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package org.nd4j.linalg.api.ops.random.custom;
import lombok.extern.slf4j.Slf4j;
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.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
@Slf4j
public class RandomBernoulli extends DynamicCustomOp {
private double p = 0.0;
public RandomBernoulli() {
//
}
public RandomBernoulli(SameDiff sd, SDVariable shape, double p){
super(null, sd, new SDVariable[]{shape});
Preconditions.checkState(p >= 0 && p <= 1.0, "Probability must be between 0 and 1 - got %s", p);
this.p = p;
addTArgument(p);
}
public RandomBernoulli(INDArray shape, INDArray out, double p){
super(null, new INDArray[]{shape}, new INDArray[]{out}, Collections.singletonList(p), (List)null);
Preconditions.checkState(p >= 0 && p <= 1.0, "Probability must be between 0 and 1 - got %s", p);
}
@Override
public String opName() {
return "random_bernoulli";
}
@Override
public List doDiff(List gradients){
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
//Input data type specifies the shape; output data type should be any float
//TODO MAKE CONFIGUREABLE - https://github.com/eclipse/deeplearning4j/issues/6854
return Collections.singletonList(DataType.FLOAT);
}
}