Java implementation of the Naive-Bayes algorithm ( Play-Tennis Example )

Naive.java

/** The class encapsulates an implementation of the Naive-Bayes algorithm
 *
 * First argument is for Outlook
 * Sunny=2 Overcast=1 Rain=0
 *
 * Second argument is for Temperature
 * Hot=2 Mild=1 Cool=0
 *
 * Third argument is for Humidity
 * High=1 Normal=0
 *
 * Fourth argument is for Wind Strength
 * Strong=1 Weak=0
 *
 * 
 * Usage with the command line : 
 *   For <rain, hot, high, false>
 *   >java Naive 0 2 1 0
 *
 * 
 * @author Tomrock D'souza, St. Francis Institute Of Technology, University of Mumbai, 2017
 * @copyright GNU General Public License v3
 * No reproduction in whole or part without maintaining this copyright notice
 * and imposing this condition on any subsequent users.
 */
public class Naive{
	public static void main(String args[]) throws Exception {
		int i;
		if(args.length!=4)
		{
			System.out.println("Wrong number of Arguments, Need :4");
			System.exit(0);
		}
		int[] argsi=new int[4];
		for(i=0;i<4;i++)
		{
			argsi[i]=Integer.parseInt(args[i]);
		}
		int p=0,n=0,m;
		double winProbablity,loseProbablity;
		int[] outLook={0,0,0,0,0,0};
		int[] temp={0,0,0,0,0,0};
		int[] humid={0,0,0,0};
		int[] wind={0,0,0,0};
		int[][] multi = new int[][]{
		/*
		 * This is the Training Set Made up of 14 rows and 5 columns Respectively
		 *
		 * First column is for Outlook
		 * Sunny=2 Overcast=1 Rain=0
		 *
		 * Second column is for Temperature
		 * Hot=2 Mild=1 Cool=0
		 *
		 * Third column is for Humidity
		 * High=1 Normal=0
		 *
		 * Fourth column is for Wind Strength
		 * Strong=1 Weak=0
		 *
		 * Fifth column is for Outcome
		 * Play=1 Not-Play=0
		 *
		 */
		  {2,2,1,0,0},
		  {2,2,1,1,0},
		  {1,2,1,0,1},
		  {0,1,1,0,1},
		  {0,0,0,0,1},
		  {0,0,0,1,0},
		  {1,0,0,1,1},
		  {2,1,1,0,0},
		  {2,0,0,0,1},
		  {0,1,0,0,1},
		  {2,1,0,1,1},
		  {1,1,1,1,1},
		  {1,2,0,0,1},
		  {0,1,1,1,0}
		};
		m=multi.length;
		
		//This For loop Uses The Training Set to Generate Probabilities
		for(i=0;i<m;i++){
			
		if(multi[i][0]==2)
			{if(multi[i][4]==1){outLook[5]++;}else{outLook[4]++;}}
		else if(multi[i][0]==1)
			{if(multi[i][4]==1){outLook[3]++;}else{outLook[2]++;}}
		else{if(multi[i][4]==1){outLook[1]++;}else{outLook[0]++;}}
		
		if(multi[i][1]==2)
			{if(multi[i][4]==1){temp[5]++;}else{temp[4]++;}}
		else if(multi[i][1]==1)
			{if(multi[i][4]==1){temp[3]++;}else{temp[2]++;}}
		else{if(multi[i][4]==1){temp[1]++;}else{temp[0]++;}}
		
		if(multi[i][2]==1)
			{if(multi[i][4]==1){humid[3]++;}else{humid[2]++;}}
		else
			{if(multi[i][4]==1){humid[1]++;}else{humid[0]++;}}
		
		if(multi[i][3]==1)
			{if(multi[i][4]==1){wind[3]++;p++;}else{wind[2]++;n++;}}
		else
			{if(multi[i][4]==1){wind[1]++;p++;}else{wind[0]++;n++;}}
			

		}
		
		//This Piece of Code Displays the Data After Training
		for(i=0;i<outLook.length;i+=2){
			System.out.println("Variable Outlook["+i/2+"]: 	"+outLook[i+1]+"/"+p+" 	"+outLook[i]+"/"+n+"\n");
			}
		for(i=0;i<temp.length;i+=2){
			System.out.println("Variable Temperate["+i/2+"]:  "+temp[i+1]+"/"+p+" 	"+temp[i]+"/"+n+"\n");
			}
		for(i=0;i<humid.length;i+=2){
			System.out.println("Variable Humidity["+i/2+"]:   "+humid[i+1]+"/"+p+" 	"+humid[i]+"/"+n+"\n");
			}
		for(i=0;i<wind.length;i+=2){
			System.out.println("Variable Windy["+i/2+"]: 	"+wind[i+1]+"/"+p+" 	"+wind[i]+"/"+n+"\n");
			}
		System.out.println("P(n):"+n+" P(p):"+p+"\nTotal Rows Parsed:"+m+"\n");

	
	winProbablity=(double)(outLook[argsi[0]*2+1]*temp[argsi[1]*2+1]*humid[argsi[2]*2+1]*wind[argsi[3]*2+1])/(p*p*p*m);
	loseProbablity=(double)(outLook[argsi[0]*2]*temp[argsi[1]*2]*humid[argsi[2]*2]*wind[argsi[3]*2])/(n*n*n*m);
	
	System.out.printf("P(p|X)= %f\nP(n|X)= %f\n",winProbablity,loseProbablity);
	if(winProbablity<loseProbablity) {System.out.println("Don't Play");}
	else {System.out.println("Must Play");}
		
	}
			
	
}