* FIX unstable cumsum in utils.random
* equal_nan = true for isclose
since numpy < 1.9 sum is as unstable as cumsum, fallback to np.cumsum
* added axis parameter to stable_cumsum
* FIX unstable sumsum in ensemble.weight_boosting and utils.stats
* FIX axis problem in stable_cumsum
* FIX unstable cumsum in mixture.gmm and mixture.dpgmm
* FIX unstable cumsum in cluster.k_means_, decomposition.pca, and manifold.locally_linear
* FIX unstable sumsum in dataset.samples_generator
* added docstring for parameter axis of stable_cumsum
* added comment for why fall back to np.cumsum when np version < 1.9
* remove unneeded stable_cumsum
* added stable_cumsum's axis testing
* FIX numpy docstring for make_sparse_spd_matrix
* change stable_cumsum from error to warning
ENH NonBLASDotWarning -> EfficiencyWarning; Improve error message
DOC Add exceptions module to modules/classes.rst
MAINT Move ConvergenceWarning, UndefinedMetricWarning et al into exceptions
MAINT Remove ChangedBehaviorWarning from base
DOC/FIX Improve DataConversionWarning's docstring
Passing 1D arrays to check_array, without setting `ensure_2d` to false now
raises a deprecation warning before reshaping it. This will later throw an
error.
All Scaler classes also throw warnings when 1D arrays are passed.
All unit tests/doctests are modified to ensure that no 1D arrays are passed,
except in explicit 1D array tests where the warnings have been silenced.
Additional tests are also included which check for different 1D array cases.
2D array tests with one samples and one features are also added and where
they failed, `check_array` call has been modified to give a more useful error
message
DOC max_iterations -> max_iter. Make it consistent with kmeans
MAINT Replace the deprecated dx parameter with d in the docstrings
MAINT Deprecation warning for max_iterations parameter.
SciPy 0.10 already has an implementation of the logistic function called
scipy.special.expit.
Using this makes RBM training 12% faster, as measured by observing the time
per iteration as reported by the plot_rbm_logistic_classification.py example,
disregarding the first iteration as an outlier (it's consistently faster than
the rest, not sure why). The speedup is in the expit function, not the
inplace operations; those are there to make benchmarking easier, but they
certainly won't hurt.
* check for NumPy >= 1.7.2 at import time, instead of in every call;
* save a function call when newer NumPy is available;
* rewrote documentation;
* put the docstring on fast_dot instead of _fast_dot;
* remove spurious warnings for matrix-vector multiplications (some of these
are suboptimal with older np.dot, but the good ones were triggering a
warning as well).
k-means can now use the memory-efficient dense implementation from
metrics.pairwise, while pairwise can use the fast sparse implementation
from k-means.
Taehoon Lee