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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.api.ops.impl.reduce3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseReduceFloatOp;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.List;
/**
* Euclidean distance
*
* @author Adam Gibson
*/
public class EuclideanDistance extends BaseReduce3Op {
public static final String OP_NAME = "euclidean";
public EuclideanDistance(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public EuclideanDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public EuclideanDistance() {}
public EuclideanDistance(INDArray x, INDArray y, int... dimensions) {
this(x, y, null, dimensions);
}
public EuclideanDistance(INDArray x, INDArray y, INDArray z) {
this(x, y, z, null);
}
public EuclideanDistance(INDArray x, INDArray y, INDArray z, int... dimensions) {
super(x, y, z,dimensions);
extraArgs = new Object[]{0.0f, 0.0f};
}
public EuclideanDistance(INDArray x, INDArray y, boolean allDistances, int... dimensions) {
this(x, y, null, allDistances, dimensions);
}
public EuclideanDistance(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimensions) {
this(x, y, z, false, allDistances, dimensions);
}
public EuclideanDistance(INDArray x, INDArray y, INDArray z, boolean keepDims, boolean allDistances, int... dimensions){
super(x, y, z, keepDims, allDistances, dimensions);
extraArgs = new Object[]{0.0f, 0.0f};
}
@Override
public int opNum() {
return 1;
}
@Override
public String opName() {
return OP_NAME;
}
@Override
public List doDiff(List i_v1) {
//ddist(x,y)/dxi = (xi-yi)/dist(x,y)
SDVariable euc = outputVariables()[0];
SDVariable difference = larg().sub(rarg());
SDVariable divBroadcastable = i_v1.get(0).div(euc);
if(!keepDims && !(dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE))){
//Not keep dims, and not full array reduction -> need to make broadcastable
divBroadcastable = f().reductionBroadcastableWithOrigShape(arg(), sameDiff.constant(Nd4j.createFromArray(dimensions)), divBroadcastable);
}
SDVariable gradX = difference.mul(divBroadcastable);
SDVariable gradY = f().neg(gradX);
return Arrays.asList(gradX, gradY);
}
}