options here
1) we're within some workspace
2) we're out of any workspace
*/
if (Nd4j.getMemoryManager().getCurrentWorkspace() == null) {
if (!isView()) {
Nd4j.getExecutioner().commit();
DataBuffer buffer = Nd4j.createBuffer(this.dataType(), this.length(), false);
Nd4j.getMemoryManager().memcpy(buffer, this.data());
return Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer());
} else {
INDArray copy = Nd4j.createUninitialized(this.dataType(), this.shape(), this.ordering());
copy.assign(this);
Nd4j.getExecutioner().commit();
return copy;
}
} else {
MemoryWorkspace workspace = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
INDArray copy = null;
if (!isView()) {
Nd4j.getExecutioner().commit();
DataBuffer buffer = Nd4j.createBuffer(this.dataType(), this.length(), false);
//Pointer.memcpy(buffer.pointer(), this.data.pointer(), this.lengthLong() * Nd4j.sizeOfDataType(this.data.dataType()));
Nd4j.getMemoryManager().memcpy(buffer, this.data());
copy = Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer()); //this.dup(this.ordering());
} else {
copy = Nd4j.createUninitialized(this.dataType(), this.shape(), this.ordering());
copy.assign(this);
Nd4j.getExecutioner().commit();
}
Nd4j.getMemoryManager().setCurrentWorkspace(workspace);
return copy;
}
}
/**
* This method detaches INDArray from current Workspace, and attaches it to Workspace above, if any.
*
* PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
* PLEASE NOTE: If current Workspace is the top-tier one, effect will be equal to detach() call - detached copy will be returned
*
* @return
*/
@Override
public INDArray leverage() {
WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
if (!isAttached())
return this;
MemoryWorkspace workspace = Nd4j.getMemoryManager().getCurrentWorkspace();
if (workspace == null) {
return this.detach();
}
MemoryWorkspace parentWorkspace = workspace.getParentWorkspace();
if (this.data.getParentWorkspace() == parentWorkspace)
return this;
// if there's no parent ws - just detach
if (parentWorkspace == null)
return this.detach();
else {
Nd4j.getExecutioner().commit();
// temporary set parent ws as current ws
Nd4j.getMemoryManager().setCurrentWorkspace(parentWorkspace);
INDArray copy = null;
if (!this.isView()) {
Nd4j.getExecutioner().commit();
DataBuffer buffer = Nd4j.createBuffer(this.length(), false);
Nd4j.getMemoryManager().memcpy(buffer, this.data());
copy = Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer());
} else {
copy = this.dup(this.ordering());
Nd4j.getExecutioner().commit();
}
// restore current ws
Nd4j.getMemoryManager().setCurrentWorkspace(workspace);
return copy;
}
}
/**
* This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id
*
* PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
* PLEASE NOTE: If Workspace with target Id wasn't created before - this array will be returned unmodified.
* PLEASE NOTE: If target workspace is the current one - this array will be returned unmodified.
*
* @param id
* @return
*/
@Override
public INDArray leverageTo(String id) {
return leverageTo(id, false);
}
/**
* This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id.
* If enforceExistence == true, and no workspace with the specified ID exists, then an {@link Nd4jNoSuchWorkspaceException}
* is thrown. Otherwise, if enforceExistance == false and no workspace with the specified ID exists, then the current
* INDArray is returned unmodified (same as {@link #leverage()}
*
* @param id ID of the workspace to leverage to
* @param enforceExistence If true, and the specified workspace does not exist: an {@link Nd4jNoSuchWorkspaceException}
* will be thrown.
* @return The INDArray, leveraged to the specified workspace
* @see #leverageTo(String)
*/
@Override
public INDArray leverageTo(String id, boolean enforceExistence) throws Nd4jNoSuchWorkspaceException {
WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
if (!isAttached())
return this;
if (!Nd4j.getWorkspaceManager().checkIfWorkspaceExists(id)) {
if(enforceExistence){
throw new Nd4jNoSuchWorkspaceException(id);
} else {
return this;
}
}
MemoryWorkspace current = Nd4j.getMemoryManager().getCurrentWorkspace();
MemoryWorkspace target = Nd4j.getWorkspaceManager().getWorkspaceForCurrentThread(id);
if (this.data.getParentWorkspace() == target)
return this;
Nd4j.getMemoryManager().setCurrentWorkspace(target);
INDArray copy = null;
if (!this.isView()) {
Nd4j.getExecutioner().commit();
DataBuffer buffer = Nd4j.createBuffer(this.dataType(), this.length(), false);
Nd4j.getMemoryManager().memcpy(buffer, this.data());
copy = Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer());
} else {
copy = this.dup(this.ordering());
Nd4j.getExecutioner().commit();
}
Nd4j.getMemoryManager().setCurrentWorkspace(current);
return copy;
}
/**
* This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id, if a workspace
* with the given ID is open and active.
*
* If the workspace does not exist, or is not active, the array is detached from any workspaces.
*
* @param id ID of the workspace to leverage to
* @return The INDArray, leveraged to the specified workspace (if it exists and is active) otherwise the detached array
* @see #leverageTo(String)
*/
public INDArray leverageOrDetach(String id){
if(!isAttached()){
return this;
}
if(!Nd4j.getWorkspaceManager().checkIfWorkspaceExistsAndActive(id)){
return detach();
}
return leverageTo(id);
}
/**
* This method pulls this INDArray into current Workspace.
*
* PLEASE NOTE: If there's no current Workspace - INDArray returned as is
*
* @return Migrated INDArray or this if no current workspace
* @see #migrate(boolean)
*/
@Override
public INDArray migrate() {
return migrate(false);
}
/**
* This method pulls this INDArray into current Workspace, or optionally detaches if no workspace is present.
