mars.tensor.random.chisquare#
- mars.tensor.random.chisquare(df, size=None, chunk_size=None, gpu=None, dtype=None)[源代码]#
Draw samples from a chi-square distribution.
When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing.
- 参数
df (float or array_like of floats) – Number of degrees of freedom, should be > 0.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifdf
is a scalar. Otherwise,mt.array(df).size
samples are drawn.chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype (data-type, optional) – Data-type of the returned tensor.
- 返回
out – Drawn samples from the parameterized chi-square distribution.
- 返回类型
Tensor or scalar
- 引发
ValueError – When df <= 0 or when an inappropriate size (e.g.
size=-1
) is given.
提示
The variable obtained by summing the squares of df independent, standard normally distributed random variables:
\[Q = \sum_{i=0}^{\mathtt{df}} X^2_i\]is chi-square distributed, denoted
\[Q \sim \chi^2_k.\]The probability density function of the chi-squared distribution is
\[p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2 - 1} e^{-x/2},\]where \(\Gamma\) is the gamma function,
\[\Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.\]引用
- 1
NIST “Engineering Statistics Handbook” http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
实际案例
>>> import mars.tensor as mt
>>> mt.random.chisquare(2,4).execute() array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])