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2028 lines (1968 loc) · 97.6 KB
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// Copyright (c) 2019 by the SciSharp Team
// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion
using System;
using System.Collections;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using Python.Runtime;
using Numpy.Models;
namespace Numpy
{
public static partial class np
{
public static partial class random {
/// <summary>
/// Random values in a given shape.
///
/// Create an array of the given shape and populate it with
/// random samples from a uniform distribution
/// over [0, 1).
///
/// Notes
///
/// This is a convenience function. If you want an interface that
/// takes a shape-tuple as the first argument, refer to
/// np.random.random_sample .
/// </summary>
/// <param name="d1">
/// The dimensions of the returned array, should all be positive.
/// If no argument is given a single Python float is returned.
/// </param>
/// <param name="d0">
/// The dimensions of the returned array, should all be positive.
/// If no argument is given a single Python float is returned.
/// </param>
/// <returns>
/// Random values.
/// </returns>
public static NDarray rand(int? d1 = null, int? d0 = null)
=> NumPy.Instance.random_rand(d1:d1, d0:d0);
}
public static partial class random {
/// <summary>
/// Return a sample (or samples) from the “standard normal” distribution.
///
/// If positive, int_like or int-convertible arguments are provided,
/// randn generates an array of shape (d0, d1, ..., dn), filled
/// with random floats sampled from a univariate “normal” (Gaussian)
/// distribution of mean 0 and variance 1 (if any of the are
/// floats, they are first converted to integers by truncation). A single
/// float randomly sampled from the distribution is returned if no
/// argument is provided.
///
/// This is a convenience function. If you want an interface that takes a
/// tuple as the first argument, use numpy.random.standard_normal instead.
///
/// Notes
///
/// For random samples from , use:
///
/// sigma * np.random.randn(...) + mu
/// </summary>
/// <param name="d1">
/// The dimensions of the returned array, should be all positive.
/// If no argument is given a single Python float is returned.
/// </param>
/// <param name="d0">
/// The dimensions of the returned array, should be all positive.
/// If no argument is given a single Python float is returned.
/// </param>
/// <returns>
/// A (d0, d1, ..., dn)-shaped array of floating-point samples from
/// the standard normal distribution, or a single such float if
/// no parameters were supplied.
/// </returns>
public static NDarray randn(int? d1 = null, int? d0 = null)
=> NumPy.Instance.random_randn(d1:d1, d0:d0);
}
public static partial class random {
/// <summary>
/// Return random integers from low (inclusive) to high (exclusive).
///
/// Return random integers from the “discrete uniform” distribution of
/// the specified dtype in the “half-open” interval [low, high). If
/// high is None (the default), then results are from [0, low).
/// </summary>
/// <param name="low">
/// Lowest (signed) integer to be drawn from the distribution (unless
/// high=None, in which case this parameter is one above the
/// highest such integer).
/// </param>
/// <param name="high">
/// If provided, one above the largest (signed) integer to be drawn
/// from the distribution (see above for behavior if high=None).
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <param name="dtype">
/// Desired dtype of the result. All dtypes are determined by their
/// name, i.e., ‘int64’, ‘int’, etc, so byteorder is not available
/// and a specific precision may have different C types depending
/// on the platform. The default value is ‘np.int’.
/// </param>
/// <returns>
/// size-shaped array of random integers from the appropriate
/// distribution, or a single such random int if size not provided.
/// </returns>
public static NDarray<int> randint(int low, int? high = null, int[] size = null, Dtype dtype = null)
=> NumPy.Instance.random_randint(low, high:high, size:size, dtype:dtype);
}
public static partial class random {
/// <summary>
/// Random integers of type np.int between low and high, inclusive.
///
/// Return random integers of type np.int from the “discrete uniform”
/// distribution in the closed interval [low, high]. If high is
/// None (the default), then results are from [1, low]. The np.int
/// type translates to the C long type used by Python 2 for “short”
/// integers and its precision is platform dependent.
