以下代码在没有缩放的情况下使用时效果很好，但是在应用缩放时，结果与实际相差太远。这是代码：
data =(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63).
model = SVR(kernel='poly', C=1e3, degree=3)
data_min = min(data)
data_max = max(data)
diff = data_max - data_min
data_scaled = []
for i in range(0,len(data)):
data_scaled.append((data[i]-data_min)/diff)
data_scaled = np.matrix(data_scaled)
data_scaled = data_scaled.reshape(-1,1)
y = (1,8,27,64,125,216,343,512,729,1000,1331,1728,2197,2744,3375,4096,4913,5832,6859,8000,9261,10648,12167,13824,15625,17576,19683,21952,24389,27000,29791,32768,35937,39304,42875,46656,50653,54872,59319,64000,68921,74088,79507,85184,91125,97336,103823,110592,117649,125000,132651,140608,148877,157464,166375,175616,185193,195112,205379,216000,226981,238328,250047)
model.fit(data_scaled, y)
predicted = model.predict(data_scaled)