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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.accum.distances;
import org.nd4j.autodiff.functions.DifferentialFunctionFactory;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseAccumulation;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Cosine similarity
* Note that you need to initialize
* a scaling constant equal to the norm2 of the
* vector
*
* @author Adam Gibson
*/
public class CosineSimilarity extends BaseAccumulation {
public static final String OP_NAME = "cosinesimilarity";
public CosineSimilarity(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public CosineSimilarity(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public CosineSimilarity() {
passThrough = true;
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x, INDArray y, long n) {
super(x, y, n);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x) {
super(x);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x, INDArray y) {
super(x, y);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z, boolean allDistances) {
this(x, y, z, x.lengthLong());
this.isComplex = allDistances;
}
public CosineSimilarity(INDArray x, INDArray y, boolean allDistances) {
this(x, y);
this.isComplex = allDistances;
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int... dimensions){
super(x, y, z, newFormat, keepDims, dimensions);
extraArgs = new Object[]{0.0f, 0.0f};
}
@Override
public Type opType() {
return Type.REDUCE3;
}
@Override
public Type getOpType() {
return opType();
}
@Override
public int opNum() {
return 2;
}
@Override
public String opName() {
return OP_NAME;
}
@Override
public List doDiff(List i_v1) {
//Let cosine(x,y) = a / b
//a = sum_i (x_i * y_i)
//b = sqrt(sum_i x_i^2) * sqrt(sum_i y_i^2) = l2(x) * l2(y)
//Then:
// dc(x,y)/dx_i = 1/b * (y - x * a / (l2(x))^2)
return doDiff(sameDiff, f(), larg(), rarg(), i_v1.get(0), dimensions);
}
public static List doDiff(SameDiff sameDiff, DifferentialFunctionFactory f, SDVariable x, SDVariable y,
SDVariable gradOut, int... dimensions){
SDVariable a = sameDiff.sum(x.mul(y),true, dimensions);
SDVariable l2x = f.norm2(x, true, dimensions);
SDVariable l2y = f.norm2(y, true, dimensions);
SDVariable b = l2x.mul(l2y);
int origRank = Shape.rankFromShape(x.getShape());
SDVariable l2xSq = sameDiff.square(l2x);
SDVariable l2ySq = sameDiff.square(l2y);
SDVariable broadcastableGrad = f.reductionBroadcastableWithOrigShape(origRank, dimensions, gradOut);
SDVariable dcdx = y.sub(x.mul(a).div(l2xSq)).div(b);
SDVariable dcdy = x.sub(y.mul(a).div(l2ySq)).div(b);
return Arrays.asList(dcdx.mul(broadcastableGrad), dcdy.mul(broadcastableGrad));
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
}