Scikit学习了几个输出的线性回归

Question

我正在尝试使用scikit学习使用多个输出进行线性回归

代码（随机数据为例）：

from sklearn import datasets, linear_model
import numpy as np

X = np.random.rand(300,10)
y = np.random.rand(300,9)
reg_model = linear_model.LinearRegression()
reg_model.fit(X,y)

我收到了明显的错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/Users/sorensonderby/Documents/workspaces/workspace/Chemoinformatics_proect/notebooks/<ipython-input-116-e235c7159573> in <module>()
      5 y = np.random.rand(300,9)
      6 reg_model = linear_model.LinearRegression()
----> 7 reg_model.fit(X,y)
      8 

/Library/Python/2.7/site-packages/scikit_learn-0.10-py2.7-macosx-10.7-intel.egg/sklearn/linear_model/base.pyc in fit(self, X, y)
    178                     linalg.lstsq(X, y)
    179 
--> 180         self._set_intercept(X_mean, y_mean, X_std)
    181         return self
    182 

/Library/Python/2.7/site-packages/scikit_learn-0.10-py2.7-macosx-10.7-intel.egg/sklearn/linear_model/base.pyc in _set_intercept(self, X_mean, y_mean, X_std)
    106         """
    107         if self.fit_intercept:
--> 108             self.coef_ = self.coef_ / X_std
    109             self.intercept_ = y_mean - np.dot(X_mean, self.coef_.T)
    110         else:

ValueError: operands could not be broadcast together with shapes (10,9) (10)

我读了适用方法的api，它说x应该是n_sample x n_features，y应该是n_sample x n_targets。 Link to fit method

我做错了什么？

Answer 1

您正在使用scikit-learn 0.10和0.13.1的文档。升级您的安装，然后再试一次 - 它应该可以工作。