def make_the_words():
# build Histogram
# my_file = open("./words.txt", "r")
# # absolute path -> ./file.ext ## more fuctional for live deploy
# lines = my_file.readlines()
filename = "transient.txt"
lines = open(filename, "r").readlines()
transient_txt_words = [] # word_list
for line in lines:
wordslist = line.split(' ')
for word in wordslist:
word = word.strip(' . , ;" \n _ ?')
transient_txt_words.append(word)
my_histogram = histogram(transient_txt_words)
# put together words into a sentence
sentence = ''
num_words = 10
''' # comment out to impliment markov
for i in range(num_words):
word = sample_by_frequency(my_histogram)
sentence = sentence + " " + word '''
# uncomment to impliment markov
markovchain = MarkovChain(transient_txt_words)
sentence = markovchain.walk(num_words)
return sentence
class TweetGenerator(object):
def __init__(self, riddle_sentences, ngram_size):
self.riddle_sentences = set(riddle_sentences)
self.chain = MarkovChain(ngram_size=ngram_size)
for sentence in self.riddle_sentences:
self.chain.train_sentence(sentence)
def get_unique_sentence(self):
while True:
s = self.chain.generate_sentence()
if s.lower() not in self.riddle_sentences:
break
return s
def generate_tweet(self):
tweet = ''
while True:
next_sentence = self.get_unique_sentence()
if len(next_sentence) > 125:
continue
if len(tweet) > 0 and len(tweet) + len(next_sentence) > 125:
break
tweet = tweet + next_sentence + ' '
return tweet[:-1].strip()
def markov(num=0):
list_of_words = words_list()
markovChain = MarkovChain(list_of_words)
sentence = markovChain.walk(10)
return sentence
class TemplateBank:
def __init__(self, title_bank):
self.markov = MarkovChain(3)
for item in title_bank.values():
self.markov.add(item['title'].replace('—', '-'))
def _random_template(self):
title = self.markov.generate()
replacements = {}
tokens = []
doc = nlp(title)
i = 0
for token in doc:
# Consider named entities as single token.
if token.ent_type_ in ('PERSON', 'FAC', 'GPE', 'LOC'):
if token.ent_iob == 1:
tokens[-2] += tokens[-1] + token.text
tokens[-1] = token.whitespace_
else:
tokens.append(token.text)
tokens.append(token.whitespace_)
replacements[i] = '[[PERSON]]' if token.ent_type_ == 'PERSON' else '[[LOC]]'
i += 2
continue
tokens.append(token.text)
tokens.append(token.whitespace_)
if token.tag_ in ("NN", "NNP"):
replacements[i] = "[[NOUN]]"
elif token.tag_ in ("NNS", "NNPS"):
replacements[i] = "[[NOUNS]]"
elif token.pos_ == "ADJ":
replacements[i] = "[[ADJ]]"
i += 2
if len(replacements) < 2:
return None
logger.debug('generated title: ' + ''.join(tokens))
# Create a template by replacing two random tokens with POS tags
for i, replacement in random.sample(replacements.items(), 2):
tokens[i] = replacement
logger.debug('generated template: ' + ''.join(tokens))
return tokens
def random_template(self):
"""Get random template from the bank."""
for i in range(0, 25):
template = self._random_template()
if template is not None:
return template
raise RecursionError("Title generation was unable to find fitting template.")
def __init__(self, filename):
self.filename = filename
self.ticks = None
self.tempo = get_tempo(filename)
self.chain = MarkovChain()
self.parse()
def __init__(self, sources, order):
'''
sources: list of text sources
order: Markov Chain order. The higher the order,
the more the model "remembers" its history.
'''
self.order = order
self.sentences = self.parse_sources(sources)
self.markov_chain = MarkovChain(order, self.sentences)
def generate_text(input, output):
if os.path.isfile(args.o):
os.remove(args.o)
num_words = args.n
contents = read_file(input)
wordlist = contents.split(' ')
markov = MarkovChain(wordlist)
with open(output, 'a+') as f:
f.write(markov.generate_text(num_words))
def run(self):
print ("Loading input and generating...")
