Data-Mining/Bayesian.java at master · DevanshuSave/Data-Mining · GitHub

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importjava.util.*;
classtable
{	Stringdata[][]={
 {"youth","high","no","fair","no"},
 {"youth","high","no","excellent","no"},
 {"middle_aged","high","no","fair","yes"},
 {"senior","medium","no","fair","yes"},
 {"senior","low","yes","fair","yes"},
 {"senior","low","yes","excellent","no"},
 {"middle_aged","low","yes","excellent","yes"},
 {"youth","medium","no","fair","no"},
 {"youth","low","yes","fair","yes"},
 {"senior","medium","yes","excellent","yes"},
 {"youth","medium","yes","excellent","yes"},
 {"middle_aged","medium","no","fair","yes"},
 {"middle_aged","high","yes","fair","yes"},
 {"senior","medium","no","excellent","no"}
 };
Stringattrib[][]={
 {"age","youth","middle_aged","senior"},
 {"income","high","medium","low"},
 {"student","no","yes"},
 {"credit_rating","fair","excellent"},
 {"buys_comp","yes","no"}
 };
Stringtemp[];
intyes,no;
intm,n,decide;
table()
	{	n=5;
m=14;
Scannerin=newScanner(System.in);
for(inti=0;i<m;i++)
System.out.println(Arrays.toString(data[i]));
System.out.println("Enter the deciding attribute");
for(inti=0;i<n;i++)
System.out.println("press "+i+" for : "+attrib[i][0]);
decide=in.nextInt();
System.out.println("Enter the tuple you wish to classify");
temp=newString[n-1];
for(inti=0;i<n-1;i++)
 {	System.out.println(attrib[i][0] +" :");
temp[i]=in.next();
 }
for(inti=0;i<m;i++)
if(data[i][4].equals("yes"))
yes++;
else
no++;
for(inti=0;i<4;i++)
for(intj=1;j<attrib[i].length;j++)
showall(attrib[i][j],i);
compute(decide);
	}
voidshowall(Stringatr,intt)
	{	intcount=0,c1=0,c2=0;
for(inti=0;i<m;i++)
if(data[i][t].equals(atr))
count++;
System.out.println("\n");
System.out.println("for attribute : "+attrib[t][0]);
for(inti=0;i<m;i++)
if(data[i][t].equals(atr) && data[i][4].equals("yes"))
c1++;
System.out.println("P( "+atr+" | yes ) : ( "+c1+" / "+yes +" ) ");
c1=0;
for(inti=0;i<m;i++)
if(data[i][t].equals(atr) && data[i][4].equals("no"))
c1++;
System.out.println("P( "+atr+" | no ) : ( "+c1+" / "+no +" ) ");
	}
voidcompute(intt)
	{	intcount=0;
intyes[][]=newint[m][5];
intansy[]=newint[n];
intansn[]=newint[n];
for(intj=1;j<3;j++)
 {	count=0;
for(inti=0;i<m;i++)
 {	if(attrib[decide][j]==data[i][decide])
count++;
 }
yes[decide][j]=count;
 }
for(inti=0;i<temp.length;i++)
 {	count=0;
for(intj=0;j<m;j++)
if(temp[i].equals(data[j][i]) && (data[j][n-1]).equals("yes"))
count++;
ansy[i]=count;
 }
ansy[n-1]=yes[n-1][1];
ansn[n-1]=yes[n-1][2];
for(inti=0;i<temp.length;i++)
 {	count=0;
for(intj=0;j<m;j++)
if(temp[i].equals(data[j][i]) && (data[j][n-1]).equals("no"))
count++;
ansn[i]=count;
 }
System.out.println(Arrays.toString(temp));
System.out.println("\n\n");
System.out.println(Arrays.toString(ansy));
doubleproby=1;
for(inti=0;i<n;i++)
proby=proby * ((double)ansy[i]/ansy[n-1]);
System.out.println("The probability of yes is :"+proby);
doubleprobn=1;
for(inti=0;i<n;i++)
probn=probn * ((double)ansn[i]/ansn[n-1]);
System.out.println("The probability of no is :"+probn);
if(probn<proby)
System.out.println("BUYS : YES");
else
System.out.println("BUYS : NO");
	}
}
classBayesian
{	publicstaticvoidmain (Stringargs[])
	{ tablet=newtable();
	}
}
/*
OUTPUT:[youth, high, no, fair, no]
[youth, high, no, excellent, no]
[middle_aged, high, no, fair, yes]
[senior, medium, no, fair, yes]
[senior, low, yes, fair, yes]
[senior, low, yes, excellent, no]
[middle_aged, low, yes, excellent, yes]
[youth, medium, no, fair, no]
[youth, low, yes, fair, yes]
[senior, medium, yes, excellent, yes]
[youth, medium, yes, excellent, yes]
[middle_aged, medium, no, fair, yes]
[middle_aged, high, yes, fair, yes]
[senior, medium, no, excellent, no]
Enterthedecidingattribute
press0for : age
press1for : income
press2for : student
press3for : credit_rating
press4for : buys_comp
4
Enterthetupleyouwishtoclassify
age :
youth
income :
medium
student :
yes
credit_rating :
fair
forattribute : age
P( youth | yes ) : ( 2 / 9 )
P( youth | no ) : ( 3 / 5 )

forattribute : age
P( middle_aged | yes ) : ( 4 / 9 )
P( middle_aged | no ) : ( 0 / 5 )

forattribute : age
P( senior | yes ) : ( 3 / 9 )
P( senior | no ) : ( 2 / 5 )

forattribute : income
P( high | yes ) : ( 2 / 9 )
P( high | no ) : ( 2 / 5 )

forattribute : income
P( medium | yes ) : ( 4 / 9 )
P( medium | no ) : ( 2 / 5 )

forattribute : income
P( low | yes ) : ( 3 / 9 )
P( low | no ) : ( 1 / 5 )

forattribute : student
P( no | yes ) : ( 3 / 9 )
P( no | no ) : ( 4 / 5 )

forattribute : student
P( yes | yes ) : ( 6 / 9 )
P( yes | no ) : ( 1 / 5 )

forattribute : credit_rating
P( fair | yes ) : ( 6 / 9 )
P( fair | no ) : ( 2 / 5 )

forattribute : credit_rating
P( excellent | yes ) : ( 3 / 9 )
P( excellent | no ) : ( 3 / 5 )
[youth, medium, yes, fair]

[2, 4, 6, 6, 9]
Theprobabilityofyesis :0.04389574759945129
Theprobabilityofnois :0.019200000000000002
BUYS : YES /*