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 plotPassiveAggressive(self):
PlotInWindow.fig = Figure()
self.labelTitle["text"] = "Passive Aggresive Algorithm"
file = self.labelForFile["text"]
file = ReadingFromFile.checkFile(file, "passiveAggressive")
fig = PassiveAggressiveAlgorithm.passiveAggressivePlotInWindow(file)
self.drawFigure(fig)
def plotLogisticRegression(self):
PlotInWindow.fig = Figure()
self.labelTitle["text"] = "Logistic Regression Algorithm"
file = self.labelForFile["text"]
file = ReadingFromFile.checkFile(file, "logistic")
fig = LogisticRegression.logisticRegressionPlotInWindow(file)
self.drawFigure(fig)
def plotPerceptron(self):
PlotInWindow.fig = Figure()
self.labelTitle["text"] = "Perceptron Algorithm"
file = self.labelForFile["text"]
file = ReadingFromFile.checkFile(file, "perceptron")
fig = Perceptron.perceptronPlotInWindow(file)
self.drawFigure(fig)
def oneVsAllPassiveAggressiveExample(file):
data = ReadingFromFile.readDataFromFile(file, ',')
x, t = Initializing.processData(data) # iz pocetnog skupa podataka razdvojimo podatke i labele
listW, listB = trainClassifiers(data, "passiveAgressive")
fig = PlotInWindow.plotLinesMulticlassOneVsAll(x, t, listW, listB)
return fig
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()
def plotMulticlassOneVsAllPassiveAggressive(self):
PlotInWindow.fig = Figure()
self.labelTitle[
"text"] = "Multiclass One Vs ALL -Logistic Regression Algorithm"
file = self.labelForFile["text"]
file = ReadingFromFile.checkFile(file, "multiclassPassiveAggressive")
fig = MulticlassOneVsAll.oneVsAllPassiveAggressiveExample(file)
canvas = FigureCanvasTkAgg(fig, master=self.window)
canvas.get_tk_widget().grid(row=5, columnspan=2)
canvas.draw()
def plotAlgorithmInWindow(file, algorithm):
data = ReadingFromFile.readDataFromFile(file, ',') # Podaci ucitani iz fajla
trainingSet, testSet = CrossValidation.makeSets(data)
x, t = Initializing.processData(trainingSet)
t = Initializing.checkLabels(t, algorithm)
kTrainingSets, kValidSets = CrossValidation.kCrossValidationMakeSets(trainingSet, 5) # Napravimo k trening i test set-ova unakrsnom validacijom (k = 5)
if algorithm == "perceptron":
w, b = Initializing.initialParam(x)
w, b = Perceptron.train(x, t, w, b)
elif algorithm == "logistic":
w, b = LogisticRegression.crossTrain(kTrainingSets,kValidSets) # Istreniramo k trening setova i kao rezultat vratimo najbolje w i najbolje b (ono w i b za koje je greska bila najmanja)
else:
c = PassiveAggressiveAlgorithm.optC(kTrainingSets, kValidSets) # Podesimo optimalni parametar c
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)
xTest, tTest = Initializing.processData(testSet)
fig = PlotInWindow.plotInWindow(xTest, w, b, tTest)
return fig