def make_markov_model(order, data):
for i in range(0, len(data) - order):
window = tuple(data[i:i + order])
if window in models:
models[window].update([data[i + order]])
else:
models[window] = Dictogram([data[i + order]])
return models
def main():
txt = open(
"/Users/ownernopassword/Desktop/Tweet-Generator-Course/harry.txt")
cleanText = refinedCorpus.refineText(txt)
histogram = Dictogram(cleanText)
stochasticSampler = StochasticSample(histogram)
return stochasticSampler.generateSentence()
def make_markov_model(data):
for i in range(0, len(data) - 1):
if data[i] in models:
# Просто присоединяем к уже существующему распределению
models[data[i]].update([data[i + 1]])
else:
models[data[i]] = Dictogram([data[i + 1]])
return models
def test_items(self):
hist_dict = Dictogram(self.text_list)
assert len(hist_dict) == 5
assert hist_dict == self.hist_dict
self.assertItemsEqual(hist_dict, self.hist_dict)
hist_list = hist_dict.items()
assert len(hist_list) == 5
self.assertItemsEqual(hist_list, self.hist_list)
def make_markov_model(data):
markov_model = dict()
for i in range(0, len(data) - 1):
if data[i] in markov_model:
markov_model[data[i]].update([data[i + 1]])
else:
markov_model[data[i]] = Dictogram([data[i + 1]])
return markov_model
def test_count(self):
hist_dict = Dictogram(self.text_list)
assert hist_dict.types == 5
assert hist_dict.tokens == 8
assert hist_dict.count('one') == 1
assert hist_dict.count('two') == 1
assert hist_dict.count('red') == 1
assert hist_dict.count('blue') == 1
assert hist_dict.count('fish') == 4
assert hist_dict.count('food') == 0
def make_markov_model(data):
markov_model = dict()
for i in range(0, len(data) - 1):
if data[i] in markov_model:
# We have to just append to the existing histogram
markov_model[data[i]].update([data[i + 1]])
else:
markov_model[data[i]] = Dictogram([data[i + 1]])
#print(markov_model)
return markov_model
def make_higher_order_markov_model(order, data):
markov_model = dict()
for i in range(0, len(data) - order):
window = tuple(data[i:i + order])
if window in markov_model:
markov_model[window].update(data[i + order])
else:
markov_model[window] = Dictogram(data[i + order])
# print(markov_model)
return markov_model
def dict_o_listo(word_list):
dictionary = {}
for word in word_list:
if word not in dictionary:
dictionary[word] = Dictogram(iterable=None)
for i in range(0, len(word_list) - 2):
first = word_list[i]
second = word_list[i + 1]
dictionary[first].update([second])
dictionary[word_list[-1]] = {word_list[0], 1}
return dictionary
def test_update(self):
hist_dict = Dictogram(self.text_list)
hist_dict.update(['two', 'blue', 'fish', 'food'])
assert hist_dict.types == 6
assert hist_dict.tokens == 12
assert hist_dict.count('one') == 1
assert hist_dict.count('two') == 2
assert hist_dict.count('red') == 1
assert hist_dict.count('blue') == 2
assert hist_dict.count('fish') == 5
assert hist_dict.count('food') == 1
def make_higher_order_markov_model(order, data):
markov_model = dict()
for i in range(0, len(data) - order):
# Create the window
window = tuple(data[i:i + order])
# Add to the dictionary
if window in markov_model:
# We have to just append to the existing Dictogram
markov_model[window].update([data[i + order]])
else:
markov_model[window] = Dictogram([data[i + order]])
return markov_model
def update(self, iterable):
"""Update the data within the model.
Args:
iterable: Any iterable type (preferably a word list)
"""
len_of_iterable = len(iterable) # O(1)
for i in range(0, len_of_iterable - 1): # O(n) n = length of iterable
if iterable[i] not in self:
self[iterable[i]] = Dictogram()
self[iterable[i]].update([iterable[i + 1]]) # O(1) updating dict
def update(self, iterable):
"""Update the data within the model.
Args:
iterable: Any iterable type (preferably a word list)
"""
for i in range(0, len(iterable) - 2):
key = tuple(iterable[i:i + 2])
if key not in self:
self[key] = Dictogram()
self[key].update([iterable[i + 2]])
def markov_chain(data):
"""markov model for 1st order"""
#Dictionary that stores windows as the key in the key-value pair and then the value
#for each key is a dictogram
markov_chain = dict()
# Looping through the ammount of indexs in the list
for index in range(0, len(data) - 1):
# If index word of list exists in dictionary then update the current index
# Store a histogram of words for each window
if data[index] in markov_chain:
markov_chain[data[index]].update([data[index + 1]])
else:
markov_chain[data[index]] = Dictogram([data[index + 1]])
return markov_chain
def nth_order_markov_model(order, data):
markov_model = dict()
for i in range(0, len(data) - order):
# Creatjng the window
window = tuple(data[i:i + order])
# If windiw is already in the markov model
if window in markov_model:
# Update the value
markov_model[window].update([data[i + order]])
else:
# Add the value
markov_model[window] = Dictogram([data[i + order]])
return markov_model
def make_markov_model(data):
markov_model = dict()
'''
Markov Model Structure
A dictionary that stores windows as the key in the key-value pair and then the value for each key is a dictogram.
A histogram of words for each window so I know what the next state can be based on a current state
'''
for i in range(0, len(data) - 1):
if data[i] in markov_model:
# We have to just append to the existing histogram
markov_model[data[i]].update([data[i + 1]])
else:
markov_model[data[i]] = Dictogram([data[i + 1]])
return markov_model
def make_higher_order_markov_model(order, data):
markov_model = dict()
'''
Nth Order Markov Model Structure
Very similar to the first order Markov Model, but in this case we store a tuple as the key in the key-value pair in the dictionary.
We do this because a tuple is a great way to represent a single list.
And we use a tuple instead of a list because a key in a dictionary should not change and tuples are immutable.
'''
for i in range(0, len(data) - order):
# Create the window
window = tuple(data[i:i + order])
# Add to the dictionary
if window in markov_model:
# We have to just append to the existing Dictogram
markov_model[window].update([data[i + order]])
else:
markov_model[window] = Dictogram([data[i + order]])
return markov_model
def make_markov_model(self, order, data):
"""
Создание модели
:param order:
:type order: int
:param data: Корпус
:type data: list
:return: Марковская модель
:rtype: dict
"""
markov_model = dict()
print(len(data))
for i in range(0, len(data) - order):
window = tuple(data[i:i + order])
if i % 100000 == 0:
print(i / 100000)
if window in markov_model:
markov_model[window].update([data[i + order]])
else:
markov_model[window] = Dictogram([data[i + order]])
return markov_model
def get_random_sentence(word_list):
histogram = Dictogram(word_list)
return " ".join(
[get_weighted_sample(histogram) for _ in range(random.randint(5, 15))])
def test_contains(self):
hist_dict = Dictogram(self.text_list)
for word in self.text_list:
assert word in hist_dict
for word in ['fishy', 'food']:
assert word not in hist_dict