///
/// This function has been deprecated. Use randint instead.
///
/// Notes
///
/// To sample from N evenly spaced floating-point numbers between a and b,
/// use:
/// </summary>
/// <param name="low">
/// Lowest (signed) integer to be drawn from the distribution (unless
/// high=None, in which case this parameter is the highest such
/// integer).
/// </param>
/// <param name="high">
/// If provided, the largest (signed) integer to be drawn from the
/// distribution (see above for behavior if high=None).
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// size-shaped array of random integers from the appropriate
/// distribution, or a single such random int if size not provided.
/// </returns>
public static NDarray<int> random_integers(int low, int? high = null, int[] size = null)
=> NumPy.Instance.random_random_integers(low, high:high, size:size);
}
public static partial class random {
/// <summary>
/// Return random floats in the half-open interval [0.0, 1.0).
///
/// Results are from the “continuous uniform” distribution over the
/// stated interval. To sample multiply
/// the output of random_sample by (b-a) and add a:
/// </summary>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// Array of random floats of shape size (unless size=None, in which
/// case a single float is returned).
/// </returns>
public static NDarray<float> random_sample(int[] size = null)
=> NumPy.Instance.random_random_sample(size:size);
}
public static partial class random {
/// <summary>
/// Return random floats in the half-open interval [0.0, 1.0).
///
/// Results are from the “continuous uniform” distribution over the
/// stated interval. To sample multiply
/// the output of random_sample by (b-a) and add a:
/// </summary>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// Array of random floats of shape size (unless size=None, in which
/// case a single float is returned).
/// </returns>
public static NDarray<float> random_(int[] size = null)
=> NumPy.Instance.random_random(size:size);
}
public static partial class random {
/// <summary>
/// Return random floats in the half-open interval [0.0, 1.0).
///
/// Results are from the “continuous uniform” distribution over the
/// stated interval. To sample multiply
/// the output of random_sample by (b-a) and add a:
/// </summary>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// Array of random floats of shape size (unless size=None, in which
/// case a single float is returned).
/// </returns>
public static NDarray<float> ranf(int[] size = null)
=> NumPy.Instance.random_ranf(size:size);
}
public static partial class random {
/// <summary>
/// Return random floats in the half-open interval [0.0, 1.0).
///
/// Results are from the “continuous uniform” distribution over the
/// stated interval. To sample multiply
/// the output of random_sample by (b-a) and add a:
/// </summary>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// Array of random floats of shape size (unless size=None, in which
/// case a single float is returned).
/// </returns>
public static NDarray<float> sample(int[] size = null)
=> NumPy.Instance.random_sample(size:size);
}
public static partial class random {
/// <summary>
/// Generates a random sample from a given 1-D array
/// </summary>
/// <param name="a">
/// If an ndarray, a random sample is generated from its elements.
/// If an int, the random sample is generated as if a were np.arange(a)
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <param name="replace">
/// Whether the sample is with or without replacement
/// </param>
/// <param name="p">
/// The probabilities associated with each entry in a.
/// If not given the sample assumes a uniform distribution over all
/// entries in a.
/// </param>
/// <returns>
/// The generated random samples
/// </returns>
public static NDarray choice(NDarray a, int[] size = null, bool? replace = true, NDarray p = null)
=> NumPy.Instance.random_choice(a, size:size, replace:replace, p:p);
}
public static partial class random {
/// <summary>
/// Generates a random sample from a given 1-D array
/// </summary>
/// <param name="a">
/// If an ndarray, a random sample is generated from its elements.
/// If an int, the random sample is generated as if a were np.arange(a)
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <param name="replace">
/// Whether the sample is with or without replacement
/// </param>
/// <param name="p">
/// The probabilities associated with each entry in a.
/// If not given the sample assumes a uniform distribution over all
/// entries in a.
