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import numpy as np
import random
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import KFold
from sklearn.externals import joblib
import matplotlib.pyplot as plt
scaler = StandardScaler()
def gen_classifier(X, y):
#scaler.fit_transform(X)
clf = KNeighborsClassifier(n_neighbors=1, weights='distance')
clf.fit(X, y)
#joblib.dump(clf, filename)
return clf
def main(datas, labels):
global scaler
# datas, labels = loadFile('data/data3.txt')
scaler.fit_transform(datas)
for i in range(len(datas)):
#datas[i] = datas[i][1000:-300]
for j in range(1050, 1700, 50):
datas.append(np.absolute(np.fft.fft(datas[i][j:j+50])))
labels.append(labels[i])
datas[i] = np.absolute(np.fft.fft(datas[i][1000:1050]))
#datas[i] = np.absolute(np.fft.fft( datas[i] ))
datas = np.array(datas)
labels = np.array(labels)
kf = KFold(len(labels), n_folds=5)
for train, test in kf:
neigh = KNeighborsClassifier(n_neighbors=1, weights='distance')
neigh.fit(datas[train], labels[train])
#print neigh.predict(datas[test])
#print labels[test]
print neigh.score(datas[test], labels[test])
if __name__ == '__main__':
main()
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