Naive Bayes Classifier:

To simply run the code for normal m,Enter

python main.py 

output is the top 5 words of spam and ham and accuracy on training and test

To run the code for varying m Enter

python main.py <start_m> <end_m> <step_size> <MP_IS_CONSTANT>

Where

<start_m> is the starting value of m   
<end_m> is the Ending value of m   
<step_size> is the skipping step of m   
<MP_IS_CONSTANT> is a boolean Value TRUE or FALSE   

<MP_IS_CONSTANT> if is TRUE, would vary p for each M such that MxP always remains = 1 if false,   
it takes p = 1/|VOCAB|

e.g run:

python main.py 1,2000,50 FALSE
python main.py 1,2000,50 TRUE

The output would be a graph along with normal output of python main.py