fileload, resolution, format = loadMidi.load('midi/bach_simple.mid')
stringNotes = convert.listToString(fileload)
mc = MarkovChain(1)
mc.add_string(stringNotes)
markovNotes = ' '.join(mc.generate_text(50))
writeMidi.writeList(convert.stringToList(markovNotes), resolution, format)
print ('Process complete, output is in ./rebuilt.mid')
def generate_words():
'''my_histogram = (lines)
sentence = ""
num_words = 10
for i in range (num_words):
word = weighted_sample(my_histogram)
sentence += " " + word
return sentence'''
markovchain = MarkovChain(
["one", "fish", "two", "fish", "red", "fish", "blue", "fish"])
return markovchain.walk(10)
def sample(self, outf, nr_frames=1e6, n=3):
'''Sample using an n-gram into the given file.'''
chain = MarkovChain(n)
chain.add_sequence(self.buf)
gen = chain.walk()
out = wave.open(outf, 'wb')
out.setparams(self.params)
out.setnframes(nr_frames)
chunk = nr_frames / 100
for k in xrange(int(nr_frames)):
if k % chunk == 0:
print k / chunk, "%"
out.writeframes(self.repr_to_pcm(next(gen)))
def gen_word():
my_file = open("./words.txt", "r")
lines = my_file.readlines()
my_histogram = histogram(lines)
sentence = ""
num_words = 10
# for i in range(num_words):
# word = sample(my_histogram)
# sentence += " " + word
markovchain = MarkovChain(lines)
sentence = markovchain.walk(num_words)
return sentence
def random_markov_phrase():
txt = load_quotes()
# Create an instance of the markov chain. By default, it uses MarkovChain.py's location to
# store and load its database files to. You probably want to give it another location, like so:
mc = MarkovChain(PATH + '/quotes_data/markov_scores')
# To generate the markov chain's language model, in case it's not present
mc.generateDatabase(textSample=txt)
# To let the markov chain generate some text, execute
sentence = mc.generateString()
generated_sentence = sentence[0].upper() + sentence[1:]
if generated_sentence[-1] not in ['?', '!']:
generated_sentence += '.'
return generated_sentence
def handle_data():
word1 = request.form["1"]
word2 = request.form["2"]
m = MarkovChain("Database/w3_.db", n = 3)
# pprint(m.query(*(options.predict[0])))
ngram = [word1, word2]
res = m.query(*ngram).keys() # Actual Result
print(res)
# word = list(res)[random.randint(0, 2)]
# print(word, end = ' ')
response = jsonify(dict(zip(["predict1", "predict2", "predict3"], list(res)[:3])))
response.headers.add('Access-Control-Allow-Origin', '*')
return response
def __init__(self, riddle_sentences, ngram_size):
self.riddle_sentences = set(riddle_sentences)
self.chain = MarkovChain(ngram_size=ngram_size)
for sentence in self.riddle_sentences:
self.chain.train_sentence(sentence)
def create_sentence(word_num):
source_text = "nietsche.txt"
with open(source_text, "r") as file:
og_text = file.read()
word_list = og_text.split()
for index, word in enumerate(word_list):
word_list[index] = word.rstrip()
chain = MarkovChain(word_list)
chain.print_chain()
sentence_words = []
sentence = chain.walk(word_num)
return sentence
def run_dir(base_path, authors_path):
text = ''
path = base_path + authors_path + '/'
files = [name for name in os.listdir(path) if '.txt' in name]
for f in files:
with open(path + f, 'r') as f:
text += f.read()
# special treatment for wittgenstein formulas
text = re.sub(r'“(.+?)”', '', text)
markov = MarkovChain(text=text)
bipolar_discourse = markov.generate(100)
print repr(bipolar_discourse)
save(authors_path + '.txt', bipolar_discourse)
def generate_words():
my_file = open("./words.txt", "r")
lines = my_file.readlines()
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
sentence = ""
num_words = 10
# for i in range(num_words):
# word = sample_by_frequency(my_histogram)
# sentence += " " + word
markovchain = MarkovChain(word_list)
