df = Parser.load_parsed_data_from_file(filename + ".parsed_encoded_data")
training_set_fraction = 0.7
training_data = df.loc[:training_set_fraction * float(df.shape[0])]
###### ###### ###### ###### ###### ###### START TRAINING ###### ###### ###### ###### ###### ######
import time
stime = time.time()
### One Class Support Vector Machine ###
import SVM
from sklearn.externals import joblib
OCSVM = SVM.trainOCSVM(training_data, tol=0.001, cache_size=2000, shrinking=False, nu=0.05, verbose=True)
joblib.dump(OCSVM, filename=filename + ".fitted_SVM_model")
clf = joblib.load(filename + ".fitted_SVM_model")
########################################
### Autoassociative NN ###
import Autoencoder
import tensorflow as tf
sess = tf.Session()
x = tf.placeholder("float", [None, df.shape[1]])
autoencoder = Autoencoder.create(x, [48, 24, 12])
EWMACost = 0
Autoencoder.train_AE(df=training_data, sess=sess, x=x,
denoising=False, verbose=False, autoencoder=autoencoder)
