mindspore.nn.probability.distribution.Uniform
- class mindspore.nn.probability.distribution.Uniform(low=None, high=None, seed=None, dtype=mstype.float32, name='Uniform')[源代码]
均匀分布(Uniform Distribution)。 连续随机分布,取值范围为 \([a, b]\) ,概率密度函数为
\[f(x, a, b) = 1 / (b - a)\]其中 \(a, b\) 为分别为均匀分布的下界和上界。
- 参数:
low (int, float, list, numpy.ndarray, Tensor) - 分布的下限。默认值:
None
。high (int, float, list, numpy.ndarray, Tensor) - 分布的上限。默认值:
None
。seed (int) - 采样时使用的种子。如果为None,则使用全局种子。默认值:
None
。dtype (mindspore.dtype) - 事件样例的类型。默认值:
mstype.float32
。name (str) - 分布的名称。默认值:
'Uniform'
。
说明
low 必须小于 high 。
dtype 必须是float类型,因为均匀分布是连续的。
- 异常:
ValueError - low 大于等于 high 。
TypeError - dtype 不是float的子类。
- 支持平台:
Ascend
GPU
CPU
样例:
>>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.distribution as msd >>> from mindspore import Tensor >>> # To initialize a Uniform distribution of the lower bound 0.0 and the higher bound 1.0. >>> u1 = msd.Uniform(0.0, 1.0, dtype=mindspore.float32) >>> # A Uniform distribution can be initialized without arguments. >>> # In this case, `high` and `low` must be passed in through arguments during function calls. >>> u2 = msd.Uniform(dtype=mindspore.float32) >>> >>> # Here are some tensors used below for testing >>> value = Tensor([0.5, 0.8], dtype=mindspore.float32) >>> low_a = Tensor([0., 0.], dtype=mindspore.float32) >>> high_a = Tensor([2.0, 4.0], dtype=mindspore.float32) >>> low_b = Tensor([-1.5], dtype=mindspore.float32) >>> high_b = Tensor([2.5, 5.], 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. >>> # Args: >>> # value (Tensor): the value to be evaluated. >>> # low (Tensor): the lower bound of the distribution. Default: self.low. >>> # high (Tensor): the higher bound of the distribution. Default: self.high. >>> # Examples of `prob`. >>> # Similar calls can be made to other probability functions >>> # by replacing 'prob' by the name of the function. >>> ans = u1.prob(value) >>> print(ans.shape) (2,) >>> # Evaluate with respect to distribution b. >>> ans = u1.prob(value, low_b, high_b) >>> print(ans.shape) (2,) >>> # `high` and `low` must be passed in during function calls. >>> ans = u2.prob(value, low_a, high_a) >>> print(ans.shape) (2,) >>> # Functions `mean`, `sd`, `var`, and `entropy` have the same arguments. >>> # Args: >>> # low (Tensor): the lower bound of the distribution. Default: self.low. >>> # high (Tensor): the higher bound of the distribution. Default: self.high. >>> # Examples of `mean`. `sd`, `var`, and `entropy` are similar. >>> ans = u1.mean() # return 0.5 >>> print(ans.shape) () >>> ans = u1.mean(low_b, high_b) # return (low_b + high_b) / 2 >>> print(ans.shape) (2,) >>> # `high` and `low` must be passed in during function calls. >>> ans = u2.mean(low_a, high_a) >>> print(ans.shape) (2,) >>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same. >>> # Args: >>> # dist (str): the type of the distributions. Should be "Uniform" in this case. >>> # low_b (Tensor): the lower bound of distribution b. >>> # high_b (Tensor): the upper bound of distribution b. >>> # low_a (Tensor): the lower bound of distribution a. Default: self.low. >>> # high_a (Tensor): the upper bound of distribution a. Default: self.high. >>> # Examples of `kl_loss`. `cross_entropy` is similar. >>> ans = u1.kl_loss('Uniform', low_b, high_b) >>> print(ans.shape) (2,) >>> ans = u1.kl_loss('Uniform', low_b, high_b, low_a, high_a) >>> print(ans.shape) (2,) >>> # Additional `high` and `low` must be passed in. >>> ans = u2.kl_loss('Uniform', low_b, high_b, low_a, high_a) >>> print(ans.shape) (2,) >>> # Examples of `sample`. >>> # Args: >>> # shape (tuple): the shape of the sample. Default: () >>> # low (Tensor): the lower bound of the distribution. Default: self.low. >>> # high (Tensor): the upper bound of the distribution. Default: self.high. >>> ans = u1.sample() >>> print(ans.shape) () >>> ans = u1.sample((2,3)) >>> print(ans.shape) (2, 3) >>> ans = u1.sample((2,3), low_b, high_b) >>> print(ans.shape) (2, 3, 2) >>> ans = u2.sample((2,3), low_a, high_a) >>> print(ans.shape) (2, 3, 2)
- property high
返回分布的上限。
- 返回:
Tensor,分布的上限值。
- property low
返回分布的下限。
- 返回:
Tensor,分布的下限值。
- cdf(value, high, low)
在给定值下计算累积分布函数(Cumulatuve Distribution Function, CDF)。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,累积分布函数的值。
- cross_entropy(dist, high_b, low_b, high, low)
计算分布a和b之间的交叉熵。
- 参数:
dist (str) - 分布的类型。
high_b (Tensor) - 对比分布的上限值。
low_b (Tensor) - 对比分布的下限值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,交叉熵的值。
- entropy(high, low)
计算熵。
- 参数:
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,熵的值。
- kl_loss(dist, high_b, low_b, high, low)
计算KL散度,即KL(a||b)。
- 参数:
dist (str) - 分布的类型。
high_b (Tensor) - 对比分布的上限值。
low_b (Tensor) - 对比分布的下限值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,KL散度。
- log_cdf(value, high, low)
计算给定值对于的累积分布函数的对数。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,累积分布函数的对数。
- log_prob(value, high, low)
计算给定值对应的概率的对数。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,累积分布函数的对数。
- log_survival(value, high, low)
计算给定值对应的生存函数的对数。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,生存函数的对数。
- mean(high, low)
计算期望。
- 参数:
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,概率分布的期望。
- mode(high, low)
计算众数。
- 参数:
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,概率分布的众数。
- prob(value, high, low)
计算给定值下的概率。对于连续是计算概率密度函数(Probability Density Function)。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,概率值。
- sample(shape, high, low)
采样函数。
- 参数:
shape (tuple) - 样本的shape。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,根据概率分布采样的样本。
- sd(high, low)
计算标准差。
- 参数:
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,概率分布的标准差。
- survival_function(value, high, low)
计算给定值对应的生存函数。
- 参数:
value (Tensor) - 要计算的值。
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,生存函数的值。
- var(high, low)
计算方差。
- 参数:
high (Tensor) - 分布的上限值。默认值:
None
。low (Tensor) - 分布的下限值。默认值:
None
。
- 返回:
Tensor,概率分布的方差。