sentence = markovchain.walk(num_words)
return sentence
def generate_words():
words_list = []
with open('./EAP.text') as f:
lines = f.readlines()
for line in lines:
for word in line.split():
words_list.append(word)
#lines = Dictogram(['one', 'fish', 'two', 'fish', 'red', 'fish', 'blue', 'fish'])
markovchain = MarkovChain(words_list)
'''sentence = ""
num_words = 20
for i in range(num_words):
word = lines.sample()
sentence += " " + word
return sentence'''
sentence = markovchain.walk(24)
return render_template('index.html', sentence=sentence)
def hello():
# hs = histogram("words.txt")
# samp = sample(hs)
my_file = open("./words.txt", "r")
lines = my_file.readlines()
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
print(word_list)
markovchain = MarkovChain(word_list)
# return samp
# num_words = 10
return (markovchain.walk(20))
def generate_words():
#Build a histogram
my_file = histogram("./text.txt")
lines = my_file.readlines()
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
sentence = ""
num_words = 10
# for i in range(num_words):
# #sample/frequency goes here
# word = sample(my_histogram)
# sentence += " " + word
# return sentence
markovchain = MarkovChain(word_list)
sentence = markovchain.walk(num_words)
return sentence
def generate_words():
#build a histogram
# my_file = open("despacito.txt","r")
lines = "one fish two fish red fish blue fish"
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
word = sample(my_histogram)
#return word
sentence = ""
num_words = 10
# for i in range(num_words):
# word = weighted_sample(my_histogram)
# sentence += " " + word
markovChain = MarkovChain(word_list)
sentence = markovChain.walk(num_words)
print("sentence", sentence)
return sentence
def generate_words():
#build a histogram
my_file = open("words.txt","r")
lines = my_file.readlines()
my_histogram = Histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
word = weighted_sample(my_histogram)
#return word
sentence = ""
num_words = 10
# for i in range(num_words):
# word = weighted_sample(my_histogram)
# sentence += " " + word
markovChain = MarkovChain(word_list)
sentence = markovChain.walk(num_words)
print("sentence", sentence)
return sentence
def test_fits_sum_to_one(self):
mc = MarkovChain(self.corpus, 1)
mc.fit()
p = mc.matrix_list[0].p
self.assertEqual(sum(p[mc.convert_word_to_number('alpha')]),1)
self.assertEqual(sum(p[mc.convert_word_to_number('beta')]),1)
self.assertEqual(sum(p[mc.convert_word_to_number('gamma')]),1)
self.assertEqual(sum(p[mc.convert_word_to_number('delta')]),1)
def main():
# read file, return list of tweets
list_of_tweets = read_file()
# returns 2 lists
# first is list of each word used in all of my tweets
# second is list of unigrams and frequencies i.e. {(word, frequency)}
lengths, list_of_words, word_freq = get_counts(list_of_tweets)
# print(word_freq)
# get top users I have replied to
top_users = get_top_users(word_freq)
# return bigrams where {(word1, word2): frequency}
bigrams = get_bigrams(list_of_tweets)
# print(bigrams)
# print(bigrams.keys())
# create model for calculating the probability of a given sentence
model = bigram_model(word_freq, bigrams)
# print(model)
#create Markov chain
m = MarkovChain()
m.learn(bigrams.keys())
# print(m.memory)
# generate length probability dictionary {length of sentence: frequency of sentences of that length}
length_prob = length_probability(lengths)
# output generated sentences, must have probability > 0.5
generated_tweets = generate_sentences(length_prob, m, model, word_freq, 50)
for tweet in generated_tweets:
print(tweet)
def main():
print("Loading data...")
text = load_data("data/philosophy").lower()
text = preprocess(text)
print("Building Markov model...")
