import os
import numpy as np
from sklearn.svm import LinearSVC
from DataReader import DataReader
data_reader = DataReader("./data/us_trial.text", "./data/us_trial.labels")
X, Y = data_reader.get_features()
len_X = len(X)
len_X = (int)(len_X / 10)
len_train = (int)(0.8 * len_X)
print(len_train)
trainX, trainY = X[0:len_train], Y[0:len_train]
trainX, trainY = np.asarray(trainX), np.asarray(trainY)
testX, testY = X[len_train:len_X], Y[len_train:len_X]
testX, testY = np.asarray(testX), np.asarray(testY)
print(len(testX))
if __name__ == '__main__':
clf = LinearSVC(random_state=0, tol=0.001)
clf.fit(trainX, trainY)
counter, len_testX = 0, len(testX)
for i in range(len_testX):
output = clf.predict(np.asarray([testX[i]]))
if output == testY[i]:
counter += 1
accuracy = counter / len_testX
accuracy = accuracy * 100
print("Accuracy: ", accuracy)
def populate_features(tweet_file, labels_file):
data_reader = DataReader(tweet_file, labels_file)
return data_reader.get_features()
