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.
/*
*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://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.
*
*
*/
package org.nd4j.linalg.api.complex;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.blas.BlasBufferUtil;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.ndarray.BaseNDArray;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.NDArrayFactory;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.conditions.Condition;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.linalg.util.LinAlgExceptions;
import org.nd4j.linalg.api.shape.Shape;
import java.util.*;
import static org.nd4j.linalg.util.ArrayUtil.calcStrides;
import static org.nd4j.linalg.util.ArrayUtil.calcStridesFortran;
/**
* ComplexNDArray for complex numbers.
*
*
* Note that the indexing scheme for a complex ndarray is 2 * length
* not length.
*
* The reason for this is the fact that imaginary components have
* to be stored alongside realComponent components.
*
* @author Adam Gibson
*/
public abstract class BaseComplexNDArray extends BaseNDArray implements IComplexNDArray {
public BaseComplexNDArray() {
}
/**
*
* @param data
* @param shape
* @param stride
*/
public BaseComplexNDArray(DataBuffer data, int[] shape, int[] stride) {
this(data, shape, stride, 0, Nd4j.order());
}
/**
*
* @param data
*/
public BaseComplexNDArray(float[] data) {
super(data);
}
/**
*
* @param buffer
* @param shape
* @param stride
* @param offset
* @param ordering
*/
public BaseComplexNDArray(DataBuffer buffer, int[] shape, int[] stride, int offset, char ordering) {
super(buffer, shape, stride, offset, ordering);
}
/**
* 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
* @param ordering
*/
public BaseComplexNDArray(float[] data, int[] shape, char ordering) {
this(data, shape, Nd4j.getComplexStrides(shape, ordering), 0, ordering);
}
/**
*
* @param shape
* @param offset
* @param ordering
*/
public BaseComplexNDArray(int[] shape, int offset, char ordering) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape) * 2),
shape, Nd4j.getComplexStrides(shape, ordering),
offset, ordering);
}
/**
*
* @param shape
*/
public BaseComplexNDArray(int[] shape) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape) * 2), shape, Nd4j.getComplexStrides(shape));
}
/**
*
* @param data
* @param shape
* @param stride
* @param ordering
*/
public BaseComplexNDArray(float[] data, int[] shape, int[] stride, char ordering) {
this(data, shape, stride, 0, ordering);
}
public BaseComplexNDArray(int[] shape, char ordering) {
this(Nd4j.createBuffer(ArrayUtil.prodLong(shape) * 2), shape, Nd4j.getComplexStrides(shape, ordering), 0, ordering);
}
/**
* Initialize the given ndarray as the real component
*
* @param m the real component
* @param stride the stride of the ndarray
* @param ordering the ordering for the ndarray
*/
public BaseComplexNDArray(INDArray m, int[] stride, char ordering) {
this(m.shape(), stride, ordering);
copyFromReal(m);
}
/**
* Construct a complex matrix from a realComponent matrix.
*/
public BaseComplexNDArray(INDArray m, char ordering) {
this(m.shape(), ordering);
copyFromReal(m);
}
/**
* Construct a complex matrix from a realComponent matrix.
*/
public BaseComplexNDArray(INDArray m) {
this(m, Nd4j.order());
}
/**
* Create with the specified ndarray as the real component
* and the given stride
*
* @param m the ndarray to use as the stride
* @param stride the stride of the ndarray
*/
public BaseComplexNDArray(INDArray m, int[] stride) {
this(m, stride, Nd4j.order());
}
/**
* Create an ndarray from the specified slices
* and the given shape
*
* @param slices the slices of the ndarray
* @param shape the final shape of the ndarray
* @param stride the stride of the ndarray
*/
public BaseComplexNDArray(List slices, int[] shape, int[] stride) {
this(slices, shape, stride, Nd4j.order());
}
/**
* Create an ndarray from the specified slices
* and the given shape
*
* @param slices the slices of the ndarray
* @param shape the final shape of the ndarray
* @param stride the stride of the ndarray
* @param ordering the ordering for the ndarray
*/
public BaseComplexNDArray(List slices, int[] shape, int[] stride, char ordering) {
this(new float[ArrayUtil.prod(shape) * 2]);
List list = new ArrayList<>();
for (int i = 0; i < slices.size(); i++) {
IComplexNDArray flattened = slices.get(i).ravel();
for (int j = 0; j < flattened.length(); j++)
list.add(flattened.getComplex(j));
}
throw new UnsupportedOperationException();
/*
this.ordering = ordering;
this.data = Nd4j.createBuffer(ArrayUtil.prod(shape) * 2);
this.stride = stride;
init(shape);
int count = 0;
for (int i = 0; i < list.size(); i++) {
putScalar(count, list.get(i));
count++;
}*/
}
/**
* Create an ndarray from the specified slices
* and the given shape
*
* @param slices the slices of the ndarray
* @param shape the final shape of the ndarray
* @param ordering the ordering of the ndarray
*/
public BaseComplexNDArray(List slices, int[] shape, char ordering) {
this(slices, shape, Nd4j.getComplexStrides(shape, ordering), ordering);
}
public BaseComplexNDArray(float[] data, int[] shape, int[] stride, int offset, Character order) {
this.data = Nd4j.createBuffer(data);
/* this.stride = ArrayUtil.copy(stride);
this.offset = offset;
this.ordering = order;
init(shape);*/
throw new UnsupportedOperationException();
}
/**
*
* @param data
*/
public BaseComplexNDArray(DataBuffer data) {
super(data);
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
*/
public BaseComplexNDArray(DataBuffer data, int[] shape, int[] stride, int offset) {
this.data = data;
/* this.stride = ArrayUtil.copy(stride);
this.offset = offset;
this.ordering = Nd4j.order();
init(shape);*/
throw new UnsupportedOperationException();
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
* @param ordering
*/
public BaseComplexNDArray(IComplexNumber[] data, int[] shape, int[] stride, int offset, char ordering) {
this(shape, stride, offset, ordering);
assert data.length <= length;
for (int i = 0; i < data.length; i++) {
putScalar(i, data[i]);
}
}
/**
*
* @param data
* @param shape
*/
public BaseComplexNDArray(DataBuffer data, int[] shape) {
this(shape);
this.data = data;
}
/**
*
* @param data
* @param shape
* @param stride
* @param offset
*/
public BaseComplexNDArray(IComplexNumber[] data, int[] shape, int[] stride, int offset) {
this(data, shape, stride, offset, Nd4j.order());
}
/**
*
* @param data
* @param shape
* @param offset
* @param ordering
*/
public BaseComplexNDArray(IComplexNumber[] data, int[] shape, int offset, char ordering) {
this(data, shape, Nd4j.getComplexStrides(shape), offset, ordering);
}
/**
*
* @param buffer
* @param shape
* @param offset
* @param ordering
*/
public BaseComplexNDArray(DataBuffer buffer, int[] shape, int offset, char ordering) {
this(buffer, shape, Nd4j.getComplexStrides(shape), offset, ordering);
}
/**
*
* @param buffer
* @param shape
* @param offset
*/
public BaseComplexNDArray(DataBuffer buffer, int[] shape, int offset) {
this(buffer, shape, Nd4j.getComplexStrides(shape), offset, Nd4j.order());
}
/**
*
* @param data
* @param order
*/
public BaseComplexNDArray(float[] data, Character order) {
this(data, new int[]{1,data.length / 2}, order);
}
/**
* Create an ndarray from the specified slices
* and the given shape
*
* @param slices the slices of the ndarray
* @param shape the final shape of the ndarray
*/
public BaseComplexNDArray(List slices, int[] shape) {
this(slices, shape, Nd4j.order());
}
/**
* Create a complex ndarray with the given complex doubles.
* Note that this maybe an easier setup than the new float
*
* @param newData the new data for this array
* @param shape the shape of the ndarray
*/
public BaseComplexNDArray(IComplexNumber[] newData, int[] shape) {
super(new float[ArrayUtil.prod(shape) * 2]);
/* init(shape);
for (int i = 0; i < length; i++)
putScalar(i, newData[i].asDouble());
*/
throw new UnsupportedOperationException();
}
/**
* Create a complex ndarray with the given complex doubles.
* Note that this maybe an easier setup than the new float
*
* @param newData the new data for this array
* @param shape the shape of the ndarray
*/
public BaseComplexNDArray(IComplexNumber[] newData, int[] shape, int[] stride) {
super(new float[ArrayUtil.prod(shape) * 2]);
/* this.stride = stride;
init(shape);
for (int i = 0; i < length; i++)
put(i, newData[i].asDouble());
*/
throw new UnsupportedOperationException();
}
/**
* Create a complex ndarray with the given complex doubles.
