nostyleplease/content/posts/test-tex.md

title	date	mathjax	draft
Test Tex	2022-08-26T22:28:10+08:00	true	true

Inline math: {{< texi \varphi >}}

Displayed math: {{< texd \begin{aligned} \varphi &\Rightarrow \psi \\ \varnothing &\rightarrow A \end{aligned} >}}

R_{\mu \nu} - {1 \over 2}g_{\mu \nu}\,R + g_{\mu \nu} \Lambda
= {8 \pi G \over c^4} T_{\mu \nu}

The equation (x_i \cdot x_j)^2 is called kernel function and is often written as k(x_i, x_j).

\arg\max_\alpha \sum_j \alpha_j - \frac{1}{2} \sum_{j,k} \alpha_j, \alpha_k y_j y_k (x_j \cdot x_k)

f(X) = \frac{1}{(2\pi)^{\frac{n}{2} |\Sigma|^{\frac{1}{2}}}} e^{ - \frac{1}{2} (X - \mu)^T \Sigma^{-1} (X - \mu)}

\mu_i = \sum_{j=1}^N \frac{p_{ij} x}{n_i} \\
\Sigma_i = \sum_{j=1}^N \frac{p_{ij} (x_j - \mu_i) (x_j - \mu_i)^T}{n_i}\\
w_i  =  \frac{n_i}{N}

S_i^{(t)} = \big \{ x_p : \big \| x_p - \mu^{(t)}_i \big \|^2 \le \big \| x_p - \mu^{(t)}_j \big \|^2 \ \forall j, 1 \le j \le k \big\}

(The error above is a demo for incorrect formulas.)