ValueError: формы (1,4) и (5,4) не выровнены: 4 (dim 1)! = 5 (dim 0) при добавлении моих переменных к моей машине прогнозирования - PullRequest
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ValueError: формы (1,4) и (5,4) не выровнены: 4 (dim 1)! = 5 (dim 0) при добавлении моих переменных к моей машине прогнозирования

1 голос
/ 18 апреля 2019

Я создаю машину прогнозирования с четырьмя переменными. Когда я добавляю переменные, все портится и выдает: 

ValueError: shapes (1,4) and (5,4) not aligned: 4 (dim 1) != 5 (dim 0)

код 

       import pandas as pd
       from pandas import DataFrame
       from sklearn import linear_model
       import tkinter as tk 
       import statsmodels.api as sm

# Подход 1: Импорт данных в Python 

Stock_Market = pd.read_csv(r'Training_Nis_New2.csv')
df = DataFrame(Stock_Market,columns=['Month 1','Month 2','Month 3','Month 
4','Month 5','Month 6','Month 7','Month 8',
                                 'Month 9','Month 10','Month 11','Month 
12','FSUTX','MMUKX','FUFRX','RYUIX','Interest R','Housing 
Sale','Unemployement Rate','Conus Average Temperature 
Rank','30FSUTX','30MMUKX','30FUFRX','30RYUIX'])


X = df[['Month 1','Interest R','Housing Sale','Unemployement Rate','Conus Average Temperature Rank']] 
# here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively,  you may add additional variables within the brackets

Y = df[['30FSUTX','30MMUKX','30FUFRX','30RYUIX']]

# with sklearn
regr = linear_model.LinearRegression()
regr.fit(X, Y)

print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)


# prediction with sklearn
# prediction with sklearn


HS=5.5
UR=6.7
CATR=8.9

New_Interest_R = 4.6



print('Predicted Stock Index Price: \n', regr.predict([[UR ,HS ,CATR 
,New_Interest_R]]))

# with statsmodel
X = df[['Month 1','Interest R','Housing Sale','Unemployement Rate','Conus Average Temperature Rank']]
Y = df['30FSUTX']
print('\n\n*** Fund = FSUTX')
X = sm.add_constant(X) # adding a constant

model = sm.OLS(Y, X).fit()
predictions = model.predict(X) 

print_model = model.summary()
print(print_model)
...