tflucke/SSH-Master-Thesis @ cb943b007e7a02f96060520adc3283e3998e98d0

Ethan Goldfarb cb943b007e brief update		%!s(int64=6) %!d(string=hai) anos
..
README.md	cb943b007e brief update	%!s(int64=6) %!d(string=hai) anos
Vector.py	a30cb9c655 preliminary commit for nn and knn classifiers and fv class	%!s(int64=6) %!d(string=hai) anos
classifier.py	ecb63ae52a fixed bugs	%!s(int64=6) %!d(string=hai) anos
logs.py	5bdc48c964 neural network using tensorflow for data classification. Note: Very preliminary	%!s(int64=6) %!d(string=hai) anos
nearestneighbors.py	cb943b007e brief update	%!s(int64=6) %!d(string=hai) anos
pcap.py	5bdc48c964 neural network using tensorflow for data classification. Note: Very preliminary	%!s(int64=6) %!d(string=hai) anos

The train(classifications: int, data: list, results: list, testdata: list, testresults: list) function should be used.

Data and Testdata should be arrays of arrays of features, ex:

Suppose there are 3 features, each a float from 0 to 1. Data could be: [[.3, .2, .3], [.3, .3, .3], [.3, .4, .5]...]

Results should be ints in an array, each result accoring to the list of features it should represent the classification of.

Make a command line utility that reads in samples, transforms them to FeatureVectors and then runs a number of classifications on them.