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/*
* ******************************************************************************
* *
* *
* * 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.random.impl;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.random.BaseRandomOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
public class BinomialDistribution extends BaseRandomOp {
private int trials;
private double probability;
public BinomialDistribution(SameDiff sd, int trials, double probability, long[] shape){
super(sd, shape);
this.trials = trials;
this.probability = probability;
this.extraArgs = new Object[] {(double) this.trials, this.probability};
}
public BinomialDistribution(SameDiff sd, int trials, double probability, DataType dataType, long[] shape){
this(sd, trials, probability, shape);
super.dataType = dataType;
}
public BinomialDistribution(int trials, double probability, DataType dt, long[] shape){
this(Nd4j.createUninitialized(dt, shape), trials, probability);
}
public BinomialDistribution() {
super();
}
/**
* This op fills Z with binomial distribution over given trials with single given probability for all trials
* @param z
* @param trials
* @param probability
*/
public BinomialDistribution(@NonNull INDArray z, int trials, double probability) {
super(z, z, z);
this.trials = trials;
this.probability = probability;
this.extraArgs = new Object[] {(double) this.trials, this.probability};
}
/**
* This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
* @param z
* @param trials
* @param probabilities array with probability value for each trial
*/
public BinomialDistribution(@NonNull INDArray z, int trials, @NonNull INDArray probabilities) {
super(z, probabilities, z);
if (trials > probabilities.length())
throw new IllegalStateException("Number of trials is > then amount of probabilities provided");
if (probabilities.elementWiseStride() < 1)
throw new IllegalStateException("Probabilities array shouldn't have negative elementWiseStride");
Preconditions.checkArgument(probabilities.dataType() == z.dataType(), "Probabilities and Z operand should have same data type");
this.trials = trials;
this.probability = 0.0;
this.extraArgs = new Object[] {(double) this.trials, this.probability};
}
/**
* This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
*
* @param z
* @param probabilities
*/
public BinomialDistribution(@NonNull INDArray z, @NonNull INDArray probabilities) {
this(z, (int) probabilities.length(), probabilities);
}
@Override
public int opNum() {
return 8;
}
@Override
public String opName() {
return "distribution_binomial";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public List calculateOutputShape(OpContext oc) {
return calculateOutputShape();
}
@Override
public List calculateOutputShape() {
LongShapeDescriptor longShapeDescriptor = LongShapeDescriptor.fromShape(shape,dataType);
return Arrays.asList(longShapeDescriptor);
}
@Override
public List doDiff(List f1) {
return Collections.emptyList();
}
@Override
public void setZ(INDArray z){
//We want all 3 args set to z for this op
this.x = z;
this.y = z;
this.z = z;
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes == null || inputDataTypes.isEmpty(), "Expected no input datatypes (no args) 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.DOUBLE);
}
@Override
public boolean isTripleArgRngOp() {
return true;
}
}