可决系数、相关系数、均方误差

#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 = 0
    varX = 0
    varY = 0
    for i in range(0, len(X)):
        diffXXBar = X[i] - xBar
        diffYYBar = Y[i] - yBar
        SSR += (diffXXBar * diffYYBar)
        varX += diffXXBar ** 2
        varY += diffYYBar ** 2
    SST = math.sqrt(varX * varY)
    return SSR / SST