Source code for mxnet.symbol.random

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"""Random distribution generator Symbol API of MXNet."""

from ..base import numeric_types, _Null
from . import _internal
from .symbol import Symbol


__all__ = ['uniform', 'normal', 'poisson', 'exponential', 'gamma', 'multinomial',
           'negative_binomial', 'generalized_negative_binomial']


def _random_helper(random, sampler, params, shape, dtype, kwargs):
    """Helper function for random generators."""
    if isinstance(params[0], Symbol):
        for i in params[1:]:
            assert isinstance(i, Symbol), \
                "Distribution parameters must all have the same type, but got " \
                "both %s and %s."%(type(params[0]), type(i))
        return sampler(*params, shape=shape, dtype=dtype, **kwargs)
    elif isinstance(params[0], numeric_types):
        for i in params[1:]:
            assert isinstance(i, numeric_types), \
                "Distribution parameters must all have the same type, but got " \
                "both %s and %s."%(type(params[0]), type(i))
        return random(*params, shape=shape, dtype=dtype, **kwargs)

    raise ValueError("Distribution parameters must be either Symbol or numbers, "
                     "but got %s."%type(params[0]))


[docs]def uniform(low=0, high=1, shape=_Null, dtype=_Null, **kwargs): """Draw random samples from a uniform distribution. Samples are uniformly distributed over the half-open interval *[low, high)* (includes *low*, but excludes *high*). Parameters ---------- low : float or Symbol Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float or Symbol Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and `high` are scalars, output shape will be `(m, n)`. If `low` and `high` are Symbols with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_uniform, _internal._sample_uniform, [low, high], shape, dtype, kwargs)
[docs]def normal(loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs): """Draw random samples from a normal (Gaussian) distribution. Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation). Parameters ---------- loc : float or Symbol Mean (centre) of the distribution. scale : float or Symbol Standard deviation (spread or width) of the distribution. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale` are Symbols with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_normal, _internal._sample_normal, [loc, scale], shape, dtype, kwargs)
[docs]def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs): """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. Parameters ---------- lam : float or Symbol Expectation of interval, should be >= 0. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `lam` is a scalar, output shape will be `(m, n)`. If `lam` is an Symbol with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `lam`. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_poisson, _internal._sample_poisson, [lam], shape, dtype, kwargs)
[docs]def exponential(scale=1, shape=_Null, dtype=_Null, **kwargs): r"""Draw samples from an exponential distribution. Its probability density function is f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}), for x > 0 and 0 elsewhere. \beta is the scale parameter, which is the inverse of the rate parameter \lambda = 1/\beta. Parameters ---------- scale : float or Symbol The scale parameter, \beta = 1/\lambda. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `scale` is a scalar, output shape will be `(m, n)`. If `scale` is an Symbol with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_exponential, _internal._sample_exponential, [1.0/scale], shape, dtype, kwargs)
[docs]def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, **kwargs): """Draw random samples from a gamma distribution. Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). Parameters ---------- alpha : float or Symbol The shape of the gamma distribution. Should be greater than zero. beta : float or Symbol The scale of the gamma distribution. Should be greater than zero. Default is equal to 1. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `alpha` and `beta` are scalars, output shape will be `(m, n)`. If `alpha` and `beta` are Symbols with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[alpha, beta)` pair. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_gamma, _internal._sample_gamma, [alpha, beta], shape, dtype, kwargs)
[docs]def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, **kwargs): """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. Parameters ---------- k : float or Symbol Limit of unsuccessful experiments, > 0. p : float or Symbol Failure probability in each experiment, >= 0 and <=1. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `k` and `p` are scalars, output shape will be `(m, n)`. If `k` and `p` are Symbols with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[k, p)` pair. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_negative_binomial, _internal._sample_negative_binomial, [k, p], shape, dtype, kwargs)
[docs]def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, **kwargs): """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. Parameters ---------- mu : float or Symbol Mean of the negative binomial distribution. alpha : float or Symbol Alpha (dispersion) parameter of the negative binomial distribution. shape : int or tuple of ints The number of samples to draw. If shape is, e.g., `(m, n)` and `mu` and `alpha` are scalars, output shape will be `(m, n)`. If `mu` and `alpha` are Symbols with shape, e.g., `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[mu, alpha)` pair. dtype : {'float16','float32', 'float64'} Data type of output samples. Default is 'float32' """ return _random_helper(_internal._random_generalized_negative_binomial, _internal._sample_generalized_negative_binomial, [mu, alpha], shape, dtype, kwargs)
[docs]def multinomial(data, shape=_Null, get_prob=True, **kwargs): """Concurrent sampling from multiple multinomial distributions. .. note:: The input distribution must be normalized, i.e. `data` must sum to 1 along its last dimension. Parameters ---------- data : Symbol An *n* dimensional array whose last dimension has length `k`, where `k` is the number of possible outcomes of each multinomial distribution. For example, data with shape `(m, n, k)` specifies `m*n` multinomial distributions each with `k` possible outcomes. shape : int or tuple of ints The number of samples to draw from each distribution. If shape is empty one sample will be drawn from each distribution. get_prob : bool If 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 w.r.t. this array to estimate gradient. """ return _internal._sample_multinomial(data, shape, get_prob, **kwargs)