generated code - from PR https://github.com/apache/incubator-mxnet/pull/13039

SymbolRandomAPIBase.scala

/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements.  See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
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* the License.  You may obtain a copy of the License at
*
*    http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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* See the License for the specific language governing permissions and
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package org.apache.mxnet

import org.apache.mxnet.annotation.Experimental

// scalastyle:off
abstract class SymbolRandomAPIBase {
  /**
  * <pre>
Draw random samples from an an approximately log-uniform
  * or Zipfian distribution without replacement.
  * 
  * This operation takes a 2-D shape `(batch_size, num_sampled)`,
  * and randomly generates *num_sampled* samples from the range of integers [0, range_max)
  * for each instance in the batch.
  * 
  * The elements in each instance are drawn without replacement from the base distribution.
  * The base distribution for this operator is an approximately log-uniform or Zipfian distribution:
  * 
  *   P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)
  * 
  * Additionaly, it also returns the number of trials used to obtain `num_sampled` samples for
  * each instance in the batch.
  * 
  * Example::
  * 
  *    samples, trials = _sample_unique_zipfian(750000, shape=(4, 8192))
  *    unique(samples[0]) = 8192
  *    unique(samples[3]) = 8192
  *    trials[0] = 16435
  * 
  * 
  * 
  * Defined in src/operator/random/unique_sample_op.cc:L66  * </pre>

  * @param range_max		The number of possible classes.
  * @param shape		2-D shape of the output, where shape[0] is the batch size, and shape[1] is the number of candidates to sample for each batch.
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def unique_zipfian[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (range_max : T, shape : Option[org.apache.mxnet.Shape] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a normal (Gaussian) distribution.
  * 
  * .. note:: The existing alias ``normal`` is deprecated.
  * 
  * Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation).
  * 
  * Example::
  * 
  *    normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478],
  *                                           [-1.23474145,  1.55807114]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L85  * </pre>

  * @param mu		Mean of the distribution.
  * @param sigma		Standard deviation of the distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def normal[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (mu : Option[T] = None, sigma : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a generalized negative binomial distribution.
  * 
  * Samples are distributed according to a generalized negative binomial distribution parametrized by
  * *mu* (mean) and *alpha* (dispersion). *alpha* is defined as *1/k* where *k* is the failure limit of the
  * number of unsuccessful experiments (generalized to real numbers).
  * Samples will always be returned as a floating point data type.
  * 
  * Example::
  * 
  *    generalized_negative_binomial(mu=2.0, alpha=0.3, shape=(2,2)) = [[ 2.,  1.],
  *                                                                     [ 6.,  4.]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L168  * </pre>

  * @param mu		Mean of the negative binomial distribution.
  * @param alpha		Alpha (dispersion) parameter of the negative binomial distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def generalized_negative_binomial[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (mu : Option[T] = None, alpha : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a Poisson distribution.
  * 
  * Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate).
  * Samples will always be returned as a floating point data type.
  * 
  * Example::
  * 
  *    poisson(lam=4, shape=(2,2)) = [[ 5.,  2.],
  *                                   [ 4.,  6.]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L132  * </pre>

  * @param lam		Lambda parameter (rate) of the Poisson distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def poisson[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (lam : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a uniform distribution.
  * 
  * .. note:: The existing alias ``uniform`` is deprecated.
  * 
  * Samples are uniformly distributed over the half-open interval *[low, high)*
  * (includes *low*, but excludes *high*).
  * 
  * Example::
  * 
  *    uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335,  0.85794562],
  *                                           [ 0.54488319,  0.84725171]]
  * 
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L66  * </pre>

  * @param low		Lower bound of the distribution.
  * @param high		Upper bound of the distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def uniform[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (low : Option[T] = None, high : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a negative binomial distribution.
  * 
  * Samples are distributed according to a negative binomial distribution parametrized by
  * *k* (limit of unsuccessful experiments) and *p* (failure probability in each experiment).
  * Samples will always be returned as a floating point data type.
  * 
  * Example::
  * 
  *    negative_binomial(k=3, p=0.4, shape=(2,2)) = [[ 4.,  7.],
  *                                                  [ 2.,  5.]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L149  * </pre>

  * @param k		Limit of unsuccessful experiments.
  * @param p		Failure probability in each experiment.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def negative_binomial[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (k : Option[T] = None, p : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Concurrent sampling from multiple multinomial distributions.
  * 
  * *data* is an *n* dimensional array whose last dimension has length *k*, where
  * *k* is the number of possible outcomes of each multinomial distribution. This
  * operator will draw *shape* samples from each distribution. If shape is empty
  * one sample will be drawn from each distribution.
  * 
  * If *get_prob* is true, a second array containing log likelihood of the drawn
  * samples will also be returned. This is usually used for reinforcement learning
  * where you can provide reward as head gradient for this array to estimate
  * gradient.
  * 
  * Note that the input distribution must be normalized, i.e. *data* must sum to
  * 1 along its last axis.
  * 
  * Examples::
  * 
  *    probs = [[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]]
  * 
  *    // Draw a single sample for each distribution
  *    sample_multinomial(probs) = [3, 0]
  * 
  *    // Draw a vector containing two samples for each distribution
  *    sample_multinomial(probs, shape=(2)) = [[4, 2],
  *                                            [0, 0]]
  * 
  *    // requests log likelihood
  *    sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3]  * </pre>

  * @param data		Distribution probabilities. Must sum to one on the last axis.
  * @param shape		Shape to be sampled from each random distribution.
  * @param get_prob		Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.
  * @param dtype		DType of the output in case this can't be inferred.
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def multinomial[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (data : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, get_prob : Option[Boolean] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from an exponential distribution.
  * 
  * Samples are distributed according to an exponential distribution parametrized by *lambda* (rate).
  * 
  * Example::
  * 
  *    exponential(lam=4, shape=(2,2)) = [[ 0.0097189 ,  0.08999364],
  *                                       [ 0.04146638,  0.31715935]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L115  * </pre>

  * @param lam		Lambda parameter (rate) of the exponential distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def exponential[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (lam : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
  /**
  * <pre>
Draw random samples from a gamma distribution.
  * 
  * Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale).
  * 
  * Example::
  * 
  *    gamma(alpha=9, beta=0.5, shape=(2,2)) = [[ 7.10486984,  3.37695289],
  *                                             [ 3.91697288,  3.65933681]]
  * 
  * 
  * Defined in src/operator/random/sample_op.cc:L100  * </pre>

  * @param alpha		Alpha parameter (shape) of the gamma distribution.
  * @param beta		Beta parameter (scale) of the gamma distribution.
  * @param shape		Shape of the output.
  * @param ctx		Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
  * @param dtype		DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
  * @return org.apache.mxnet.Symbol
  */
@Experimental
def gamma[T: SymbolOrValue : scala.reflect.runtime.universe.TypeTag] (alpha : Option[T] = None, beta : Option[T] = None, shape : Option[org.apache.mxnet.Shape] = None, ctx : Option[String] = None, dtype : Option[String] = None, name : String = null, attr : Map[String, String] = null): org.apache.mxnet.Symbol
}