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/*******************************************************************************
* 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.factory;
import lombok.NonNull;
import lombok.val;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.blas.*;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.random.impl.Range;
import org.nd4j.linalg.api.rng.distribution.Distribution;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JArraySizeException;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.common.primitives.AtomicDouble;
import org.nd4j.common.util.ArrayUtil;
import java.util.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* Base NDArrayFactory class.
*
* Allows specification or data opType and row (c) or column(fortran) major order
*
* @author Adam Gibson
*/
public abstract class BaseNDArrayFactory implements NDArrayFactory {
// We don't really care about dtype field we'll use context instead
// protected DataType dtype;
protected char order;
protected Blas blas;
protected Level1 level1;
protected Level2 level2;
protected Level3 level3;
protected Lapack lapack;
public BaseNDArrayFactory() {}
@Override
public Lapack lapack() {
if (lapack == null)
createLapack();
return lapack;
}
@Override
public Blas blas() {
if (blas == null)
createBlas();
return blas;
}
@Override
public Level1 level1() {
if (level1 == null)
createLevel1();
return level1;
}
@Override
public Level2 level2() {
if (level2 == null)
createLevel2();
return level2;
}
@Override
public Level3 level3() {
if (level3 == null)
createLevel3();
return level3;
}
/**
*
* Initialize with the given data opType and ordering
* The ndarray factory will use this for
* @param dtype the data opType
* @param order the ordering in mem
*/
protected BaseNDArrayFactory(DataType dtype, Character order) {
// this.dtype = dtype;
if (Character.toLowerCase(order) != 'c' && Character.toLowerCase(order) != 'f')
throw new IllegalArgumentException("Order must either be c or f");
this.order = Character.toLowerCase(order);
}
/**
* @param dtype the data opType
* @param order the ordering
*/
protected BaseNDArrayFactory(DataType dtype, char order) {
// this.dtype = dtype;
if (Character.toLowerCase(order) != 'c' && Character.toLowerCase(order) != 'f')
throw new IllegalArgumentException("Order must either be c or f");
this.order = Character.toLowerCase(order);
}
/**
* Sets the order. Primarily for testing purposes
*
* @param order
*/
@Override
public void setOrder(char order) {
Preconditions.checkArgument(order == 'c' || order == 'f', "Order specified must be either c or f: got %s", String.valueOf(order));
this.order = order;
}
@Override
public INDArray rand(long[] shape, double min, double max, org.nd4j.linalg.api.rng.Random rng) {
Nd4j.getRandom().setSeed(rng.getSeed());
return Nd4j.getDistributions().createUniform(min, max).sample(shape);
}
@Override
public INDArray rand(int[] shape, double min, double max, org.nd4j.linalg.api.rng.Random rng) {
Nd4j.getRandom().setSeed(rng.getSeed());
return Nd4j.getDistributions().createUniform(min, max).sample(shape);
}
@Override
public INDArray rand(long rows, long columns, double min, double max, org.nd4j.linalg.api.rng.Random rng) {
Nd4j.getRandom().setSeed(rng.getSeed());
return rand(new long[] {rows, columns}, min, max, rng);
}
/**
* Sets the data opType
*
* @param dtype
*/
@Override
public void setDType(DataType dtype) {
assert dtype == DataType.DOUBLE || dtype == DataType.FLOAT
|| dtype == DataType.INT : "Invalid opType passed, must be float or double";
// this.dtype = dtype;
}
@Override
public INDArray create(int[] shape, DataType dataType, MemoryWorkspace workspace) {
return create(shape, Nd4j.createBuffer(shape, dataType));
}
/**
* Returns the order for this ndarray for internal data storage
*
* @return the order (c or f)
*/
@Override
public char order() {
return order;
}
/**
* Returns the data opType for this ndarray
*
* @return the data opType for this ndarray
*/
@Override
public DataType dtype() {
return Nd4j.dataType();
}
@Override
public INDArray create(int[] ints, int[] ints1, int[] stride, long offset) {
return create(Nd4j.createBuffer(ints), ints1, stride, offset);
}
@Override
public INDArray create(long rows, long columns, char ordering) {
return create(new long[] {rows, columns}, ordering);
}
/**
* Returns a vector with all of the elements in every nd array
* equal to the sum of the lengths of the ndarrays
*
* @param matrices the ndarrays to getFloat a flattened representation of
* @return the flattened ndarray
*/
@Override
public INDArray toFlattened(Collection matrices) {
return toFlattened('c', matrices.toArray(new INDArray[matrices.size()]));
}
@Override
public INDArray toFlattened(int length, Iterator... matrices) {
List arr = new ArrayList<>();
for (Iterator arrs : matrices) {
while (arrs.hasNext())
arr.add(arrs.next());
}
return toFlattened(arr);
}
/**
* Returns a column vector where each entry is the nth bilinear
* product of the nth slices of the two tensors.