/// </param>
/// <returns>
/// The generated random samples
/// </returns>
public static NDarray choice(int a, int[] size = null, bool? replace = true, NDarray p = null)
=> NumPy.Instance.random_choice(a, size:size, replace:replace, p:p);
}
public static partial class random {
/// <summary>
/// Return random bytes.
/// </summary>
/// <param name="length">
/// Number of random bytes.
/// </param>
/// <returns>
/// String of length length.
/// </returns>
public static string bytes(int length)
=> NumPy.Instance.random_bytes(length);
}
public static partial class random {
/// <summary>
/// Modify a sequence in-place by shuffling its contents.
///
/// This function only shuffles the array along the first axis of a
/// multi-dimensional array. The order of sub-arrays is changed but
/// their contents remains the same.
/// </summary>
/// <param name="x">
/// The array or list to be shuffled.
/// </param>
public static void shuffle(NDarray x)
=> NumPy.Instance.random_shuffle(x);
}
public static partial class random {
/// <summary>
/// Randomly permute a sequence, or return a permuted range.
///
/// If x is a multi-dimensional array, it is only shuffled along its
/// first index.
/// </summary>
/// <param name="x">
/// If x is an integer, randomly permute np.arange(x).
/// If x is an array, make a copy and shuffle the elements
/// randomly.
/// </param>
/// <returns>
/// Permuted sequence or array range.
/// </returns>
public static NDarray permutation(NDarray x)
=> NumPy.Instance.random_permutation(x);
}
public static partial class random {
/// <summary>
/// Randomly permute a sequence, or return a permuted range.
///
/// If x is a multi-dimensional array, it is only shuffled along its
/// first index.
/// </summary>
/// <param name="x">
/// If x is an integer, randomly permute np.arange(x).
/// If x is an array, make a copy and shuffle the elements
/// randomly.
/// </param>
/// <returns>
/// Permuted sequence or array range.
/// </returns>
public static NDarray permutation(int x)
=> NumPy.Instance.random_permutation(x);
}
public static partial class random {
/// <summary>
/// Draw samples from a Beta distribution.
///
/// The Beta distribution is a special case of the Dirichlet distribution,
/// and is related to the Gamma distribution. It has the probability
/// distribution function
///
/// where the normalisation, B, is the beta function,
///
/// It is often seen in Bayesian inference and order statistics.
/// </summary>
/// <param name="a">
/// Alpha, positive (>0).
/// </param>
/// <param name="b">
/// Beta, positive (>0).
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if a and b are both scalars.
/// Otherwise, np.broadcast(a, b).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized beta distribution.
/// </returns>
public static NDarray beta(NDarray<float> a, NDarray<float> b, int[] size = null)
=> NumPy.Instance.random_beta(a, b, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a binomial distribution.
///
/// Samples are drawn from a binomial distribution with specified
/// parameters, n trials and p probability of success where
/// n an integer >= 0 and p is in the interval [0,1]. (n may be
/// input as a float, but it is truncated to an integer in use)
///
/// Notes
///
/// The probability density for the binomial distribution is
///
/// where is the number of trials, is the probability
/// of success, and is the number of successes.
///
/// When estimating the standard error of a proportion in a population by
/// using a random sample, the normal distribution works well unless the
/// product p*n <=5, where p = population proportion estimate, and n =
/// number of samples, in which case the binomial distribution is used
/// instead. For example, a sample of 15 people shows 4 who are left
/// handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
/// so the binomial distribution should be used in this case.
///
/// References
/// </summary>
/// <param name="n">
/// Parameter of the distribution, >= 0. Floats are also accepted,
/// but they will be truncated to integers.
/// </param>
/// <param name="p">
/// Parameter of the distribution, >= 0 and <=1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if n and p are both scalars.
/// Otherwise, np.broadcast(n, p).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized binomial distribution, where
/// each sample is equal to the number of successes over the n trials.
