def strat_markov_chain(play):
"""You can copy and modify this strategy."""
ret_val = result.Result()
for act in play.gen_acts():
for scene in act.gen_scenes():
ret_val.set_act_scene(act, scene)
mc = MarkovChain.MarkovChain()
actors_seen = []
while True:
# Ask the markov chain for the most likely guess
if len(actors_seen):
most_probable = mc.most_probable(actors_seen[-1])
else:
most_probable = tuple()
# If multiple values choose one randomly
if len(most_probable) > 1:
my_choice = random.choice(most_probable)
elif len(most_probable) == 1:
my_choice = most_probable[0]
else:
# Not enough information
my_choice = ''
actual_actor = ret_val.guess(my_choice)
if actual_actor is None:
break
else:
# Record the transition
if len(actors_seen):
mc.add(actors_seen[-1], actual_actor)
actors_seen.append(actual_actor)
return ret_val
def MarkovTrumpReactiveTweetGen(inp):
ret = ""
chain = MarkovChain.MarkovChain(csvParser.ImportTrumpData())
if (inp == ''):
ret = MarkovTweetGen(chain)
else:
ret = MarkovReactiveTweetGen(chain, inp)
if (ret.endswith(',')):
ret = ret[0:-1]
if (not (ret.endswith(".")) and not (ret.endswith("!"))
and not (ret.endswith("?"))):
ret += '.'
return ret
def __init__(self):
self.memory = []
self.markov_graph = MarkovChain.MarkovChain()
self.active_input_pattern_index = -1
self.prev_active_input_pattern_index = -1
self.temporal_groups = []
self.active_temporal_group_index = -1
# In the format (x1, y1, x2, y2) where (x1, y1) is the top left of the rectangle
# and (x2, y2) is the bottom right of the rectangle. All rectangles are in the 4th
# quadrant of the Euclidean plane where the negative y axis is positive.
self.receptive_field_dimensions = None
def chainBuilder(self):
parsedEpisodes = getEpisodes(self.episodes)
for e in parsedEpisodes:
castList = e.getCast()
for c in castList:
if c not in self.suppCharacters and c not in self.mainCharacters:
self.suppCharacters.add(c)
directChain = MarkovChain(2) #Change direction order here
stageDirections = getAllStageDirs(self.episodes)
for direction in stageDirections:
directChain.addData(direction.getChainableSource())
self.chains["STAGEDIR"] = directChain
for i in self.mainCharacters:
self.characterChain(i)
for m in self.suppCharacters:
self.characterChain(m)
from MarkovChain import *
m = MarkovChain()
m.transition_table = [[0.9, 0.05, 0.05], [0.5, 0.1, 0.4], [0, 1, 0]]
m.nodes = ["alive", "zombie", "dead"]
for i in m.generator():
print(i)
num_nodes = len(G)
num_items = len(G)
k = 50
item_distributions = ['uniform', 'direct', 'inverse', 'ego']
for item_distribution in item_distributions:
if item_distribution == 'ego':
iterations = 10
else:
iterations = 1
for iteration in xrange(iterations):
print "Evaluating item distribution {}".format(item_distribution)
__builtin__.mc = MarkovChain(num_nodes=num_nodes,
num_items=num_items,
item_distribution=item_distribution,
G=G)
print "Starting evaluation of methods"
methods = [
random_nodes, highest_item_nodes,
highest_closeness_centrality_nodes,
highest_in_degree_centrality_nodes,
highest_in_probability_nodes,
highest_betweenness_centrality_nodes, smart_greedy_parallel
]
for method in methods:
print "Evaluating method {}".format(method.func_name)
get_objective_evolution(method, k, iteration)
def setUp(self):
self._c = MarkovChain.MarkovChain()
def characterChain(self, character):
tempChain = MarkovChain(2) #Change line order here
characterLines = getAllCharLines(self.episodes, character)
for line in characterLines:
tempChain.addData(line.getChainableSource())
self.chains[character] = tempChain
word1 = inp
retSen = word1.capitalize()
else:
if (inp.endswith('s')):
word1 = "are"
else:
word1 = "is"
retSen = inp.capitalize() + ' ' + word1
for it in range(length - 1):
word2 = random.choice(MC[word1])
# Check for custom end of tweet token
if (word2 == "@b@"):
break
word1 = word2
retSen += " " + word1
if (not (retSen.endswith(".")) and not (retSen.endswith("!"))
and not (retSen.endswith("?"))):
retSen += '.'
print(retSen)
return retSen
if (__name__ == "__main__"):
#TweetCreator(input1)
chain = MarkovChain.MarkovChain(csvParser.ImportTrumpData())
for it in range(10):
print("Test #" + str(it) + ':')
MarkovTrumpReactiveTweetGen("")
#! /c/Anaconda/python
from MarkovChain import *
mc = MarkovChain("./markov")
# To generate the markov chain's language model, in case it's not present
mc.generateDatabase("ThisThis string of Text. is This is a string of Text. a string of Text.It won't is a string of Text. This is a string of Text. It won't generate string of Text. an interesting string of Text.database though.")
# To let the markov chain generate some text, execute
print mc.generateString()
if not channel:
channel = input("Channel: ")
if not dict_name:
print("No dict name specified. Setting it to channel name.")
dict_name = channel
username = ""
auth_token = ""
with open("creds.txt", 'r') as creds_file:
username = creds_file.readline()
auth_token = creds_file.readline()
irc_bot = TwitchChat.TwitchChat(username, auth_token, channel)
markov_chain = MarkovChain.MarkovChain(save_dir, dict_name)
while True:
msg = irc_bot.next_msg()
if msg:
markov_chain.take_message(msg)
if markov_chain.iterations() % 5 == 1:
print("Channel:", markov_chain.dict_name)
print("Iterations:", markov_chain.iterations())
print("Time elapsed:", int(markov_chain.time_elapsed()), "seconds")
print("Message:", markov_chain.make_message())
print()
markov_chain.save_progress()
