Source code for mxnet.ndarray.random

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

from ..base import numeric_types, _Null
from ..context import current_context
from . import _internal
from .ndarray import NDArray


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


def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
    """Helper function for random generators."""
    if isinstance(params[0], NDArray):
        for i in params[1:]:
            assert isinstance(i, NDArray), \
                "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, out=out, **kwargs)
    elif isinstance(params[0], numeric_types):
        if ctx is None:
            ctx = current_context()
        if shape is _Null and out is None:
            shape = 1
        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, ctx=ctx, out=out, **kwargs)

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


[docs]def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray 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 NDArray 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 NDArrays 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' ctx : Context Device context of output. Default is current context. Overridden by `low.context` when `low` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.uniform(0, 1) [ 0.54881352] >>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0)) [ 0.92514056] >>> mx.nd.random.uniform(-1, 1, shape=(2,)) [ 0.71589124 0.08976638] >>> low = mx.nd.array([1,2,3]) >>> high = mx.nd.array([2,3,4]) >>> mx.nd.random.uniform(low, high, shape=2) [[ 1.78653979 1.93707538] [ 2.01311183 2.37081361] [ 3.30491424 3.69977832]] """ return _random_helper(_internal._random_uniform, _internal._sample_uniform, [low, high], shape, dtype, ctx, out, kwargs)
[docs]def normal(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray Mean (centre) of the distribution. scale : float or NDArray 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 NDArrays 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' ctx : Context Device context of output. Default is current context. Overridden by `loc.context` when `loc` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.normal(0, 1) [ 2.21220636] >>> mx.nd.random.normal(0, 1, ctx=mx.gpu(0)) [ 0.29253659] >>> mx.nd.random.normal(-1, 1, shape=(2,)) [-0.2259962 -0.51619542] >>> loc = mx.nd.array([1,2,3]) >>> scale = mx.nd.array([2,3,4]) >>> mx.nd.random.normal(loc, scale, shape=2) [[ 0.55912292 3.19566321] [ 1.91728961 2.47706747] [ 2.79666662 5.44254589]] """ return _random_helper(_internal._random_normal, _internal._sample_normal, [loc, scale], shape, dtype, ctx, out, kwargs)
[docs]def poisson(lam=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray 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 NDArray 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' ctx : Context Device context of output. Default is current context. Overridden by `lam.context` when `lam` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.poisson(1) [ 1.] >>> mx.nd.random.poisson(1, shape=(2,)) [ 0. 2.] >>> lam = mx.nd.array([1,2,3]) >>> mx.nd.random.poisson(lam, shape=2) [[ 1. 3.] [ 3. 2.] [ 2. 3.]] """ return _random_helper(_internal._random_poisson, _internal._sample_poisson, [lam], shape, dtype, ctx, out, kwargs)
[docs]def exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs): r"""Draw samples from an exponential distribution. Its probability density function is .. math:: 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 NDArray 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 NDArray 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' ctx : Context Device context of output. Default is current context. Overridden by `scale.context` when `scale` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.exponential(1) [ 0.79587454] >>> mx.nd.random.exponential(1, shape=(2,)) [ 0.89856035 1.25593066] >>> scale = mx.nd.array([1,2,3]) >>> mx.nd.random.exponential(scale, shape=2) [[ 0.41063145 0.42140478] [ 2.59407091 10.12439728] [ 2.42544937 1.14260709]] """ return _random_helper(_internal._random_exponential, _internal._sample_exponential, [1.0/scale], shape, dtype, ctx, out, kwargs)
[docs]def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray The shape of the gamma distribution. Should be greater than zero. beta : float or NDArray 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 NDArrays 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' ctx : Context Device context of output. Default is current context. Overridden by `alpha.context` when `alpha` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.gamma(1, 1) [ 1.93308783] >>> mx.nd.random.gamma(1, 1, shape=(2,)) [ 0.48216391 2.09890771] >>> alpha = mx.nd.array([1,2,3]) >>> beta = mx.nd.array([2,3,4]) >>> mx.nd.random.gamma(alpha, beta, shape=2) [[ 3.24343276 0.94137681] [ 3.52734375 0.45568955] [ 14.26264095 14.0170126 ]] """ return _random_helper(_internal._random_gamma, _internal._sample_gamma, [alpha, beta], shape, dtype, ctx, out, kwargs)
[docs]def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray Limit of unsuccessful experiments, > 0. p : float or NDArray 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 NDArrays 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' ctx : Context Device context of output. Default is current context. Overridden by `k.context` when `k` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.negative_binomial(10, 0.5) [ 4.] >>> mx.nd.random.negative_binomial(10, 0.5, shape=(2,)) [ 3. 4.] >>> k = mx.nd.array([1,2,3]) >>> p = mx.nd.array([0.2,0.4,0.6]) >>> mx.nd.random.negative_binomial(k, p, shape=2) [[ 3. 2.] [ 4. 4.] [ 0. 5.]] """ return _random_helper(_internal._random_negative_binomial, _internal._sample_negative_binomial, [k, p], shape, dtype, ctx, out, kwargs)
[docs]def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, ctx=None, out=None, **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 NDArray Mean of the negative binomial distribution. alpha : float or NDArray 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 NDArrays 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' ctx : Context Device context of output. Default is current context. Overridden by `mu.context` when `mu` is an NDArray. out : NDArray Store output to an existing NDArray. Examples -------- >>> mx.nd.random.generalized_negative_binomial(10, 0.5) [ 19.] >>> mx.nd.random.generalized_negative_binomial(10, 0.5, shape=(2,)) [ 30. 21.] >>> mu = mx.nd.array([1,2,3]) >>> alpha = mx.nd.array([0.2,0.4,0.6]) >>> mx.nd.random.generalized_negative_binomial(mu, alpha, shape=2) [[ 4. 0.] [ 3. 2.] [ 6. 2.]] """ return _random_helper(_internal._random_generalized_negative_binomial, _internal._sample_generalized_negative_binomial, [mu, alpha], shape, dtype, ctx, out, kwargs)
[docs]def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **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 : NDArray 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. out : NDArray Store output to an existing NDArray. dtype : str or numpy.dtype Data type of the sample output array. The default is int32. Note that the data type of the log likelihood array is the same with that of `data`. Examples -------- >>> probs = mx.nd.array([[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]]) >>> mx.nd.random.multinomial(probs) [3 1] >>> mx.nd.random.multinomial(probs, shape=2) [[4 4] [1 2]] >>> mx.nd.random.multinomial(probs, get_prob=True) [3 2] [-1.20397282 -1.60943794] """ return _internal._sample_multinomial(data, shape, get_prob, out=out, dtype=dtype, **kwargs)
[docs]def shuffle(data, **kwargs): """Shuffle the elements randomly. This shuffles the array along the first axis. The order of the elements in each subarray does not change. For example, if a 2D array is given, the order of the rows randomly changes, but the order of the elements in each row does not change. Parameters ---------- data : NDArray Input data array. out : NDArray Array to store the result. Examples -------- >>> data = mx.nd.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> mx.nd.random.shuffle(data) [[ 0. 1. 2.] [ 6. 7. 8.] [ 3. 4. 5.]] >>> mx.nd.random.shuffle(data) [[ 3. 4. 5.] [ 0. 1. 2.] [ 6. 7. 8.]] """ return _internal._shuffle(data, **kwargs)