/// </returns>
public static NDarray binomial(NDarray<int> n, NDarray<float> p, int[] size = null)
=> NumPy.Instance.random_binomial(n, p, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a binomial distribution.
///
/// Samples are drawn from a binomial distribution with specified
/// parameters, n trials and p probability of success where
/// n an integer >= 0 and p is in the interval [0,1]. (n may be
/// input as a float, but it is truncated to an integer in use)
///
/// Notes
///
/// The probability density for the binomial distribution is
///
/// where is the number of trials, is the probability
/// of success, and is the number of successes.
///
/// When estimating the standard error of a proportion in a population by
/// using a random sample, the normal distribution works well unless the
/// product p*n <=5, where p = population proportion estimate, and n =
/// number of samples, in which case the binomial distribution is used
/// instead. For example, a sample of 15 people shows 4 who are left
/// handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
/// so the binomial distribution should be used in this case.
///
/// References
/// </summary>
/// <param name="n">
/// Parameter of the distribution, >= 0. Floats are also accepted,
/// but they will be truncated to integers.
/// </param>
/// <param name="p">
/// Parameter of the distribution, >= 0 and <=1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if n and p are both scalars.
/// Otherwise, np.broadcast(n, p).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized binomial distribution, where
/// each sample is equal to the number of successes over the n trials.
/// </returns>
public static NDarray binomial(int n, NDarray<float> p, int[] size = null)
=> NumPy.Instance.random_binomial(n, p, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a chi-square distribution.
///
/// When df independent random variables, each with standard normal
/// distributions (mean 0, variance 1), are squared and summed, the
/// resulting distribution is chi-square (see Notes). This distribution
/// is often used in hypothesis testing.
///
/// Notes
///
/// The variable obtained by summing the squares of df independent,
/// standard normally distributed random variables:
///
/// is chi-square distributed, denoted
///
/// The probability density function of the chi-squared distribution is
///
/// where is the gamma function,
///
/// References
/// </summary>
/// <param name="df">
/// Number of degrees of freedom, should be > 0.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if df is a scalar. Otherwise,
/// np.array(df).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized chi-square distribution.
/// </returns>
public static NDarray chisquare(NDarray<float> df, int[] size = null)
=> NumPy.Instance.random_chisquare(df, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from the Dirichlet distribution.
///
/// Draw size samples of dimension k from a Dirichlet distribution. A
/// Dirichlet-distributed random variable can be seen as a multivariate
/// generalization of a Beta distribution. Dirichlet pdf is the conjugate
/// prior of a multinomial in Bayesian inference.
///
/// Notes
///
/// Uses the following property for computation: for each dimension,
/// draw a random sample y_i from a standard gamma generator of shape
/// alpha_i, then
/// is
/// Dirichlet distributed.
///
/// References
/// </summary>
/// <param name="alpha">
/// Parameter of the distribution (k dimension for sample of
/// dimension k).
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. Default is None, in which case a
/// single value is returned.
/// </param>
/// <returns>
/// The drawn samples, of shape (size, alpha.ndim).
/// </returns>
public static NDarray dirichlet(NDarray alpha, int[] size = null)
=> NumPy.Instance.random_dirichlet(alpha, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from an exponential distribution.
///
/// Its probability density function is
///
/// for x > 0 and 0 elsewhere. is the scale parameter,
/// which is the inverse of the rate parameter .
/// The rate parameter is an alternative, widely used parameterization
/// of the exponential distribution [3].
///
/// The exponential distribution is a continuous analogue of the
/// geometric distribution. It describes many common situations, such as
/// the size of raindrops measured over many rainstorms [1], or the time
/// between page requests to Wikipedia [2].
///
/// References
/// </summary>
/// <param name="scale">
/// The scale parameter, .
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if scale is a scalar. Otherwise,
/// np.array(scale).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized exponential distribution.
