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package org.nd4j.linalg.api.ops.impl.transforms.custom;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
public class LessThanOrEqual extends BaseDynamicTransformOp {
public LessThanOrEqual() {}
public LessThanOrEqual( SameDiff sameDiff, SDVariable x, SDVariable y) {
this(sameDiff, new SDVariable[]{x,y}, false);
}
public LessThanOrEqual( SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
super(sameDiff, args, inPlace);
}
public LessThanOrEqual( INDArray[] inputs, INDArray[] outputs) {
super(inputs, outputs);
}
public LessThanOrEqual(INDArray x, INDArray y, INDArray z){
this(new INDArray[]{x, y}, new INDArray[]{z});
}
public LessThanOrEqual(INDArray x, INDArray y){
this(new INDArray[]{x, y}, null);
}
@Override
public String opName() {
return "less_equal";
}
@Override
public String onnxName() {
return "LessEqual";
}
@Override
public String tensorflowName() {
return "LessEqual";
}
@Override
public List doDiff(List f1) {
//2 inputs, not continuously differentiable but 0s almost everywhere
return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Input datatypes must be same type: got %s", dataTypes);
return Collections.singletonList(DataType.BOOL);
}
}