import java.io.*;
import java.util.*;
import java.lang.*;

public class Bayespam
{
	// This a class with two counters (for regular and for spam)
	static class Multiple_Counter
	{
		int counter_spam    = 0;
		int counter_regular = 0;
	}


	public static void main(String[] args)
	throws IOException
	{
		// Location of the directory (the path) taken from the cmd line (first arg)
		File dir_location      = new File( args[0] );
		
		// Listing of the directory (should contain 2 subdirectories: regular/ and spam/)
		File[] dir_listing     = new File[0];

		// Check if the cmd line arg is a directory and list it
		if ( dir_location.isDirectory() )
		{
			dir_listing = dir_location.listFiles();
		}
		else
		{
			System.out.println( "- Error: cmd line arg not a directory.\n" );
		        Runtime.getRuntime().exit(0);
		}
		
		// Listings of the two subdirectories (regular/ and spam/)
		File[] listing_regular = new File[0];
		File[] listing_spam    = new File[0];
		
		// Check that there are 2 subdirectories
		if ( dir_listing.length == 2 )
		{
			listing_regular = dir_listing[0].listFiles();
			listing_spam    = dir_listing[1].listFiles();
		}
		else
		{
			System.out.println( "- Error: specified directory does not contain two subdirectories.\n" );
		        Runtime.getRuntime().exit(0);
		}

		// Print out the number of messages in regular and in spam
		System.out.println( "\t number of regular messages is: " + listing_regular.length );
		System.out.println( "\t number of spam messages is: "    + listing_spam.length );
		
		// Create a hash table for the vocabulary (word searching is very fast in a hash table)
		Hashtable vocab = new Hashtable();
		Multiple_Counter old_cnt   = new Multiple_Counter();

		// Read the e-mail messages
		// The regular mail
		for ( int i = 0; i < listing_regular.length; i ++ )
		{
			FileInputStream i_s = new FileInputStream( listing_regular[i] );
			BufferedReader in = new BufferedReader(new InputStreamReader(i_s));
	                String line;
			String word;
			
        	        while ((line = in.readLine()) != null)					// read a line
			{
				StringTokenizer st = new StringTokenizer(line);			// parse it into words
		
				while (st.hasMoreTokens())
				{
					word = st.nextToken();
					
					if ( vocab.containsKey(word) )				// check if word exists already in the vocabulary
					{
						old_cnt = (Multiple_Counter)vocab.get(word);	// get the counter from the hashtable
						old_cnt.counter_regular ++;			// and increment it
					
						vocab.put(word, old_cnt);
					}
					else
					{
						Multiple_Counter fresh_cnt = new Multiple_Counter();
						fresh_cnt.counter_regular = 1;
						fresh_cnt.counter_spam    = 0;
						
						vocab.put(word, fresh_cnt);			// put the new word with its new counter into the hashtable
					}
				}
			}

                	in.close();
		}
		// The spam mail
		for ( int i = 0; i < listing_spam.length; i ++ )
		{
			FileInputStream i_s = new FileInputStream( listing_spam[i] );
			BufferedReader in = new BufferedReader(new InputStreamReader(i_s));
	                String line;
			String word;
			
        	        while ((line = in.readLine()) != null)					// read a line
			{
				StringTokenizer st = new StringTokenizer(line);			// parse it into words
		
				while (st.hasMoreTokens())
				{
					word = st.nextToken();
					
					if ( vocab.containsKey(word) )				// check if word exists already in the vocabulary
					{
						old_cnt = (Multiple_Counter)vocab.get(word);	// get the counter from the hashtable
						old_cnt.counter_spam ++;			// and increment it
					
						vocab.put(word, old_cnt);
					}
					else
					{
						Multiple_Counter fresh_cnt = new Multiple_Counter();
						fresh_cnt.counter_regular = 0;
						fresh_cnt.counter_spam    = 1;
						
						vocab.put(word, fresh_cnt);			// put the new word with its new counter into the hashtable
					}
				}
			}

                	in.close();
		}
		
		// Print out the hash table
		for (Enumeration e = vocab.keys() ; e.hasMoreElements() ;)
		{	
			String word;
			
			word = (String)e.nextElement();
			old_cnt  = (Multiple_Counter)vocab.get(word);
			
			System.out.println( word + " | in regular: " + old_cnt.counter_regular + 
			                             " in spam: "    + old_cnt.counter_spam);
		}
		
		// Now all students must continue from here:
		// A priori class probabilities must be computed from the number of regular and spam messages
		// The vocabulary must be clean: punctuation and digits must be removed, case insensitive
		// Conditional probabilities must be computed for every word
		// A priori probabilities must be computed for every word
		// Zero probabilities must be replaced by a small estimated value
		// Bayes rule must be applied on new messages, followed by argmax classification
		// Errors must be computed on the test set (FAR = false accept rate (misses), FRR = false reject rate (false allarms))
		// Improve the code and the performance (speed, accuracy)
	}
}