def predict(data, samples, classifier='SVM',
classification='combined', selectFeatures=('CUK', 10)):
"""
Learns the data-set with the given classifier and
gives a prediction for each of the samples.
"""
if (classification == "trained"):
classifyTrained = True
classifySurface = False
elif (classification == 'surface'):
classifyTrained = False
classifySurface = True
else:
classifyTrained = True
classifySurface = True
if (classifier == "SVM"):
clf = cl.classifyDataSVM(data, classifyTrained,
classifySurface, selectFeatures,scaling=False)
elif (classifier == "DT"):
clf = cl.classifyDataDT(data, classifyTrained,
classifySurface, selectFeatures,scaling=False)
elif (classifier == "KNN"):
clf = cl.classifyDataKNN(data, classifyTrained,
classifySurface, selectFeatures,scaling=False)
elif (classifier == "LogReg"):
clf = cl.classifyDataLR(data, classifyTrained,
classifySurface, selectFeatures,scaling=False)
else:
print (str(classifier) + " is not a valid option")
[samples, _,_,_] = clf.extractData(samples,scaling=False)
predictions = [clf.predict(s) for s in samples]
return predictions
def evalDT(data, classifyTrained, classifySurface, selectFeatures):
classifier = cl.classifyDataDT(data, classifyTrained,
classifySurface, selectFeatures)
classifier.crossValidation()
# classifier.showFeatureImportances()
classifier.createTreePdf()
if (plotSurfaces):
classifier.plotDecisionSurface()