def __init__(self, msg, size, active_unlearner, label, distance_opt,
working_set=None, separate=True):
# Clustroid specs
self.clustroid = msg[1] # index of msg
self.label = label
self.common_features = []
self.separate = separate
self.size = size # arbitrarily set to 100
self.active_unlearner = active_unlearner # point to calling au instance
self.opt = distance_opt
# The data
self.working_set = working_set
self.train_y = self.working_set[0]
self.train_x = self.working_set[1]
self.pol_y = self.working_set[2]
self.pol_x = self.working_set[3]
self.data_y, self.data_x = h.compose_set(self.working_set)
time_1 = time.time()
self.vec_data_x = vectorize_set(self.data_x)
print 'Vectorizing data_x took: ', h.sec_to_english(time.time() - time_1)
self.ham = set()
self.spam = set()
if 'frequency' in self.opt:
self.cluster_word_frequency = msg[0] # actual vector representation of msg
self.added = [] # keeps track of order emails are added
self.dist_list = self.distance_array(self.separate) # returns list containing dist from all emails in phantom space to center clustroid
self.cluster_set = self.make_cluster() # adds closest emails to cluster
self.divide() # adds cluster emails to ham and spam
def update_dist_list(self, t=False):
"""Updates self.dist_list for the frequency method"""
if t:
time_1 = time.time()
indices = [train[1] for train in self.dist_list] # get array of indices
self.dist_list = [(distance(self.vec_data_x[i], self.cluster_word_frequency, self.opt), i) for i in indices]
self.dist_list.sort()
if t:
time_2 = time.time()
print 'update_dist_list took: ', h.sec_to_english(time_2 - time_1)
def update_dist_list(self, t=False):
"""Updates self.dist_list for the frequency method"""
if t:
time_1 = time.time()
indices = [train[1]
for train in self.dist_list] # get array of indices
self.dist_list = [(distance(self.vec_data_x[i],
self.cluster_word_frequency, self.opt), i)
for i in indices]
self.dist_list.sort()
if t:
time_2 = time.time()
print 'update_dist_list took: ', h.sec_to_english(time_2 - time_1)
def __init__(self,
msg,
size,
active_unlearner,
label,
distance_opt,
working_set=None,
separate=True):
# Clustroid specs
self.clustroid = msg[1] # index of msg
self.label = label
self.common_features = []
self.separate = separate
self.size = size # arbitrarily set to 100
self.active_unlearner = active_unlearner # point to calling au instance
self.opt = distance_opt
# The data
self.working_set = working_set
self.train_y = self.working_set[0]
self.train_x = self.working_set[1]
self.pol_y = self.working_set[2]
self.pol_x = self.working_set[3]
self.data_y, self.data_x = h.compose_set(self.working_set)
time_1 = time.time()
self.vec_data_x = vectorize_set(self.data_x)
print 'Vectorizing data_x took: ', h.sec_to_english(time.time() -
time_1)
self.ham = set()
self.spam = set()
if 'frequency' in self.opt:
self.cluster_word_frequency = msg[
0] # actual vector representation of msg
self.added = [] # keeps track of order emails are added
self.dist_list = self.distance_array(
self.separate
) # returns list containing dist from all emails in phantom space to center clustroid
self.cluster_set = self.make_cluster(
) # adds closest emails to cluster
self.divide() # adds cluster emails to ham and spam
