没有循环匹配指定的签名和转换错误

我是 python 和机器学习的初学者。当我尝试将数据放入 statsmodels.formula.api OLS.fit() 时，出现以下错误

回溯（最近一次调用最后一次）：

文件“”，第 47 行，位于 regressor_OLS = sm.OLS(y , X_opt).fit()

文件 “E：\ Anaconda \ lib \ site-packages \ statsmodels \回归\ Linear_model.py”， 第 190 行，适合 self.pinv_wexog，奇异值 = pinv_extended(self.wexog)

文件“E:\Anaconda\lib\site-packages\statsmodels\tools\tools.py”， 第 342 行，在 pinv_extended 中 u, s, vt = np.linalg.svd(X, 0)

文件“E:\Anaconda\lib\site-packages\numpy\linalg\linalg.py”，行 第1404章 u, s, vt = gufunc(a, 签名=签名, extobj=extobj)

类型错误：没有与指定签名和转换匹配的循环 找到 ufunc svd_n_s

code

#Importing Libraries
import numpy as np # linear algebra
import pandas as pd # data processing
import matplotlib.pyplot as plt #Visualization


#Importing the dataset
dataset = pd.read_csv('Video_Games_Sales_as_at_22_Dec_2016.csv')
#dataset.head(10) 

#Encoding categorical data using panda get_dummies function . Easier and straight forward than OneHotEncoder in sklearn
#dataset = pd.get_dummies(data = dataset , columns=['Platform' , 'Genre' , 'Rating' ] , drop_first = True ) #drop_first use to fix dummy varible trap 


dataset=dataset.replace('tbd',np.nan)

#Separating Independent & Dependant Varibles
#X = pd.concat([dataset.iloc[:,[11,13]], dataset.iloc[:,13: ]] , axis=1).values  #Getting important  variables
X = dataset.iloc[:,[10,12]].values
y = dataset.iloc[:,9].values #Dependant Varible (Global sales)


#Taking care of missing data
from sklearn.preprocessing import Imputer
imputer =  Imputer(missing_values = 'NaN' , strategy = 'mean' , axis = 0)
imputer = imputer.fit(X[:,0:2])
X[:,0:2] = imputer.transform(X[:,0:2])


#Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2 , random_state = 0)

#Fitting Mutiple Linear Regression to the Training Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

#Predicting the Test set Result
y_pred = regressor.predict(X_test)


#Building the optimal model using Backward Elimination (p=0.050)
import statsmodels.formula.api as sm
X = np.append(arr = np.ones((16719,1)).astype(float) , values = X , axis = 1)

X_opt = X[:, [0,1,2]]
regressor_OLS = sm.OLS(y , X_opt).fit()
regressor_OLS.summary() 

Dataset

数据集链接 https://www.dropbox.com/s/w2hq4t0utbvk7bu/Video_Games_Sales_as_at_22_Dec_2016.csv?dl=0

在 stack-overflow 或 google 上找不到任何有助于解决此问题的内容。

尝试指定

dtype = '浮动'

创建矩阵时。 例子：

a=np.matrix([[1,2],[3,4]], dtype='float')

希望这有效！