def simpler(X,Y):
'''一元回归分析'''
alpha = mytools.correlation(Y,X) * Y.std() / X.std()
beta = Y.mean() - X.mean() * alpha
class fit(object):
'''模型本体'''
def __init__(self, alpha, beta):
self.alpha = alpha
self.beta = beta
self.error = (1 - mytools.correlation(Y,X) ** 2) * Y.var()
self.express = "y = %.2fx + %.2f" % (alpha, beta)
self.goodness = mytools.correlation(Y,X) ** 2
def plot(self):
pylab.scatter(X,Y)
pylab.show()
def pre(self,Xvalue):
return self.alpha * Xvalue + self.beta
return fit(alpha, beta)
def __init__(self, alpha, beta):
self.alpha = alpha
self.beta = beta
self.error = (1 - mytools.correlation(Y,X) ** 2) * Y.var()
self.express = "y = %.2fx + %.2f" % (alpha, beta)
self.goodness = mytools.correlation(Y,X) ** 2
