Maria@1: # -*- coding: utf-8 -*-
Maria@1: """
Maria@1: Created on Mon Oct  5 14:59:39 2015
Maria@1: 
Maria@1: @author: mariapanteli
Maria@1: """
Maria@1: """Selection of classifiers"""
Maria@1: 
Maria@1: import numpy
Maria@1: 
Maria@1: from sklearn.neighbors import KNeighborsClassifier
Maria@1: from sklearn.naive_bayes import GaussianNB
Maria@1: from sklearn.cross_validation import cross_val_predict
Maria@1: from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
Maria@1: from sklearn import svm
Maria@1: from sklearn import metrics
Maria@1: 
Maria@1: def classifyKNN(X, Y, nn=1, kfold=5):
Maria@1:     model = KNeighborsClassifier(n_neighbors=nn, metric='euclidean')      
Maria@1:     Y_pred = cross_val_predict(model, X, Y, cv=kfold)
Maria@1:     accuracies = []
Maria@1:     for i in range(len(Y_pred)):
Maria@1:         accuracies.append(metrics.accuracy_score([Y[i]], [Y_pred[i]]))
Maria@1:     return numpy.asarray(accuracies)
Maria@1: 
Maria@1: def classifySVM(X, Y, kfold=5):
Maria@1:     model = svm.SVC(kernel='linear')
Maria@1:     Y_pred = cross_val_predict(model, X, Y, cv=kfold)
Maria@1:     accuracies = []
Maria@1:     for i in range(len(Y_pred)):
Maria@1:         accuracies.append(metrics.accuracy_score([Y[i]], [Y_pred[i]]))
Maria@1:     return numpy.asarray(accuracies)
Maria@1: 
Maria@1: def classifyLDA(X, Y, kfold=5):
Maria@1:     model = LDA(n_components=20)
Maria@1:     Y_pred = cross_val_predict(model, X, Y, cv=kfold)
Maria@1:     accuracies = []
Maria@1:     for i in range(len(Y_pred)):
Maria@1:         accuracies.append(metrics.accuracy_score([Y[i]], [Y_pred[i]]))
Maria@1:     return numpy.asarray(accuracies)
Maria@1: 
Maria@1: def classifyNB(X, Y, kfold=5):
Maria@1:     model = GaussianNB()
Maria@1:     Y_pred = cross_val_predict(model, X, Y, cv=kfold)
Maria@1:     accuracies = []
Maria@1:     for i in range(len(Y_pred)):
Maria@1:         accuracies.append(metrics.accuracy_score([Y[i]], [Y_pred[i]]))
Maria@1:     return numpy.asarray(accuracies)
Maria@1: