The problem with slicing is when the index column is not the first one. Would
there be a way to do it with slicing.
I’m also not sure about the first method. The inputs of reshape should be
vector, new shape and order. What does the line below do?
Thank yuou,
From: Artem [mailto:barmaley....@gmail.com]
Sent: Wednesday, February 11, 2015 1:39 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] regression with one independent variable
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<mailto: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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