mindspore.nn.probability.distribution.LogNormal
- class mindspore.nn.probability.distribution.LogNormal(loc=None, scale=None, seed=0, dtype=mstype.float32, name='LogNormal')[source]
LogNormal distribution. A log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. The log-normal distribution has the range \((0, \inf)\) with the pdf as
\[f(x, \mu, \sigma) = 1 / x\sigma\sqrt{2\pi} \exp(-(\ln(x) - \mu)^2 / 2\sigma^2).\]where \(\mu, \sigma\) are the mean and the standard deviation of the underlying normal distribution respectively. It is constructed as the exponential transformation of a Normal distribution.
- Parameters
loc (int, float, list, numpy.ndarray, Tensor) – The mean of the underlying Normal distribution. Default: None.
scale (int, float, list, numpy.ndarray, Tensor) – The standard deviation of the underlying Normal distribution. Default: None.
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: ‘LogNormal’.
- Supported Platforms:
Ascend
GPU
Note
scale must be greater than zero. dist_spec_args are loc and scale. dtype must be a float type because LogNormal distributions are continuous.
- Raises
ValueError – When scale <= 0.
TypeError – When the input dtype is not a subclass of float.
Examples
>>> import numpy as np >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> class Prob(nn.Cell): ... def __init__(self): ... super(Prob, self).__init__() ... self.ln = msd.LogNormal(np.array([0.3]), np.array([[0.2], [0.4]]), dtype=mindspore.float32) ... def construct(self, x_): ... return self.ln.prob(x_) >>> pdf = Prob() >>> output = pdf(Tensor([1.0, 2.0], dtype=mindspore.float32)) >>> print(output.shape) (2, 2)
- 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: None.
Returns
Tensor, the sample following the distribution.
- sd(loc, scale)
The standard deviation.
Parameters
loc (Tensor) - the loc parameter of the distribution. Default value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: 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 value: None.
scale (Tensor) - the scale parameter of the distribution. Default value: None.
Returns
Tensor, the variance of the distribution.