added code for Bayesian algorithm

algorithms/artificial-intelligence/Bayesian/AItest.cpp

@@ -0,0 +1,86 @@
+#include<bits/stdc++.h>
+using namespace std;
+
+int main()
+{
+    vector<string>test;
+    vector<string>feature;
+    vector<string>cls;
+    vector<string>like;
+    vector<string>mapc;
+
+	string str;
+	ifstream read("cat.txt");
+
+	while(getline (read,str))
+    {
+		test.push_back(str);
+	}
+	read.close();
+
+	/*for(int i=0;i<test.size();i++){
+		cout<<test[i]<<"\n";
+	}*/
+	FILE *fptr;
+	if ((fptr = fopen("likelihood.txt", "r")) == NULL)
+    {
+        printf("Error! opening file");
+        exit(1);
+    }
+
+	char f[100], c[100], l[100], m[100];
+	string c1,c2,m1,m2;
+	while(fscanf(fptr, "%s %s %s %s", f, c, l, m)!=EOF){
+		string s1="";
+		string s2="";
+		string s3="";
+		string s4="";
+
+		for(int i=0; i<strlen(f); i++){
+            s1 += f[i] ;
+        }
+        feature.push_back(s1);
+
+		for(int i=0; i<strlen(c); i++){
+            s2 += c[i] ;
+        }
+        cls.push_back(s2);
+
+		for(int i=0; i<strlen(l); i++){
+            s3 += l[i] ;
+        }
+        like.push_back(s3);
+
+		for(int i=0; i<strlen(m); i++){
+            s4 += m[i] ;
+        }
+        mapc.push_back(s4);
+	}
+
+	for(int i=0;i<test.size();i++){
+		for(int j=0;j<feature.size();j++){
+		    if(test[i]==feature[j])
+		    {
+			if(cls[j]=="dog")
+			{
+			    c1 = cls[j];
+			    m1 = mapc[j];
+			}
+			else
+			{
+			    c2 = cls[j];
+			    m2 = mapc[j];
+			}
+		    }
+		}
+		if(m1>m2){
+		    cout<<test[i]<<"\t--> "<<"dog"<<"\n";
+		}
+		else{
+		    cout<<test[i]<<"\t--> "<<"cat"<<"\n";
+		}
+	}
+
+	return 0;
+
+}

