org.nd4j.linalg.api.ops.random.impl.UniformDistribution Maven / Gradle / Ivy
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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.
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* * SPDX-License-Identifier: Apache-2.0
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*/
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.DataBuffer;
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.api.shape.options.ArrayOptionsHelper;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
public class UniformDistribution extends BaseRandomOp {
private double from;
private double to;
public UniformDistribution() {
super();
}
public UniformDistribution(SameDiff sd, double from, double to, long[] shape) {
super(sd, shape);
this.from = from;
this.to = to;
this.extraArgs = new Object[] {this.from, this.to};
}
public UniformDistribution(SameDiff sd, double from, double to, DataType dataType, long[] shape) {
this(sd, from, to, shape);
this.dataType = dataType;
}
public UniformDistribution(double min, double max, DataType datatype, long... shape){
this(Nd4j.createUninitialized(datatype, shape), min, max);
this.shape = shape;
}
/**
* This op fills Z with random values within from...to boundaries
* @param z
* @param from
* @param to
*/
public UniformDistribution(@NonNull INDArray z, double from, double to) {
super(null, null, z);
this.from = from;
this.to = to;
this.extraArgs = new Object[] {this.from, this.to};
this.shape = z.shape();
}
/**
* This op fills Z with random values within 0...1
* @param z
*/
public UniformDistribution(@NonNull INDArray z) {
this(z, 0.0, 1.0);
}
/**
* This op fills Z with random values within 0...to
* @param z
*/
public UniformDistribution(@NonNull INDArray z, double to) {
this(z, 0.0, to);
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "distribution_uniform";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public List doDiff(List f1) {
return Collections.emptyList();
}
@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 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);
}
}