# coding=utf-8import matplotlib.pyplot as pltimport numpy as npimport scipy as spimport csvfrom scipy.stats import normfrom sklearn.pipeline import Pipelinefrom sklearn.linear_model import LinearRegressionfrom sklearn.preprocessing import PolynomialFeaturesfrom sklearn import linear_model''''' 數(shù)據(jù)導(dǎo)入 '''def loadDataSet(fileName): dataMat = [] labelMat = [] csvfile = file(fileName, 'rb') reader = csv.reader(csvfile) b = 0 for line in reader:  if line[50] is '':   b += 1  else:   dataMat.append(float(line[41])/100*20+30)   labelMat.append(float(line[25])*100) csvfile.close() print "absence time number: %d" % b return dataMat,labelMatxArr,yArr = loadDataSet('data.csv')x = np.array(xArr)y = np.array(yArr)# x = np.arange(0, 1, 0.002)# y = norm.rvs(0, size=500, scale=0.1)# y = y + x ** 2def rmse(y_test, y): return sp.sqrt(sp.mean((y_test - y) ** 2))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)plt.scatter(x, y, s=5)#分別進(jìn)行1,2,3,6次擬合degree = [1, 2,3, 6]y_test = []y_test = np.array(y_test)for d in degree: #普通 # clf = Pipeline([('poly', PolynomialFeatures(degree=d)), #     ('linear', LinearRegression(fit_intercept=False))]) # clf.fit(x[:, np.newaxis], y) # 嶺回歸 clf = Pipeline([('poly', PolynomialFeatures(degree=d)),     ('linear', linear_model.Ridge())]) clf.fit(x[:, np.newaxis], y) y_test = clf.predict(x[:, np.newaxis]) print('多項(xiàng)式參數(shù)%s' %clf.named_steps['linear'].coef_) print('rmse=%.2f, R2=%.2f, R22=%.2f, clf.score=%.2f' %   (rmse(y_test, y),   R2(y_test, y),   R22(y_test, y),   clf.score(x[:, np.newaxis], y))) plt.plot(x, y_test, linewidth=2)plt.grid()plt.legend(['1', '2','3', '6'], loc='upper left')plt.show()