Uniform¶
均匀分布
概率密度函数（pdf）为：
\[ \begin{align}\begin{aligned}pdf(x; a, b) = \frac{1}{Z}, a <=x < b\\Z = b - a\end{aligned}\end{align} \]
上面的数学公式中：
\(low = a\) 。 \(high = b\) 。 \(Z\)：正态分布常量。
参数 low 和 high 的维度必须能够支持广播。
参数¶
low (int|float|list|numpy.ndarray|Tensor) - 均匀分布的下边界。数据类型为 int、float、list、numpy.ndarray 或 Tensor。
high (int|float|list|numpy.ndarray|Tensor) - 均匀分布的上边界。数据类型为 int、float、list、numpy.ndarray 或 Tensor。
name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。
代码示例¶
import paddle
from paddle.distribution import Uniform
# Without broadcasting, a single uniform distribution [3, 4]:
u1 = Uniform(low=3.0, high=4.0)
# 2 distributions [1, 3], [2, 4]
u2 = Uniform(low=[1.0, 2.0], high=[3.0, 4.0])
# 4 distributions
u3 = Uniform(low=[[1.0, 2.0], [3.0, 4.0]],
high=[[1.5, 2.5], [3.5, 4.5]])
# With broadcasting:
u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
# Complete example
value_tensor = paddle.to_tensor([0.8], dtype="float32")
uniform = Uniform([0.], [2.])
sample = uniform.sample([2])
# a random tensor created by uniform distribution with shape: [2, 1]
entropy = uniform.entropy()
# [0.6931472] with shape: [1]
lp = uniform.log_prob(value_tensor)
# [-0.6931472] with shape: [1]
p = uniform.probs(value_tensor)
# [0.5] with shape: [1]