* Note that this maybe an easier setup than the new float
*
* @param newData the new data for this array
* @param shape the shape of the ndarray
* @param ordering the ordering for the ndarray
*/
public BaseComplexNDArray(IComplexNumber[] newData, int[] shape, char ordering) {
super(new float[ArrayUtil.prod(shape) * 2]);
/* this.ordering = ordering;
//init(shape);
for (int i = 0; i < length; i++)
put(i, newData[i]);*/
throw new UnsupportedOperationException();
}
/**
* Initialize with the given data,shape and stride
*
* @param data the data to use
* @param shape the shape of the ndarray
* @param stride the stride of the ndarray
*/
public BaseComplexNDArray(float[] data, int[] shape, int[] stride) {
this(data, shape, stride, 0, Nd4j.order());
}
/**
*
* @param data
* @param shape
*/
public BaseComplexNDArray(float[] data, int[] shape) {
this(data, shape, 0);
}
public BaseComplexNDArray(float[] data, int[] shape, int offset, char ordering) {
this(data, shape, ordering == NDArrayFactory.C ? calcStrides(shape, 2) : calcStridesFortran(shape, 2), offset, ordering);
}
public BaseComplexNDArray(float[] data, int[] shape, int 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 BaseComplexNDArray(int[] shape, int[] stride, int offset) {
this(new float[ArrayUtil.prod(shape) * 2], shape, stride, 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 for the ndarray
*/
public BaseComplexNDArray(int[] shape, int[] stride, int offset, char ordering) {
this(new float[ArrayUtil.prod(shape) * 2], shape, stride, offset);
this.shapeInformation = Shape.createShapeInformation(shape,stride,offset,stride[stride.length - 1],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
*/
public BaseComplexNDArray(int[] shape, int[] stride, char ordering) {
this(shape, stride, 0, 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
*/
public BaseComplexNDArray(int[] shape, int[] stride) {
this(shape, stride, 0);
}
/**
* @param shape
* @param offset
*/
public BaseComplexNDArray(int[] shape, int offset) {
this(shape, offset, Nd4j.order());
}
/**
* Creates a new n times mComplexDoubleMatrix.
*
* @param newRows the number of rows (n) of the new matrix.
* @param newColumns the number of columns (m) of the new matrix.
*/
public BaseComplexNDArray(int newRows, int newColumns) {
this(new int[]{newRows, newColumns});
}
/**
* Creates a new n times mComplexDoubleMatrix.
*
* @param newRows the number of rows (n) of the new matrix.
* @param newColumns the number of columns (m) of the new matrix.
* @param ordering the ordering of the ndarray
*/
public BaseComplexNDArray(int newRows, int newColumns, char ordering) {
this(new int[]{newRows, newColumns}, ordering);
}
public BaseComplexNDArray(float[] data, int[] shape, int[] stride, int offset) {
this(data, shape, stride, offset, Nd4j.order());
}
/**
*
* @param real
*/
protected void copyFromReal(INDArray real) {
if(!Shape.shapeEquals(shape(),real.shape()))
throw new IllegalStateException("Unable to copy array. Not the same shape");
INDArray linear = real.linearView();
IComplexNDArray thisLinear = linearView();
for (int i = 0; i < linear.length(); i++) {
thisLinear.putScalar(i, Nd4j.createComplexNumber(linear.getDouble(i),0.0));
}
}
@Override
protected IComplexNDArray create(DataBuffer data, int[] shape, int[] strides) {
return Nd4j.createComplex(data,shape,strides,offset(),ordering());
}
/**
* Copy real numbers to arr
* @param arr the arr to copy to
*/
protected void copyRealTo(INDArray arr) {
INDArray linear = arr.linearView();
IComplexNDArray thisLinear = linearView();
if(arr.isScalar())
arr.putScalar(0,getReal(0));
else
for (int i = 0; i < linear.length(); i++) {
arr.putScalar(i, thisLinear.getReal(i));
}
}
/**
* Copy imaginary numbers to the given
* ndarray
* @param arr the array to copy imaginary numbers to
*/
protected void copyImagTo(INDArray arr) {
INDArray linear = arr.linearView();
IComplexNDArray thisLinear = linearView();
if(arr.isScalar())
arr.putScalar(0,getReal(0));
else
for (int i = 0; i < linear.length(); i++) {
arr.putScalar(i, thisLinear.getImag(i));
}
}
@Override
public int blasOffset() {
return offset();
}
@Override
public IComplexNDArray linearViewColumnOrder() {
if(length >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Length can not be >= Integer.MAX_VALUE");
return Nd4j.createComplex(data, new int[]{(int)length, 1}, offset());
}
/**
* Returns a linear view reference of shape
* 1,length(ndarray)
*
* @return the linear view of this ndarray
*/
@Override
public IComplexNDArray linearView() {
if (isVector() || isScalar() || length() == 1 || length() == size(0))
return this;
return this;
}
@Override
public void resetLinearView() {
}
@Override
public IComplexNumber getComplex(int i, IComplexNumber result) {
if(!isVector() || i >= length())
throw new IllegalArgumentException("Given index >= length " + length());
IComplexNumber d = getComplex(i);
return result.set(d.realComponent(), d.imaginaryComponent());
}
@Override
public IComplexNumber getComplex(int i, int j, IComplexNumber result) {
IComplexNumber d = getComplex(i, j);
return result.set(d.realComponent(), d.imaginaryComponent());
}
@Override
public IComplexNDArray putScalar(int j, int i, IComplexNumber conji) {
return putScalar(new int[]{j, i}, conji);
}
/**
* Returns an ndarray with 1 if the element is epsilon equals
*
* @param other the number to compare
* @return a copied ndarray with the given
* binary conditions
*/
@Override
public IComplexNDArray eps(Number other) {
return dup().epsi(other);
}
/**
* Returns an ndarray with 1 if the element is epsilon equals
*
* @param other the number to compare
* @return a copied ndarray with the given
* binary conditions
*/
@Override
public IComplexNDArray eps(IComplexNumber other) {
return dup().epsi(other);
}
/**
* Returns an ndarray with 1 if the element is epsilon equals
*
* @param other the number to compare
* @return a copied ndarray with the given
* binary conditions
*/
@Override
public IComplexNDArray epsi(IComplexNumber other) {
IComplexNDArray linear = linearView();
double otherVal = other.realComponent().doubleValue();
for (int i = 0; i < linearView().length(); i++) {
IComplexNumber n = linear.getComplex(i);
double real = n.realComponent().doubleValue();
double diff = Math.abs(real - otherVal);
if (diff <= Nd4j.EPS_THRESHOLD)
linear.putScalar(i, Nd4j.createDouble(1, 0));
else
linear.putScalar(i, Nd4j.createDouble(0, 0));
}
return this;
}
/**
* Returns an ndarray with 1 if the element is epsilon equals
*
* @param other the number to compare
* @return a copied ndarray with the given
* binary conditions
*/
@Override
public IComplexNDArray epsi(Number other) {
IComplexNDArray linear = linearView();
double otherVal = other.doubleValue();
for (int i = 0; i < linearView().length(); i++) {
IComplexNumber n = linear.getComplex(i);
double real = n.realComponent().doubleValue();
double diff = Math.abs(real - otherVal);
if (diff <= Nd4j.EPS_THRESHOLD)
linear.putScalar(i, Nd4j.createDouble(1, 0));
else
linear.putScalar(i, Nd4j.createDouble(0, 0));
}
return this;
}
/**
* epsilon equals than comparison:
* If the given number is less than the
* comparison number the item is 0 otherwise 1
*
* @param other the number to compare
* @return
*/
@Override
public IComplexNDArray eps(INDArray other) {
return dup().epsi(other);
}
/**
* In place epsilon equals than comparison:
* If the given number is less than the
* comparison number the item is 0 otherwise 1
*
* @param other the number to compare
* @return
*/
@Override
public IComplexNDArray epsi(INDArray other) {
IComplexNDArray linear = linearView();
if (other instanceof IComplexNDArray) {
IComplexNDArray otherComplex = (IComplexNDArray) other;
IComplexNDArray otherComplexLinear = otherComplex.linearView();
for (int i = 0; i < linearView().length(); i++) {
IComplexNumber n = linear.getComplex(i);
IComplexNumber otherComplexNumber = otherComplexLinear.getComplex(i);
double real = n.absoluteValue().doubleValue();
double otherAbs = otherComplexNumber.absoluteValue().doubleValue();
double diff = Math.abs(real - otherAbs);
if (diff <= Nd4j.EPS_THRESHOLD)
linear.putScalar(i, Nd4j.createDouble(1, 0));
else
linear.putScalar(i, Nd4j.createDouble(0, 0));
}
}
return this;
}
@Override
public IComplexNDArray lt(Number other) {
return dup().lti(other);
}
@Override
public IComplexNDArray lti(Number other) {
IComplexNDArray linear = linearView();
double val = other.doubleValue();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue() < val ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
return this;
}
@Override
public IComplexNDArray eq(Number other) {
return dup().eqi(other);
}
@Override
public IComplexNDArray eqi(Number other) {
IComplexNDArray linear = linearView();
double val = other.doubleValue();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue() == val ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
return this;
}
@Override
public IComplexNDArray gt(Number other) {
return dup().gti(other);
}
@Override
public IComplexNDArray gti(Number other) {
IComplexNDArray linear = linearView();
double val = other.doubleValue();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue() > val ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
return this;
}
@Override
public IComplexNDArray lt(INDArray other) {
return dup().lti(other);
}
@Override
public IComplexNDArray lti(INDArray other) {
if (other instanceof IComplexNDArray) {
IComplexNDArray linear = linearView();
IComplexNDArray otherLinear = (IComplexNDArray) other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
< otherLinear.getComplex(i).absoluteValue().doubleValue() ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
} else {
IComplexNDArray linear = linearView();
INDArray otherLinear = other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
< otherLinear.getDouble(i) ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
}
return this;
}
@Override
public IComplexNDArray eq(INDArray other) {
return dup().eqi(other);
}
@Override
public IComplexNDArray eqi(INDArray other) {
if (other instanceof IComplexNDArray) {
IComplexNDArray linear = linearView();
IComplexNDArray otherLinear = (IComplexNDArray) other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
== otherLinear.getComplex(i).absoluteValue().doubleValue() ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
} else {
IComplexNDArray linear = linearView();
INDArray otherLinear = other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
== otherLinear.getDouble(i) ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
}
return this;
}
@Override
public IComplexNDArray neq(INDArray other) {
return dup().neqi(other);
}
@Override
