MAINT remove some dead code from the LibSVM wrapper

We ship a hacked-up v300 and won't support an older version anyway.
Fix OOB score calculation for non-contiguous targets

OOB scores of RandomForestClassifier are incorrectly calculated
because of confusion between predicted labels and indices of a target.
For example, when you label the `digits` dataset from 1 or greater
number but 0, OOB score becomes nearly zero.
This patch will fix this error, and a test is added in order to avoid
regression.
Merge pull request #2685 from bicycle1885/fix-oob-score

Fix OOB score calculation for non-contiguous targets
DOC optimize Phimeca logo, size halved
MAINT some more pointer safety in LibSVM

All pointers are asigned to NULL after delete
and are checked not NULL before reference in
svm_free_and_destroy_model and svm_free_model_content.

Conflicts:
	sklearn/svm/src/libsvm/svm.cpp
MAINT import LibSVM 310

version number changed to 310

Conflicts:
	README
	java/libsvm/svm.m4
add a comment for sigmoid_predict in svm.cpp
MAINT import LibSVM patch from upstream

int i; in svm_predict_values for openmp

Conflicts:
	sklearn/svm/src/libsvm/svm.cpp
Merge pull request #2673 from larsmans/libsvm-310

MAINT update LibSVM to version 310
ENH filter out zeros early in FeatureHasher

Fixes #2665.
DOC: typo in binary_tree docs
FIX PCA.score_samples didn't do input validation
DOC missing parts of docstrings

Added documentation for attributes of GridSearchCV, RidgeCV, and
LassoCV. Fixed typo in "dual_gaps" documentation.

Fixes #2693.
FIX Do one run with MiniBatchKMeans and explicit centers

* this will fix issue #2686
* if n_init is not 1, it will be set to one and a warning will be printed
* added a test case which checks for the warning
* modified other test cases so that they do not cause this warning
FIX MBKMeans w/ explicit centers and n_init>1, part 2

* don't change a user-set attribute in fit
* specify, in the warning, which argument to change
ENH show top term per cluster in doc. k-means example
FIX n_init bug in k-means

Sorry, should have tested.
ENH make_pipeline and make_union utility functions
MAINT refactor fast_dot

* 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).
Upgrade to joblib 0.8.0a