The short answer is: "kind of". These two Github issues explain what's going on more in-depth: https://github.com/scipy/scipy/issues/3995 https://github.com/scipy/scipy/issues/4239
As for the warning only showing once, that's Python's default behavior for warnings: http://stackoverflow.com/q/22661745/10601 -CJ On Fri, Nov 20, 2015 at 2:40 PM, <josef.p...@gmail.com> wrote: > Is this intentional? > > > >>> exog > <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format> > > >>> np.asarray(exog) > array(<50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>, dtype=object) > > > I'm just a newbie who thought to use the usual pattern. > > > .... > > >>> np.asarray(exog).dot(beta) > array([ <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>, > <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>, > <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>, > <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>, > <50x5 sparse matrix of type '<class 'numpy.float64'>' > with 50 stored elements in Compressed Sparse Column format>], dtype=object) > C:\programs\WinPython-64bit-3.4.3.1\python-3.4.3.amd64\lib\site-packages\scipy\sparse\compressed.py:306: > SparseEfficiencyWarning: Comparing sparse matrices using >= and <= is > inefficient, using <, >, or !=, instead. > "using <, >, or !=, instead.", SparseEfficiencyWarning) > > seems to warn only once > > >>> y = np.asarray(exog).dot(beta) > >>> y.shape > (5,) > > > >>> np.__version__ > '1.9.2rc1' > > >>> scipy.__version__ > '0.15.1' > > > > Josef > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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