Reorganizing code and adding unstaged code

Former-commit-id: de014d962df43aec20626c3e07cb058adf68970a

Author: akashzcoder

#I just work with main CSV

@@ -0,0 +1,89 @@
+import csv
+import random
+import math
+
+def loadCsv (myfile):
+    lines = csv.reader(open(myfile,""))
+    dataset = list(lines)
+    for i in range(len(dataset)):
+        dataset[i]= [float(x) for x in dataset[i]]
+    return dataset
+
+
+
+def separateByClass(dataset):
+    separated = {}
+    for i in range(len(dataset)):
+            vector= dataset[i]
+    if(vector[-1] not in separated):
+        separated[vector[-1]]= []
+        separated[vector[-1]].append(vector)
+    return separated
+
+
+def P_root(m,p_root):
+   
+ return round (decimal(m) ** decimal((1/float(p_root))),k)
+
+
+
+def MinkowskiDistance(x,xi,p):
+    return P_root(sum(pow((a-b),p) for a, b in zip(x, xi)),p)
+
+
+def Neighbors(train_dataSet, test_dataset, k):
+    distances = []
+    neighbor = []
+    L_train=len(train_dataSet)
+    L_test=len(test_dataset)
+    for x in range(L_train):
+        dis = MinkowskiDistance(test_dataset,train_dataSet,L_test)
+        distances.append((train_dataSet, dis))
+        distances.sort(key=operator.itemgetter(1))
+   
+    for x in range(k):
+        neighbor.append(distances[x][0])
+        
+        return neighbor
+
+def Response(neighbor):
+    classVotes = {}
+    for x in range(len(neighbor)):
+            response = neighbor[x][-1]
+            if response in classVotes:
+                classVotes[response] += 1
+    else:
+        classVotes[response] = 1
+    sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
+
+    return sortedVotes[0][0]
+
+def accuracy(test,predict):
+    correct=0
+    for i in test:
+        if test ==predict:
+            correct +=1
+           
+            return float(corect/len(test))*100.00
+        
+        
+def main():
+    myfile = ''
+    train_dataSet=[]
+    test_dataset=[]
+    splitRatio = 0.67
+    dataset = loadCsv(myfile)
+    train_dataSet, test_dataset = splitDataset(dataset, splitRatio)
+    predictions=[]
+    k = 3
+    for x in range(len(test_dataset)):
+        neighbor = Neighbors(train_dataSet, test_dataset[x], k)
+        result = Response(neighbor)
+        predictions.append(result)
+        
+    predictions = getPredictions(summaries, testSet)
+    accuracy = getAccuracy(testSet, predictions)
+    print('Accuracy: {0}%').format(accuracy)
+
+    
+    main()