MAINT remove default values from private funcs in GMM

Removed default values for 'mean' and 'covars' arguments.
They did not have the excepted format causing the functions
to fail when called with default arguments.
ENH faster sampling in GMMs

A few optimizations for sampling with full covariance matrix:
1) use eigh instead of svd, which are equivalent (up to numerical precision),
2) use broadcasting instead of matrix multiplication for the product U s^(1/2),
3) simpler construction of the random vectors.
Merge branch 'pr/3120'

Fixes #3120, "Bad default arguments in gmm.py".
FIX numerical stability in GMM with eigh sampling
Merge pull request #3151 from rajatkhanduja/fixing_examples

Updated examples to use pyplot for plotting instead of pylab
adding conditional import to lfw.py for python3 support
Some more examples updated to use matplotlib.pyplot
Modified example 'plot_stock_market.py' to use matplotlib.pyplot

Modified despite #3153 because
- Error is not related to pylab or matplotlib.pyplot
- Removing the symbol 'DELL' from the example solves the problem
Merge pull request #3152 from jdowns/lfw-conditional-import

[MRG+1] adding conditional import to lfw.py for python3 support
Merge pull request #3154 from rajatkhanduja/fixing_examples

[MRG+1] Updated remaining examples to use pyplot for plotting instead of pylab
fix wrong confidence interval
Extend utils.sparsefuncs: inplace scale and axis min/max
Remove HMM examples

Fixes #2646
Adding string={pearson, spearman} option to increasing argument in IsotonicRegression.
Adding increasing and decreasing tests for both Pearson and Spearman increasing argument options
Merge pull request #3156 from IvicaJovic/fixes

DOC Fix wrong confidence interval
FIX: use clip(0) instead of abs()

This removes spurious tiny negatives from the output of eig.
DOC : fix alpha docstring in dict learning
Refactoring increasing_bool set into _check_increasing method
PEP8ing isotonic tests