/// </returns>
public static NDarray exponential(NDarray<float> scale = null, int[] size = null)
=> NumPy.Instance.random_exponential(scale, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from an F distribution.
///
/// Samples are drawn from an F distribution with specified parameters,
/// dfnum (degrees of freedom in numerator) and dfden (degrees of
/// freedom in denominator), where both parameters should be greater than
/// zero.
///
/// The random variate of the F distribution (also known as the
/// Fisher distribution) is a continuous probability distribution
/// that arises in ANOVA tests, and is the ratio of two chi-square
/// variates.
///
/// Notes
///
/// The F statistic is used to compare in-group variances to between-group
/// variances. Calculating the distribution depends on the sampling, and
/// so it is a function of the respective degrees of freedom in the
/// problem. The variable dfnum is the number of samples minus one, the
/// between-groups degrees of freedom, while dfden is the within-groups
/// degrees of freedom, the sum of the number of samples in each group
/// minus the number of groups.
///
/// References
/// </summary>
/// <param name="dfnum">
/// Degrees of freedom in numerator, should be > 0.
/// </param>
/// <param name="dfden">
/// Degrees of freedom in denominator, should be > 0.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if dfnum and dfden are both scalars.
/// Otherwise, np.broadcast(dfnum, dfden).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized Fisher distribution.
/// </returns>
public static NDarray f(NDarray<float> dfnum, NDarray<float> dfden, int[] size = null)
=> NumPy.Instance.random_f(dfnum, dfden, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a Gamma distribution.
///
/// Samples are drawn from a Gamma distribution with specified parameters,
/// shape (sometimes designated “k”) and scale (sometimes designated
/// “theta”), where both parameters are > 0.
///
/// Notes
///
/// The probability density for the Gamma distribution is
///
/// where is the shape and the scale,
/// and is the Gamma function.
///
/// The Gamma distribution is often used to model the times to failure of
/// electronic components, and arises naturally in processes for which the
/// waiting times between Poisson distributed events are relevant.
///
/// References
/// </summary>
/// <param name="shape">
/// The shape of the gamma distribution. Should be greater than zero.
/// </param>
/// <param name="scale">
/// The scale of the gamma distribution. Should be greater than zero.
/// Default is equal to 1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if shape and scale are both scalars.
/// Otherwise, np.broadcast(shape, scale).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized gamma distribution.
/// </returns>
public static NDarray gamma(Shape shape, NDarray<float> scale = null, int[] size = null)
=> NumPy.Instance.random_gamma(shape, scale:scale, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from the geometric distribution.
///
/// Bernoulli trials are experiments with one of two outcomes:
/// success or failure (an example of such an experiment is flipping
/// a coin). The geometric distribution models the number of trials
/// that must be run in order to achieve success. It is therefore
/// supported on the positive integers, k = 1, 2, ....
///
/// The probability mass function of the geometric distribution is
///
/// where p is the probability of success of an individual trial.
/// </summary>
/// <param name="p">
/// The probability of success of an individual trial.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if p is a scalar. Otherwise,
/// np.array(p).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized geometric distribution.
/// </returns>
public static NDarray geometric(NDarray<float> p, int[] size = null)
=> NumPy.Instance.random_geometric(p, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a Gumbel distribution.
///
/// Draw samples from a Gumbel distribution with specified location and
/// scale. For more information on the Gumbel distribution, see
/// Notes and References below.
///
/// Notes
///
/// The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme
/// Value Type I) distribution is one of a class of Generalized Extreme
/// Value (GEV) distributions used in modeling extreme value problems.
/// The Gumbel is a special case of the Extreme Value Type I distribution
/// for maximums from distributions with “exponential-like” tails.
///
/// The probability density for the Gumbel distribution is
///
/// where is the mode, a location parameter, and
/// is the scale parameter.
