ENH vectorized Viterbi decoding

For (20 states, 30k samples) data it makes StructuredPerceptron.fit 4x faster
MAINT "and contributors" in license
DOC expand README
FIX verbose output from perceptron
ENH speed up perceptron training two-fold

kernprof shows that the line

    w -= lr * safe_sparse_dot(Y_diff.T, X_i)

is now the bottleneck @ 75.9% of the time in a chunking demo w/
65k features and 20 classes.
ENH further optimization of perceptron fit algo

Computation of weight updates now twice as fast.
ENH rewrite Viterbi decoder in Cython

>10x faster on set of ~850 sentences w/ mean length of ~23.7 tokens
and 65k features.
MAINT expand gitignore w/ Cython and kernprof cruft
ENH lengths support (optional) in score and predict
MAINT move decoder tests to _decode subdir
TST add tests for count_trans function
FIX enhance input validation

Fixes #3.
DOC "getting started" in README
MAINT drop indices from SequenceKFold

They're going to be deprecated in scikit-learn soon,
https://github.com/scikit-learn/scikit-learn/pull/2334
ENH repeated cross-validation
ENH return lengths in cross-validation

Fixes #2.
ENH add an example script