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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 TruncatedNormalDistribution extends BaseRandomOp {
private double mean;
private double stddev;
public TruncatedNormalDistribution() {
super();
}
public TruncatedNormalDistribution(SameDiff sd, double mean, double stddev, long[] shape){
super(sd, shape);
this.mean = mean;
this.stddev = stddev;
this.extraArgs = new Object[] {this.mean, this.stddev};
}
public TruncatedNormalDistribution(SameDiff sd, double mean, double stddev, DataType dataType, long[] shape) {
super(sd, shape);
this.mean = mean;
this.stddev = stddev;
this.extraArgs = new Object[] {this.mean, this.stddev};
}
public TruncatedNormalDistribution(double mean, double stddev, DataType datatype, long... shape){
this(Nd4j.createUninitialized(datatype, shape), mean, stddev);
}
/**
* This op fills Z with random values within stddev..mean..stddev boundaries
* @param z
* @param mean
* @param stddev
*/
public TruncatedNormalDistribution(@NonNull INDArray z, double mean, double stddev) {
super(z,z,z);
this.mean = mean;
this.stddev = stddev;
this.extraArgs = new Object[] {this.mean, this.stddev};
}
public TruncatedNormalDistribution(@NonNull INDArray z, @NonNull INDArray means, double stddev) {
super(z, means, z);
if (z.length() != means.length())
throw new IllegalStateException("Result length should be equal to provided Means length");
if (means.elementWiseStride() < 1)
throw new IllegalStateException("Means array can't have negative EWS");
this.mean = 0.0;
this.stddev = stddev;
this.extraArgs = new Object[] {this.mean, this.stddev};
}
/**
* This op fills Z with random values within -1.0..0..1.0
* @param z
*/
public TruncatedNormalDistribution(@NonNull INDArray z) {
this(z, 0.0, 1.0);
}
/**
* This op fills Z with random values within stddev..0..stddev
* @param z
*/
public TruncatedNormalDistribution(@NonNull INDArray z, double stddev) {
this(z, 0.0, stddev);
}
@Override
public int opNum() {
return 11;
}
@Override
public String opName() {
return "distribution_truncated";
}
@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 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 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 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;
}
}