import numpy as np
from sklearn import svm

f = open("bc.train.0")
data = np.loadtxt(f)
train = data[:,1:]
trainlabels = data[:,0]

f = open("bc.test.0")
data = np.loadtxt(f)
test = data[:,1:]
testlabels = data[:,0]

clf = svm.LinearSVC()
clf.fit(train,trainlabels)
prediction = clf.predict(test)
print(prediction)

err = 0
for i in range(0, len(prediction), 1):
    if(prediction[i] != testlabels[i]):
        err += 1
err = err/len(testlabels)
print(err)