what is in python math

https://discuss.python.org/t/pep-791-imath-module-for-integer-spe...

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PEP 791: imath - Discussions on Python.org

I would like to vote for the option to just update the documentation so that it no longer claims that math only returns floats (as mentioned in the introduction). Discussions on Python.org PEP 791: imath --- module for integer-specific mathematics functions

https://blog.csdn.net/m0_74766454/article/details/148155961

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【Python打卡Day30】模块与包的导入@浙大疏锦行 - CSDN博客

from math import *`：这种方法将 `math` 模块中的所有公开的函数和变量导入到当前的命名空间中，可以直接使用这些函数和变量而无需 `math.` 前缀。学习python本身就是个伪命题，就像你说学习科目一样，你没说清晰你学习的具体科目是什么，也没说学这个科目的哪些 ...

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python中模块的用法_python做一个软件的全过程_激活谷

python中模块的用法_python做一个软件的全过程在 Python 中实现模块化编程可以通过以下步骤创建模块将相关的函数变量或类封装到一个独立的 py 文件中这个文件就称为模块例如创建一个名为 math operations py 的文件并添加数学运算函数 python math operations pydef add x y return x y def subtract x y return x

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python开根号运算_python软件怎么运行_激活谷

在Python中，计算一个数的平方根（开根号）可以通过以下几种方法表示： 1. 使用`math`模块的`sqrt`函数： import math result = math.sqrt（x）. 2.

https://en.wikipedia.org/wiki/Dirichlet_distribution

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Dirichlet distribution - Wikipedia

In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted ⁡ (), is a family of continuous multivariate probability distributions parameterized by a vector α of positive reals.It is a multivariate generalization of the beta distribution, [1] hence its alternative name of multivariate beta distribution (MBD). [2]

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Dirichlet distribution

In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted ⁡ (), is a family of continuous multivariate probability distributions parameterized by a vector α of positive reals.It is a multivariate generalization of the beta distribution, hence its alternative name of multivariate beta distribution (MBD).

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Beta

Dirichlet distribution

Parameters

K ≥ 2 {\displaystyle K\geq 2} number of categories (integer) α = (α 1, …, α K) {\displaystyle {\boldsymbol {\alpha }}=(\alpha _{1},\ldots,\alpha _{K})} concentration parameters, where α i > 0 {\displaystyle \alpha _{i}>0}

Support

x 1, …, x K {\displaystyle x_{1},\ldots,x_{K}} where x i ∈ {\displaystyle x_{i}\in } and ∑ i = 1 K x i = 1 {\displaystyle \sum _{i=1}^{K}x_{i}=1}

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1 B (α) ∏ i = 1 K x i α i − 1 {\displaystyle {\frac {1}{\mathrm {B} ({\boldsymbol {\alpha }})}}\prod _{i=1}^{K}x_{i}^{\alpha _{i}-1}} where B (α) = ∏ i = 1 K Γ (α i) Γ (α 0) {\displaystyle \mathrm {B} ({\boldsymbol {\alpha }})={\frac {\prod _{i=1}^{K}\Gamma (\alpha _{i})}{\Gamma {\bigl (}\alpha _{0}{\bigr)}}}} where α 0 = ∑ i = 1 K α i {\displaystyle \alpha _{0}=\sum _{i=1}^{K}\alpha _{i}}

Mean

E = α i α 0 {\displaystyle \operatorname {E} ={\frac {\alpha _{i}}{\alpha _{0}}}} E = ψ (α i) − ψ (α 0) {\displaystyle \operatorname {E} =\psi (\alpha _{i})-\psi (\alpha _{0})} (where ψ {\displaystyle \psi } is the digamma function)

Mode

x i = α i − 1 α 0 − K, α i > 1. {\displaystyle x_{i}={\frac {\alpha _{i}-1}{\alpha _{0}-K}},\quad \alpha _{i}>1.}

Variance

Var = α ~ i (1 − α ~ i) α 0 + 1, {\displaystyle \operatorname {Var} ={\frac {{\tilde {\alpha }}_{i}(1-{\tilde {\alpha }}_{i})}{\alpha _{0}+1}},} Cov = δ i j α ~ i − α ~ i α ~ j α 0 + 1 {\displaystyle \operatorname {Cov} ={\frac {\delta _{ij}\,{\tilde {\alpha }}_{i}-{\tilde {\alpha }}_{i}{\tilde {\alpha }}_{j}}{\alpha _{0}+1}}} where α ~ i = α i α 0 {\displaystyle {\tilde {\alpha }}_{i}={\frac {\alpha _{i}}{\alpha _{0}}}}, and δ i j {\displaystyle \delta _{ij}} is the Kronecker delta

Entropy

H (X) = log B (α) {\displaystyle H(X)=\log \mathrm {B} ({\boldsymbol {\alpha }})} + (α 0 − K) ψ (α 0) − {\displaystyle +(\alpha _{0}-K)\psi (\alpha _{0})-} ∑ j = 1 K (α j − 1) ψ (α j) {\displaystyle \sum _{j=1}^{K}(\alpha _{j}-1)\psi (\alpha _{j})} with α 0 {\displaystyle \alpha _{0}} defined as for variance, above; and ψ {\displaystyle \psi } is the digamma function

Method of moments

α i = E (E (1 − E) V − 1) {\displaystyle \alpha _{i}=E\left({\frac {E(1-E)}{V}}-1\right)} where j {\displaystyle j} is any index, possibly i {\displaystyle i} itself

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Parameters

Support

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Mean

Mode

Variance

Entropy

Method of moments

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