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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.functions.DifferentialFunctionFactory;
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.ops.executioner.OpExecutioner;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
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 BaseReduce3Op {
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() {
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z, int... dimensions) {
super(x, y, z, dimensions);
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineSimilarity(INDArray x, INDArray y, int... dimensions) {
this(x, y, null, dimensions);
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z) {
this(x, y, z, null);
}
public CosineSimilarity(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimension) {
this(x, y, z, dimension);
this.isComplex = allDistances;
}
public CosineSimilarity(INDArray x, INDArray y, boolean allDistances, int... dimension) {
this(x, y, null, allDistances, dimension);
}
public CosineSimilarity(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 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), keepDims, dimensions);
}
public static List doDiff(SameDiff sameDiff, DifferentialFunctionFactory f, SDVariable x, SDVariable y,
SDVariable gradOut, boolean keepDims, 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);
SDVariable l2xSq = sameDiff.math().square(l2x);
SDVariable l2ySq = sameDiff.math().square(l2y);
SDVariable broadcastableGrad;
if(keepDims || dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE)){
//keepDims or full array reduction
broadcastableGrad = gradOut;
} else {
broadcastableGrad = sameDiff.f().reductionBroadcastableWithOrigShape(x, sameDiff.constant(Nd4j.createFromArray(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));
}
}