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trainClassifiers.py
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trainClassifiers.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 3 14:44:49 2019
@author: janz
"""
from sklearn import svm
from sklearn import tree
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
def trainClassifiers (X, y):
classifiers = [];
for i in range(len(X)):
#Train SVM Clasifier
model = svm.SVC(gamma = 0.001, decision_function_shape='ovo')
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#Train Decision Tree Classifier
model = tree.DecisionTreeClassifier()
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#Naive Bayes Classifier
model = GaussianNB()
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#Multi layer perceptron Classifier
model = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#KNN Classifier
model = KNeighborsClassifier(n_neighbors=3)
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#Linear Discriminant Analysis Classifier
model = LinearDiscriminantAnalysis()
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
#Logistic Regression Classifier
model = LogisticRegression()
classifiers.append(model.fit(X[i].astype(float), y[i].astype(float)))
return classifiers