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org.nd4j.linalg.api.ops.impl.reduce3.JaccardDistance Maven / Gradle / Ivy
/*
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* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.reduce3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.util.SameDiffUtils;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.List;
public class JaccardDistance extends BaseReduce3Op {
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(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions) {
super(sameDiff, i_v, dimensions);
}
public JaccardDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public JaccardDistance() {
}
public JaccardDistance(INDArray x, INDArray y, int... dimensions) {
this(x, y, null, false, dimensions);
}
public JaccardDistance(INDArray x, INDArray y, boolean allDistances, int... dimensions) {
super(x, y, allDistances, dimensions);
}
public JaccardDistance(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimensions) {
this(x, y, z, false, allDistances, dimensions);
this.isComplex = allDistances;
}
public JaccardDistance(INDArray x, INDArray y, INDArray z) {
this(x, y, z, false, null);
}
public JaccardDistance(INDArray x, INDArray y, boolean allDistances) {
this(x, y);
this.isComplex = allDistances;
}
public JaccardDistance(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};
}
public JaccardDistance(INDArray x, INDArray y, INDArray z, int... dimensions) {
super(x, y, z, dimensions);
}
public JaccardDistance(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public JaccardDistance(SameDiff sd, SDVariable x, SDVariable y, boolean keepDims, boolean isComplex, int[] dimensions) {
super(sd,x,y,keepDims,isComplex,dimensions);
}
public JaccardDistance(INDArray x, INDArray y, boolean keepDims, boolean isComplex, int[] dimensions) {
super(x,y,null,keepDims,isComplex,dimensions);
}
@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 = sameDiff.math.min(larg(), rarg());
SDVariable max = sameDiff.math.max(larg(), rarg());
SDVariable sumMax = max.sum(true, dimensions);
SDVariable sumMin = min.sum(true, dimensions);
DataType d = arg().dataType();
SDVariable xIsMin = sameDiff.eq(min, larg()).castTo(d);
SDVariable xIsMax = sameDiff.eq(max, larg()).castTo(d);
SDVariable yIsMin = sameDiff.eq(min, rarg()).castTo(d);
SDVariable yIsMax = sameDiff.eq(max, rarg()).castTo(d);
SDVariable sqSumMax = sameDiff.math.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(keepDims || dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE)){
//KeepDims or full array reduction - already broadcastable
bcGradOut = f1.get(0);
} else {
bcGradOut = SameDiffUtils.reductionBroadcastableWithOrigShape(arg(), sameDiff.constant(Nd4j.createFromArray(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());
}
@Override
public DataType resultType() {
return Nd4j.defaultFloatingPointType();
}
}