Hi Luca,
>From the docs,
fit(*X*, *y*,
*n_jobs=1*)<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.fit>
Fit linear model.
Parameters :
*X* : numpy array or sparse matrix of shape [n_samples,n_features]
Training data
With your example code, my traceback is
*------------------------------------------------------------*
*Traceback (most recent call last):*
* File "<ipython console>", line 1, in <module>*
* File "/home/jaques/scikit-learn/sklearn/linear_model/base.py", line 363,
in fit*
* X, y, self.fit_intercept, self.normalize, self.copy_X)*
* File "/home/jaques/scikit-learn/sklearn/linear_model/base.py", line 103,
in center_data*
* X_std = np.ones(X.shape[1])*
*IndexError: tuple index out of range*
Hence why your reshaping fixes the problem.
to match the API, your X, must be of shape [n_samples,n_features]
So I don't think an issue is necessary, as it is expected, although, having
a better error message in terms of what the input should be could be useful.
Thoughts, list?
Hope this helps
Kind Regards,
Jaques
2013/9/24 Luca Cerone <[email protected]>
> Dear all,
>
> I have noticed that the Linear Regression fails to perform the prediction
> if performed on
> with a dataset and target that are normal array.
>
> You can replicate this as follows:
>
> from pylab import linspace, permutation, randn
> from sklearn import linear_model
>
> >>>
> clf = linear_model.LinearRegression()
>
> x = linspace(0,1,201)
> noise = 0.2 * randn(*x.shape)
> y = 0.5 + 2 * x + noise
>
> clf.fit(x,y)
>
> fails with the following message:
>
> TypeError Traceback (most recent call last)
> <ipython-input-134-5c1831092d7a> in <module>()
> ----> 1 clf.fit(x,y)
>
> /home/lcerone/CNOVE/local/lib/python2.7/site-packages/sklearn/linear_model/base.pyc
> in fit(self, X, y, n_jobs)
> 361
> 362 X, y, X_mean, y_mean, X_std = self._center_data(
> --> 363 X, y, self.fit_intercept, self.normalize, self.copy_X)
> 364
> 365 if sp.issparse(X):
>
> /home/lcerone/CNOVE/local/lib/python2.7/site-packages/sklearn/linear_model/base.pyc
> in center_data(X, y, fit_intercept, normalize, copy, sample_weight)
> 98 if normalize:
> 99 X_std = np.sqrt(np.sum(X ** 2, axis=0))
> --> 100 X_std[X_std == 0] = 1
> 101 X /= X_std
> 102 else:
>
> TypeError: 'numpy.float64' object does not support item assignment
>
> <<<
>
> This however can be solved by reshaping the arrays x,y (x has only 1
> dimension and so does y)
>
> xx = x.reshape((x.shape[0],-1))
> yy = y.reshape((y.shape[0],-1))
> clf.fit(xx,yy)
>
> correctly solves the regression problem.
>
> Similary if now I try to run prediction using an array zz generated using
> linspace the task fails, but can be solved easily by reshaping the array zz.
>
> I was wondering if this is the intended behaviour or if I should submit an
> issue on github.
>
> Have a nice day,
> Cheers,
> Luca
>
>
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