new_seq_item.append(item)
expanded.append(new_seq_item)
if (len(value["sequence"]) == 0):
new_seq_item = []
for item in value["itemset"]:
new_seq_item.append(item)
expanded.append(new_seq_item)
num_users = float(self.attributes["user"]["domain"]) * float(
value["support"])
for i in range(int(num_users)):
selected = random.randrange(
1, int(self.attributes["user"]["domain"]))
selected_user = self.attributes["user"]["represent"] + str(
selected)
if selected_user not in self.exp_sequences:
self.exp_sequences[selected_user] = {}
self.exp_sequences[selected_user][key] = expanded
if __name__ == '__main__':
attribute = Attribute()
attribute.load_dist_json()
attribute.load_attr_cvs()
pattern = Pattern(attribute.sem_to_rep)
pattern.load()
expanded_seq = Sequencepat(attribute.distribution, pattern.patterns)
expanded_seq.expand()
print expanded_seq.exp_sequences
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from attribute import Attribute
import random
# normal function, parameter mu and sigma is the fit parameter
def normal(k, mu, sigma):
return 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (k - mu)**2 / (2 * sigma**2) )
attribute = Attribute()
attribute.load_dist_json()
for key, value in attribute.distribution.iteritems():
name = key
type = value["type"]
domain = int(value["domain"])
# only draw normal distribution
if (type != "normal" and type != "random"):
continue;
mu = int(value["mu"])
sigma = int(value["sigma"])
#s = np.random.normal(mu, sigma, 1000) # 1000 random number following normal distribution
s = []
eventfile = "output/events.txt"
with open(eventfile, 'rb') as file:
