def perceptronExample(file):
file = ReadingFromFile.checkFile(file, "perceptron")
data = ReadingFromFile.readDataFromFile(file,
',') # Podaci ucitani iz fajla
trainingSet, testSet = CrossValidation.makeSets(data)
x, t = Initializing.processData(trainingSet)
t = Initializing.checkLabels(t, "perceptron")
w, b = Initializing.initialParam(x)
w, b = Perceptron.train(x, t, w, b)
Plot.plotData(x, t)
Plot.plotLine(x, w, b)
plt.show()
def PassiveAggressiveAlgorithmExample(file):
file = ReadingFromFile.checkFile(file, "passiveAggressive")
data = ReadingFromFile.readDataFromFile(file, ',')
trainingSet, testSet = CrossValidation.makeSets(
data) # Napravimo trening i test set
kTrainingSets, kValidSets = CrossValidation.kCrossValidationMakeSets(
trainingSet,
5) # Napravimo k trening i test set-ova unakrsnom validacijom (k = 5)
c = PassiveAggressiveAlgorithm.optC(kTrainingSets, kValidSets)
w, b = PassiveAggressiveAlgorithm.crossTrain(
kTrainingSets, kValidSets, c
) # Istreniramo k trening setova i kao rezultat vratimo najbolje w i najbolje b (ono w i b za koje je greska bila najmanja)
x, t = Initializing.processData(
testSet) # Rezultat crtamo i merimo nad test skupom podataka
t = Initializing.checkLabels(t, "passiveAggressive")
Plot.plotData(x, t)
Plot.plotLine(x, w, b)
plt.show()