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https://umatechnology.org/how-to-install-python-on-windows-11-6/

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How to Install Python on Windows 11 - UMA Technology

Installing Visual Studio Code as an Example: Download Visual Studio Code: Visit code.visualstudio.com to download VS Code. Run the Installer: Follow the on-screen instructions to install the application. Install the Python Extension: Open Visual Studio Code and navigate to the Extensions view by clicking on the Extensions icon in the sidebar.

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

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Dirichlet distribution - Wikipedia

Below is example Python code to draw the sample: params = [a1, a2,..., ak] sample = [random. gammavariate (a, 1) for a in params] sample = [v / sum (sample) for v in sample] This formulation is correct regardless of how the Gamma distributions are parameterized (shape/scale vs. shape/rate) because they are equivalent when scale and rate equal 1.0.

https://forums.unrealengine.com/t/ue-5-5-using-python-or-a-bluepr...

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(UE 5.5) Using python or a blueprint, is there a way to see if a joint ...

Given a skeleton, I have been trying to see if it has a “neck_01” joint for example. There doesn’t seem to a way as far as I can see. In python, I got so far as getting the bone tree nodes, but I don’t know how to progress into each BoneNode to get its name. This brings up another related question. Is there a way to know all of the options usable for get_editor_property()? I’ve run ...

https://www.php.cn/fr/faq/1796813006.html

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Le tutoriel Ampère Porting Advisor-Industrie informatique-php.cn

Originally, it was coded as a Python module that analyzed known incompatibilities for C and Fortran code. This tutorial walks you through building and using the tool and how to act on issues identified by the tool. The Ampere Porting Advisor is a command line tool that analyzes source code for known code patterns and dependency libraries.

https://blog.csdn.net/zhubeibei168/article/details/148177816

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自然语言处理之文本生成：Markov Chains与深度学习结合-CSDN博客

文章浏览阅读344次，点赞6次，收藏14次。神经网络是一种模仿人脑神经元结构的计算模型，用于处理复杂的输入输出关系。它由大量的节点（或称为神经元）组成，这些节点通过连接权重相互连接，形成多层结构。神经网络可以分为输入层、隐藏层和输出层。

https://discourse.julialang.org/t/julia-based-course-performance-...

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Julia based course - Performance Engineering of Software Systems

Respected Sir, I liked the course (Performance Engineering of Software Systems) .It enhanced my understanding of software performance. I request that you @stevengj with Prof. Charles Leiserson, Julian Shun and Prof.Alan Edelman @alanedelman should repeat this course with Julia in place of Python examples. I think that it would revolutionize the learning of Julia software performance.

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Dirichlet distribution

Below is example Python code to draw the sample: params = sample = sample = This formulation is correct regardless of how the Gamma distributions are parameterized (shape/scale vs. shape/rate) because they are equivalent when scale and rate equal 1.0.

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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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