*/
@Override
public INDArray bilinearProducts(INDArray curr, INDArray in) {
Preconditions.checkArgument(curr.rank() == 3, "Argument 'curr' must be rank 3. Got input with rank: %s", curr.rank());
if (in.columns() != 1) {
throw new AssertionError("Expected a column vector");
}
if (in.rows() != curr.size(curr.shape().length - 1)) {
throw new AssertionError("Number of rows in the input does not match number of columns in tensor");
}
if (curr.size(curr.shape().length - 2) != curr.size(curr.shape().length - 1)) {
throw new AssertionError("Can only perform this operation on a SimpleTensor with square slices");
}
INDArray ret = Nd4j.create(curr.slices(), 1);
INDArray inT = in.transpose();
for (int i = 0; i < curr.slices(); i++) {
INDArray slice = curr.slice(i);
INDArray inTTimesSlice = inT.mmul(slice);
ret.putScalar(i, Nd4j.getBlasWrapper().dot(inTTimesSlice, in));
}
return ret;
}
@Override
public INDArray toFlattened(INDArray... matrices) {
return toFlattened(Nd4j.order(), Arrays.asList(matrices));
}
@Override
public INDArray toFlattened(char order, INDArray... matrices) {
return toFlattened(order, Arrays.asList(matrices));
}
/**
* Create the identity ndarray
*
* @param n the number for the identity
* @return
*/
@Override
public INDArray eye(long n) {
INDArray ret = Nd4j.create(n, n);
for (int i = 0; i < n; i++) {
ret.put(i, i, 1.0);
}
return ret.reshape(n, n);
}
/**
* Rotate a matrix 90 degrees
*
* @param toRotate the matrix to rotate
* @return the rotated matrix
*/
@Override
public void rot90(INDArray toRotate) {
if (!toRotate.isMatrix())
throw new IllegalArgumentException("Only rotating matrices");
INDArray start = toRotate.transpose();
for (int i = 0; i < start.rows(); i++)
start.putRow(i, reverse(start.getRow(i)));
}
/**
* Reverses the passed in matrix such that m[0] becomes m[m.length - 1] etc
*
* @param reverse the matrix to reverse
* @return the reversed matrix
*/
@Override
public INDArray rot(INDArray reverse) {
INDArray ret = Nd4j.create(reverse.shape());
if (reverse.isVector())
return reverse(reverse);
else {
for (int i = 0; i < reverse.slices(); i++) {
ret.putSlice(i, reverse(reverse.slice(i)));
}
}
return ret.reshape(reverse.shape());
}
/**
* Reverses the passed in matrix such that m[0] becomes m[m.length - 1] etc
*
* @param reverse the matrix to reverse
* @return the reversed matrix
*/
@Override
public INDArray reverse(INDArray reverse) {
// FIXME: native method should be used instead
INDArray rev = reverse.reshape(-1);
INDArray ret = Nd4j.create(rev.shape());
int count = 0;
for (long i = rev.length() - 1; i >= 0; i--) {
ret.putScalar(count++, rev.getFloat(i));
}
return ret.reshape(reverse.shape());
}
/**
* Array of evenly spaced values.