public IComplexNDArray neq(Number other) {
return dup().neqi(other);
}
@Override
public IComplexNDArray neqi(Number other) {
IComplexNDArray linear = linearView();
double otherVal = other.doubleValue();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
!= otherVal ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
return this;
}
@Override
public IComplexNDArray neqi(INDArray other) {
if (other instanceof IComplexNDArray) {
IComplexNDArray linear = linearView();
IComplexNDArray otherLinear = (IComplexNDArray) other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
!= otherLinear.getComplex(i).absoluteValue().doubleValue() ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
} else {
IComplexNDArray linear = linearView();
INDArray otherLinear = other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
!= otherLinear.getDouble(i) ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
}
return this;
}
@Override
public IComplexNDArray gt(INDArray other) {
return dup().gti(other);
}
@Override
public IComplexNDArray gti(INDArray other) {
if (other instanceof IComplexNDArray) {
IComplexNDArray linear = linearView();
IComplexNDArray otherLinear = (IComplexNDArray) other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
> otherLinear.getComplex(i).absoluteValue().doubleValue() ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
} else {
IComplexNDArray linear = linearView();
INDArray otherLinear = other.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, linear.getComplex(i).absoluteValue().doubleValue()
> otherLinear.getDouble(i) ? Nd4j.createComplexNumber(1, 0) : Nd4j.createComplexNumber(0, 0));
}
}
return this;
}
@Override
public IComplexNDArray rdiv(Number n, INDArray result) {
return dup().rdivi(n, result);
}
@Override
public IComplexNDArray rdivi(Number n, INDArray result) {
return rdivi(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray rsub(Number n, INDArray result) {
return dup().rsubi(n, result);
}
@Override
public IComplexNDArray rsubi(Number n, INDArray result) {
return rsubi(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray div(Number n, INDArray result) {
return dup().divi(n, result);
}
@Override
public IComplexNDArray divi(Number n, INDArray result) {
return divi(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray mul(Number n, INDArray result) {
return dup().muli(n, result);
}
@Override
public IComplexNDArray muli(Number n, INDArray result) {
return muli(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray sub(Number n, INDArray result) {
return dup().subi(n, result);
}
@Override
public IComplexNDArray subi(Number n, INDArray result) {
return subi(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray add(Number n, INDArray result) {
return dup().addi(n, result);
}
@Override
public IComplexNDArray addi(Number n, INDArray result) {
return addi(Nd4j.createDouble(n.doubleValue(), 0), result);
}
@Override
public IComplexNDArray dup() {
return (IComplexNDArray) Shape.toOffsetZeroCopy(this);
}
@Override
public IComplexNDArray rsubRowVector(INDArray rowVector) {
return dup().rsubiRowVector(rowVector);
}
@Override
public IComplexNDArray rsubiRowVector(INDArray rowVector) {
return doRowWise(rowVector, 't');
}
@Override
public IComplexNDArray rsubColumnVector(INDArray columnVector) {
return dup().rsubiColumnVector(columnVector);
}
@Override
public IComplexNDArray rsubiColumnVector(INDArray columnVector) {
return doColumnWise(columnVector, 'h');
}
@Override
public IComplexNDArray rdivRowVector(INDArray rowVector) {
return dup().rdiviRowVector(rowVector);
}
@Override
public IComplexNDArray rdiviRowVector(INDArray rowVector) {
return doRowWise(rowVector, 't');
}
@Override
public IComplexNDArray rdivColumnVector(INDArray columnVector) {
return dup().rdiviColumnVector(columnVector);
}
@Override
public IComplexNDArray rdiviColumnVector(INDArray columnVector) {
return doColumnWise(columnVector, 't');
}
@Override
protected IComplexNDArray doRowWise(INDArray rowVector, char operation) {
return (IComplexNDArray) super.doRowWise(rowVector,operation);
}
@Override
protected IComplexNDArray doColumnWise(INDArray columnVector, char operation) {
return (IComplexNDArray) super.doColumnWise(columnVector,operation);
}
/**
* Returns the squared (Euclidean) distance.
*/
@Override
public double squaredDistance(INDArray other) {
double sd = 0.0;
if (other instanceof IComplexNDArray) {
IComplexNDArray n = (IComplexNDArray) other;
IComplexNDArray nLinear = n.linearView();
for (int i = 0; i < length; i++) {
IComplexNumber diff = linearView().getComplex(i).sub(nLinear.getComplex(i));
double d = diff.absoluteValue().doubleValue();
sd += d * d;
}
return sd;
}
for (int i = 0; i < length; i++) {
INDArray linear = other.linearView();
IComplexNumber diff = linearView().getComplex(i).sub(linear.getDouble(i));
double d = diff.absoluteValue().doubleValue();
sd += d * d;
}
return sd;
}
/**
* Returns the (euclidean) distance.
*/
@Override
public double distance2(INDArray other) {
return Math.sqrt(squaredDistance(other));
}
/**
* Returns the (1-norm) distance.
*/
@Override
public double distance1(INDArray other) {
float d = 0.0f;
if (other instanceof IComplexNDArray) {
IComplexNDArray n2 = (IComplexNDArray) other;
IComplexNDArray n2Linear = n2.linearView();
for (int i = 0; i < length; i++) {
IComplexNumber n = getComplex(i).sub(n2Linear.getComplex(i));
d += n.absoluteValue().doubleValue();
}
return d;
}
INDArray linear = other.linearView();
for (int i = 0; i < length; i++) {
IComplexNumber n = linearView().getComplex(i).sub(linear.getDouble(i));
d += n.absoluteValue().doubleValue();
}
return d;
}
@Override
public IComplexNDArray put(INDArrayIndex[] indices, INDArray element) {
super.put(indices,element);
return this;
}
@Override
public IComplexNDArray normmax(int...dimension) {
return Nd4j.createComplex(super.normmax(dimension));
}
@Override
public Number normmaxNumber() {
return normmaxComplex().absoluteValue();
}
@Override
public IComplexNumber normmaxComplex() {
return normmax(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray prod(int...dimension) {
return Nd4j.createComplex(super.prod(dimension));
}
@Override
public Number prodNumber() {
return prodComplex().absoluteValue();
}
@Override
public IComplexNumber prodComplex() {
return prod(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray mean(int...dimension) {
return Nd4j.createComplex(super.mean(dimension));
}
@Override
public Number meanNumber() {
return meanComplex().absoluteValue();
}
@Override
public IComplexNumber meanComplex() {
return mean(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray var(int...dimension) {
return Nd4j.createComplex(super.var(dimension));
}
@Override
public Number varNumber() {
return varComplex().absoluteValue();
}
@Override
public IComplexNumber varComplex() {
return var(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray max(int...dimension) {
return Nd4j.createComplex(super.max(dimension));
}
@Override
public Number maxNumber() {
return maxComplex().absoluteValue();
}
@Override
public IComplexNumber maxComplex() {
return max(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray sum(int...dimension) {
return Nd4j.createComplex(super.sum(dimension));
}
@Override
public Number sumNumber() {
return sumComplex().absoluteValue();
}
@Override
public IComplexNumber sumComplex() {
return sum(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray min(int...dimension) {
return Nd4j.createComplex(super.min(dimension));
}
@Override
public Number minNumber() {
return minComplex().absoluteValue();
}
@Override
public IComplexNumber minComplex() {
return min(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray norm1(int...dimension) {
return Nd4j.createComplex(super.norm1(dimension));
}
@Override
public Number norm1Number() {
return norm1Complex().absoluteValue();
}
@Override
public IComplexNumber norm1Complex() {
return norm1(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray std(int...dimension) {
return Nd4j.createComplex(super.std(dimension));
}
@Override
public Number stdNumber() {
return stdComplex().absoluteValue();
}
@Override
public IComplexNumber stdComplex() {
return std(Integer.MAX_VALUE).getComplex(0);
}
@Override
public IComplexNDArray norm2(int...dimension) {
return Nd4j.createComplex(super.norm2(dimension));
}
@Override
public Number norm2Number() {
return norm2Complex().absoluteValue();
}
@Override
public IComplexNumber norm2Complex() {
return norm2(Integer.MAX_VALUE).getComplex(0);
}
/**
* Inserts the element at the specified index
*
* @param i the row insert into
* @param j the column to insert into
* @param element a scalar ndarray
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray put(int i, int j, Number element) {
return (IComplexNDArray) super.put(i, j, Nd4j.scalar(element));
}
/**
* @param indexes
* @param value
* @return
*/
@Override
public IComplexNDArray put(int[] indexes, double value) {
//int ix = offset;
/* if (indexes.length != shape.length)
throw new IllegalArgumentException("Unable to applyTransformToDestination values: number of indices must be equal to the shape");
for (int i = 0; i < shape.length; i++)
ix += indexes[i] * stride[i];
data.put(ix, value);
return this;*/
throw new UnsupportedOperationException();
}
@Override
public IComplexNDArray put(int i, int j, IComplexNumber complex) {
return putScalar(new int[]{i, j}, complex);
}
/**
* Assigns the given matrix (put) to the specified slice
*
* @param slice the slice to assign
* @param put the slice to transform
* @return this for chainability
*/
@Override
public IComplexNDArray putSlice(int slice, IComplexNDArray put) {
if (isScalar()) {
assert put.isScalar() : "Invalid dimension. Can only insert a scalar in to another scalar";
put(0, put.getScalar(0));
return this;
} else if (isVector()) {
assert put.isScalar() || put.isVector() &&
put.length() == length() : "Invalid dimension on insertion. Can only insert scalars input vectors";
if (put.isScalar())
putScalar(slice, put.getComplex(0));
else
for (int i = 0; i < length(); i++)
putScalar(i, put.getComplex(i));
return this;
}
assertSlice(put, slice);
IComplexNDArray view = slice(slice);
if (put.length() == 1)
putScalar(slice, put.getComplex(0));
else if (put.isVector())
for (int i = 0; i < put.length(); i++)
view.putScalar(i, put.getComplex(i));
else {
assert Shape.shapeEquals(view.shape(),put.shape());
IComplexNDArray linear = view.linearView();
IComplexNDArray putLinearView = put.linearView();
for(int i = 0; i < linear.length(); i++) {
linear.putScalar(i,putLinearView.getComplex(i));
}
}
return this;
}
/**
* Mainly here for people coming from numpy.
