我已经编译了用于绘制多项式图的代码，但它没有进行绘图。我使用了来自scikit-learn的SVR（支持向量回归），我的代码如下。它没有显示任何错误消息，只是显示了我的数据。我不知道发生了什么。有人知道吗？甚至在变量控制台上也没有显示任何关于我的数据的描述。

import pandas as pdimport numpy as npfrom sklearn.svm import SVRfrom sklearn import cross_validationfrom sklearn.linear_model import LinearRegressionimport matplotlib.pyplot as pltdf = pd.read_csv('coffee.csv')print(df)df = df[['Date','Amount_prod','Beverage_index']]x = np.array(df.Amount_prod)y = np.array(df.Beverage_index)x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.2)x_train = np.pad(x, [(0,0)], mode='constant')x_train.reshape((26,1))y_train = np.pad(y, [(0,0)], mode='constant')y_train.reshape((26,1))x_train = np.arange(26).reshape((26, 1))x_train = x.reshape((26, 1))c = x.Tnp.all(x_train == c)x_test = np.arange(6).reshape((-1,1))x_test = x.reshape((-1,1))c2 = x.Tnp.all(x_test == c2)y_test = np.arange(6).reshape((-1,1))y_test = y.reshape((-1,1))c2 = y.Tnp.all(y_test ==c2)svr_poly = SVR(kernel='poly', C=1e3, degree=2)y_poly = svr_poly.fit(x_train,y_train).predict(x_train)plt.scatter(x_train, y_train, color='black')plt.plot(x_train,  y_poly)plt.show()

数据样本:

 Date   Amount_prod Beverage_index    1990    83000         78    1991    102000        78    1992    94567         86    1993    101340        88    1994    96909         123    1995    92987         101    1996    103489        99    1997    99650         109    1998    107849        110    1999    123467        90    2000    112586        67    2001    113485        67    2002    108765        90

回答：

尝试下面的代码。支持向量机期望其输入具有零均值和单位方差。阻塞的不是绘图，而是对fit的调用。

from sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import StandardScalersvr_poly = make_pipeline(StandardScaler(), SVR(kernel='poly', C=1e3, degree=2))y_poly = svr_poly.fit(x_train,y_train).predict(x_train)