mindspore.nn.probability.distribution.Gumbel
- class mindspore.nn.probability.distribution.Gumbel(loc, scale, seed=0, dtype=mstype.float32, name='Gumbel')[source]
Gumbel distribution. A Gumbel distributio is a continuous distribution with the range of all real numbers and the probability density function:
\[f(x, a, b) = 1 / b \exp(\exp(-(x - a) / b) - x)\]Where \(a, b\) are loc and scale parameter respectively.
- Parameters
loc (int, float, list, numpy.ndarray, Tensor) – The location of Gumbel distribution.
scale (int, float, list, numpy.ndarray, Tensor) – The scale of Gumbel distribution.
seed (int) – the seed used in sampling. The global seed is used if it is None. Default:
0
.dtype (mindspore.dtype) – type of the distribution. Default:
mstype.float32
.name (str) – the name of the distribution. Default:
'Gumbel'
.
Note
scale must be greater than zero. dist_spec_args are loc and scale. dtype must be a float type because Gumbel 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 numpy as np >>> import mindspore.nn.probability.distribution as msd >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> class Prob(nn.Cell): ... def __init__(self): ... super(Prob, self).__init__() ... self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=mindspore.float32) ... ... def construct(self, x_): ... return self.gum.prob(x_) >>> value = np.array([1.0, 2.0]).astype(np.float32) >>> pdf = Prob() >>> output = pdf(Tensor(value, dtype=mindspore.float32))
- property loc
Return the loc parameter of the distribution.
- Returns
Tensor, the loc parameter of the distribution.
- property scale
Return the scale parameter of the distribution.
- Returns
Tensor, the scale parameter of the distribution.
- cdf(value, loc, scale)
Compute the cumulatuve distribution function(CDF) of the given value.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the cumulatuve distribution function for the given input.
- cross_entropy(dist, loc_b, scale_b, loc, scale)
Compute the cross entropy of two distribution.
- Parameters
dist (str) - the type of the other distribution.
loc_b (Tensor) - the loc parameter of the other distribution.
scale_b (Tensor) - the scale parameter of the other distribution.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the cross entropy.
- entropy(loc, scale)
Compute the value of the entropy.
- Parameters
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the entropy.
- kl_loss(dist, loc_b, scale_b, loc, scale)
Compute the value of the K-L loss between two distribution, namely KL(a||b).
- Parameters
dist (str) - the type of the other distribution.
loc_b (Tensor) - the loc parameter of the other distribution.
scale_b (Tensor) - the scale parameter of the other distribution.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the K-L loss.
- log_cdf(value, loc, scale)
Compute the log value of the cumulatuve distribution function.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the log value of the cumulatuve distribution function.
- log_prob(value, loc, scale)
the log value of the probability.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the log value of the probability.
- log_survival(value, loc, scale)
Compute the log value of the survival function.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the K-L loss.
- mean(loc, scale)
Compute the mean value of the distribution.
- Parameters
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the mean of the distribution.
- mode(loc, scale)
Compute the mode value of the distribution.
- Parameters
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the mode of the distribution.
- prob(value, loc, scale)
The probability of the given value. For the continuous distribution, it is the probability density function.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the probability.
- sample(shape, loc, scale)
Generate samples.
- Parameters
shape (tuple) - the shape of the sample.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the sample following the distribution.
- sd(loc, scale)
The standard deviation.
- Parameters
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the standard deviation of the distribution.
- survival_function(value, loc, scale)
Compute the value of the survival function.
- Parameters
value (Tensor) - the value to compute.
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the value of the survival function.
- var(loc, scale)
Compute the variance of the distribution.
- Parameters
loc (Tensor) - the loc parameter of the distribution. Default:
None
.scale (Tensor) - the scale parameter of the distribution. Default:
None
.
- Returns
Tensor, the variance of the distribution.