* This is equivalent to a call to permute
*
* @param dimension the dimension to swap
* @param with the one to swap it with
* @return the swapped axes view
*/
public IComplexNDArray swapAxes(int dimension, int with) {
return (IComplexNDArray) super.swapAxes(dimension, with);
}
/**
* Compute complex conj (in-place).
*/
@Override
public IComplexNDArray conji() {
IComplexNDArray reshaped = linearView();
IComplexDouble c = Nd4j.createDouble(0.0, 0);
for (int i = 0; i < length; i++) {
IComplexNumber conj = reshaped.getComplex(i, c).conj();
reshaped.putScalar(i, conj);
}
return this;
}
@Override
public IComplexNDArray hermitian() {
IComplexNDArray result = Nd4j.createComplex(shape());
IComplexDouble c = Nd4j.createDouble(0, 0);
for (int i = 0; i < slices(); i++)
for (int j = 0; j < columns; j++)
result.putScalar(j, i, getComplex(i, j, c).conji());
return result;
}
/**
* Compute complex conj.
*/
@Override
public IComplexNDArray conj() {
return dup().conji();
}
@Override
public INDArray getReal() {
INDArray result = Nd4j.create(shape());
IComplexNDArray linearView = linearView();
INDArray linearRet = result.linearView();
for (int i = 0; i < linearView.length(); i++) {
linearRet.putScalar(i, linearView.getReal(i));
}
return result;
}
@Override
public double getImag(int i) {
return getComplex(i).imaginaryComponent().doubleValue();
}
@Override
public double getReal(int i) {
return getComplex(i).realComponent().doubleValue();
}
@Override
public IComplexNDArray putReal(int rowIndex, int columnIndex, double value) {
data.put(2 * index(rowIndex, columnIndex) + offset(), value);
return this;
}
@Override
public IComplexNDArray putImag(int rowIndex, int columnIndex, double value) {
data.put(index(rowIndex, columnIndex) + 1 + offset(), value);
return this;
}
@Override
public IComplexNDArray putReal(int i, float v) {
super.putScalar(i,v);
return this;
}
@Override
public IComplexNDArray putImag(int i, float v) {
/*int offset = this.offset + Shape.offsetFor(this, i) + 1;
data.put(offset,v);*/
return this;
}
@Override
public IComplexNumber getComplex(int i) {
if(i >= length())
throw new IllegalArgumentException("Index " + i + " >= " + length());
int[] dimensions = Shape.ind2sub(this,i);
return getComplex(dimensions);
}
@Override
public IComplexNumber getComplex(int i, int j) {
return getComplex(new int[]{i, j});
}
@Override
public IComplexNumber getComplex(int... indices) {
//
// int ix = offset;
// for (int i = 0; i < indices.length; i++)
// ix += indices[i] * stride[i];
//
// return data.getComplex(ix);
throw new UnsupportedOperationException();
}
/**
* Get realComponent part of the matrix.
*/
@Override
public INDArray real() {
/* INDArray ret = Nd4j.create(shape);
copyRealTo(ret);
return ret;*/
throw new UnsupportedOperationException();
}
/**
* Get imaginary part of the matrix.
*/
@Override
public INDArray imag() {
/* INDArray ret = Nd4j.create(shape);
copyImagTo(ret);
return ret;*/
throw new UnsupportedOperationException();
}
/**
* Inserts the element at the specified index
*
* @param i the index insert into
* @param element a scalar ndarray
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray put(int i, IComplexNDArray element) {
if (element == null)
throw new IllegalArgumentException("Unable to insert null element");
assert element.isScalar() : "Unable to insert non scalar element";
int idx = linearIndex(i);
IComplexNumber n = element.getComplex(0);
data.put(idx, n.realComponent().doubleValue());
data.put(idx + 1, n.imaginaryComponent().doubleValue());
return this;
}
/**
* Fetch a particular number on a multi dimensional scale.
*
* @param indexes the indexes to getFromOrigin a number from
* @return the number at the specified indices
*/
@Override
public IComplexNDArray getScalar(int... indexes) {
/* int ix = offset;
for (int i = 0; i < shape.length; i++) {
ix += indexes[i] * stride[i];
}
return Nd4j.scalar(Nd4j.createDouble(data.getDouble(ix), data.getDouble(ix + 1)));*/
throw new UnsupportedOperationException();
}
/**
* Validate dimensions are equal
*
* @param other the other ndarray to compare
*/
@Override
public void checkDimensions(INDArray other) {
}
/**
* Assigns the given matrix (put) to the specified slice
*
* @param slice the slice to assign
* @param put the slice to put
* @return this for chainability
*/
@Override
public IComplexNDArray putSlice(int slice, INDArray put) {
return putSlice(slice,Nd4j.createComplex(put));
}
@Override
public IComplexNDArray subArray(int[] offsets, int[] shape, int[] stride) {
return (IComplexNDArray) super.subArray(offsets, shape, stride);
}
/**
* Inserts the element at the specified index
*
* @param indices the indices to insert into
* @param element a scalar ndarray
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray put(int[] indices, INDArray element) {
/* if (!element.isScalar())
throw new IllegalArgumentException("Unable to insert anything but a scalar");
if(isRowVector() && indices.length == 2 && indices[0] == 0) {
int idx = linearIndex(indices[1]);
if(element instanceof IComplexNDArray) {
IComplexNDArray arr2 = (IComplexNDArray) element;
data.put(idx,arr2.getComplex(0));
}
else
data.put(idx,element.getDouble(0));
}
else {
int ix = offset;
if (indices.length != shape.length)
throw new IllegalArgumentException("Unable to op values: number of indices must be equal to the shape");
for (int i = 0; i < shape.length; i++)
ix += indices[i] * stride[i];
if (element instanceof IComplexNDArray) {
IComplexNumber element2 = ((IComplexNDArray) element).getComplex(0);
data.put(ix, element2.realComponent().doubleValue());
data.put(ix + 1, element2.imaginaryComponent().doubleValue());
} else {
double element2 = element.getDouble(0);
data.put(ix, element2);
data.put(ix + 1, 0);
}
}
return this;
*/
throw new UnsupportedOperationException();
}
/**
* Inserts the element at the specified index
*
* @param i the row insert into
* @param j the column to insert into
* @param element a scalar ndarray
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray put(int i, int j, INDArray element) {
return put(new int[]{i, j}, element);
}
@Override
protected IComplexNDArray create(BaseNDArray baseNDArray) {
return Nd4j.createComplex(baseNDArray);
}
protected IComplexNDArray createScalar(double d) {
return Nd4j.createComplex(Nd4j.scalar(d));
}
protected INDArray createScalarForIndex(int i,boolean applyOffset) {
return Nd4j.createComplex(data(), new int[]{1, 1}, new int[]{1, 1}, applyOffset ? offset() + i : i);
}
/**
* 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 IComplexNDArray slice(int slice) {
return (IComplexNDArray) super.slice(slice);
}
@Override
public int elementStride() {
return 2;
}
@Override
protected IComplexNDArray create(int[] shape) {
return Nd4j.createComplex(shape, Nd4j.getComplexStrides(shape, ordering()), 0);
}
@Override
protected IComplexNDArray create(int[] shape,int[] strides,int offset) {
return Nd4j.createComplex(shape, strides, offset);
}
@Override
protected int[] getStrides(int[] shape,char ordering) {
return Nd4j.getComplexStrides(shape, ordering);
}
@Override
protected IComplexNDArray create(DataBuffer data, int[] newShape, int[] newStrides, int offset, char ordering) {
return Nd4j.createComplex(data, newShape, newStrides, offset, ordering);
}
/**
* 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 IComplexNDArray slice(int slice, int dimension) {
return (IComplexNDArray) super.slice(slice,dimension);
}
@Override
protected IComplexNDArray newShape(int[] newShape, char ordering) {
return Nd4j.createComplex(super.newShape(newShape,ordering));
}
@Override
protected IComplexNDArray create(DataBuffer data, int[] newShape, int[] newStrides, int offset) {
return Nd4j.createComplex(data, newShape, newStrides, offset);
}
/**
* Replicate and tile array to fill out to the given shape
*
* @param shape the new shape of this ndarray
* @return the shape to fill out to
*/
@Override
public IComplexNDArray repmat(int[] shape) {
return (IComplexNDArray) super.repmat(shape);
}
/**
* Assign all of the elements in the given
* ndarray to this ndarray
*
* @param arr the elements to assign
* @return this
*/
@Override
public IComplexNDArray assign(IComplexNDArray arr) {
if (!arr.isScalar())
LinAlgExceptions.assertSameLength(this, arr);
IComplexNDArray linear = linearView();
IComplexNDArray otherLinear = arr.linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, otherLinear.getComplex(i));
}
return this;
}
@Override
public void assign(IComplexNumber aDouble) {
IComplexNDArray linear = linearView();
for (int i = 0; i < linear.length(); i++) {
linear.putScalar(i, aDouble);
}
}
/**
* Get whole rows from the passed indices.
