RNG¶
- class RNG(seed=None, device=None)[源代码]¶
RNG公开了一些生成随机数的方法。实际案例
>>> import megengine.random as rand >>> rng = rand.RNG(seed=100) >>> x = rng.uniform(size=(2, 2)) >>> x.numpy() array([[0.84811664, 0.6147553 ], [0.59429836, 0.64727545]], dtype=float32)
- beta(alpha, beta, size=None)[源代码]¶
服从贝塔分布 \(\operatorname{Beta}(\alpha, \beta)\) 的随机变量。
对应的概率密度函数为
\[p(x)=\frac{1}{\mathrm{~B}(\alpha, \beta)} x^{\alpha-1}(1-x)^{\beta-1} \quad \text { for } \alpha, \beta>0,\]其中 \(\mathrm{~B}(\alpha, \beta)\) 是 Beta 函数，
\[\mathrm{~B}(\alpha, \beta)=\int_{0}^{1} t^{\alpha-1}(1-t)^{\beta-1} d t.\]- 参数
alpha (Union[Tensor, float]) – the alpha parameter of the distribution. Must be positive.
beta (Union[Tensor, float]) – the beta parameter of the distribution. Must be positive.
size (Optional[Iterable[int]]) – the size of output tensor. If alpha and beta are scalars and given size is, e.g., (m, n), then the output shape is (m, n). If alpha or beta is a Tensor and given size is, e.g., (m, n), then the output shape is (m, n) + broadcast(alpha, beta).shape. Default: None.
- 返回
tensor. The random variable with Beta distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.beta(alpha=2, beta=1, size=(2, 2)) >>> x.numpy() array([[0.6172312 , 0.9789006 ], [0.50004643, 0.9775796 ]], dtype=float32) >>> alpha = mge.Tensor([[0.5], ... [ 3]], dtype="float32") >>> beta = mge.Tensor([0.5,5], dtype="float32") >>> x = rand.beta(alpha=alpha, beta=beta) >>> x.numpy() array([[0.0075407 , 0.1275094 ], [0.96331763, 0.22299217]], dtype=float32) >>> x = rand.beta(alpha=alpha, beta=beta, size=2) >>> x.numpy() array([[[0.46863747, 0.13819647], [0.8646759 , 0.16014215]],
- [[0.0682759 , 0.04448463],
[0.97733796, 0.19206746]]], dtype=float32)
- gamma(shape, scale=1, size=None)[源代码]¶
服从伽玛分布 \(\Gamma(k, \theta)\) 的随机变量。
对应的概率密度函数为
\[p(x)=x^{k-1} \frac{e^{-x / \theta}}{\theta^{k} \Gamma(k)} \quad \text { for } x>0 \quad k, \theta>0,\]其中 \(\Gamma(k)\) 是 gamma 函数，
\[\Gamma(k)=(k-1) ! \quad \text { for } \quad k \quad \text{is positive integer}.\]- 参数
shape (Union[Tensor, float]) – the shape parameter (sometimes designated “k”) of the distribution. Must be positive.
scale (Union[Tensor, float]) – the scale parameter (sometimes designated “theta”) of the distribution. Must be positive. Default: 1.
size (Optional[Iterable[int]]) – the size of output tensor. If shape and scale are scalars and given size is, e.g., (m, n), then the output shape is (m, n). If shape or scale is a Tensor and given size is, e.g., (m, n), then the output shape is (m, n) + broadcast(shape, scale).shape. The broadcast rules are consistent with numpy.broadcast. Default: None.
