#均方误差根def rmse(y_test, y):return sp.sqrt(sp.mean((y_test - y) ** 2))#与均值相比的优秀程度，介于[0~1]。0表示不如均值。1表示完美预测.def R2(y_test, y_true):return 1 - ((y_test - y_true) ** 2).sum() / ((y_true - y_true.mean()) ** 2).sum()def R22(y_test, y_true):y_mean = np.array(y_true)y_mean[:] = y_mean.mean()return 1 - rmse(y_test, y_true) / rmse(y_mean, y_true)def computeCorrelation(X, Y):xBar = np.mean(X)yBar = np.mean(Y)SSR = 0varX = 0varY = 0for i in range(0, len(X)):diffXXBar = X[i] - xBardiffYYBar = Y[i] - yBarSSR += (diffXXBar * diffYYBar)varX += diffXXBar ** 2varY += diffYYBar ** 2SST = math.sqrt(varX * varY)return SSR / SST
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