*
* @param rindices
*/
@Override
public IComplexNDArray getRows(int[] rindices) {
INDArray rows = Nd4j.create(rindices.length, columns());
for (int i = 0; i < rindices.length; i++) {
rows.putRow(i, getRow(rindices[i]));
}
return (IComplexNDArray) rows;
}
@Override
public IComplexNDArray put(INDArrayIndex[] indices, IComplexNumber element) {
return put(indices, Nd4j.scalar(element));
}
@Override
public IComplexNDArray put(INDArrayIndex[] indices, IComplexNDArray element) {
super.put(indices,element);
return this;
}
@Override
protected INDArray create(int[] shape, char ordering) {
return Nd4j.createComplex(shape, ordering);
}
@Override
public IComplexNDArray put(INDArrayIndex[] indices, Number element) {
return put(indices, Nd4j.scalar(element));
}
@Override
public IComplexNDArray putScalar(int i, IComplexNumber value) {
int[] dimensions = Shape.ind2sub(this, i);
return putScalar(dimensions,value);
}
@Override
protected IComplexNDArray create(DataBuffer buffer) {
return Nd4j.createComplex(buffer, new int[]{1, (int) buffer.length()});
}
@Override
protected IComplexNDArray create(int rows, int length) {
return create(new int[]{rows,length});
}
@Override
protected IComplexNDArray create(DataBuffer data,int[] shape,int offset) {
return Nd4j.createComplex(data, shape, offset);
}
protected IComplexNDArray create(INDArray baseNDArray) {
return Nd4j.createComplex(baseNDArray);
}
/**
* Get the vector along a particular dimension
*
* @param index the index of the vector to get
* @param dimension the dimension to getScalar the vector from
* @return the vector along a particular dimension
*/
@Override
public IComplexNDArray vectorAlongDimension(int index, int dimension) {
return (IComplexNDArray) super.vectorAlongDimension(index,dimension);
}
/**
* Cumulative sum along a dimension
*
* @param dimension the dimension to perform cumulative sum along
* @return the cumulative sum along the specified dimension
*/
@Override
public IComplexNDArray cumsumi(int dimension) {
/*if (isVector()) {
IComplexNumber s = Nd4j.createDouble(0, 0);
for (int i = 0; i < length; i++) {
s.addi(getComplex(i));
putScalar(i, s);
}
} else if (dimension == Integer.MAX_VALUE || dimension == shape.length - 1) {
IComplexNDArray flattened = ravel().dup();
IComplexNumber prevVal = flattened.getComplex(0);
for (int i = 1; i < flattened.length(); i++) {
IComplexNumber d = prevVal.add((flattened.getComplex(i)));
flattened.putScalar(i, d);
prevVal = d;
}
return flattened;
} else {
for (int i = 0; i < vectorsAlongDimension(dimension); i++) {
IComplexNDArray vec = vectorAlongDimension(i, dimension);
vec.cumsumi(0);
}
}
return this;*/
throw new UnsupportedOperationException();
}
/**
* 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 IComplexNDArray dimShuffle(Object[] rearrange, int[] newOrder, boolean[] broadCastable) {
return (IComplexNDArray) super.dimShuffle(rearrange,newOrder,broadCastable);
}
/**
* Cumulative sum along a dimension (in place)
*
* @param dimension the dimension to perform cumulative sum along
* @return the cumulative sum along the specified dimension
*/
@Override
public IComplexNDArray cumsum(int dimension) {
return dup().cumsumi(dimension);
}
/**
* Assign all of the elements in the given
* ndarray to this nedarray
*
* @param arr the elements to assign
* @return this
*/
@Override
public INDArray assign(INDArray arr) {
return assign((IComplexNDArray) arr);
}
@Override
public IComplexNDArray putScalar(int i, double value) {
return put(i, Nd4j.scalar(value));
}
@Override
public INDArray putScalar(int[] i, double value) {
super.putScalar(i, value);
return putScalar(i, Nd4j.createComplexNumber(value, 0));
}
@Override
public IComplexNDArray putScalar(int[] indexes, IComplexNumber complexNumber) {
/* int ix = offset;
for (int i = 0; i < shape.length; i++) {
if(indexes[i] >= size(i))
throw new IllegalArgumentException("Illegal index " + i + " size at this index is " + size(i));
ix += indexes[i] * stride[i];
}
data.put(ix, complexNumber.asFloat().realComponent().doubleValue());
data.put(ix + 1, complexNumber.asFloat().imaginaryComponent().doubleValue());
return this;*/
throw new UnsupportedOperationException();
}
/**
* Negate each element.
*/
@Override
public IComplexNDArray neg() {
return dup().negi();
}
/**
* Negate each element (in-place).
*/
@Override
public IComplexNDArray negi() {
return (IComplexNDArray) Transforms.neg(this);
}
@Override
public IComplexNDArray rdiv(Number n) {
return rdiv(n, this);
}
@Override
public IComplexNDArray rdivi(Number n) {
return rdivi(n, this);
}
@Override
public IComplexNDArray rsub(Number n) {
return rsub(n, this);
}
@Override
public IComplexNDArray rsubi(Number n) {
return rsubi(n, this);
}
@Override
public IComplexNDArray div(Number n) {
return dup().divi(n);
}
@Override
public IComplexNDArray divi(Number n) {
return divi(Nd4j.complexScalar(n));
}
@Override
public IComplexNDArray mul(Number n) {
return dup().muli(n);
}
@Override
public IComplexNDArray muli(Number n) {
return muli(Nd4j.complexScalar(n));
}
@Override
public IComplexNDArray sub(Number n) {
return dup().subi(n);
}
@Override
public IComplexNDArray subi(Number n) {
return subi(Nd4j.complexScalar(n));
}
@Override
public IComplexNDArray add(Number n) {
return dup().addi(n);
}
@Override
public IComplexNDArray addi(Number n) {
return addi(Nd4j.complexScalar(n));
}
/**
* Returns a subset of this array based on the specified
* indexes
*
* @param indexes the indexes in to the array
* @return a view of the array with the specified indices
*/
@Override
public IComplexNDArray get(INDArrayIndex... indexes) {
return (IComplexNDArray) super.get(indexes);
}
@Override
public IComplexNDArray cond(Condition condition) {
return dup().condi(condition);
}
@Override
public IComplexNDArray condi(Condition condition) {
IComplexNDArray linear = linearView();
for (int i = 0; i < length(); i++) {
boolean met = condition.apply(linear.getComplex(i));
IComplexNumber put = Nd4j.createComplexNumber(met ? 1 : 0, 0);
linear.putScalar(i, put);
}
return this;
}
/**
* Get whole columns from the passed indices.