mc = MarkovChain()
mc.train_ngram(1, text)
mc.train_ngram(2, text)
mc.train_ngram(3, text)
bot = Bot(mc)
bot.run()
def render_page():
my_list = read_file('tintern_abbey.txt')
chain = MarkovChain(my_list)
num_words = int(10) - 1
my_sentence = chain.walk(num_words)
my_list2 = read_file("the_rime.txt")
chain2 = MarkovChain(my_list2)
num_words2 = int(10) - 1
my_sentence2 = chain2.walk(num_words2)
return render_template('index.html',
sentence=my_sentence,
sentence2=my_sentence2)
def __init__(self, bot_token, master=None):
self.bot_token = bot_token
self.session_id = None
self.acl = {master.upper(): 1000} if master is not None else {}
self._default_handlers()
self.mc = MarkovChain()
self.nn_temp = 0.7
self.seq_num = None
self.hb_every = -1
self.hb_task = None
self.ident = ('', '', '') #user,disc,nick
self.ident_id = ''
self.guilds = {}
self.cmd_re = re.compile('\\.(\\S+)')
self.approver = srl_approve.SRLApprove()
class MarkovTests(unittest.TestCase):
# setup chain object
source_file = "the_black_cat.txt"
with open(source_file, encoding="utf-8") as f:
text = f.read()
m = MarkovChain(text)
def test_start(self):
"""Check that the correct start is used"""
start = "From"
predicted = self.m.predict(20, start)
self.assertTrue(predicted.startswith(start))
pass
def test_length(self):
"""Check that the chain outputs the correct number of words"""
n = 100
predicted = self.m.predict(100)
tokens = findall(d_re_pattern, predicted, d_re_flags)
expected = n
actual = len(tokens)
self.assertEqual(actual, expected)
def main():
# Get our training data, Brown corpus
b_sents = brown.sents()
b_pos = nltk.pos_tag_sents(b_sents)
# Filter to only verb phrases
b_verbs = verb_filter(b_pos)
# Fit our MarkovChain
b_mc = MarkovChain(order=1)
b_mc.fit(b_verbs)
b_mc.normalize_transition_matrix()
examples = load_examples('examples.json')
for ex in examples:
compare_sentences(mc=b_mc, s1=ex['good'], s2=ex['bad'])
def render_page():
my_list = read_file('plato.txt')
chain = MarkovChain(my_list)
num_words = int(10) - 1
my_sentence = chain.walk(num_words)
my_sentence_2 = chain.walk(num_words)
my_sentence_3 = chain.walk(num_words)
my_sentence_4 = chain.walk(num_words)
my_sentence_5 = chain.walk(num_words)
return render_template('index.html', sentence=my_sentence,
sentence2=my_sentence_2,
sentence3=my_sentence_3,
sentence4=my_sentence_4,
sentence5=my_sentence_5)
def test_finds_highest_order_solution_first(self):
mc = MarkovChain(self.corpus, 3)
mc.fit()
nextw = mc.next_word(['alpha','beta','gamma'])
self.assertEqual(nextw, 'delta')
def test_probs_stop_across_lines(self):
mc = MarkovChain(self.corpus, 3)
mc.fit()
nextw = mc.next_word(['alpha','epsilon'])
self.assertEqual(nextw, '\n') # Nothing follows epsilon
# drop punctuation (optional)
#text = text.replace(";", "").replace(".", "").replace("!", "").replace(",", "")
text = text.replace('"', "")
return text
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('length', type=int, help='Maximum number of words to generate.')
parser.add_argument('--lines', type=int, help='Number of lines to generate (default=1).', default=1)
args = parser.parse_args()
chain = MarkovChain()
if not os.path.isfile("table.json"):
f = open("source.txt")
lines = [christmas_parser(f.read())]
f.close()
chain.create_table(lines)
chain.save_table("table.json")
else:
f = open("table.json")
raw_table = f.read()
f.close()
chain.parse_table(raw_table)
##########
# BASED ON https://github.com/thomasboyt/python-markov/blob/master/examples/AChristmasMarkov/christmas_markov.py
##########
import os, json
from markov import MarkovChain
def sanitize(text):
replaced_chars = {
"\n": " ",
"\r": "",
";": "",
".": "",
"!": "",
",": "",
"?": "",
'"': ""
}
for c in replaced_chars:
text = text.replace(c, replaced_chars[c])
return text
chain = MarkovChain()
f = open("markov_source.txt")
source = sanitize(f.read())
f.close()
chain.create_table(source)
