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/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* 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
******************************************************************************/
//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
import static org.nd4j.linalg.factory.NDValidation.isSameType;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
public class NDRandom {
public NDRandom() {
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
* with the specified probability. Array values will have value 1 with probability P and value 0 with probability
* 1-P.
*
* @param p Probability of value 1
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray bernoulli(double p, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution(p, datatype, shape));
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
* with the specified number of trials and probability.
*
* @param nTrials Number of trials parameter for the binomial distribution
* @param p Probability of success for each trial
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray binomial(int nTrials, double p, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.BinomialDistribution(nTrials, p, datatype, shape));
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
* P(x) = lambda * exp(-lambda * x)
*
* Inputs must satisfy the following constraints:
* Must be positive: lambda > 0
*
* @param lambda lambda parameter
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray exponential(double lambda, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
Preconditions.checkArgument(lambda > 0, "Must be positive");
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.custom.RandomExponential(lambda, datatype, shape))[0];
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
* i.e., {@code log(x) ~ N(mean, stdev)}
*
* @param mean Mean value for the random array
* @param stddev Standard deviation for the random array
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray logNormal(double mean, double stddev, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.LogNormalDistribution(mean, stddev, datatype, shape));
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
* N(mean, stdev)
*
* @param mean Mean value for the random array
* @param stddev Standard deviation for the random array
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray normal(double mean, double stddev, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.GaussianDistribution(mean, stddev, datatype, shape));
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
* N(mean, stdev). However, any values more than 1 standard deviation from the mean are dropped and re-sampled
*
* @param mean Mean value for the random array
* @param stddev Standard deviation for the random array
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray normalTruncated(double mean, double stddev, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.TruncatedNormalDistribution(mean, stddev, datatype, shape));
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
* U(min,max)
*
* @param min Minimum value
* @param max Maximum value.
* @param datatype Data type of the output variable
* @param shape Shape of the new random INDArray, as a 1D array (Size: AtLeast(min=0))
* @return output Tensor with the given shape where values are randomly sampled according to a %OP_NAME% distribution (NUMERIC type)
*/
public INDArray uniform(double min, double max, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.random.impl.UniformDistribution(min, max, datatype, shape));
}
}
