Initial work on decision trees

	Random Forest has occasional disastrous accuracy:
	 never seen that happen in WEKA
ChiMerge seems to improve accuracy
ID3 algorithm working
Passes all the tests
Reduced-error pruning
Identified source of the low accuracy
Corrected a problem with pruning, actual ID3 decision tree type

	Going to modify Bagging to select attributes on its own
Correction to randomisation and train-test split
Not sure if this bagging version is better or not

	More more similar to "Attribute bagging:improving accuracy of classifier ensembles by using random feature subsets" (Brill)
Fixed race conditions
Examples for RandomForest, ID3 and Random trees
Package documentation
Merge branch 'randomforests-upstream' of github.com:Sentimentron/golearn into randomforests-upstream
Making Fit() more idiomatic
Benchmarking
@mish15's re-write of Predict()
Merge pull request #33 from Sentimentron/randomforests-upstream

ID3 and random decision trees and random forests
Add Travis CI banner to README

Signed-off-by: Pekka Enberg <penberg@iki.fi>
Install libatlas-base-dev for Blas package

The Go blas package requires libcblas to be present on the system.
Travis environments run Ubuntu Linux so we need to install
libatlas-base-dev.

Signed-off-by: Pekka Enberg <penberg@iki.fi>
Merge pull request #37 from penberg/travis-cblas

Install libatlas-base-dev for Blas package