/usr/include/torch/Random.h

// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//                
// This file is part of Torch 3.1.
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#ifndef RANDOM_INC
#define RANDOM_INC

#include "general.h"

namespace Torch {

/** Random class which contains several static random methods.
    
    These methods are based on a uniform random generator,
    named "Mersenne Twister", available at:
    http://www.math.keio.ac.jp/matumoto/emt.html.
    Copyright Makoto Matsumoto and Takuji Nishimura.
    (Have a look inside the implementation file for details).

    The random generator can be initialized with the manualSeed() method.
    Otherwise, it will be automatically initialized with a seed based
    on the current computer clock.

    @author Ronan Collobert (collober@idiap.ch)
*/
class Random
{
  public:
    // The seed used to initialize the random generator.
    static unsigned long the_initial_seed;

    // Internal variables for the Mersenne Twister generator
    static const int n;
    static const int m;
    static unsigned long state[]; /* the array for the state vector  */
    static int left;
    static int initf;
    static unsigned long *next;

    // Internal variables for the normal distribution generator
    static real normal_x;
    static real normal_y;
    static real normal_rho;
    static bool normal_is_valid;

    // Internal method for the Mersenne Twister generator
    static void nextState();

    /// Initializes the random number generator with the computer clock.
    static void seed();

    /// Initializes the random number generator with the given long "the_seed_".
    static void manualSeed(unsigned long the_seed_);

    /// Returns the starting seed used.
    static unsigned long getInitialSeed();

    /// Generates a uniform 32 bits integer.
    static unsigned long random();

    /// Generates a uniform random number on [0,1[.
    static real uniform();

    /// Returns in #indices# #n_indices# shuffled. (between 0 and #n_indices-1#).
    static void getShuffledIndices(int *indices, int n_indices);

    /// Shuffles tabular, which contains #n_elems# of size #size_elem#.
    static void shuffle(void *tabular, int size_elem, int n_elems);

    /// Generates a uniform random number on [a,b[ (b>a).
    static real boundedUniform(real a, real b);

    /** Generates a random number from a normal distribution.
        (With mean #mean# and standard deviation #stdv >= 0#).
    */
    static real normal(real mean=0, real stdv=1);

    /** Generates a random number from an exponential distribution.
        The density is $p(x) = lambda * exp(-lambda * x)$, where
        lambda is a positive number.
    */
    static real exponential(real lambda);

    /** Returns a random number from a Cauchy distribution.
        The Cauchy density is $p(x) = sigma/(pi*(sigma^2 + (x-median)^2))$
    */
    static real cauchy(real median=0, real sigma=1);

    /** Generates a random number from a log-normal distribution.
        (#mean > 0# is the mean of the log-normal distribution
        and #stdv# is its standard deviation).
    */
    static real logNormal(real mean, real stdv);

    /** Generates a random number from a geometric distribution.
        It returns an integer #i#, where $p(i) = (1-p) * p^(i-1)$.
        p must satisfy $0 < p < 1$.
    */
    static int geometric(real p);

    /// Returns true with probability $p$ and false with probability $1-p$ (p > 0).
    static bool bernouilli(real p=0.5);
};

}

#endif
libtorch3-dev 3.1-2.1build1 / usr / include / torch / Random.h