*
* @param begin the begin of the range
* @param end the end of the range
* @return the range vector
*/
@Override
public INDArray arange(double begin, double end, double step) {
long length = (long)Math.floor((end-begin)/step);
DynamicCustomOp op = new Range(begin, end, step, DataType.FLOAT);
INDArray out = Nd4j.create(op.calculateOutputShape().get(0));
op.setOutputArgument(0, out);
Nd4j.exec(op);
return out;
}
/**
* Copy a to b
*
* @param a the origin matrix
* @param b the destination matrix
*/
@Override
public void copy(INDArray a, INDArray b) {
b.assign(a);
}
/**
* Generates a random matrix between min and max
*
* @param shape the number of rows of the matrix
* @param min the minimum number
* @param max the maximum number
* @param rng the rng to use
* @return a random matrix of the specified shape and range
*/
@Override
public INDArray rand(int[] shape, float min, float max, org.nd4j.linalg.api.rng.Random rng) {
//ensure shapes that wind up being scalar end up with the write shape
if (shape.length == 1 && shape[0] == 0) {
shape = new int[] {1, 1};
}
return Nd4j.getDistributions().createUniform(min, max).sample(shape);
}
@Override
public INDArray rand(long[] shape, float min, float max, org.nd4j.linalg.api.rng.Random rng) {
//ensure shapes that wind up being scalar end up with the write shape
if (shape.length == 1 && shape[0] == 0) {
shape = new long[] {1, 1};
}
return Nd4j.getDistributions().createUniform(min, max).sample(shape);
}
/**
* Generates a random matrix between min and max
*
* @param rows the number of rows of the matrix
* @param columns the number of columns in the matrix
* @param min the minimum number
* @param max the maximum number
* @param rng the rng to use
* @return a random matrix of the specified shape and range
*/
@Override
public INDArray rand(long rows, long columns, float min, float max, org.nd4j.linalg.api.rng.Random rng) {
return rand(new long[] {rows, columns}, min, max, rng);
}
/**
* Merge the vectors and append a bias.
* Each vector must be either row or column vectors.
* An exception is thrown for inconsistency (mixed row and column vectors)
*
* @param vectors the vectors to merge
* @return the merged ndarray appended with the bias
*/
@Override
public INDArray appendBias(INDArray... vectors) {
Preconditions.checkArgument(vectors != null && vectors.length > 0, "vectros must be not null and have at least one element");
int size = 0;
for (INDArray vector : vectors) {
size += vector.rows();
Preconditions.checkArgument(vectors[0].dataType() == vector.dataType(), "appendBias: all arrays must have same type");
}
INDArray result = Nd4j.create(vectors[0].dataType(), size + 1, vectors[0].columns());
int index = 0;
for (INDArray vector : vectors) {
INDArray put = toFlattened(vector, Nd4j.ones(vector.dataType(), 1));
result.put(new INDArrayIndex[] {NDArrayIndex.interval(index, index + vector.rows() + 1),
NDArrayIndex.interval(0, vectors[0].columns())}, put);
index += vector.rows();
}
return result;
}
/**
* Create a random ndarray with the given shape using the given rng
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @param r the random generator to use
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(long rows, long columns, org.nd4j.linalg.api.rng.Random r) {
return rand(new long[] {rows, columns}, r);
}
/**
* Create a random ndarray with the given shape using the given rng
*
* @param rows the number of rows in the matrix
* @param columns the columns of the ndarray
* @param seed the seed to use
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(long rows, long columns, long seed) {
Nd4j.getRandom().setSeed(seed);
return rand(new long[] {rows, columns}, Nd4j.getRandom());
}
/**
* Create a random ndarray with the given shape using
* the current time as the seed
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(long rows, long columns) {
return rand(new long[] {rows, columns}, System.currentTimeMillis());
}
/**
* Create a random (uniform 0-1) NDArray with the specified shape and order
* @param order Order ('c' or 'f') of the output array
* @param rows Number of rows of the output array
* @param columns Number of columns of the output array
*/
@Override
public INDArray rand(char order, long rows, long columns) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextDouble(order, new long[] {rows, columns});
}
/**
* Random normal using the given rng
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @param r the random generator to use
* @return
*/
@Override
public INDArray randn(long rows, long columns, org.nd4j.linalg.api.rng.Random r) {
return randn(new long[] {rows, columns}, r);
}
/**
* Random normal using the current time stamp
* as the seed
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return
*/
@Override
public INDArray randn(long rows, long columns) {
return randn(new long[] {rows, columns}, System.currentTimeMillis());
}
/**
* Generate a random normal N(0,1) with the specified order and shape
* @param order Order of the output array
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return
*/
@Override
public INDArray randn(char order, long rows, long columns) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextGaussian(order, new long[] {rows, columns});
}
/**
* Random normal using the specified seed
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return
*/
@Override
public INDArray randn(long rows, long columns, long seed) {
Nd4j.getRandom().setSeed(seed);
return randn(new long[] {rows, columns}, Nd4j.getRandom());
}
/**
* Create a random ndarray with the given shape using the given rng
*
* @param shape the shape of the ndarray
* @param r the random generator to use
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(int[] shape, Distribution r) {
INDArray ret = r.sample(shape);
return ret;
}
/**
* Create a random ndarray with the given shape using the given rng
*
* @param shape the shape of the ndarray
* @param r the random generator to use
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(int[] shape, org.nd4j.linalg.api.rng.Random r) {
INDArray ret = r.nextDouble(shape);
return ret;
}
@Override
public INDArray rand(long[] shape, org.nd4j.linalg.api.rng.Random r) {
INDArray ret = r.nextDouble(shape);
return ret;
}
/**
* Create a random ndarray with the given shape using the given rng
*
* @param shape the shape of the ndarray
* @param seed the seed to use
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(int[] shape, long seed) {
Nd4j.getRandom().setSeed(seed);
return rand(shape, Nd4j.getRandom());
}
@Override
public INDArray rand(long[] shape, long seed) {
Nd4j.getRandom().setSeed(seed);
return rand(shape, Nd4j.getRandom());
}
/**
* Create a random ndarray with the given shape using
* the current time as the seed
*
* @param shape the shape of the ndarray
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(int[] shape) {
return rand(shape, System.currentTimeMillis());
}
@Override
public INDArray rand(long[] shape) {
return rand(shape, System.currentTimeMillis());
}
/**
* Create a random ndarray with the given shape and order
*
* @param shape the shape of the ndarray
* @return the random ndarray with the specified shape
*/
@Override
public INDArray rand(char order, int[] shape) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextDouble(order, shape);
}
@Override
public INDArray rand(char order, long[] shape) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextDouble(order, shape);
}
/**
* Random normal using the given rng
*
* @param shape the shape of the ndarray
* @param r the random generator to use
* @return
*/
@Override
public INDArray randn(int[] shape, org.nd4j.linalg.api.rng.Random r) {
return r.nextGaussian(shape);
}
@Override
public INDArray randn(long[] shape, org.nd4j.linalg.api.rng.Random r) {
return r.nextGaussian(shape);
}
/**
* Random normal using the current time stamp
* as the seed
*
* @param shape the shape of the ndarray
* @return
*/
@Override
public INDArray randn(char order, int[] shape) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextGaussian(order, shape);
}
@Override
public INDArray randn(char order, long[] shape) {
Shape.assertValidOrder(order);
return Nd4j.getRandom().nextGaussian(order, shape);
}
/**
* Random normal N(0,1) with the specified shape and
*
* @param shape the shape of the ndarray
* @return
*/
@Override
public INDArray randn(int[] shape) {
return randn(shape, System.currentTimeMillis());
}
@Override
public INDArray randn(long[] shape) {
return randn(shape, System.currentTimeMillis());
}
/**
* Random normal using the specified seed
*
* @param shape the shape of the ndarray
* @return
*/
@Override
public INDArray randn(int[] shape, long seed) {
Nd4j.getRandom().setSeed(seed);
return randn(shape, Nd4j.getRandom());
}
@Override
public INDArray randn(long[] shape, long seed) {
Nd4j.getRandom().setSeed(seed);
return randn(shape, Nd4j.getRandom());
}
/**
* Creates a row vector with the data
*
* @param data the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray create(double[] data) {
return create(data, new int[] {1, data.length});
}
/**
* Creates a row vector with the data
*
* @param data the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray create(float[] data) {
return create(data, new long[] {data.length});
}
/**
* Creates a row vector with the specified number of columns
*
* @param columns the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray create(long columns) {
return create(new long[] {columns});
}
/**
* Creates a row vector with the specified number of columns
*
* @param rows the rows of the ndarray
* @param columns the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray zeros(long rows, long columns) {
return zeros(new long[] {rows, columns});
}
/**
* This method produces concatenated array, that consist from tensors, fetched from source array, against some dimension and specified indexes
*
* @param source source tensor
* @param sourceDimension dimension of source tensor
* @param indexes indexes from source array
* @return
*/
@Override
public INDArray pullRows(INDArray source, int sourceDimension, int[] indexes, char order) {
Shape.assertValidOrder(order);
long vectorLength = source.shape()[sourceDimension];
INDArray ret = Nd4j.createUninitialized(new long[] {indexes.length, vectorLength}, order);
for (int cnt = 0; cnt < indexes.length; cnt++) {
ret.putRow(cnt, source.tensorAlongDimension((int) indexes[cnt], sourceDimension));
}
return ret;
}
/**
* This method produces concatenated array, that consist from tensors, fetched from source array, against some dimension and specified indexes
*
* @param source source tensor
* @param sourceDimension dimension of source tensor
* @param indexes indexes from source array
* @return
*/
@Override
public INDArray pullRows(INDArray source, int sourceDimension, int[] indexes) {
return pullRows(source, sourceDimension, indexes, Nd4j.order());
}
/**
* Creates a row vector with the specified number of columns
*
* @param columns the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray zeros(long columns) {
return zeros(new long[] {columns});
}
/**
* Creates an ndarray with the specified value
* as the only value in the ndarray
*
* @param shape the shape of the ndarray
* @param value the value to assign
* @return the created ndarray
*/
@Override
public INDArray valueArrayOf(int[] shape, double value) {
INDArray ret = Nd4j.createUninitialized(shape, Nd4j.order());
ret.assign(value);
return ret;
}
@Override
public INDArray valueArrayOf(long[] shape, double value) {
INDArray ret = Nd4j.createUninitialized(shape, Nd4j.order());
ret.assign(value);
return ret;
}
@Override
public INDArray create(int[] shape, int[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
//ensure shapes that wind up being scalar end up with the write shape
long length = ArrayUtil.prodLong(shape);
if(length == 0)
return scalar(0.0);
return create(Nd4j.createBuffer(length), shape, stride, offset, ordering);
}
/**
* Creates a row vector with the specified number of columns
*
* @param rows the number of rows in the matrix
* @param columns the columns of the ndarray
* @param value the value to assign
* @return the created ndarray
*/
@Override
public INDArray valueArrayOf(long rows, long columns, double value) {
INDArray create = createUninitialized(new long[] {rows, columns}, Nd4j.order());
create.assign(value);
return create;
}
/**
* Creates a row vector with the specified number of columns
*
* @param rows the number of rows in the matrix
* @param columns the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray ones(long rows, long columns) {
return ones(new long[] {rows, columns});
}
/**
* Creates a row vector with the specified number of columns
*
* @param columns the columns of the ndarray
* @return the created ndarray
*/
@Override
public INDArray ones(long columns) {
return ones(new long[] {columns});
}
@Override
public INDArray create(float[] data, int[] shape, char ordering) {
Shape.assertValidOrder(ordering);
long length = ArrayUtil.prodLong(shape);
if(length == 0)
return scalar(0.0);
return create(Nd4j.createBuffer(data), shape, Nd4j.getStrides(shape, ordering), 0, ordering);
}
/**
* concatenate ndarrays along a dimension
*
* @param dimension the dimension to concatenate along
* @param toConcat the ndarrays to concatenate
* @return the concatenate ndarrays
*/
@Override
public INDArray concat(int dimension, INDArray... toConcat) {
if (toConcat.length == 1)
return toConcat[0];
int sumAlongDim = 0;
boolean allC = toConcat[0].ordering() == 'c';
long[] outputShape = ArrayUtil.copy(toConcat[0].shape());
outputShape[dimension] = sumAlongDim;
for (int i = 0; i < toConcat.length; i++) {
sumAlongDim += toConcat[i].size(dimension);
allC = allC && toConcat[i].ordering() == 'c';
for (int j = 0; j < toConcat[i].rank(); j++) {
if (j != dimension && toConcat[i].size(j) != outputShape[j] && !toConcat[i].isVector()) {
throw new IllegalArgumentException(
"Illegal concatenation at array " + i + " and shape element " + j);
}
}
}
long[] sortedStrides = Nd4j.getStrides(outputShape);
INDArray ret = Nd4j.create(outputShape, sortedStrides);
allC &= (ret.ordering() == 'c');
if (toConcat[0].isScalar()) {
INDArray retLinear = ret.reshape(-1);
for (int i = 0; i < retLinear.length(); i++)
retLinear.putScalar(i, toConcat[i].getDouble(0));
return ret;
}
if (dimension == 0 && allC) {
int currBuffer = 0;
int currBufferOffset = 0;
for (int i = 0; i < ret.length(); i++) {
ret.data().put(i, toConcat[currBuffer].data()
.getDouble(toConcat[currBuffer].offset() + currBufferOffset++));
if (currBufferOffset >= toConcat[currBuffer].length()) {
currBuffer++;
currBufferOffset = 0;
}
}
return ret;
}
int arrOffset = 0;
if (ret.tensorsAlongDimension(dimension) > Integer.MAX_VALUE)
throw new ND4JArraySizeException();
INDArray[] retAlongDimensionArrays = new INDArray[(int) ret.tensorsAlongDimension(dimension)];
for (int i = 0; i < retAlongDimensionArrays.length; i++)
retAlongDimensionArrays[i] = ret.tensorAlongDimension(i, dimension);
for (INDArray arr : toConcat) {
long arrTensorLength = -1;
if (arr.tensorsAlongDimension(dimension) != ret.tensorsAlongDimension(dimension))
throw new IllegalStateException("Illegal concatenate. Tensors along dimension must be same length.");
for (int i = 0; i < arr.tensorsAlongDimension(dimension); i++) {
INDArray retLinear = retAlongDimensionArrays[i];
INDArray arrTensor = arr.tensorAlongDimension(i, dimension);
arrTensorLength = arrTensor.length();
for (int j = 0; j < arrTensor.length(); j++) {
int idx = j + arrOffset;
retLinear.putScalar(idx, arrTensor.getDouble(j));
}
}
//bump the sliding window
arrOffset += arrTensorLength;
}
return ret;
}
/**
* Concatenates two matrices horizontally.
* Matrices must have identical
* numbers of rows.
