import DPlib from sklearn import neighbors, metrics, svm client = 'AlienVault' data_path = '/home/hugo/DATA/' data_file = 'BRENNT_' + client + '_Data.csv' test_file = 'BRENNT_' + client + '_Test.csv' aux_path = client + '/' cat_list = [2,5,6,23,24,25,26,27] stats_file = client + '.stats' name_list = client + '.names' DPlib.getLabels(data_path, data_file, cat_list, aux_path, stats_file) DATA, LABEL = DPlib.getAllModData(data_path, data_file, aux_path, name_list, stats_file) tDATA, tLABEL = DPlib.getAllModData(data_path, test_file, aux_path, name_list, stats_file) clfkNNu = neighbors.KNeighborsClassifier(3, 'uniform', p=5) clfkNNd = neighbors.KNeighborsClassifier(3, 'distance', p=5) clfkNNc = neighbors.NearestCentroid() clfkNNu.fit(DATA, LABEL) clfkNNd.fit(DATA, LABEL) clfkNNc.fit(DATA, LABEL) pLABELkNNu = clfkNNu.predict(tDATA) pLABELkNNd = clfkNNd.predict(tDATA) pLABELkNNc = clfkNNc.predict(tDATA) V = [pLABELkNNu, pLABELkNNd, pLABELkNNc]
import DPlib
from sklearn import neighbors, metrics, svm
client = 'AlienVault'
data_path = '/home/hugo/DATA/'
data_file = 'BRENNT_' + client + '_Data.csv'
test_file = 'BRENNT_' + client + '_Test.csv'
aux_path = client + '/'
cat_list = [2, 5, 6, 23, 24, 25, 26, 27]
stats_file = client + '.stats'
name_list = client + '.names'
DPlib.getLabels(data_path, data_file, cat_list, aux_path, stats_file)
DATA, LABEL = DPlib.getAllModData(data_path, data_file, aux_path, name_list,
stats_file)
tDATA, tLABEL = DPlib.getAllModData(data_path, test_file, aux_path, name_list,
stats_file)
clfkNNu = neighbors.KNeighborsClassifier(3, 'uniform', p=5)
clfkNNd = neighbors.KNeighborsClassifier(3, 'distance', p=5)
clfkNNc = neighbors.NearestCentroid()
clfkNNu.fit(DATA, LABEL)
clfkNNd.fit(DATA, LABEL)
clfkNNc.fit(DATA, LABEL)
pLABELkNNu = clfkNNu.predict(tDATA)
pLABELkNNd = clfkNNd.predict(tDATA)
pLABELkNNc = clfkNNc.predict(tDATA)
