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/*-
*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://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.
*
*
*/
package org.nd4j.linalg.api.ops.impl.indexaccum;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.complex.IComplexNumber;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseIndexAccumulation;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.List;
/**
* Calculate the index of min value over a vector
*
* @author Alex Black
*/
public class IMin extends BaseIndexAccumulation {
public IMin(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public IMin(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public IMin() {
}
public IMin(INDArray x, INDArray y, long n) {
super(x, y, n);
}
public IMin(INDArray x) {
super(x);
}
public IMin(INDArray x, INDArray y) {
super(x, y);
}
@Override
public int opNum() {
return 1;
}
@Override
public String opName() {
return "imin";
}
@Override
public float zeroFloat() {
return Float.MAX_VALUE;
}
@Override
public double zeroDouble() {
return Double.MAX_VALUE;
}
@Override
public float zeroHalf() {
return 65503.0f;
}
@Override
public IComplexNumber zeroComplex() {
return Nd4j.createComplexNumber(Double.MAX_VALUE, 0);
}
@Override
public String onnxName() {
return "ArgMin";
}
@Override
public String tensorflowName() {
return "argmin";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
//Not differentiable, but (assuming no ties) output does not change for a given infinitesimal change in the input
return Arrays.asList(f().zerosLike(arg()));
}
}