Source code for mindspore.nn.probability.distribution.logistic

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"""Logistic Distribution"""
import numpy as np
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore._checkparam import Validator
from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import check_greater_zero
from ._utils.custom_ops import exp_generic, log_generic


[docs]class Logistic(Distribution): r""" Logistic distribution. A Logistic distributio is a continuous distribution with the range :math:`(-\inf, \inf)` and the probability density function: .. math:: f(x, a, b) = 1 / b \exp(\exp(-(x - a) / b) - x). where a and b are loc and scale parameter respectively. Args: loc (float, list, numpy.ndarray, Tensor): The location of the Logistic distribution. Default: None. scale (float, list, numpy.ndarray, Tensor): The scale of the Logistic distribution. Default: None. seed (int): The seed used in sampling. The global seed is used if it is None. Default: None. dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32. name (str): The name of the distribution. Default: 'Logistic'. Note: `scale` must be greater than zero. `dist_spec_args` are `loc` and `scale`. `dtype` must be a float type because Logistic distributions are continuous. Raises: ValueError: When scale <= 0. TypeError: When the input `dtype` is not a subclass of float. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> # To initialize a Logistic distribution of loc 3.0 and scale 4.0. >>> l1 = msd.Logistic(3.0, 4.0, dtype=mindspore.float32) >>> # A Logistic distribution can be initialized without arguments. >>> # In this case, `loc` and `scale` must be passed in through arguments. >>> l2 = msd.Logistic(dtype=mindspore.float32) >>> >>> # Here are some tensors used below for testing >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32) >>> loc_a = Tensor([2.0], dtype=mindspore.float32) >>> scale_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32) >>> loc_b = Tensor([1.0], dtype=mindspore.float32) >>> scale_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32) >>> >>> # Private interfaces of probability functions corresponding to public interfaces, including >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, >>> # have the same arguments as follows. >>> # Args: >>> # value (Tensor): the value to be evaluated. >>> # loc (Tensor): the location of the distribution. Default: self.loc. >>> # scale (Tensor): the scale of the distribution. Default: self.scale. >>> # Examples of `prob`. >>> # Similar calls can be made to other probability functions >>> # by replacing 'prob' by the name of the function >>> ans = l1.prob(value) >>> print(ans.shape) (3,) >>> # Evaluate with respect to distribution b. >>> ans = l1.prob(value, loc_b, scale_b) >>> print(ans.shape) (3,) >>> # `loc` and `scale` must be passed in during function calls >>> ans = l1.prob(value, loc_a, scale_a) >>> print(ans.shape) (3,) >>> # Functions `mean`, `mode`, `sd`, `var`, and `entropy` have the same arguments. >>> # Args: >>> # loc (Tensor): the location of the distribution. Default: self.loc. >>> # scale (Tensor): the scale of the distribution. Default: self.scale. >>> # Example of `mean`. `mode`, `sd`, `var`, and `entropy` are similar. >>> ans = l1.mean() >>> print(ans.shape) () >>> ans = l1.mean(loc_b, scale_b) >>> print(ans.shape) (3,) >>> # `loc` and `scale` must be passed in during function calls. >>> ans = l1.mean(loc_a, scale_a) >>> print(ans.shape) (3,) >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: () >>> # loc (Tensor): the location of the distribution. Default: self.loc. >>> # scale (Tensor): the scale of the distribution. Default: self.scale. >>> ans = l1.sample() >>> print(ans.shape) () >>> ans = l1.sample((2,3)) >>> print(ans.shape) (2, 3) >>> ans = l1.sample((2,3), loc_b, scale_b) >>> print(ans.shape) (2, 3, 3) >>> ans = l1.sample((2,3), loc_a, scale_a) >>> print(ans.shape) (2, 3, 3) """ def __init__(self, loc=None, scale=None, seed=None, dtype=mstype.float32, name="Logistic"): """ Constructor of Logistic. """ param = dict(locals()) param['param_dict'] = {'loc': loc, 'scale': scale} valid_dtype = mstype.float_type Validator.check_type_name( "dtype", dtype, valid_dtype, type(self).__name__) super(Logistic, self).__init__(seed, dtype, name, param) self._loc = self._add_parameter(loc, 'loc') self._scale = self._add_parameter(scale, 'scale') if self._scale is not None: check_greater_zero(self._scale, "scale") # ops needed for the class self.cast = P.Cast() self.consttensor = P.ScalarToTensor() self.dtypeop = P.DType() self.exp = exp_generic self.expm1 = P.Expm1() self.fill = P.Fill() self.less = P.Less() self.log = log_generic self.log1p = P.Log1p() self.logicalor = P.LogicalOr() self.erf = P.Erf() self.greater = P.Greater() self.sigmoid = P.Sigmoid() self.squeeze = P.Squeeze(0) self.select = P.Select() self.shape = P.Shape() self.softplus = self._softplus self.sqrt = P.Sqrt() self.uniform = C.uniform self.neg = P.Neg() self.threshold = np.log(np.finfo(np.float32).eps) + 1. self.tiny = np.finfo(np.float).tiny self.sd_const = np.pi/np.sqrt(3) def _softplus(self, x): too_small = self.less(x, self.threshold) too_large = self.greater(x, -self.threshold) too_small_value = self.exp(x) too_large_value = x too_small_or_too_large = self.logicalor(too_small, too_large) ones = self.fill(self.dtypeop(x), self.shape(x), 1.0) x = self.select(too_small_or_too_large, ones, x) y = self.log(self.exp(x) + 1.0) return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y)) def extend_repr(self): """Display instance object as string.""" if self.is_scalar_batch: s = 'location = {}, scale = {}'.format(self._loc, self._scale) else: s = 'batch_shape = {}'.format(self._broadcast_shape) return s @property def loc(self): """ Return the location of the distribution after casting to dtype. Output: Tensor, the loc parameter of the distribution. """ return self._loc @property def scale(self): """ Return the scale of the distribution after casting to dtype. Output: Tensor, the scale parameter of the distribution. """ return self._scale def _get_dist_type(self): return "Logistic" def _get_dist_args(self, loc=None, scale=None): if loc is None: loc = self.loc else: self.checktensor(loc, 'loc') if scale is None: scale = self.scale else: self.checktensor(scale, 'scale') return loc, scale def _mean(self, loc=None, scale=None): """ The mean of the distribution. """ loc, scale = self._check_param_type(loc, scale) return loc def _mode(self, loc=None, scale=None): """ The mode of the distribution. """ loc, scale = self._check_param_type(loc, scale) return loc def _sd(self, loc=None, scale=None): """ The standard deviation of the distribution. """ _, scale = self._check_param_type(loc, scale) return scale * self.consttensor(self.sd_const, self.dtypeop(scale)) def _entropy(self, loc=None, scale=None): r""" Evaluate entropy. .. math:: H(X) = \log(scale) + 2. """ loc, scale = self._check_param_type(loc, scale) return self.log(scale) + 2. def _log_prob(self, value, loc=None, scale=None): r""" Evaluate log probability. Args: value (Tensor): The value to be evaluated. loc (Tensor): The location of the distribution. Default: self.loc. scale (Tensor): The scale of the distribution. Default: self.scale. .. math:: z = (x - \mu) / \sigma L(x) = -z * -2. * softplus(-z) - \log(\sigma) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) loc, scale = self._check_param_type(loc, scale) z = (value - loc) / scale return -z - 2. * self.softplus(-z) - self.log(scale) def _cdf(self, value, loc=None, scale=None): r""" Evaluate the cumulative distribution function on the given value. Args: value (Tensor): The value to be evaluated. loc (Tensor): The location of the distribution. Default: self.loc. scale (Tensor): The scale the distribution. Default: self.scale. .. math:: cdf(x) = sigmoid((x - loc) / scale) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) loc, scale = self._check_param_type(loc, scale) z = (value - loc) / scale return self.sigmoid(z) def _log_cdf(self, value, loc=None, scale=None): r""" Evaluate the log cumulative distribution function on the given value. Args: value (Tensor): The value to be evaluated. loc (Tensor): The location of the distribution. Default: self.loc. scale (Tensor): The scale the distribution. Default: self.scale. .. math:: log_cdf(x) = -softplus(-(x - loc) / scale) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) loc, scale = self._check_param_type(loc, scale) z = (value - loc) / scale return (-1) * self.softplus(-z) def _survival_function(self, value, loc=None, scale=None): r""" Evaluate the survival function on the given value. Args: value (Tensor): The value to be evaluated. loc (Tensor): The location of the distribution. Default: self.loc. scale (Tensor): The scale the distribution. Default: self.scale. .. math:: survival(x) = sigmoid(-(x - loc) / scale) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) loc, scale = self._check_param_type(loc, scale) z = (value - loc) / scale return self.sigmoid(-z) def _log_survival(self, value, loc=None, scale=None): r""" Evaluate the log survival function on the given value. Args: value (Tensor): The value to be evaluated. loc (Tensor): The location of the distribution. Default: self.loc. scale (Tensor): The scale the distribution. Default: self.scale. .. math:: survival(x) = -softplus((x - loc) / scale) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) loc, scale = self._check_param_type(loc, scale) z = (value - loc) / scale return (-1) * self.softplus(z) def _sample(self, shape=(), loc=None, scale=None): """ Sampling. Args: shape (tuple): The shape of the sample. Default: (). loc (Tensor): The location of the samples. Default: self.loc. scale (Tensor): The scale of the samples. Default: self.scale. Returns: Tensor, with the shape being shape + batch_shape. """ shape = self.checktuple(shape, 'shape') loc, scale = self._check_param_type(loc, scale) batch_shape = self.shape(loc + scale) origin_shape = shape + batch_shape if origin_shape == (): sample_shape = (1,) else: sample_shape = origin_shape l_zero = self.consttensor(self.tiny, mstype.float32) h_one = self.consttensor(1.0, mstype.float32) sample_uniform = self.uniform(sample_shape, l_zero, h_one, self.seed) sample = self.log(sample_uniform) - self.log1p(self.neg(sample_uniform)) sample = sample * scale + loc value = self.cast(sample, self.dtype) if origin_shape == (): value = self.squeeze(value) return value