///
/// The Gumbel (named for German mathematician Emil Julius Gumbel) was used
/// very early in the hydrology literature, for modeling the occurrence of
/// flood events. It is also used for modeling maximum wind speed and
/// rainfall rates. It is a “fat-tailed” distribution - the probability of
/// an event in the tail of the distribution is larger than if one used a
/// Gaussian, hence the surprisingly frequent occurrence of 100-year
/// floods. Floods were initially modeled as a Gaussian process, which
/// underestimated the frequency of extreme events.
///
/// It is one of a class of extreme value distributions, the Generalized
/// Extreme Value (GEV) distributions, which also includes the Weibull and
/// Frechet.
///
/// The function has a mean of and a variance
/// of .
///
/// References
/// </summary>
/// <param name="loc">
/// The location of the mode of the distribution. Default is 0.
/// </param>
/// <param name="scale">
/// The scale parameter of the distribution. Default is 1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if loc and scale are both scalars.
/// Otherwise, np.broadcast(loc, scale).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized Gumbel distribution.
/// </returns>
public static NDarray gumbel(NDarray<float> loc = null, NDarray<float> scale = null, int[] size = null)
=> NumPy.Instance.random_gumbel(loc:loc, scale:scale, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a Hypergeometric distribution.
///
/// Samples are drawn from a hypergeometric distribution with specified
/// parameters, ngood (ways to make a good selection), nbad (ways to make
/// a bad selection), and nsample = number of items sampled, which is less
/// than or equal to the sum ngood + nbad.
///
/// Notes
///
/// The probability density for the Hypergeometric distribution is
///
/// where and
///
/// for P(x) the probability of x successes, g = ngood, b = nbad, and
/// n = number of samples.
///
/// Consider an urn with black and white marbles in it, ngood of them
/// black and nbad are white. If you draw nsample balls without
/// replacement, then the hypergeometric distribution describes the
/// distribution of black balls in the drawn sample.
///
/// Note that this distribution is very similar to the binomial
/// distribution, except that in this case, samples are drawn without
/// replacement, whereas in the Binomial case samples are drawn with
/// replacement (or the sample space is infinite). As the sample space
/// becomes large, this distribution approaches the binomial.
///
/// References
/// </summary>
/// <param name="ngood">
/// Number of ways to make a good selection. Must be nonnegative.
/// </param>
/// <param name="nbad">
/// Number of ways to make a bad selection. Must be nonnegative.
/// </param>
/// <param name="nsample">
/// Number of items sampled. Must be at least 1 and at most
/// ngood + nbad.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if ngood, nbad, and nsample
/// are all scalars. Otherwise, np.broadcast(ngood, nbad, nsample).size
/// samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized hypergeometric distribution.
/// </returns>
public static NDarray hypergeometric(NDarray<int> ngood, NDarray<int> nbad, NDarray<int> nsample, int[] size = null)
=> NumPy.Instance.random_hypergeometric(ngood, nbad, nsample, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from the Laplace or double exponential distribution with
/// specified location (or mean) and scale (decay).
///
/// The Laplace distribution is similar to the Gaussian/normal distribution,
/// but is sharper at the peak and has fatter tails. It represents the
/// difference between two independent, identically distributed exponential
/// random variables.
///
/// Notes
///
/// It has the probability density function
///
/// The first law of Laplace, from 1774, states that the frequency
/// of an error can be expressed as an exponential function of the
/// absolute magnitude of the error, which leads to the Laplace
/// distribution. For many problems in economics and health
/// sciences, this distribution seems to model the data better
/// than the standard Gaussian distribution.
///
/// References
/// </summary>
/// <param name="loc">
/// The position, , of the distribution peak. Default is 0.
/// </param>
/// <param name="scale">
/// , the exponential decay. Default is 1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if loc and scale are both scalars.
/// Otherwise, np.broadcast(loc, scale).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized Laplace distribution.