algorithms/artificial-intelligence/Bayesian/AItraining.cpp

@@ -0,0 +1,152 @@
+#include<bits/stdc++.h>
+using namespace std ;
+
+vector<string>avalue ; //attribute value
+vector<string>cvalue ; //class value
+
+int numberofobject = 0;
+double priorD=0, priorC=0;
+
+int findOccurance(string att, string cl)
+{
+    FILE *fptr;
+    char a[100], c[100] ; //a-attribute, c-class
+    int count = 0 ;
+    if ((fptr = fopen("data.txt", "r")) == NULL) {
+        printf("Error! opening file");
+        exit(1);
+    }
+    while (fscanf(fptr, "%s %s", a, c)!= EOF){
+        string s = "" ;
+        string s2 = "" ;
+
+        for(int i = 0 ; i < strlen(a) ; i++){ //converting char array into string
+            s += a[i] ;
+         }
+         for(int i = 0 ; i < strlen(c) ; i++){
+            s2 += c[i] ;
+         }
+         if(s == att && s2 == cl) count++ ;
+    }
+    fclose(fptr);
+    return count ;
+}
+
+int main() {
+    char a[100], c[100];
+    FILE *fptr;
+    if ((fptr = fopen("data.txt", "r")) == NULL){
+        printf("Error! opening file");
+        exit(1);
+    }
+    string s = "" ;
+
+    int dogc=0, catc=0;
+
+    while (fscanf(fptr, "%s %s", a, c)!= EOF){
+         //printf("%s %s", a, c);
+         //printf("\n") ;
+         numberofobject++ ; //counting total objects.
+
+         bool flag = false ;
+         string s = "" ;
+         for(int i = 0 ; i < strlen(a) ; i++){
+            s += a[i] ;
+         }
+
+         for(int i = 0 ; i < avalue.size() ; i++){
+            if(s == avalue[i]) {
+                flag = true ;
+            }
+         }
+         if(!flag)
+            avalue.push_back(s) ;
+
+
+         flag = false ;
+         s = "" ;
+         for(int i = 0 ; i < strlen(c) ; i++){
+            s += c[i] ;
+         }
+
+         for(int i = 0 ; i < cvalue.size() ; i++){
+            if(s == cvalue[i]) {
+                flag = true ;
+            }
+         }
+         if(!flag)
+            cvalue.push_back(s) ;
+
+         if(s=="dog"){ //counting number of dogs and cats.
+            dogc++;
+         }
+         if(s=="cat"){
+            catc++;
+         }
+    }
+
+    printf("%d\n\n", numberofobject) ;
+    for(int i = 0 ; i < avalue.size() ; i++){
+
+        cout<< avalue[i] << endl ;
+    }
+
+    for(int i = 0 ; i < cvalue.size() ; i++){
+        cout<< cvalue[i] << endl ;
+    }
+
+    //calculating likelihood and writing in a file
+    ofstream myfile("likelihood.txt");
+    myfile<<"Feature"<<"\t"<<"Class"<<"\t"<<"Likelihood"<<"\t"<<"Classifier"<<"\n";
+    myfile<<"-------"<<"\t"<<"-----"<<"\t"<<"-----------"<<"\t"<<"----------"<<"\n";
+
+    for(int i = 0 ; i < cvalue.size() ; i++){
+        for(int j = 0 ; j < avalue.size() ; j++){
+            int n = findOccurance(avalue[j], cvalue[i]) ; //finding occurences of big-dog, small-cat, medium-dog etc.
+
+            cout << avalue[j] << "|" << cvalue[i] << " = "<< n << endl;
+
+            //calculating likelihood
+            double liked=0, likec=0;
+            if(cvalue[i]=="dog"){
+                liked = (n*1.00)/dogc;
+                priorD = (dogc*1.00)/numberofobject;
+
+                myfile<<avalue[j]<<"\t"<<cvalue[i]<<"\t"<<liked<<"\t\t"<<priorD*liked<<"\n";
+                cout<<liked<<endl;
+            }
+            else{
+                likec = (n*1.00)/catc;
+                priorC = (catc*1.00)/numberofobject;
+
+                myfile<<avalue[j]<<"\t"<<cvalue[i]<<"\t"<<likec<<"\t"<<priorC*likec<<"\n";
+                cout<<likec<<endl;
+            }
+        }
+    }
+    fclose(fptr);
+
+    //priorD = (dogc*1.00)/numberofobject;
+    //priorC = (catc*1.00)/numberofobject;
+
+    myfile<<"\n\n"<<"Prior\n-----\n"<<"P(Dog) = "<<priorD<<"\n";
+    myfile<<"\n"<<"P(Cat) = "<<priorC<<"\n";
+    myfile.close();
+
+    cout<<"\n"<<"Dog count: "<<dogc<<"\n"<<"Cat count: "<<catc<<"\n\n";
+
+    //cout<<priorC<<" "<<priorD<<"\n\n\n";
+
+
+    string myText;
+    ifstream readFile("likelihood.txt");
+
+    while (getline (readFile, myText)){
+        cout << myText<<"\n";
+    }
+    readFile.close();
+
+
+    return 0;
+}
+

algorithms/artificial-intelligence/Bayesian/data.txt

@@ -0,0 +1,30 @@
+big dog
+big dog
+big cat
+big dog
+small cat
+small cat
+small cat
+small dog
+small dog
+small cat
+big dog
+big dog
+big cat
+big cat
+big dog
+small cat
+big dog
+small cat
+big dog
+small dog
+small dog
+small cat
+small cat
+big cat
+big dog
+medium cat
+medium cat
+big dog
+medium dog
+big dog

algorithms/artificial-intelligence/Bayesian/testFile.txt

@@ -0,0 +1,6 @@
+big
+big
+small
+medium
+small
+medium