def _dev_int_context(self, ec):
segment_choices = []
for segment in self.segments:
results = map(lambda x: reduce(operator.mul, \
map(lambda i: self.app_pool[x][i][ec[i]], xrange(self.feature_num)), 1), \
segment)
score = reduce(operator.mul, results, 1)
segment_choices.append((segment, score))
app_choices = []
for app in self.app_pool.keys():
score = reduce(operator.mul, map(lambda i: self.app_pool[app][i][ec[i]], xrange(self.feature_num)), 1)
app_choices.append((app, score))
int_context = []
segment = nonrandom.choice(segment_choices)
# turning_point = random.choice(xrange(1, self.l)) if random.random() > 0.3 else -1
for i in xrange(self.l):
# if i == turning_point:
if random.random() > 0.9:
segment = nonrandom.choice(filter(lambda x: x!= segment, segment_choices))
s = list(segment)
for j in xrange(self.s):
if random.random() < self.c:
s[j] = nonrandom.choice(app_choices) if random.random() > 0.7 else None
s = filter(lambda x: x, s)
if not s:
s = list(segment)
int_context.extend(s)
return int_context
def _generate_segments(self, app_usage_distrs):
# TODO: decide parameters
session_len = 5
I = 3
l = 50
apps = app_usage_distrs.keys()
segments = []
size = nonrandom.choice(
map(lambda x: (x, I**x * math.exp(-I) / math.factorial(x)),
xrange(2, 6)))
segment = random.sample(apps, size)
segments.append(segment)
for i in xrange(l):
prev_segment = segments[-1]
# size = nonrandom.choice(map(lambda x: (x, I**x*math.exp(-I)/math.factorial(x)), xrange(2, 6)))
size = 4
# random.choice([2, 3])
# TODO: decide how many items are preserved
fragment_size = int(0.6 * len(prev_segment))
if fragment_size > 0:
# segment = [prev_segment[i] for i in \
# sorted(random.sample(xrange(len(prev_segment)), fragment_size))]
segment = random.sample(prev_segment, fragment_size)
if len(segment) < size:
segment.extend(
random.sample(filter(lambda x: x not in segment, apps),
size - len(segment)))
# random.shuffle(segment)
segment = random.sample(apps, size)
segments.append(segment)
return segments
def _generate_segments(self, app_usage_distrs):
# TODO: decide parameters
session_len = 5
I = 3
l = 50
apps = app_usage_distrs.keys()
segments = []
size = nonrandom.choice(map(lambda x: (x, I**x*math.exp(-I)/math.factorial(x)), xrange(2, 6)))
segment = random.sample(apps, size)
segments.append(segment)
for i in xrange(l):
prev_segment = segments[-1]
# size = nonrandom.choice(map(lambda x: (x, I**x*math.exp(-I)/math.factorial(x)), xrange(2, 6)))
size = 4
# random.choice([2, 3])
# TODO: decide how many items are preserved
fragment_size = int(0.6*len(prev_segment))
if fragment_size > 0:
# segment = [prev_segment[i] for i in \
# sorted(random.sample(xrange(len(prev_segment)), fragment_size))]
segment = random.sample(prev_segment, fragment_size)
if len(segment) < size:
segment.extend(random.sample(filter(lambda x: x not in segment, apps), size - len(segment)))
# random.shuffle(segment)
segment = random.sample(apps, size)
segments.append(segment)
return segments
def _fill_segment(self, int_context, initiator, segment_len, candidates,
test):
segment = []
if len(int_context) > 0:
predecessor = int_context[-1]
else:
predecessor = ''
choices = list(candidates)
initiator = nonrandom.choice(choices)
if initiator == predecessor and predecessor != '' and initiator == test:
choices = filter(lambda x: x[0] != predecessor, choices)
initiator = nonrandom.choice(choices)
"""
choices = map(lambda x: (x, self.launching_contrib[x][initiator]), self.launching_contrib)
choices = filter(lambda x: x[0] in map(lambda y: y[0], candidates), choices)
if sum(v for k, v in choices) == 0:
choices = list(candidates)
"""
# """
p = initiator
while len(segment) < segment_len - 1:
# choices = list(candidates)
choices = map(lambda x: (x, self.launching_contrib[x][p]),
self.launching_contrib)
# if sum(v for k, v in choices) == 0:
# app = random.choice(self.launching_contrib.keys())
app = nonrandom.choice(choices)
while app in segment or app == initiator:
choices = filter(lambda x: x[0] not in [initiator] + segment,
choices)
# choices = map(lambda x: (x, self.launching_contrib[x][p]), self.launching_contrib)
app = nonrandom.choice(choices)
segment.append(app)
# p = app
# """
random.shuffle(segment)
segment.insert(0, initiator)
# random.shuffle(segment)
return segment
def _fill_segment(self, int_context, initiator, segment_len, candidates, test):
segment = []
if len(int_context) > 0:
predecessor = int_context[-1]
else:
predecessor = ''
choices = list(candidates)
initiator = nonrandom.choice(choices)
if initiator == predecessor and predecessor != '' and initiator == test:
choices = filter(lambda x: x[0] != predecessor, choices)
initiator = nonrandom.choice(choices)
"""
choices = map(lambda x: (x, self.launching_contrib[x][initiator]), self.launching_contrib)
