make anomaly.py a class for further modularity

and convert tests / usecases accordingly;
Just create an instance of nupic.algorithms.anomaly.Anomaly()
and call the computeAnomalyScore() method
add test for anomaly speedup in numpy.sum()

yields cca 300x speedup! (only of array is numpy, not python array) which is this case
speedup: use numpy.sum() where applicable

instead of python's sum() on numpy array (much slower)

found by: grep -R 'numpy.*.sum()' $NUPIC
implement "new" anomaly using temporal pooler
use TemporalPoolerAnomaly class in CLAModel

this class was extracted from clamodel previously, so it's just moving around
CumulativeAnomaly (sliding window) implementation
add AnomalyChooser class that let's you select from

avail anomaly implementations
Anomaly: remove separate classes and use parameters to method instead
Anomaly: prepare for Subutai's likelihood anomaly
cumulative Anomaly: fix + unittest

fixed bug: self._buf must be float array!
TMP use slide-window

Conflicts:
	py/nupic/frameworks/opf/clamodel.py
Revert "TMP use slide-window"

This reverts commit be713848ac4a073baff175baaf1b33cc292b1971.
Revert "Revert "TMP use slide-window""

This reverts commit d7e635fad0c5128a2a43d9110df82e82c509795c.
TMP: sliding window 100
Merge branch 'master' into anomaly_modular
Anomaly: helper method for movingAverage()
Anomaly: separate  helper "pure anomaly" function
Merge branch 'master' into anomaly_modular
make _pureAnomaly() a @staticmethod
Anomaly: add mode for outputting the probability as "anomaly score"