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org.nd4j.linalg.api.ops.impl.accum.distances.JaccardDistance Maven / Gradle / Ivy
/*******************************************************************************
* 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.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.List;
/**
* Jaccard distance (dissimilarity)
*
* @author [email protected]
*/
public class JaccardDistance extends BaseAccumulation {
public JaccardDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int... dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
extraArgs = new Object[]{0.0f, 0.0f};
}
public JaccardDistance() {
passThrough = false;
}
public JaccardDistance(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 JaccardDistance(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 JaccardDistance(INDArray x) {
super(x);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public JaccardDistance(INDArray x, INDArray y) {
super(x, y);
passThrough = Nd4j.getExecutioner().executionMode() == OpExecutioner.ExecutionMode.JAVA;
extraArgs = new Object[]{0.0f, 0.0f};
}
public JaccardDistance(INDArray x, INDArray y, INDArray z, boolean allDistances) {
this(x, y, z, x.lengthLong());
this.isComplex = allDistances;
}
public JaccardDistance(INDArray x, INDArray y, boolean allDistances) {
this(x, y);
this.isComplex = allDistances;
}
public JaccardDistance(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 6;
}
@Override
public String opName() {
return "jaccarddistance";
}
@Override
public List doDiff(List f1) {
//Jaccard distance: https://en.wikipedia.org/wiki/Jaccard_index#Generalized_Jaccard_similarity_and_distance
//J(x,y) = 1 - sum_i min(x_i, y_i) / sum_i max(x_i, y_i)
SDVariable min = f().min(larg(), rarg());
SDVariable max = f().max(larg(), rarg());
SDVariable sumMax = max.sum(true, dimensions);
SDVariable sumMin = min.sum(true, dimensions);
SDVariable xIsMin = f().eq(min, larg());
SDVariable xIsMax = f().eq(max, larg());
SDVariable yIsMin = f().eq(min, rarg());
SDVariable yIsMax = f().eq(max, rarg());
SDVariable sqSumMax = f().square(sumMax);
SDVariable dldx = xIsMax.mul(sumMin).sub(xIsMin.mul(sumMax)).div(sqSumMax);
SDVariable dldy = yIsMax.mul(sumMin).sub(yIsMin.mul(sumMax)).div(sqSumMax);
SDVariable bcGradOut;
if(dimensions == null){
bcGradOut = f1.get(0);
} else {
int inRank = arg().getArr().rank();
bcGradOut = f().reductionBroadcastableWithOrigShape(inRank, dimensions, f1.get(0));
}
return Arrays.asList(dldx.mul(bcGradOut), dldy.mul(bcGradOut));
}
@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());
}
}