def start():
featuresList=[]
labelsList=[]
featuresList, labelsList= loadFile("output.txt")
print 'Normalizing array...'
normalizearray(featuresList)
alldata = ClassificationDataSet( len(featuresList[0]), 1, nb_classes=8, class_labels=['ffi_brainmatter','ffi_neuron','ffi_vacuole','ffi_astrocyte', 'wt_brainmatter', 'wt_neuron', 'wt_vacuole', 'wt_astrocyte'] )
for i in range(len(labelsList)):
alldata.appendLinked(featuresList[i], labelsList[i])
#print 'All data: ', alldata
#print 'Statisticcs: ', alldata.calculateStatistics()
newK=fSel.getTreeFeatures(featuresList, labelsList);
newK=newK.shape[1]
print "K= ", newK
reducedFeatures= fSel.getBestK(featuresList,labelsList, 'f_classif', newK)
reducedData=ClassificationDataSet( len(reducedFeatures[0]), 1, nb_classes=8, class_labels=['ffi_brainmatter','ffi_neuron','ffi_vacuole','ffi_astrocyte', 'wt_brainmatter', 'wt_neuron', 'wt_vacuole', 'wt_astrocyte'] )
#prep reducedData object with reduced feature list
for i in range(len(labelsList)):
reducedData.appendLinked(reducedFeatures[i], labelsList[i])
print 'Splitting test and training data...'
tstdata, trndata = alldata.splitWithProportion( 0.30 )
reducedTestData, reducedTrainData=reducedData.splitWithProportion(0.3)
print 'Number of training and test patterns: ', len(trndata), len(tstdata)
trndata._convertToOneOfMany(bounds=[0,1])
tstdata._convertToOneOfMany(bounds=[0,1])
reducedTestData._convertToOneOfMany(bounds=[0,1])
reducedTrainData._convertToOneOfMany(bounds=[0,1])
#print "Number of training patterns: ", len(trndata)
print "Input and output dimensions: ", trndata.indim, trndata.outdim
#print "Sample (input, target, class):"
#print trndata['input'][0], trndata['target'][0], trndata['class'][0]
#print trndata['input'][1], trndata['target'][1], trndata['class'][1]
buildFNN(tstdata, trndata)
print "___________________________________________FEATURE REDUCTION________________________________________________"
buildFNN(reducedTestData, reducedTrainData)
def start(fName, K=None):
'''
inputFile, number of features to be selected, default is none
'''
X,y= readFile.loadFile(fName)
getMask(X, y, K)