def __init__(self, list_words=None):
self.corpus = list_words
self.words = {} # [String: Stochastic]
# iterate corpus
for index, current_word in enumerate(self.corpus):
adjacent_word = None # None is equal to stop
def get_adjacent_word():
try:
# TODO: end of sentence
return self.corpus[index + 1]
except IndexError:
return None
# TODO: identify the beginning of a sentence
adjacent_word = get_adjacent_word()
stochastic_for_current_word = None
if current_word in self.words:
stochastic_for_current_word = self.words[current_word]
else:
stochastic_for_current_word = Stochastic()
self.words[current_word] = stochastic_for_current_word
stochastic_for_current_word.add_count(adjacent_word)
def __init__(self, words, order):
self.corpus = re.findall(r"[\w']+|[.,!?;]", words)
self.words = HashTable(32)
self.markov_order = order
# iterate corpus
previous_element = None
previous_element_list = None
if len(self.corpus) < self.markov_order + 1 and self.markov_order > 0:
print("invalid corpus")
else:
max_iteration = len(self.corpus) - self.markov_order + 1
for index in range(0, max_iteration):
adjacent_index = index + self.markov_order
current_element_list = self.corpus[index:adjacent_index]
current_element = ' '.join(current_element_list)
# check: at beginning of sentence, create/append current_word to self.words[None]
if index == 0 or previous_element_list[0] in OrderedMarkovChain.end_punctuation:
stochastic_for_beginning_of_sentence = None
try:
stochastic_for_beginning_of_sentence = self.words[None]
except KeyError:
# stochastic for None is not made, so make one
stochastic_for_beginning_of_sentence = Stochastic()
self.words[None] = stochastic_for_beginning_of_sentence
stochastic_for_beginning_of_sentence.add_count(current_element)
# grab adjacent word, can be None to represent end of the sentence
def get_adjacent_word():
try:
adjacent_word = self.corpus[adjacent_index]
temp_list = current_element_list[1:] # current_element without the first index
temp_element = ' '.join(temp_list)
return adjacent_word
except IndexError:
# end of list_words, thus end of sentence
return None
adjacent_element = get_adjacent_word()
# print(current_element, "->{}".format(adjacent_element))
# if current_word is an ending puncuation, then adjacent_word is None
if current_element_list[-1] in OrderedMarkovChain.end_punctuation:
adjacent_element = None
# continue
stochastic_for_current_word = None
if current_element in self.words:
stochastic_for_current_word = self.words[current_element]
else:
stochastic_for_current_word = Stochastic()
self.words[current_element] = stochastic_for_current_word
stochastic_for_current_word.add_count(adjacent_element)
previous_element = current_element
previous_element_list = current_element_list
def init_nature(self, naturelabel):
if naturelabel == 'stochastic':
self.nature = Stochastic(self.matchID)
elif naturelabel == 'deterministic':
self.nature = Deterministic(self.matchID)
else:
self.nature = Adversarial(self.matchID)
def __init__(self, N, eta, nature):
self.N = N
self.weights = [1] * self.N
self.eta = eta
self.T = 100
self.matchID = 0
self.h = [0] * self.N
self.naturelabel = nature
self.nature = Stochastic(self.matchID)
self.learner_loss = [0] * self.T
self.expert_loss = [[0 for i in range(self.N)] for j in range(self.T)]
self.sum_learner = 0
self.sum_experts = [0] * self.N
self.init_nature(self.naturelabel)
from minimax import Minimax
from alpha_beta import AlphaBeta
from stochastic import Stochastic
if __name__ == '__main__':
start = time.time()
# Initialize game board
gameboard = Board()
# Initialize red agent
idx = input('Please choose a type for RED:\n'
'1. Stochastic\n'
'2. Alpha Beta\n')
if idx == '1':
RED = Stochastic('red')
elif idx == '2':
RED = AlphaBeta('red')
# Initialize blue agent
idx = input('Please choose a type for BLUE:\n'
'1. Stochastic\n'
'2. Alpha Beta\n')
if idx == '1':
BLUE = Stochastic('blue')
elif idx == '2':
BLUE = AlphaBeta('blue')
# Mark order or two agents
_red_ = 1
_blue_ = 0
def stochastic_test():
test_stochastic = Stochastic('words_sample.txt')
sentence = test_stochastic.create_sentence(10000)
words_list = sentence.split(" ")
sentence_histogram = test_stochastic.create_histogram(words_list)
print(sentence_histogram)