我的bayes算法，为thomas bayes命名。
tomas的Python项目详细描述
# Bayes Classifier
## Principle
### Naive Bayes
$$
p(c|x)=\frac{p(x|c)p(c)}{p(x)}\sim p(x|c)p(c)\\
\sim \prod_ip(x_i|c)p(c) = \prod_ip(x_i,c)p(c)^{1-n}~~~~~~~~~\text{(Naive condition)}\\
\sim\prod_i\frac{N(x_i,c)}{N}p(c)^{1-n}
$$
### Semi Naive Bayes
$$
p(c|x,y)=\sim p(x|c)p(c|y)\\
\sim \prod_ip(x_i|c)p(c|y) ~~~~~~~~~\text{(Semi-Naive condition)}
$$
where $p(c|y)$ will be estimated by say of neural networks.
### Hemi Naive Bayes, in more general form
When $y$ is empty, it is equiv. to the naive one.
$$
p(c|x,y_1,\cdots y_m)
\sim \prod_ip(x_i|c)\prod_ip(c|y_i)p(c)^{1-m} ~~~~~~~~~~(Hemi-condition)\\
\sim \prod_ip(x_i|c)\prod_if_c(y_i)p(c)^{1-m}\\
\sim \prod_ip(x_i,c)\prod_if_c(y_i)p(c)^{1-m-n}
$$
## Predict
$$
\frac{p(c|x,y)}{p(c'|x,y)}= \prod_i(\frac{p(x_i|c)}{p(x_i|c')})\frac{p(c|y)}{p(c'|y)}\\
= \prod_i(\frac{p(x_i,c)}{p(x_i,c')})\frac{p(c|y)}{p(c'|y)}(\frac{p(c')}{p(c)})^n
~~~~~~~~~\text{(Semi-Naive condition)}\\
\sim \prod_i(\frac{N(x_i,c)}{N(x_i,{p（c y）}{p（c y）}{p（c y）}（\frac{n（c）}{n（c）}{n（c）}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~…，y（m）}\sim…(\frac{N(c')}{N(c)})^{n+m-1}\prod_i\frac{p(c|y_i)}{p(c'|y_i)} ~~~~~~~~~(\text{Hemi-condition})
$$
### 0-1 cases
$$
r = \frac{p(1|x,y)}{p(0|x,y)}\sim \prod_i(\frac{N(x_i,1)}{N(x_i,0)})\frac{p(1|y)}{1-p(1|y)}(\frac{N(0)}{N(1)})^n (Semi)\\
r \sim \prod_i(\frac{N(x_i,1)}{N(x_i,0)})\prod_i\frac{p(1|y_i)}{1-p(1|y_i)}(\frac{N(0)}{N(1)})^{n+m-1} (Hemi)
$$
iff $r\geq 1$, $(x,y)$ is in class 1, else in class 0.
## Estimate (for continuous rv)
$p(x)\sim \frac{N(x)}{N}, N(x):$ the number of samples in a neighborhood of $x$
## Principle
### Naive Bayes
$$
p(c|x)=\frac{p(x|c)p(c)}{p(x)}\sim p(x|c)p(c)\\
\sim \prod_ip(x_i|c)p(c) = \prod_ip(x_i,c)p(c)^{1-n}~~~~~~~~~\text{(Naive condition)}\\
\sim\prod_i\frac{N(x_i,c)}{N}p(c)^{1-n}
$$
### Semi Naive Bayes
$$
p(c|x,y)=\sim p(x|c)p(c|y)\\
\sim \prod_ip(x_i|c)p(c|y) ~~~~~~~~~\text{(Semi-Naive condition)}
$$
where $p(c|y)$ will be estimated by say of neural networks.
### Hemi Naive Bayes, in more general form
When $y$ is empty, it is equiv. to the naive one.
$$
p(c|x,y_1,\cdots y_m)
\sim \prod_ip(x_i|c)\prod_ip(c|y_i)p(c)^{1-m} ~~~~~~~~~~(Hemi-condition)\\
\sim \prod_ip(x_i|c)\prod_if_c(y_i)p(c)^{1-m}\\
\sim \prod_ip(x_i,c)\prod_if_c(y_i)p(c)^{1-m-n}
$$
## Predict
$$
\frac{p(c|x,y)}{p(c'|x,y)}= \prod_i(\frac{p(x_i|c)}{p(x_i|c')})\frac{p(c|y)}{p(c'|y)}\\
= \prod_i(\frac{p(x_i,c)}{p(x_i,c')})\frac{p(c|y)}{p(c'|y)}(\frac{p(c')}{p(c)})^n
~~~~~~~~~\text{(Semi-Naive condition)}\\
\sim \prod_i(\frac{N(x_i,c)}{N(x_i,{p（c y）}{p（c y）}{p（c y）}（\frac{n（c）}{n（c）}{n（c）}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~…，y（m）}\sim…(\frac{N(c')}{N(c)})^{n+m-1}\prod_i\frac{p(c|y_i)}{p(c'|y_i)} ~~~~~~~~~(\text{Hemi-condition})
$$
### 0-1 cases
$$
r = \frac{p(1|x,y)}{p(0|x,y)}\sim \prod_i(\frac{N(x_i,1)}{N(x_i,0)})\frac{p(1|y)}{1-p(1|y)}(\frac{N(0)}{N(1)})^n (Semi)\\
r \sim \prod_i(\frac{N(x_i,1)}{N(x_i,0)})\prod_i\frac{p(1|y_i)}{1-p(1|y_i)}(\frac{N(0)}{N(1)})^{n+m-1} (Hemi)
$$
iff $r\geq 1$, $(x,y)$ is in class 1, else in class 0.
## Estimate (for continuous rv)
$p(x)\sim \frac{N(x)}{N}, N(x):$ the number of samples in a neighborhood of $x$
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