You are viewing a single comment's thread. Return to all comments →
def std(X): temp=[] X_mean=float(sum(X)/len(X)) for val in X: temp.append((val-X_mean)**2) return((sum(temp))**0.5) def cov(X,Y): temp=[] X_mean=float(sum(X)/len(X)) Y_mean=float(sum(Y)/len(Y)) for i,j in zip(X,Y): temp.append((i-X_mean)*(j-Y_mean)) return sum(temp) def pearson(X,Y): return(cov(X,Y)/(std(X)*std(Y))) def main(): n = int(input()) M = [] P = [] C = [] for i in range(n): marks = input().split() marks = [int(a) for a in marks] M.append(marks[0]) P.append(marks[1]) C.append(marks[2]) rMP = pearson(M,P) rPC = pearson(P,C) rCM = pearson(C,M) print("%.2f"%rMP) print("%.2f"%rPC) print("%.2f"%rCM) if(__name__=="__main__"): main()
Day 5: Computing the Correlation
You are viewing a single comment's thread. Return to all comments →