*
* @param cindices
*/
@Override
public IComplexNDArray getColumns(int[] cindices) {
return (IComplexNDArray) super.getColumns(cindices);
}
/**
* Insert a row in to this array
* Will throw an exception if this
* ndarray is not a matrix
*
* @param row the row insert into
* @param toPut the row to insert
* @return this
*/
@Override
public IComplexNDArray putRow(int row, INDArray toPut) {
return (IComplexNDArray) super.putRow(row,toPut);
}
/**
* Insert a column in to this array
* Will throw an exception if this
* ndarray is not a matrix
*
* @param column the column to insert
* @param toPut the array to put
* @return this
*/
@Override
public IComplexNDArray putColumn(int column, INDArray toPut) {
assert toPut.isVector() && toPut.length() == rows : "Illegal length for row " + toPut.length() + " should have been " + columns;
IComplexNDArray r = getColumn(column);
if (toPut instanceof IComplexNDArray) {
IComplexNDArray putComplex = (IComplexNDArray) toPut;
for (int i = 0; i < r.length(); i++) {
IComplexNumber n = putComplex.getComplex(i);
r.putScalar(i, n);
}
} else {
for (int i = 0; i < r.length(); i++)
r.putScalar(i, Nd4j.createDouble(toPut.getDouble(i), 0));
}
return this;
}
/**
* Returns the element at the specified row/column
* This will throw an exception if the
*
* @param row the row of the element to return
* @param column the row of the element to return
* @return a scalar indarray of the element at this index
*/
@Override
public IComplexNDArray getScalar(int row, int column) {
return getScalar(new int[]{row, column});
}
/**
* Returns the element at the specified index
*
* @param i the index of the element to return
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray getScalar(int i) {
return Nd4j.scalar(getComplex(i));
}
/**
* Inserts the element at the specified index
*
* @param i the index insert into
* @param element a scalar ndarray
* @return a scalar ndarray of the element at this index
*/
@Override
public IComplexNDArray put(int i, INDArray element) {
if (element == null)
throw new IllegalArgumentException("Unable to insert null element");
assert element.isScalar() : "Unable to insert non scalar element";
if (element instanceof IComplexNDArray) {
IComplexNDArray n1 = (IComplexNDArray) element;
IComplexNumber n = n1.getComplex(0);
put(i, n);
} else
putScalar(i, Nd4j.createDouble(element.getDouble(0), 0.0));
return this;
}
public void put(int i, IComplexNumber element) {
int idx = linearIndex(i);
data.put(idx, element.realComponent().doubleValue());
data.put(idx + 1, element.imaginaryComponent().doubleValue());
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray diviColumnVector(INDArray columnVector) {
for (int i = 0; i < columns(); i++) {
getColumn(i).divi(columnVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray divColumnVector(INDArray columnVector) {
return dup().diviColumnVector(columnVector);
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray diviRowVector(INDArray rowVector) {
for (int i = 0; i < rows(); i++) {
getRow(i).divi(rowVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray divRowVector(INDArray rowVector) {
return dup().diviRowVector(rowVector);
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray muliColumnVector(INDArray columnVector) {
for (int i = 0; i < columns(); i++) {
getColumn(i).muli(columnVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray mulColumnVector(INDArray columnVector) {
return dup().muliColumnVector(columnVector);
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray muliRowVector(INDArray rowVector) {
for (int i = 0; i < rows(); i++) {
getRow(i).muli(rowVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray mulRowVector(INDArray rowVector) {
return dup().muliRowVector(rowVector);
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray subiColumnVector(INDArray columnVector) {
for (int i = 0; i < columns(); i++) {
getColumn(i).subi(columnVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray subColumnVector(INDArray columnVector) {
return dup().subiColumnVector(columnVector);
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray subiRowVector(INDArray rowVector) {
for (int i = 0; i < rows(); i++) {
getRow(i).subi(rowVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray subRowVector(INDArray rowVector) {
return dup().subiRowVector(rowVector);
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray addiColumnVector(INDArray columnVector) {
for (int i = 0; i < columns(); i++) {
getColumn(i).addi(columnVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param columnVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray addColumnVector(INDArray columnVector) {
return dup().addiColumnVector(columnVector);
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray addiRowVector(INDArray rowVector) {
for (int i = 0; i < rows(); i++) {
getRow(i).addi(rowVector.getScalar(i));
}
return this;
}
/**
* In place addition of a column vector
*
* @param rowVector the column vector to add
* @return the result of the addition
*/
@Override
public IComplexNDArray addRowVector(INDArray rowVector) {
return dup().addiRowVector(rowVector);
}
/**
* Perform a copy matrix multiplication
*
* @param other the other matrix to perform matrix multiply with
* @return the result of the matrix multiplication
*/
@Override
public IComplexNDArray mmul(INDArray other) {
return (IComplexNDArray) super.mmul(other);
}
/**
* 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 IComplexNDArray mmul(INDArray other, INDArray result) {
return dup().mmuli(other, result);
}
/**
* in place (element wise) division of two matrices
*
* @param other the second ndarray to divide
* @return the result of the divide
*/
@Override
public IComplexNDArray div(INDArray other) {
return dup().divi(other);
}
/**
* copy (element wise) division of two matrices
*
* @param other the second ndarray to divide
* @param result the result ndarray
* @return the result of the divide
*/
@Override
public IComplexNDArray div(INDArray other, INDArray result) {
return dup().divi(other, result);
}
/**
* copy (element wise) multiplication of two matrices
*
* @param other the second ndarray to multiply
* @return the result of the addition
*/
@Override
public IComplexNDArray mul(INDArray other) {
return dup().muli(other);
}
/**
* copy (element wise) multiplication of two matrices
*
* @param other the second ndarray to multiply
* @param result the result ndarray
* @return the result of the multiplication
*/
@Override
public IComplexNDArray mul(INDArray other, INDArray result) {
return dup().muli(other, result);
}
/**
* copy subtraction of two matrices
*
* @param other the second ndarray to subtract
* @return the result of the addition
*/
@Override
public IComplexNDArray sub(INDArray other) {
return dup().subi(other);
}
/**
* copy subtraction of two matrices
*
* @param other the second ndarray to subtract
* @param result the result ndarray
* @return the result of the subtraction
*/
@Override
public IComplexNDArray sub(INDArray other, INDArray result) {
return dup().subi(other, result);
}
/**
* copy addition of two matrices
*
* @param other the second ndarray to add
* @return the result of the addition
*/
@Override
public IComplexNDArray add(INDArray other) {
return dup().addi(other);
}
/**
* copy addition of two matrices
*
* @param other the second ndarray to add
* @param result the result ndarray
* @return the result of the addition
*/
@Override
public IComplexNDArray add(INDArray other, INDArray result) {
return dup().addi(other, result);
}
/**
* Perform an copy matrix multiplication
*
* @param other the other matrix to perform matrix multiply with
* @return the result of the matrix multiplication
*/
@Override
public IComplexNDArray mmuli(INDArray other) {
return mmuli(other, this);
}
/**
* 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 IComplexNDArray mmuli(INDArray other, INDArray result) {
IComplexNDArray otherArray = (IComplexNDArray) other;
IComplexNDArray resultArray = (IComplexNDArray) result;
if (other.shape().length > 2) {
for (int i = 0; i < other.slices(); i++) {
resultArray.putSlice(i, slice(i).mmul(otherArray.slice(i)));
}
return resultArray;
}
LinAlgExceptions.assertMultiplies(this, other);
if (other.isScalar()) {
return muli(otherArray.getComplex(0), resultArray);
}
if (isScalar()) {
return otherArray.muli(getComplex(0), resultArray);
}
/* check sizes and resize if necessary */
//assertMultipliesWith(other);
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.
