def main():
# trainFile = "../../data/spambase/missing_values/{}_percent_missing_train.txt"
# testFile = "../../data/spambase/missing_values/{}_percent_missing_test.txt"
for i in range(10):
print "Working on data with {} testing set".format(i)
# Step 1: loading data
print "Loading data..."
# trainX, trainY, testX, testY = util.loadData(trainFile.format(i*10), testFile.format(i*10))
# data = loadtxt('../../data/spambase/spambase.data', delimiter=',')
# trainX, trainY, testX, testY = util.initialData(data)
##### gammas
data = loadtxt('../../data/spambase/spambase.data', delimiter=',')
trainX, trainY, testX, testY = util.initialGammaData(data, i)
# # Step 2: training data
print "Training data..."
# model = NaiveBayes.train(trainX, trainY)
# model = NaiveBayes.train_missing_value(trainX, trainY)
##### gammas
model = NaiveBayes.train_gamma(trainX, trainY)
# # Step 3: predict test data
print "Predicting data..."
# predict_y = NaiveBayes.test(testX, model)
# predict_y = NaiveBayes.test_missing_value(testX, model)
##### gammas
train_y = NaiveBayes.test_gamma(trainX, model)
test_y = NaiveBayes.test_gamma(testX, model)
# # # Step 4: Calculate the Accuracy.
print "Accuracy..."
# accuracy = sum(predict_y == testY) / float(testY.size)
# print "Accuracy on testing : {:.2f}%".format(accuracy*100)
# print "....Done...."
##### gammas
accuracy = sum(train_y == trainY) / float(trainY.size)
print "Accuracy on training : {:.2f}%".format(accuracy*100)
accuracy = sum(test_y == testY) / float(testY.size)
print "Accuracy on test : {:.2f}%".format(accuracy*100)