choices = filter(lambda x: x[0] in map(lambda y: y[0], candidates), choices)
if sum(v for k, v in choices) == 0:
choices = list(candidates)
"""
# """
p = initiator
while len(segment) < segment_len - 1:
# choices = list(candidates)
choices = map(lambda x: (x, self.launching_contrib[x][p]), self.launching_contrib)
# if sum(v for k, v in choices) == 0:
# app = random.choice(self.launching_contrib.keys())
app = nonrandom.choice(choices)
while app in segment or app == initiator:
choices = filter(lambda x: x[0] not in [initiator] + segment, choices)
# choices = map(lambda x: (x, self.launching_contrib[x][p]), self.launching_contrib)
app = nonrandom.choice(choices)
segment.append(app)
# p = app
# """
random.shuffle(segment)
segment.insert(0, initiator)
# random.shuffle(segment)
return segment
def _develop_int_context(self, ec, session_len, app_usage_distrs):
choices = []
for segment in self.segments:
results = map(lambda x: reduce(operator.mul, \
map(lambda i: app_usage_distrs[x][i][ec[i]], xrange(self.feature_num)), 1), \
segment)
score = reduce(operator.mul, results, 1)
choices.append((segment, score))
# for app in app_usage_distrs:
# score = reduce(operator.mul, map(lambda i: app_usage_distrs[app][i][ec[i]], xrange(self.feature_num)), 1)
# choices.append(([app], score))
# int_context = []
# while len(int_context) < session_len:
# size = nonrandom.choice(map(lambda x: (x, 3**x*math.exp(-3)/math.factorial(x)), \
# xrange(2, session_len - len(int_context) + 1)))
# if len(int_context) > 0:
# int_context.extend(nonrandom.choice(filter(lambda x: len(x[0]) == size and x[0][0] != int_context[-1], choices)))
# else:
# int_context.extend(nonrandom.choice(filter(lambda x: len(x[0]) == size, choices)))
# return int_context
c = 0.3
int_context = []
segment = nonrandom.choice(filter(lambda x: len(x[0]) == 3, choices))
for i in xrange(3):
s = filter(lambda x: random.random() > c, segment)
int_context.extend(s)
if not int_context:
int_context = segment
return int_context
# return nonrandom.choice(choices)+nonrandom.choice(choices)+nonrandom.choice(choices)
return nonrandom.choice(filter(lambda x: len(x[0]) == 4, choices))
# nonrandom.choice(filter(lambda x: len(x[0]) == 4, choices))
return random.choice(self.segments)+random.choice(self.segments)+random.choice(self.segments)
"""
def _develop_int_context(self, ec, session_len, app_usage_distrs):
choices = []
for segment in self.segments:
results = map(lambda x: reduce(operator.mul, \
map(lambda i: app_usage_distrs[x][i][ec[i]], xrange(self.feature_num)), 1), \
segment)
score = reduce(operator.mul, results, 1)
choices.append((segment, score))
# for app in app_usage_distrs:
# score = reduce(operator.mul, map(lambda i: app_usage_distrs[app][i][ec[i]], xrange(self.feature_num)), 1)
# choices.append(([app], score))
# int_context = []
# while len(int_context) < session_len:
# size = nonrandom.choice(map(lambda x: (x, 3**x*math.exp(-3)/math.factorial(x)), \
# xrange(2, session_len - len(int_context) + 1)))
# if len(int_context) > 0:
# int_context.extend(nonrandom.choice(filter(lambda x: len(x[0]) == size and x[0][0] != int_context[-1], choices)))
# else:
# int_context.extend(nonrandom.choice(filter(lambda x: len(x[0]) == size, choices)))
# return int_context
c = 0.3
int_context = []
segment = nonrandom.choice(filter(lambda x: len(x[0]) == 3, choices))
for i in xrange(3):
s = filter(lambda x: random.random() > c, segment)
int_context.extend(s)
if not int_context:
int_context = segment
return int_context
# return nonrandom.choice(choices)+nonrandom.choice(choices)+nonrandom.choice(choices)
return nonrandom.choice(filter(lambda x: len(x[0]) == 4, choices))
# nonrandom.choice(filter(lambda x: len(x[0]) == 4, choices))
return random.choice(self.segments) + random.choice(
self.segments) + random.choice(self.segments)
"""
def _dev_int_context(self, ec):
segment_choices = []
for segment in self.segments:
results = map(lambda x: reduce(operator.mul, \
map(lambda i: self.app_pool[x][i][ec[i]], xrange(self.feature_num)), 1), \
segment)
score = reduce(operator.mul, results, 1)
segment_choices.append((segment, score))
app_choices = []
for app in self.app_pool.keys():
score = reduce(
operator.mul,
map(lambda i: self.app_pool[app][i][ec[i]],
xrange(self.feature_num)), 1)
app_choices.append((app, score))
int_context = []
segment = nonrandom.choice(segment_choices)
# turning_point = random.choice(xrange(1, self.l)) if random.random() > 0.3 else -1
for i in xrange(self.l):
# if i == turning_point:
if random.random() > 0.9:
segment = nonrandom.choice(
filter(lambda x: x != segment, segment_choices))
s = list(segment)
for j in xrange(self.s):
if random.random() < self.c:
s[j] = nonrandom.choice(
app_choices) if random.random() > 0.7 else None
s = filter(lambda x: x, s)
if not s:
s = list(segment)
int_context.extend(s)
return int_context
