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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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//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
public class SDRandom extends SDOps {
public SDRandom(SameDiff sameDiff) {
super(sameDiff);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution(sd,p, datatype, shape).outputVariable();
}
/**
* 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 name name May be null. Name for the output variable
* @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 SDVariable bernoulli(String name, double p, DataType datatype, long... shape) {
Preconditions.checkArgument(shape.length >= 0, "shape has incorrect size/length. Expected: shape.length >= 0, got %s", shape.length);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution(sd,p, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.BinomialDistribution(sd,nTrials, p, datatype, shape).outputVariable();
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
* with the specified number of trials and probability.
*
* @param name name May be null. Name for the output variable
* @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 SDVariable binomial(String name, 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);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.BinomialDistribution(sd,nTrials, p, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.custom.RandomExponential(sd,lambda, datatype, shape).outputVariable();
}
/**
* 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 name name May be null. Name for the output variable
* @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 SDVariable exponential(String name, 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");
SDVariable out = new org.nd4j.linalg.api.ops.random.custom.RandomExponential(sd,lambda, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.LogNormalDistribution(sd,mean, stddev, datatype, shape).outputVariable();
}
/**
* 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 name name May be null. Name for the output variable
* @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 SDVariable logNormal(String name, 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);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.LogNormalDistribution(sd,mean, stddev, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.GaussianDistribution(sd,mean, stddev, datatype, shape).outputVariable();
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
* N(mean, stdev)
*
* @param name name May be null. Name for the output variable
* @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 SDVariable normal(String name, 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);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.GaussianDistribution(sd,mean, stddev, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.TruncatedNormalDistribution(sd,mean, stddev, datatype, shape).outputVariable();
}
/**
* 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 name name May be null. Name for the output variable
* @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 SDVariable normalTruncated(String name, 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);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.TruncatedNormalDistribution(sd,mean, stddev, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* 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 SDVariable 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 new org.nd4j.linalg.api.ops.random.impl.UniformDistribution(sd,min, max, datatype, shape).outputVariable();
}
/**
* Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
* U(min,max)
*
* @param name name May be null. Name for the output variable
* @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 SDVariable uniform(String name, 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);
SDVariable out = new org.nd4j.linalg.api.ops.random.impl.UniformDistribution(sd,min, max, datatype, shape).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
}