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[英]PolynomialFeatures LinearRegression ValueError: shapes not aligned
[英]LinearRegression Predict- ValueError: matrices are not aligned
我一直在搜索谷歌,無法弄清楚我做錯了什么。 我對python很新,並試圖在股票上使用scikit,但我在嘗試預測時得到錯誤“ValueError:矩陣不對齊”。
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()
錯誤行是
model.predict(close[0][-20:])
我已經嘗試將其嵌套在列表中。 使它成為一個numpy數組。 我在谷歌上找到的任何東西,但我不知道我在這里做什么。
這個錯誤意味着什么,為什么會發生?
試圖通過簡單的線性回歸預測股票價格? :^ |。 無論如何,這是你需要改變的:
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]])
請記住,在構建模型時, .fit
方法的X
和y
應該具有[n_samples,n_features]
的形狀。 這同樣適用於.predict
方法。
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