/// </returns>
public static NDarray laplace(NDarray<float> loc = null, NDarray<float> scale = null, int[] size = null)
=> NumPy.Instance.random_laplace(loc:loc, scale:scale, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a logistic distribution.
///
/// Samples are drawn from a logistic distribution with specified
/// parameters, loc (location or mean, also median), and scale (>0).
///
/// Notes
///
/// The probability density for the Logistic distribution is
///
/// where = location and = scale.
///
/// The Logistic distribution is used in Extreme Value problems where it
/// can act as a mixture of Gumbel distributions, in Epidemiology, and by
/// the World Chess Federation (FIDE) where it is used in the Elo ranking
/// system, assuming the performance of each player is a logistically
/// distributed random variable.
///
/// References
/// </summary>
/// <param name="loc">
/// Parameter of the distribution. Default is 0.
/// </param>
/// <param name="scale">
/// Parameter of the distribution. Should be greater than zero.
/// Default is 1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if loc and scale are both scalars.
/// Otherwise, np.broadcast(loc, scale).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized logistic distribution.
/// </returns>
public static NDarray logistic(NDarray<float> loc = null, NDarray<float> scale = null, int[] size = null)
=> NumPy.Instance.random_logistic(loc:loc, scale:scale, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a log-normal distribution.
///
/// Draw samples from a log-normal distribution with specified mean,
/// standard deviation, and array shape. Note that the mean and standard
/// deviation are not the values for the distribution itself, but of the
/// underlying normal distribution it is derived from.
///
/// Notes
///
/// A variable x has a log-normal distribution if log(x) is normally
/// distributed. The probability density function for the log-normal
/// distribution is:
///
/// where is the mean and is the standard
/// deviation of the normally distributed logarithm of the variable.
/// A log-normal distribution results if a random variable is the product
/// of a large number of independent, identically-distributed variables in
/// the same way that a normal distribution results if the variable is the
/// sum of a large number of independent, identically-distributed
/// variables.
///
/// References
/// </summary>
/// <param name="mean">
/// Mean value of the underlying normal distribution. Default is 0.
/// </param>
/// <param name="sigma">
/// Standard deviation of the underlying normal distribution. Should
/// be greater than zero. Default is 1.
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if mean and sigma are both scalars.
/// Otherwise, np.broadcast(mean, sigma).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized log-normal distribution.
/// </returns>
public static NDarray lognormal(NDarray<float> mean = null, NDarray<float> sigma = null, int[] size = null)
=> NumPy.Instance.random_lognormal(mean:mean, sigma:sigma, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a logarithmic series distribution.
///
/// Samples are drawn from a log series distribution with specified
/// shape parameter, 0 < p < 1.
///
/// Notes
///
/// The probability density for the Log Series distribution is
///
/// where p = probability.
///
/// The log series distribution is frequently used to represent species
/// richness and occurrence, first proposed by Fisher, Corbet, and
/// Williams in 1943 [2]. It may also be used to model the numbers of
/// occupants seen in cars [3].
///
/// References
/// </summary>
/// <param name="p">
/// Shape parameter for the distribution. Must be in the range (0, 1).
/// </param>
/// <param name="size">
/// Output shape. If the given shape is, e.g., (m, n, k), then
/// m * n * k samples are drawn. If size is None (default),
/// a single value is returned if p is a scalar. Otherwise,
/// np.array(p).size samples are drawn.
/// </param>
/// <returns>
/// Drawn samples from the parameterized logarithmic series distribution.
/// </returns>
public static NDarray logseries(NDarray<float> p, int[] size = null)
=> NumPy.Instance.random_logseries(p, size:size);
}
public static partial class random {
/// <summary>
/// Draw samples from a multinomial distribution.
///
/// The multinomial distribution is a multivariate generalisation of the
/// binomial distribution. Take an experiment with one of p
/// possible outcomes. An example of such an experiment is throwing a dice,
/// where the outcome can be 1 through 6. Each sample drawn from the