*/
IComplexNDArray temp = Nd4j.createComplex(resultArray.shape());
if (otherArray.columns() == 1) {
Nd4j.getBlasWrapper().level2().gemv(BlasBufferUtil.getCharForTranspose(temp),BlasBufferUtil.getCharForTranspose(this),Nd4j.UNIT,this,otherArray,Nd4j.ZERO,temp);
} else {
Nd4j.getBlasWrapper().level3().gemm(BlasBufferUtil.getCharForTranspose(temp),BlasBufferUtil.getCharForTranspose(this),BlasBufferUtil.getCharForTranspose(other),Nd4j.UNIT,this,otherArray,Nd4j.ZERO,temp);
}
Nd4j.getBlasWrapper().copy(temp, resultArray);
} else {
if (otherArray.columns() == 1) {
Nd4j.getBlasWrapper().level2().gemv(
BlasBufferUtil.getCharForTranspose(resultArray)
,BlasBufferUtil.getCharForTranspose(this)
,Nd4j.UNIT
,this
,otherArray
,Nd4j.ZERO
,resultArray);
}
else {
Nd4j.getBlasWrapper().level3().gemm(
BlasBufferUtil.getCharForTranspose(resultArray)
,BlasBufferUtil.getCharForTranspose(this)
,BlasBufferUtil.getCharForTranspose(other)
,Nd4j.UNIT
,this
,otherArray
,Nd4j.ZERO
,resultArray);
}
}
return resultArray;
}
@Override
public int secondaryStride() {
return super.secondaryStride() / 2;
}
/**
* in place (element wise) division of two matrices
*
* @param other the second ndarray to divide
* @return the result of the divide
*/
@Override
public IComplexNDArray divi(INDArray other) {
return divi(other, this);
}
/**
* in place (element wise) division of two matrices
*
* @param other the second ndarray to divide
* @param result the result ndarray
* @return the result of the divide
*/
@Override
public IComplexNDArray divi(INDArray other, INDArray result) {
IComplexNDArray cOther = (IComplexNDArray) other;
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray linear = linearView();
IComplexNDArray cOtherLinear = cOther.linearView();
IComplexNDArray cResultLinear = cResult.linearView();
if (other.isScalar())
return divi(cOther.getComplex(0), result);
IComplexNumber c = Nd4j.createComplexNumber(0, 0);
IComplexNumber d = Nd4j.createComplexNumber(0, 0);
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, linear.getComplex(i, c).divi(cOtherLinear.getComplex(i, d)));
return cResult;
}
/**
* in place (element wise) multiplication of two matrices
*
* @param other the second ndarray to multiply
* @return the result of the addition
*/
@Override
public IComplexNDArray muli(INDArray other) {
return muli(other, this);
}
/**
* in place (element wise) multiplication of two matrices
*
* @param other the second ndarray to multiply
* @param result the result ndarray
* @return the result of the multiplication
*/
@Override
public IComplexNDArray muli(INDArray other, INDArray result) {
IComplexNDArray cOther = (IComplexNDArray) other;
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray linear = linearView();
IComplexNDArray cOtherLinear = cOther.linearView();
IComplexNDArray cResultLinear = cResult.linearView();
if (other.isScalar())
return muli(cOther.getComplex(0), result);
IComplexNumber c = Nd4j.createComplexNumber(0, 0);
IComplexNumber d = Nd4j.createComplexNumber(0, 0);
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, linear.getComplex(i, c).muli(cOtherLinear.getComplex(i, d)));
return cResult;
}
/**
* in place subtraction of two matrices
*
* @param other the second ndarray to subtract
* @return the result of the addition
*/
@Override
public IComplexNDArray 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 IComplexNDArray subi(INDArray other, INDArray result) {
IComplexNDArray cOther = (IComplexNDArray) other;
IComplexNDArray cResult = (IComplexNDArray) result;
if (other.isScalar())
return subi(cOther.getComplex(0), result);
if (result == this)
Nd4j.getBlasWrapper().axpy(Nd4j.NEG_UNIT, cOther, cResult);
else if (result == other) {
if (data.dataType() == (DataBuffer.Type.DOUBLE)) {
Nd4j.getBlasWrapper().scal(Nd4j.NEG_UNIT.asDouble(), cResult);
Nd4j.getBlasWrapper().axpy(Nd4j.UNIT, this, cResult);
} else {
Nd4j.getBlasWrapper().scal(Nd4j.NEG_UNIT.asFloat(), cResult);
Nd4j.getBlasWrapper().axpy(Nd4j.UNIT, this, cResult);
}
} else {
Nd4j.getBlasWrapper().copy(this, result);
Nd4j.getBlasWrapper().axpy(Nd4j.NEG_UNIT, cOther, cResult);
}
return cResult;
}
/**
* in place addition of two matrices
*
* @param other the second ndarray to add
* @return the result of the addition
*/
@Override
public IComplexNDArray addi(INDArray other) {
return addi(other, this);
}
/**
* in place addition of two matrices
*
* @param other the second ndarray to add
* @param result the result ndarray
* @return the result of the addition
*/
@Override
public IComplexNDArray addi(INDArray other, INDArray result) {
IComplexNDArray cOther = (IComplexNDArray) other;
IComplexNDArray cResult = (IComplexNDArray) result;
if (cOther.isScalar()) {
return cResult.addi(cOther.getComplex(0), result);
}
if (isScalar()) {
return cOther.addi(getComplex(0), result);
}
if (result == this) {
Nd4j.getBlasWrapper().axpy(Nd4j.UNIT, cOther, cResult);
} else if (result == other) {
Nd4j.getBlasWrapper().axpy(Nd4j.UNIT, this, cResult);
} else {
INDArray resultLinear = result.linearView();
INDArray otherLinear = other.linearView();
INDArray linear = linearView();
for (int i = 0; i < resultLinear.length(); i++) {
resultLinear.putScalar(i, otherLinear.getDouble(i) + linear.getDouble(i));
}
}
return (IComplexNDArray) result;
}
@Override
public IComplexNDArray rdiv(IComplexNumber n, INDArray result) {
return dup().rdivi(n, result);
}
@Override
public IComplexNDArray rdivi(IComplexNumber n, INDArray result) {
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray cResultLinear = cResult.linearView();
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, n.div(getComplex(i)));
return cResult;
}
@Override
public IComplexNDArray rsub(IComplexNumber n, INDArray result) {
return dup().rsubi(n, result);
}
@Override
public IComplexNDArray rsubi(IComplexNumber n, INDArray result) {
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray cResultLinear = cResult.linearView();
IComplexNDArray thiLinear = linearView();
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, n.sub(thiLinear.getComplex(i)));
return cResult;
}
@Override
public IComplexNDArray div(IComplexNumber n, INDArray result) {
return dup().divi(n, result);
}
@Override
public IComplexNDArray divi(IComplexNumber n, INDArray result) {
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray cResultLinear = cResult.linearView();
IComplexNDArray thisLinear = linearView();
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, thisLinear.getComplex(i).div(n));
return cResult;
}
@Override
public IComplexNDArray mul(IComplexNumber n, INDArray result) {
return dup().muli(n, result);
}
@Override
public IComplexNDArray muli(IComplexNumber n, INDArray result) {
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray cResultLinear = cResult.linearView();
IComplexNDArray thiLinear = linearView();
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, thiLinear.getComplex(i).mul(n));
return cResult;
}
@Override
public IComplexNDArray sub(IComplexNumber n, INDArray result) {
return dup().subi(n, result);
}
@Override
public IComplexNDArray subi(IComplexNumber n, INDArray result) {
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray cResultLinear = cResult.linearView();
IComplexNDArray linear = linearView();
for (int i = 0; i < length; i++)
cResultLinear.putScalar(i, linear.getComplex(i).sub(n));
return cResult;
}
@Override
public IComplexNDArray add(IComplexNumber n, INDArray result) {
return dup().addi(n, result);
}
@Override
public IComplexNDArray addi(IComplexNumber n, INDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = (IComplexNDArray) result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, linear.getComplex(i).add(n));
}
return (IComplexNDArray) result;
}
@Override
public IComplexNDArray rdiv(IComplexNumber n) {
return dup().rdivi(n, this);
}
@Override
public IComplexNDArray rdivi(IComplexNumber n) {
return rdivi(n, this);
}
@Override
public IComplexNDArray rsub(IComplexNumber n) {
return rsub(n, this);
}
@Override
public IComplexNDArray rsubi(IComplexNumber n) {
return rsubi(n, this);
}
@Override
public IComplexNDArray div(IComplexNumber n) {
return div(n, this);
}
@Override
public IComplexNDArray divi(IComplexNumber n) {
return divi(n, this);
}
@Override
public IComplexNDArray mul(IComplexNumber n) {
return dup().muli(n);
}
@Override
public IComplexNDArray muli(IComplexNumber n) {
return muli(n, this);
}
@Override
public IComplexNDArray sub(IComplexNumber n) {
return dup().subi(n);
}
@Override
public IComplexNDArray subi(IComplexNumber n) {
return subi(n, this);
}
@Override
public IComplexNDArray add(IComplexNumber n) {
return dup().addi(n);
}
@Override
public IComplexNDArray addi(IComplexNumber n) {
return addi(n, this);
}
@Override
public IComplexNDArray putReal(int rowIndex, int columnIndex, float value) {
return putReal(new int[]{rowIndex, columnIndex}, value);
}
@Override
public IComplexNDArray putReal(int[] indices, float value) {
return putReal(indices, (double) value);
}
@Override
public IComplexNDArray putImag(int[] indices, float value) {
return putImag(indices, (double) value);
}
@Override
public IComplexNDArray putReal(int[] indices, double value) {
/* int ix = offset;
for (int i = 0; i < indices.length; i++)
ix += indices[i] * stride[i];
data.put(ix, value);
return this;*/
throw new UnsupportedOperationException();
}
@Override
public IComplexNDArray putImag(int[] indices, double value) {
/*int ix = offset;
for (int i = 0; i < indices.length; i++)
ix += indices[i] * stride[i];
data.put(ix + 1, value);
return this;*/
throw new UnsupportedOperationException();
}
@Override
public IComplexNDArray putImag(int rowIndex, int columnIndex, float value) {
return putReal(new int[]{rowIndex, columnIndex}, value);
}
@Override
public IComplexNDArray put(int[] indexes, float value) {
return put(indexes, (double) value);
}
@Override
public IComplexNDArray neqi(IComplexNumber other) {
IComplexNDArray ret = linearView();
for (int i = 0; i < length(); i++) {
IComplexNumber num = ret.getComplex(i);
ret.putScalar(i, num.neqc(other));
}
return this;
}
@Override
public IComplexNDArray neq(IComplexNumber other) {
return dup().neqi(other);
}
@Override
public IComplexNDArray lt(IComplexNumber other) {
return dup().lti(other);
}
@Override
public IComplexNDArray lti(IComplexNumber other) {
IComplexNDArray ret = linearView();
for (int i = 0; i < length(); i++) {
IComplexNumber num = ret.getComplex(i);
ret.putScalar(i, num.lt(other));
}
return this;
}
@Override
public IComplexNDArray eq(IComplexNumber other) {
return dup().eqi(other);
}
@Override
public IComplexNDArray eqi(IComplexNumber other) {
return dup().eqi(other);
}
@Override
public IComplexNDArray gt(IComplexNumber other) {
return dup().gti(other);
}
@Override
public IComplexNDArray gti(IComplexNumber other) {
IComplexNDArray ret = linearView();
for (int i = 0; i < length(); i++) {
IComplexNumber num = ret.getComplex(i);
ret.putScalar(i, num.gt(other));
}
return this;
}
/**
* Return transposed copy of this matrix.
