mindspore.nn.probability.distribution.Exponential
- class mindspore.nn.probability.distribution.Exponential(rate=None, seed=None, dtype=mstype.float32, name='Exponential')[source]
Exponential Distribution. An Exponential distributio is a continuous distribution with the range \([0, \inf)\) and the probability density function:
\[f(x, \lambda) = \lambda \exp(-\lambda x)\]where \(\lambda\) is the rate of the distribution.
- Parameters
rate (int, float, list, numpy.ndarray, Tensor) – The inverse scale. 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:
'Exponential'
.
Note
rate must be strictly greater than 0. dist_spec_args is rate. dtype must be a float type because Exponential distributions are continuous.
- Raises
ValueError – When rate <= 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 Exponential distribution of the probability 0.5. >>> e1 = msd.Exponential(0.5, dtype=mindspore.float32) >>> # An Exponential distribution can be initialized without arguments. >>> # In this case, `rate` must be passed in through `args` during function calls. >>> e2 = msd.Exponential(dtype=mindspore.float32) >>> # Here are some tensors used below for testing >>> value = Tensor([1, 2, 3], dtype=mindspore.float32) >>> rate_a = Tensor([0.6], dtype=mindspore.float32) >>> rate_b = Tensor([0.2, 0.5, 0.4], dtype=mindspore.float32) >>> # Private interfaces of probability functions corresponding to public interfaces, including >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, are the same as follows. >>> # Args: >>> # value (Tensor): the value to be evaluated. >>> # rate (Tensor): the rate of the distribution. Default: self.rate. >>> # Examples of `prob`. >>> # Similar calls can be made to other probability functions >>> # by replacing `prob` by the name of the function. >>> ans = e1.prob(value) >>> print(ans.shape) (3,) >>> # Evaluate with respect to distribution b. >>> ans = e1.prob(value, rate_b) >>> print(ans.shape) (3,) >>> # `rate` must be passed in during function calls. >>> ans = e2.prob(value, rate_a) >>> print(ans.shape) (3,) >>> # Functions `mean`, `sd`, 'var', and 'entropy' have the same arguments as follows. >>> # Args: >>> # rate (Tensor): the rate of the distribution. Default: self.rate. >>> # Examples of `mean`. `sd`, `var`, and `entropy` are similar. >>> ans = e1.mean() # return 2 >>> print(ans.shape) () >>> ans = e1.mean(rate_b) # return 1 / rate_b >>> print(ans.shape) (3,) >>> # `rate` must be passed in during function calls. >>> ans = e2.mean(rate_a) >>> print(ans.shape) (1,) >>> # Interfaces of `kl_loss` and `cross_entropy` are the same. >>> # Args: >>> # dist (str): The name of the distribution. Only 'Exponential' is supported. >>> # rate_b (Tensor): the rate of distribution b. >>> # rate_a (Tensor): the rate of distribution a. Default: self.rate. >>> # Examples of `kl_loss`. `cross_entropy` is similar. >>> ans = e1.kl_loss('Exponential', rate_b) >>> print(ans.shape) (3,) >>> ans = e1.kl_loss('Exponential', rate_b, rate_a) >>> print(ans.shape) (3,) >>> # An additional `rate` must be passed in. >>> ans = e2.kl_loss('Exponential', rate_b, rate_a) >>> print(ans.shape) (3,) >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: () >>> # probs1 (Tensor): the rate of the distribution. Default: self.rate. >>> ans = e1.sample() >>> print(ans.shape) () >>> ans = e1.sample((2,3)) >>> print(ans.shape) (2, 3) >>> ans = e1.sample((2,3), rate_b) >>> print(ans.shape) (2, 3, 3) >>> ans = e2.sample((2,3), rate_a) >>> print(ans.shape) (2, 3, 1)
- property rate
Return rate.
- Returns
Tensor, the rate of the distribution.
- cdf(value, rate)
Compute the cumulatuve distribution function(CDF) of the given value.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the cumulatuve distribution function for the given input.
- cross_entropy(dist, rate_b, rate)
Compute the cross entropy of two distribution.
- Parameters
dist (str) - the type of the other distribution.
rate_b (Tensor) - the rate of the other distribution.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the cross entropy.
- entropy(rate)
Compute the value of the entropy.
- Parameters
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the entropy.
- kl_loss(dist, rate_b, rate)
Compute the value of the K-L loss between two distribution, namely KL(a||b).
- Parameters
dist (str) - the type of the other distribution.
rate_b (Tensor) - the rate of the other distribution.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the K-L loss.
- log_cdf(value, rate)
Compute the log value of the cumulatuve distribution function.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the log value of the cumulatuve distribution function.
- log_prob(value, rate)
the log value of the probability.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the log value of the probability.
- log_survival(value, rate)
Compute the log value of the survival function.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the K-L loss.
- mean(rate)
Compute the mean value of the distribution.
- Parameters
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the mean of the distribution.
- mode(rate)
Compute the mode value of the distribution.
- Parameters
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the mode of the distribution.
- prob(value, rate)
The probability of the given value. For the continuous distribution, it is the probability density function.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the probability.
- sample(shape, rate)
Generate samples.
- Parameters
shape (tuple) - the shape of the sample.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the sample following the distribution.
- sd(rate)
The standard deviation.
- Parameters
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the standard deviation of the distribution.
- survival_function(value, rate)
Compute the value of the survival function.
- Parameters
value (Tensor) - the value to compute.
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the value of the survival function.
- var(rate)
Compute the variance of the distribution.
- Parameters
rate (Tensor) - the rate of the distribution. Default:
None
.
- Returns
Tensor, the variance of the distribution.