def main():
iris = datasets.load_iris()
irisData = iris.data[:, [2, 3]]
irisClass = iris.target
dataTrainingSet, dataTestSet, classTrainingSet, classTestSet = train_test_split(
irisData, irisClass, test_size=0.3, random_state=1, stratify=irisClass)
# =============== Perceptron ====================
# Perceptron 1
classTrainingSubset1 = np.copy(classTrainingSet)
classTrainingSubset1 = classTrainingSubset1[(classTrainingSubset1 != 2)]
dataTrainingSubset1 = np.copy(dataTrainingSet)
dataTrainingSubset1 = dataTrainingSubset1[(classTrainingSet != 2)]
classTrainingSubset1[(classTrainingSubset1 != 0)] = -1
classTrainingSubset1[(classTrainingSubset1 != -1)] = 1
perceptron1 = Perceptron(learningRate=0.1, iterationsToStop=10)
perceptron1.learn(dataTrainingSubset1, classTrainingSubset1)
# Perceptron 2
classTrainingSubset2 = np.copy(classTrainingSet)
classTrainingSubset2 = classTrainingSubset2[(classTrainingSubset2 != 1)]
dataTrainingSubset2 = np.copy(dataTrainingSet)
dataTrainingSubset2 = dataTrainingSubset2[(classTrainingSet != 1)]
classTrainingSubset2[(classTrainingSubset2 != 2)] = -1
classTrainingSubset2[(classTrainingSubset2 != -1)] = 1
perceptron2 = Perceptron(learningRate=0.1, iterationsToStop=10)
perceptron2.learn(dataTrainingSubset2, classTrainingSubset2)
# Perceptron 3
classTrainingSubset3 = np.copy(classTrainingSet)
classTrainingSubset3 = classTrainingSubset3[(classTrainingSubset3 != 0)]
dataTrainingSubset3 = np.copy(dataTrainingSet)
dataTrainingSubset3 = dataTrainingSubset3[(classTrainingSet != 0)]
classTrainingSubset3[(classTrainingSubset3 != 1)] = -1
perceptron3 = Perceptron(learningRate=0.35, iterationsToStop=850)
perceptron3.learn(dataTrainingSubset3, classTrainingSubset3)
multiPerceptron = MultiPerceptron(perceptron1, perceptron2, perceptron3)
plot_decision_regions(X=dataTestSet,
y=classTestSet,
classifier=multiPerceptron)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$x_2$')
plt.title('Perceptron')
plt.legend(loc='upper left')
plt.show()
# =============== Logistic regression ====================
classTrainingSubset1[(classTrainingSubset1 != 1)] = 0
logisticRegression1 = LogisticRegression(learningRate=0.05,
iterationsToStop=1000,
random_state=1)
logisticRegression1.learn(dataTrainingSubset1, classTrainingSubset1)
logisticRegression1.printProbability(dataTrainingSubset1)
classTrainingSubset2[(classTrainingSubset2 != 1)] = 0
logisticRegression2 = LogisticRegression(learningRate=0.05,
iterationsToStop=1000,
random_state=1)
logisticRegression2.learn(dataTrainingSubset2, classTrainingSubset2)
logisticRegression2.printProbability(dataTrainingSubset2)
classTrainingSubset3[(classTrainingSubset3 != 1)] = 0
logisticRegression3 = LogisticRegression(learningRate=0.15,
iterationsToStop=1500,
random_state=1)
logisticRegression3.learn(dataTrainingSubset3, classTrainingSubset3)
logisticRegression3.printProbability(dataTrainingSubset3)
multiLogisticRegression = MultiLogisticRegression(logisticRegression1,
logisticRegression2,
logisticRegression3)
plot_decision_regions(X=dataTestSet,
y=classTestSet,
classifier=multiLogisticRegression)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$x_2$')
plt.title('Logistic regression')
plt.legend(loc='lower right')
plt.show()