chain.save_table("markov_source.json")
def __init__(self, title_bank):
self.markov = MarkovChain(3)
for item in title_bank.values():
self.markov.add(item['title'].replace('—', '-'))
print "example:"
print "python algorithmic.music.py 1 data/lilypond-template.ly data/entchen.ly 300"
sys.exit(1)
order = int(sys.argv[1])
template = sys.argv[2]
filename = sys.argv[3]
length = int(sys.argv[4])
lilypond = "lilypond"
opencmd = "xdg-open"
if sys.platform.startswith("darwin"):
lilypond = "/Applications/LilyPond.app/Contents/Resources/bin/lilypond"
opencmd = "open"
m = MarkovChain(order)
m.observe_file(filename, True)
start = m.get_random_prestate()
result = m.random_walk(length, start)
def tactify(tuplelist, resolution, tact):
# tuplelist: eingabeliste
# resolution: kleinste note
# tact: wieviele resolution-noten einen takt geben (ignored)
# immer 4/4 Takt
takt = resolution # in 16teln
count = 0
output = []
order = 1 length = 50 text = """ g8 e8 e4 f8 d8 d4 c8 d8 e8 f8 g8 g8 g4 g8 e8 e4 f8 d8 d4 c8 e8 g8 g8 c4 r4 g8 e8 e4 f8 d8 d4 c8 d8 e8 f8 g8 g8 g4 g8 e8 e4 f8 d8 d4 c8 e8 g8 g8 c4 r4 """ m = MarkovChain(order) print "observing %s" % text spacer() m.observe_string(text, True) print "TODO make logging verbose for observe()..." spacer() m.print_transitions() spacer() m.print_matrix() start = m.get_random_prestate() spacer() result = m.random_walk_string(length, start)
def __init__(self, nick, log=False):
GenericChatBot.__init__(self, nick, log)
MucRoomHandler.__init__(self, nick)
self.responder = MarkovChain(PidginLogs('~/.purple/logs/jabber/'))
class MidiParser:
def __init__(self, filename):
self.filename = filename
self.ticks = None
self.tempo = get_tempo(filename)
self.chain = MarkovChain()
self.parse()
def parse(self):
"""
reads midi and makes markov insertions
"""
midi = mido.MidiFile(self.filename)
self.ticks = midi.ticks_per_beat
prev = []
curr = []
for track in midi.tracks:
for msg in track:
if msg.type is 'note_on':
if msg.time is 0:
curr.append(msg.note)
else:
self.make_transition(prev, curr, msg.time)
prev = curr
curr = []
def make_transition(self, prev, curr, time):
"""
inserts all state transition possibilities into markov
"""
print(prev, curr, time)
for i in prev:
for j in curr:
self.chain.add(i, j, self.millisecond(time))
def millisecond(self, ticks):
"""
tick to millisecond converter
"""
try:
ms = ((ticks / self.ticks) * self.tempo) / 1000
return int(ms - (ms % 250) + 250)
except TypeError:
raise TypeError(
"Could not read a tempo and ticks_per_beat from midi")
def get_markov(self):
return self.chain
#!/usr/bin/env python import sys from markov import MarkovChain if len(sys.argv) != 4: print "usage:" print "python lorem.ipsum.py $ORDER $DATAFILE $OUTLENGTH" print "example:" print "python lorem.ipsum.py 3 data.txt 300" sys.exit(1) order = int(sys.argv[1]) filename = sys.argv[2] length = int(sys.argv[3]) m = MarkovChain(order) m.observe_file(filename, True) start = m.get_random_prestate() result = m.random_walk_string(length, start) print result
def randomized_markov(word_list, num):
markov_chain = MarkovChain(word_list)
return f'''
from markov import MarkovChain
dan = MarkovChain('dan.txt')
# baldur = MarkovChain('baldur.txt')
print(dan.get_new_sentence(12).upper())
# print(baldur.get_new_sentence(15))
def get(self):
#-------------MAIN SCRIPT-----------------------
#app_status = state.load()
client = memcache.Client()
print client
#logName = app_status.get('log_name', 'logs/app_log.log')
#logger = self.set_up_logging(logName)
#Create and train markov chain
chainTrainingFile = './data/fortunes.txt'
fortuneChain = MarkovChain(1)
fortuneChain.learn(chainTrainingFile)
#lastChainUpdate = datetime.datetime.now()
#updateFrequency = 2*60*60 #in seconds
#logger.info('Markov Chain Initial Trained on '+ chainTrainingFile + ' at ' + str(lastChainUpdate))
#Authenticate twitter account
#Note: Only do this when ready to go live!
