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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