*
* @param arrs
*/
public INDArray hstack(@NonNull INDArray... arrs) {
int firstRank = arrs[0].rank();
Preconditions.checkState(firstRank > 0 && firstRank <= 2, "Only rank 1 and 2 arrays may be horizontally stacked; first input has rank %ndRank shape %nhShape", arrs[0], arrs[0]);
for( int i=1; i list, int[] shape);
/**
* Creates an ndarray with the specified shape
*
* @param rows the rows of the ndarray
* @param columns the columns of the ndarray
* @param stride the stride for the ndarray
* @param offset the offset of the ndarray
* @return the instance
*/
@Override
public INDArray create(long rows, long columns, int[] stride, long offset) {
/*
if (Nd4j.dataType() == DataType.DOUBLE)
return create(new double[rows * columns], new int[] {rows, columns}, stride, offset);
if (Nd4j.dataType() == DataType.FLOAT || Nd4j.dataType() == DataType.HALF)
return create(new float[rows * columns], new int[] {rows, columns}, stride, offset);
if (Nd4j.dataType() == DataType.INT)
return create(new int[rows * columns], new int[] {rows, columns}, stride, offset);
throw new IllegalStateException("Illegal data opType " + Nd4j.dataType());
*/
throw new UnsupportedOperationException();
}
/**
* Creates an ndarray with the specified shape
*
* @param shape the shape of the ndarray
* @param stride the stride for the ndarray
* @param offset the offset of the ndarray
* @return the instance
*/
@Override
public INDArray create(int[] shape, int[] stride, long offset) {
DataBuffer buffer = Nd4j.createBuffer(ArrayUtil.prodLong(shape));
return create(buffer, shape, stride, offset);
}
@Override
public INDArray create(long[] shape, long[] stride, long offset) {
DataBuffer buffer = Nd4j.createBuffer(ArrayUtil.prodLong(shape));
return create(buffer, shape, stride, offset);
}
/**
* Creates an ndarray with the specified shape
*
* @param rows the rows of the ndarray
* @param columns the columns of the ndarray
* @param stride the stride for the ndarray
* @return the instance
*/
@Override
public INDArray create(long rows, long columns, int[] stride) {
return create(new long[] {rows, columns}, ArrayUtil.toLongArray(stride));
}
@Override
public INDArray create(long[] shape, long[] stride) {
return create(shape, stride, 0, Nd4j.order());
}
@Override
public INDArray create(long[] shape, long[] stride, long offset, char ordering) {
Shape.assertValidOrder(ordering);
return create(Nd4j.createBuffer(ArrayUtil.prodLong(shape)), shape, stride, offset, ordering);
}
/**
* Creates an ndarray with the specified shape
*
* @param shape the shape of the ndarray
* @param stride the stride for the ndarray
* @return the instance
*/
@Override
public INDArray create(int[] shape, int[] stride) {
return create(shape, stride, 0);
}
/**
* Creates an ndarray with the specified shape
*
* @param rows the rows of the ndarray
* @param columns the columns of the ndarray
* @return the instance
*/
@Override
public INDArray create(long rows, long columns) {
return create(new long[] {rows, columns});
}
/**
* Creates an ndarray with the specified shape
*
* @param shape the shape of the ndarray
* @return the instance
*/
@Override
public INDArray create(long[] shape) {
//ensure shapes that wind up being scalar end up with the write shape
return create(shape, Nd4j.getStrides(shape), 0L);
}
/**
* Creates an ndarray with the specified shape
*
* @param shape the shape of the ndarray
* @return the instance
*/
@Override
public INDArray create(int[] shape) {
return create(shape, Nd4j.getStrides(shape), 0);
}
/**
* Create a scalar nd array with the specified value and offset
*
* @param value the value of the scalar
* @param offset the offset of the ndarray
* @return the scalar nd array
*/
@Override
public INDArray scalar(float value, long offset) {
return create(new float[] {value}, new int[0], new int[0], offset);
}
/**
