org.nd4j.linalg.api.ops.impl.reduce3.CosineDistance Maven / Gradle / Ivy
The newest version!
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * 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.linalg.api.ndarray.INDArray;
import java.util.Arrays;
import java.util.List;
public class CosineDistance extends BaseReduce3Op {
public CosineDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int... dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public CosineDistance(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions) {
super(sameDiff, i_v, dimensions);
}
public CosineDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public CosineDistance() {
}
public CosineDistance(INDArray x, INDArray y, INDArray z) {
this(x, y, z, null);
}
public CosineDistance(INDArray x, INDArray y, INDArray z, int... dimension) {
super(x, y, z, dimension);
extraArgs = new Object[]{0.0f, 0.0f};
}
public CosineDistance(INDArray x, INDArray y, int... dimension) {
this(x, y, null, dimension);
}
public CosineDistance(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimension) {
this(x, y, z, dimension);
this.isComplex = allDistances;
}
public CosineDistance(INDArray x, INDArray y, boolean allDistances, int... dimension) {
this(x, y, null, allDistances, dimension);
}
public CosineDistance(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 CosineDistance(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public CosineDistance(SameDiff sd, SDVariable x, SDVariable y, boolean keepDims, boolean isComplex, int[] dimensions) {
super(sd,x,y,keepDims,isComplex,dimensions);
}
public CosineDistance(INDArray x, INDArray y, boolean keepDims, boolean isComplex, int[] dimensions) {
super(x,y,null,keepDims,isComplex,dimensions);
}
@Override
public int opNum() {
return 5;
}
@Override
public String opName() {
return "cosinedistance";
}
@Override
public List doDiff(List i_v1) {
//Cosine distance = 1 - cosine similarity
//Therefore: just need to negate gradients from cosine similarity...
List diff = CosineSimilarity.doDiff(sameDiff, larg(), rarg(), i_v1.get(0), keepDims, dimensions);
return Arrays.asList(sameDiff.math.neg(diff.get(0)), sameDiff.math.neg(diff.get(1)));
}
}