- 返回
tensor. The random variable with Gamma distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.gamma(shape=2, scale=1, size=(2, 2)) >>> x.numpy() array([[0.97447544, 1.5668875 ], [1.0069491 , 0.3078318 ]], dtype=float32) >>> shape = mge.Tensor([[ 1], ... [10]], dtype="float32") >>> scale = mge.Tensor([1,5], dtype="float32") >>> x = rand.gamma(shape=shape, scale=scale) >>> x.numpy() array([[ 0.11312152, 3.0799196 ], [10.973469 , 29.596972 ]], dtype=float32) >>> x = rand.gamma(shape=shape, scale=scale, size=2) >>> x.numpy() array([[[4.35868073e+00, 1.22415285e+01], [1.02696848e+01, 4.19773598e+01]],
- [[7.73875117e-02, 6.06766164e-01],
[1.22881927e+01, 8.13445740e+01]]], dtype=float32)
- normal(mean=0, std=1, size=None)[源代码]¶
服从高斯分布的随机变量 \(N(\mu, \sigma)\)
- 参数
- 返回
tensor. The random variable with Gaussian distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.normal(mean=0, std=1, size=(2, 2)) >>> x.numpy() array([[ 1.5534291 , -0.28356555], [ 2.2230418 , -0.92425716]], dtype=float32)
- permutation(n, *, dtype='int32')[源代码]¶
- 打乱一个张量，或者返回给定范围的随机张量
如果 n 是一个张量，只会沿着第一个轴打乱
- 参数
- 返回
The output tensor.
实际案例
>>> import numpy as np >>> import megengine.random as rand >>> x = rand.permutation(10, dtype="int32") >>> x.numpy() array([8, 4, 0, 3, 5, 6, 2, 1, 7, 9], dtype=int32) >>> x = rand.permutation(10, dtype="float32") >>> x.numpy() array([1., 3., 0., 2., 4., 8., 7., 9., 6., 5.], dtype=float32) >>> x = mge.tensor(np.arange(18)).reshape(6,3) >>> x = rand.permutation(x) >>> x.numpy() array([[15, 16, 17], [ 6, 7, 8], [ 0, 1, 2], [ 3, 4, 5], [12, 13, 14], [ 9, 10, 11]], dtype=int32)
- poisson(lam, size=None)[源代码]¶
服从泊松分布 \(\operatorname{Poisson}(\lambda)\) 的随机变量。
对应的概率密度函数为
\[f(k ; \lambda)=\frac{\lambda^{k} e^{-\lambda}}{k !},\]其中 k 是出现的次数 \(({\displaystyle k=0,1,2...})\) 。
- 参数
- 返回
tensor. The random variable with Poisson distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.poisson(lam=2., size=(1, 3)) >>> x.numpy() array([[1., 2., 2.]], dtype=float32) >>> lam = mge.Tensor([[1.,1.], ... [10,10]], dtype="float32") >>> x = rand.poisson(lam=lam) >>> x.numpy() array([[ 1., 2.], [11., 11.]], dtype=float32) >>> x = rand.poisson(lam=lam, size=(1,3)) >>> x.numpy() array([[[[ 2., 1.], [10., 8.]],
- [[ 5., 2.],
[10., 10.]],
- [[ 1., 2.],
[ 8., 10.]]]], dtype=float32)
- shuffle(inp)[源代码]¶
打乱张量的内容原地修改张量。这个函数只是沿着第一个轴打乱张量。张量顺序改变但不改变值
- 参数
inp (
Tensor) – 输入张量。- 返回
None.
实际案例
>>> import numpy as np >>> import megengine.random as rand >>> x = mge.tensor(np.arange(10)) >>> rand.shuffle(x) >>> x.numpy() array([4, 5, 9, 6, 2, 8, 1, 0, 3, 7], dtype=int32) >>> y = mge.tensor(np.arange(18)).reshape(6,3) >>> rand.shuffle(y) >>> y.numpy() array([[ 3, 4, 5], [ 6, 7, 8], [15, 16, 17], [ 0, 1, 2], [12, 13, 14], [ 9, 10, 11]], dtype=int32)
- uniform(low=0, high=1, size=None)[源代码]¶
Random variable with uniform distribution \(U(low, high)\).
- 参数
- 返回
tensor. The random variable with uniform distribution.
- 返回类型
Return type
实际案例
>>> import megengine.random as rand >>> x = rand.uniform(size=(2, 2)) >>> x.numpy() array([[0.28603864, 0.3156649 ], [0.42066026, 0.9805052 ]], dtype=float32)