fit expects 2-dimensional input, whereas X[:, 0] is one dimensional. You
can either reshape it manually:
regr.fit(x_train[:, 0].reshape((x_train.shape[0], 1)), x_train[:, 1])
or use slices to select continuous range of columns:
regr.fit(x_train[:, :1], x_train[:, 1])
What exception tells you is that x_train is of wrong shape. If x_train was
2-dimensional, it's shape would have a 2nd element (with index 1).
On Wed, Feb 11, 2015 at 9:26 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> I’m trying to make a linear regression with one independent variable and
> one or more dependent variables.
>
>
>
> That does not seem to work. Is that a limitation of regression function?
>
>
>
> regr.fit(x_train[:, 0], x_train[:, 1])
>
>
>
> File
> "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/base.py", line
> 355, in fit
>
> X, y, self.fit_intercept, self.normalize, self.copy_X)
>
> File
> "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/base.py", line
> 104, in center_data
>
> X_std = np.ones(X.shape[1])
>
> IndexError: tuple index out of range
>
>
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