LinearRegression Predict- ValueError：矩阵未对齐

Question

我一直在搜索谷歌，但无法弄清楚我做错了什么。我对 python 很陌生，并试图在股票上使用 scikit，但在尝试预测时出现错误“ValueError：矩阵未对齐”。

import datetime

import numpy as np
import pylab as pl
from matplotlib import finance
from matplotlib.collections import LineCollection

from sklearn import cluster, covariance, manifold, linear_model

from sklearn import datasets, linear_model

###############################################################################
# Retrieve the data from Internet

# Choose a time period reasonnably calm (not too long ago so that we get
# high-tech firms, and before the 2008 crash)
d1 = datetime.datetime(2003, 01, 01)
d2 = datetime.datetime(2008, 01, 01)

# kraft symbol has now changed from KFT to MDLZ in yahoo
symbol_dict = {
    'AMZN': 'Amazon'}

symbols, names = np.array(symbol_dict.items()).T

quotes = [finance.quotes_historical_yahoo(symbol, d1, d2, asobject=True)
          for symbol in symbols]

open = np.array([q.open for q in quotes]).astype(np.float)
close = np.array([q.close for q in quotes]).astype(np.float)

# The daily variations of the quotes are what carry most information
variation = close - open

#########

pl.plot(range(0, len(close[0])-20), close[0][:-20], color='black')

model = linear_model.LinearRegression(normalize=True)
model.fit([close[0][:-1]], [close[0][1:]])

print(close[0][-20:])
model.predict(close[0][-20:])


#pl.plot(range(0, 20), model.predict(close[0][-20:]), color='red')

pl.show()

错误行是

model.predict(close[0][-20:])

我尝试将它嵌套在一个列表中。用 numpy 使其成为一个数组。我可以在谷歌上找到任何东西，但我不知道我在这里做什么。

这个错误是什么意思，为什么会发生？

Answer 1

试图通过简单的线性回归来预测股票价格？ :^|。无论如何，这是你需要改变的：

In [19]:

M=model.fit(close[0][:-1].reshape(-1,1), close[0][1:].reshape(-1,1))
In [31]:

M.predict(close[0][-20:].reshape(-1,1))
Out[31]:
array([[ 90.92224274],
       [ 94.41875811],
       [ 93.19997275],
       [ 94.21895723],
       [ 94.31885767],
       [ 93.030142  ],
       [ 90.76240203],
       [ 91.29187436],
       [ 92.41075928],
       [ 89.0940647 ],
       [ 85.10803717],
       [ 86.90624508],
       [ 89.39376602],
       [ 90.59257129],
       [ 91.27189427],
       [ 91.02214318],
       [ 92.86031126],
       [ 94.25891741],
       [ 94.45871828],
       [ 92.65052033]])

请记住，当您构建模型时，X 和 y 的 .fit 方法应该具有 [n_samples,n_features] 的形状。 .predict 方法也是如此。