org.nd4j.linalg.api.ops.random.custom.RandomExponential Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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.random.custom;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.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;
/**
* Random exponential distribution: p(x) = lambda * exp(-lambda * x)
*
* @author [email protected]
*/
@Slf4j
public class RandomExponential extends DynamicCustomOp {
private double lambda = 0.0;
public RandomExponential() {
//
}
public RandomExponential(SameDiff sd, SDVariable shape, double lambda){
super(null, sd, new SDVariable[]{shape});
Preconditions.checkState(lambda >= 0, "Lambda parameter must be > 0 - got %s", lambda);
this.lambda = lambda;
addTArgument(lambda);
}
public RandomExponential(INDArray shape,INDArray out, double lambda){
super(null, new INDArray[]{shape}, new INDArray[]{out}, Collections.singletonList(lambda), (List)null);
this.lambda = lambda;
}
@Override
public String opName() {
return "random_exponential";
}
@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/deeplearning4j/deeplearning4j/issues/6854
return Collections.singletonList(DataType.FLOAT);
}
}