* Create a scalar nd array with the specified value and offset
*
* @param value the value of the scalar
* @param offset the offset of the ndarray
* @return the scalar nd array
*/
@Override
public INDArray scalar(double value, long offset) {
return create(new double[] {value}, new int[0], new int[0], offset);
}
/**
* Create a scalar nd array with the specified value and offset
*
* @param value the value of the scalar
* @param offset the offset of the ndarray
* @return the scalar nd array
*/
@Override
public INDArray scalar(int value, long offset) {
return create(new int[] {value}, new long[0], new long[0], DataType.INT, Nd4j.getMemoryManager().getCurrentWorkspace());
}
/**
* Create a scalar ndarray with the specified offset
*
* @param value the value to initialize the scalar with
* @return the created ndarray
*/
@Override
public INDArray scalar(Number value) {
MemoryWorkspace ws = Nd4j.getMemoryManager().getCurrentWorkspace();
if (value instanceof Double || value instanceof AtomicDouble) /* note that org.nd4j.linalg.primitives.AtomicDouble extends org.nd4j.shade.guava.util.concurrent.AtomicDouble */
return scalar(value.doubleValue());
else if (value instanceof Float)
return scalar(value.floatValue());
else if (value instanceof Long || value instanceof AtomicLong)
return create(new long[] {value.longValue()}, new long[] {}, new long[] {}, DataType.LONG, ws);
else if (value instanceof Integer || value instanceof AtomicInteger)
return create(new int[] {value.intValue()}, new long[] {}, new long[] {}, DataType.INT, ws);
else if (value instanceof Short)
return create(new short[] {value.shortValue()}, new long[] {}, new long[] {}, DataType.SHORT, ws);
else if (value instanceof Byte)
return create(new byte[] {value.byteValue()}, new long[] {}, new long[] {}, DataType.BYTE, ws);
else
throw new UnsupportedOperationException("Unsupported data type: [" + value.getClass().getSimpleName() + "]");
}
/**
* Create a scalar nd array with the specified value and offset
*
* @param value the value of the scalar
* @return the scalar nd array
*/
@Override
public INDArray scalar(double value) {
return create(new double[] {value}, new long[0], new long[0], DataType.DOUBLE, Nd4j.getMemoryManager().getCurrentWorkspace());
}
@Override
public INDArray scalar(float value) {
return create(new float[] {value}, new long[0], new long[0], DataType.FLOAT, Nd4j.getMemoryManager().getCurrentWorkspace());
}
@Override
public INDArray create(float[] data, int[] shape, long offset) {
return create(Nd4j.createBuffer(data), shape, offset);
}
public abstract INDArray create(float[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace);
@Override
public INDArray create(float[] data, char order) {
val shape = new long[] {data.length};
val stride = Nd4j.getStrides(shape, order);
return create(data, shape, stride, order, DataType.FLOAT);
}
@Override
public INDArray create(float[] data, int[] shape, int[] stride, char order, long offset) {
return create(Nd4j.createBuffer(data), shape, stride, order, offset);
}
@Override
public INDArray create(double[] data, char order) {
Shape.assertValidOrder(order);
return create(data, new long[] {data.length}, new long[]{1}, DataType.DOUBLE, Nd4j.getMemoryManager().getCurrentWorkspace());
}
@Override
public INDArray create(double[] data, int[] shape, int[] stride, char order, long offset) {
return create(Nd4j.createBuffer(data), shape, stride, order, offset);
}
@Override
public INDArray create(DataBuffer buffer, int[] shape, int[] stride, char order, long offset) {
Shape.assertValidOrder(order);
return create(buffer, shape, stride, offset, order);
}
@Override
public INDArray create(int[] data, int[] shape, int[] stride, char order, long offset) {
Shape.assertValidOrder(order);
return create(Nd4j.createBuffer(data), shape, stride, order, offset);
}
}
