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My Bayes algorithm, for the name of Thomas Bayes.

Project description

# 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)}\\

### 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,c')})\frac{p(c|y)}{p(c'|y)}(\frac{N(c')}{N(c)})^n ~~~~~~~~~~~~~~~~~~~~~\text{(estimate)}

\frac{p(c|x,y_1,\cdots, y_m)}{p(c'|x,y_1,...,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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