Update collab-filter.py for final movie recommendation referring to task A.3

collab-filter.py

@@ -39,6 +39,7 @@ userFile.close()
 #data is complete at this point
 
 # Pearson Correlation Basic Implementation Using for Loop
+
 pearson = []
 rows = len(data)
 users = data[rows-1]
@@ -68,9 +69,35 @@ for x in range(1,rows-1): # does not include (rows-1)
 			summ = nume / deno
 		else:
 			summ = 1
-		pearson.append((summ,lis[0]))
+		data[x].append(summ)
 	else:
-		pearson.append((0,lis[0]))
+		data[x].append(0)
+# explicit choice to take summ = 1 for the corner case ... However, we were told no such "Pathological Cases" would be given
+# print(data)
+
+# Humanity's Answer : Suggesting Movies
+review = []
+for i in range(1,cols):
+	if(float(users[i]) == -1):
+		movie_name = data[0][i]
+		movie_rate = 0
+		sumcorrcoeff = 0
+		for g in range(1,rows-1):
+			if(float(data[g][i]) >= 0):
+				movie_rate += float(data[g][i])*float(data[g][cols])
+				sumcorrcoeff += abs(float(data[g][cols]))
+		if(sumcorrcoeff > 0):
+			movie_rate = movie_rate / sumcorrcoeff
+		else: # 0/0 format
+			movie_rate = 0
+		review.append((movie_rate,movie_name))
+
+review.sort(reverse=True)
+number = len(review)
+number = min(3,number)
+print("The top movie recommendations for this particular user are :")
+for x in range(number):
+	print(review[x][1])
 
-pearson.sort(reverse=True)
-print(pearson)
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+# Tried this, but didn't work, TypeError: map() takes 1 positional argument but 2 were given 
+# movie_rate = sum(map(lambda g: func(float(data[g][i]))*float(data[g][cols]), range(1,rows-1)))
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