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)