auth = OAuthHandler(secrets['CONSUMER_KEY'], secrets['CONSUMER_SECRET'])
auth.set_access_token(secrets['ACCESS_TOKEN'], secrets['ACCESS_TOKEN_SECRET'])
api = API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
#logger.info('Authentication Successful')
#mostRecentReply = app_status.get('recent_reply', 0)
mostRecentReply = client.gets('recent_reply')
if mostRecentReply is None:
mostRecentReply = 0
memcache.add(key='recent_reply', value=0)
print "MOST RECENT", mostRecentReply
#waitTime = 1 * 60 # In Seconds
#logger.info('Wait time set to ' + str(waitTime) + ' seconds')
#Run forever
#while True:
#If we're on startup, fastforward to current tweet
if mostRecentReply is 0:
#Grab all mentions
mentions = api.mentions_timeline()
#Do not reply to these tweets (as they are old)
if len(mentions) > 0:
mostRecentReply = mentions[0].id
#logger.info('Fast-forwarding most recent reply to ' + str(mostRecentReply))
#Check if we need to retrain the chain (do once per hour)
#if (datetime.datetime.now() - lastChainUpdate).total_seconds() > updateFrequency:
#Retrain chain
# fortuneChain.learn(chainTrainingFile)
# lastChainUpdate = datetime.datetime.now()
# logger.info('Markov chain retrained at ' + str(lastChainUpdate))
print mostRecentReply
#Get tweets directed at account since last check
mentions = api.mentions_timeline(since_id = mostRecentReply)
mentions.reverse()
print len(mentions)
for mention in mentions:
#print mention.text
#print mention.author.screen_name
#logger.info(str(mention))
#Generate a fortune
fortune = self.generateFortune(fortuneChain)
#logger.info(str(fortune))
#Send that user a reply with their fortune
statusRet = api.update_status(status='@' + mention.author.screen_name +
' ' + fortune, in_reply_to_status_id = mention.id)
#logger.info('Replied to ' + mention.author.screen_name)
#Update most recent reply if there's something newer
if len(mentions) > 0:
mostRecentReply = mentions[-1].id
#logger.info('Updating most recent reply to ' + str(mostRecentReply))
#Set the value of the current tweet in the memcache.
tries = 40
print "SETTING REPLY", mostRecentReply
print "TRIES", tries
while tries > 0:
tries -= 1
reply = client.gets('recent_reply')
print reply
if client.cas('recent_reply', mostRecentReply):
break
print tries
#app_status['recent_reply'] = mostRecentReply
#Wait for a period before looping again
#time.sleep(waitTime)
#state.save(app_status)
#logging.shutdown()
self.response.write(MAIN_PAGE_HTML)
def walk_corpus(fname):
with open(fname, 'r') as f:
words = f.read().split()
chain = MarkovChain(5)
chain.add_sequence(words)
return chain.walk()
def test_second_best_solution_next(self):
mc = MarkovChain(self.corpus, 3)
mc.fit()
# TODO: Make sure it tests for beta delta together first?
nextw = mc.next_word(['beta','delta'])
self.assertEqual(nextw, 'alpha')
def walk_corpus():
chain = MarkovChain(mc_nodes)
chain.add_sequence(corpus)
return chain.walk()
if "reply_to" in args.config:
reply_to = args.config["reply_to"]
subject = args.payload["subject"][0]
if subject.strip() == "":
subject = "[no subject]"
text_body = args.payload["text"][0]
text_in = text_body.split("\n")[0]
sender = args.payload["from"][0]
print "Sender: %s" % (sender,)
print "Got text: %s" % (text_body,)
print "Using text: %s" % (text_in,)
chain = MarkovChain()
f = open("markov_source.json")
raw_table = f.read()
f.close()
chain.parse_table(raw_table)
text_words = [word.strip() for word in text_in.split(" ")]
text_in = chain.generate_chain(length=len(text_words) + 1, words=text_words)
print "New text: %s" % (text_in,)
from_param = from_address
if from_name is not None:
from_param = (from_address, from_name)
message = sendgrid.Message(from_param, subject, text_in)
#!/usr/bin/env python import sys from markov import MarkovChain if len(sys.argv) != 2: print "usage:" print "python brownian.py $OUTLENGTH" print "example:" print "python brownian.py 300" sys.exit(1) m = MarkovChain(1) min, max = -999, +999 for i in range(min, max+1): m.observe( (str(i),) , str(i-1) ) m.observe( (str(i),) , str(i+1) ) m.observe( (str(min-1),) , str(min-1) ) m.observe( (str(max+1),) , str(max+1) ) start = ["0"] #result = m.random_walk_string(int(sys.argv[1]), start) #print result from pylab import * result = m.random_walk(int(sys.argv[1]), start) result = [int(x) for x in result] plot(result)