*/
@Override
public IComplexNDArray transposei() {
return Nd4j.createComplex(super.transposei());
}
/**
* Return transposed copy of this matrix.
*/
@Override
public IComplexNDArray transpose() {
return transposei();
}
@Override
public IComplexNDArray addi(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, linear.getComplex(i).addi(n));
}
return result;
}
@Override
public IComplexNDArray subi(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, linear.getComplex(i).subi(n));
}
return result;
}
@Override
public IComplexNDArray muli(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
IComplexNumber n3 = linear.getComplex(i);
cResult.putScalar(i, n3.mul(n));
}
return result;
}
@Override
public IComplexNDArray divi(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, linear.getComplex(i).div(n));
}
return result;
}
@Override
public IComplexNDArray rsubi(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, n.sub(linear.getComplex(i)));
}
return result;
}
@Override
public IComplexNDArray rdivi(IComplexNumber n, IComplexNDArray result) {
IComplexNDArray linear = linearView();
IComplexNDArray cResult = result.linearView();
for (int i = 0; i < length(); i++) {
cResult.putScalar(i, n.div(linear.getComplex(i)));
}
return result;
}
/**
* Reshape the ndarray in to the specified dimensions,
* possible errors being thrown for invalid shapes
*
* @param shape
* @return
*/
@Override
public IComplexNDArray reshape(int...shape) {
return (IComplexNDArray) super.reshape(shape);
}
@Override
public IComplexNDArray reshape(char order, int... newShape) {
return (IComplexNDArray) super.reshape(order, newShape);
}
@Override
public IComplexNDArray reshape(char order, int rows, int columns) {
return (IComplexNDArray) super.reshape(order, rows, columns);
}
/**
* Set the value of the ndarray to the specified value
*
* @param value the value to assign
* @return the ndarray with the values
*/
@Override
public IComplexNDArray assign(Number value) {
IComplexNDArray one = linearView();
for (int i = 0; i < one.length(); i++)
one.putScalar(i, Nd4j.createDouble(value.doubleValue(), 0));
return this;
}
/**
* Reverse division
*
* @param other the matrix to divide from
* @return
*/
@Override
public IComplexNDArray rdiv(INDArray other) {
return dup().rdivi(other);
}
/**
* Reverse divsion (in place)
*
* @param other
* @return
*/
@Override
public IComplexNDArray rdivi(INDArray other) {
return rdivi(other, this);
}
/**
* Reverse division
*
* @param other the matrix to subtract from
* @param result the result ndarray
* @return
*/
@Override
public IComplexNDArray rdiv(INDArray other, INDArray result) {
return dup().rdivi(other, result);
}
/**
* Reverse division (in-place)
*
* @param other the other ndarray to subtract
* @param result the result ndarray
* @return the ndarray with the operation applied
*/
@Override
public IComplexNDArray rdivi(INDArray other, INDArray result) {
return (IComplexNDArray) other.divi(this, result);
}
/**
* Reverse subtraction
*
* @param other the matrix to subtract from
* @param result the result ndarray
* @return
*/
@Override
public IComplexNDArray rsub(INDArray other, INDArray result) {
return dup().rsubi(other, result);
}
/**
* @param other
* @return
*/
@Override
public IComplexNDArray rsub(INDArray other) {
return dup().rsubi(other);
}
/**
* @param other
* @return
*/
@Override
public IComplexNDArray rsubi(INDArray other) {
return rsubi(other, this);
}
/**
* Reverse subtraction (in-place)
*
* @param other the other ndarray to subtract
* @param result the result ndarray
* @return the ndarray with the operation applied
*/
@Override
public IComplexNDArray rsubi(INDArray other, INDArray result) {
return (IComplexNDArray) other.subi(this, result);
}
public IComplexNumber max() {
IComplexNDArray reshape = ravel();
IComplexNumber max = reshape.getComplex(0);
for (int i = 1; i < reshape.length(); i++) {
IComplexNumber curr = reshape.getComplex(i);
double val = curr.realComponent().doubleValue();
if (val > curr.realComponent().doubleValue())
max = curr;
}
return max;
}
public IComplexNumber min() {
IComplexNDArray reshape = ravel();
IComplexNumber min = reshape.getComplex(0);
for (int i = 1; i < reshape.length(); i++) {
IComplexNumber curr = reshape.getComplex(i);
double val = curr.realComponent().doubleValue();
if (val < curr.realComponent().doubleValue())
min = curr;
}
return min;
}
/**
* Reshape the matrix. Number of elements must not change.
*
* @param newRows
* @param newColumns
*/
@Override
public IComplexNDArray reshape(int newRows, int newColumns) {
return reshape(new int[]{newRows, newColumns});
}
/**
* Get the specified column
*
* @param c
*/
@Override
public IComplexNDArray getColumn(int c) {
return (IComplexNDArray) super.getColumn(c);
}
/**
* Get a copy of a row.
*
* @param r
*/
@Override
public IComplexNDArray getRow(int r) {
return (IComplexNDArray) super.getRow(r);
}
/**
* Compare two matrices. Returns true if and only if other is also a
* ComplexDoubleMatrix which has the same size and the maximal absolute
* difference in matrix elements is smaller than 1e-6.
*
* @param o
*/
@Override
public boolean equals(Object o) {
if (!(o instanceof IComplexNDArray)) {
return false;
}
IComplexNDArray n = (IComplexNDArray) o;
//epsilon equals
if (isScalar() && n.isScalar()) {
IComplexNumber c = n.getComplex(0);
return FastMath.abs(getComplex(0).sub(c).realComponent().doubleValue()) < Nd4j.EPS_THRESHOLD;
}
else if (isVector() && n.isVector()) {
for (int i = 0; i < length; i++) {
IComplexNumber nComplex = n.getComplex(i);
IComplexNumber thisComplex = getComplex(i);
if(!nComplex.equals(thisComplex))
return false;
}
return true;
}
if (!Shape.shapeEquals(shape(), n.shape()))
return false;
//epsilon equals
if (isScalar()) {
IComplexNumber c = n.getComplex(0);
return getComplex(0).sub(c).absoluteValue().doubleValue() < Nd4j.EPS_THRESHOLD;
}
else if (isVector()) {
for (int i = 0; i < length; i++) {
IComplexNumber curr = getComplex(i);
IComplexNumber comp = n.getComplex(i);
if(!curr.equals(comp))
return false;
}
return true;
}
for (int i = 0; i < slices(); i++) {
IComplexNDArray sliceI = slice(i);
IComplexNDArray nSliceI = n.slice(i);
if (!sliceI.equals(nSliceI))
return false;
}
return true;
}
/**
* Broadcasts this ndarray to be the specified shape
*
* @param shape the new shape of this ndarray
* @return the broadcasted ndarray
*/
@Override
public IComplexNDArray broadcast(int[] shape) {
return (IComplexNDArray) super.broadcast(shape);
}
/**
* 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 getScalar the element of a non scalar");
int idx = linearIndex(0);
return Nd4j.createDouble(data.getDouble(idx), data.getDouble(idx + 1));
}
/**
* See: http://www.mathworks.com/help/matlab/ref/permute.html
*
* @param rearrange the dimensions to swap to
* @return the newly permuted array
*/
@Override
public IComplexNDArray permute(int[] rearrange) {
return (IComplexNDArray) super.permute(rearrange);
}
/**
* Flattens the array for linear indexing
*
* @return the flattened version of this array
*/
@Override
public IComplexNDArray ravel() {
if(length >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Length can not be >= Integer.MAX_VALUE");
IComplexNDArray ret = Nd4j.createComplex((int)length, ordering());
IComplexNDArray linear = linearView();
for(int i = 0; i < length(); i++) {
ret.putScalar(i,linear.getComplex(i));
}
return ret;
}
/**
* Generate string representation of the matrix.
*/
@Override
public String toString() {
if (isScalar()) {
return element().toString();
} else if (isVector()) {
StringBuilder sb = new StringBuilder();
sb.append("[");
if(length >= Integer.MAX_VALUE)
throw new IllegalArgumentException("Length can not be >= Integer.MAX_VALUE");
long numElementsToPrint = Nd4j.MAX_ELEMENTS_PER_SLICE < 0 ? length : Nd4j.MAX_ELEMENTS_PER_SLICE;
for (int i = 0; i < length; i++) {
sb.append(getComplex(i));
if (i < length - 1)
sb.append(" ,");
if (i >= numElementsToPrint) {
long numElementsLeft = length - i;
//set towards the end of the buffer
if (numElementsLeft > numElementsToPrint) {
i += numElementsLeft - numElementsToPrint - 1;
sb.append(" ,... ,");
}
}
}
sb.append("]\n");
return sb.toString();
}
/* StringBuilder sb = new StringBuilder();
int length = shape[0];
sb.append("[");
if (length > 0) {
sb.append(slice(0).toString());
int slices = Nd4j.MAX_SLICES_TO_PRINT > 0 ? Nd4j.MAX_SLICES_TO_PRINT : slices();
if (slices > slices())
slices = slices();
for (int i = 1; i < slices; i++) {
sb.append(slice(i).toString());
if (i < length - 1)
sb.append(" ,");
}
}
sb.append("]\n");
return sb.toString();*/
throw new UnsupportedOperationException();
}
}
