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
def main():
txt = read_text_file('fish.txt')
text_list = list_convert(text)
my_histogram = histogram(text_list)
test_random(my_histogram)
test_random_weight(my_histogram)
def main():
'''reads in histogram and returns random word'''
txt = read_text_file('fish.txt')
text_list = list_convert(text)
my_histogram = histogram(text_list)
test_random(my_histogram)
test_random_weight(my_histogram)
def main():
'''This program reads in a histogram and returns a random word'''
#read in text file, store as list
txt = read_textfile("short_story.txt")
txt_list = convert_to_list(txt)
#create histogram based on list
my_histogram = histogram(txt_list)
test_randomness(my_histogram)
test_weighted_randomness(my_histogram)
def compare_structures_using_file(input_file):
histogram_linked_list = histogram(input_file)
frequency_linked = frequency
histogram_tuples = extra_sec_3.histogram_tuples(input_file)
frequency_tuples = extra_sec_3.frequency_tuples
histogram_dict = word_frequency.histogram(input_file)
frequency_dict = word_frequency.frequency
three_functions = [frequency_linked, frequency_tuples, frequency_dict]
three_hgrms = [histogram_linked_list, histogram_tuples, histogram_dict]
three_types = ["linked_list", "tuples", "dictionary"]
time_many_funcs(20000, 'loremipsum', three_hgrms, three_functions, three_types)
key]:
# return word within range
return key
# change range to decrease liklihood of same word repeating
word_range += histogram[key]
if __name__ == "__main__":
'''
Entry point of program, opens and closes words.txt file for program to use
Holds empty dictionary for program to use
'''
filename = 'words.txt'
word_histogram = {}
with open(filename, 'r') as f:
words = f.read().split(' ')
hist = histogram(words)
print(dict_sample(hist))
# '''
# calculate range
# add frequency to determine range
# see if word is in that range
# take random number
# find word at the index generated by random number
# return random word
# '''
def hash_table(source_text): # O(n) Complexity to create hash table
# dictionary structure -- hash table of words, frequencies
histogram = wf.histogram(source_text)
return histogram
def random_word(list):
return random.choice(list)
def randomnes_check(hist):
n_of_times = word_frequency.unique_words(hist)
list = stochastic_list(hist)
chosen = {}
for i in range(n_of_times * 100):
word = random_word(list)
if chosen.get(word, 0) != 0:
chosen[word] += 1
else:
chosen[word] = 1
return chosen
if __name__ == "__main__":
path = sys.argv[1]
hist = word_frequency.histogram(path)
check = randomnes_check(hist)
for key, value in check.items():
print(key + "\t" + str(value))
print(len(check.keys()))
import word_frequency as wf
import random
histogram = wf.histogram('Biggie_Smalls_Ready_To_Die.txt')
def stochastic_sampler(histogram):
totalLyricsList = []
for word, total_use in histogram.items():
# [brian] You could replace the below code with:
# totalLyricsList.extend([word]*total_use)
for x in range(total_use):
totalLyricsList.append(word)
return random.choice(totalLyricsList)
def percentage_checker(word, wordList):
word_count = 0
actual_frequency = 0.0
for w in wordList:
if w == word:
word_count += 1
actual_frequency = float(word_count) / len(wordList)
return actual_frequency
testList = []
for x in range(100):
if x < 13:
testList.append('a')
else:
testList.append('b')
def convert_histogram(): #text
# text_stuff = text.split()
# word_counts = word_frequency.histogram(text)
word_counts = word_frequency.histogram(word_frequency.get_words())
# print(word_counts)
return word_counts
test = 0
print("---------------RESULTS---------------")
# Prints all the frequencies
for word in keys:
print("Test Frequency: {} -> {} Actual Frequency: {} -> {}".format(word, freq[word]/trials, word, histogram[word]/total))
if __name__ == '__main__':
time_start = time.clock()
trials = 1000
source_text = "text_files/" + sys.argv[1]
histogram = word_frequency.histogram(source_text)
true_rand = gen_list(histogram)
random_trials(trials, histogram)
#print("Random word: {}".format(random_word(histogram, rand)))
print("---------------STATISTICS---------------")
time_end = time.clock()
time_diff = time_end - time_start
#print("Time taken: {} seconds".format(time_diff))
print("Time taken: %.3f seconds" % (time_diff))
print("Memory consumed: {} Bytes".format(sys.getsizeof(true_rand)))
print()
def main():
'''Return random word from histogram'''
txt = read_textfile("sample_text.txt")
txt_list = convert_to_list(txt)
histogram1 = histogram(txt_list)
return new_list
def random_word(list):
return random.choice(list)
def randomnes_check(hist):
n_of_times = word_frequency.unique_words(hist)
list = stochastic_list(hist)
chosen = {}
for i in range(n_of_times * 100):
word = random_word(list)
if chosen.get(word, 0) != 0:
chosen[word] += 1
else:
chosen[word] = 1
return chosen
if __name__ == "__main__":
path = sys.argv[1]
hist = word_frequency.histogram(path)
check = randomnes_check(hist)
for key, value in check.items():
print(key + "\t" + str(value))
print(len(check.keys()))
def random_word_colorbonus(input_histogram):
my_vals = list(input_histogram.values())
my_keys = list(input_histogram.keys())
enemies = 0
winning_idx = 0
for idx, each_val in enumerate(my_vals):
if each_val == 0:
continue
multiplier = 1
each_word = my_keys[idx]
if each_word in ['red', 'blue', 'yellow', 'green', 'orange', 'purple']:
multiplier = 2
friends_lots = each_val * multiplier
result = random.randint(1, (friends_lots + enemies))
if result <= friends_lots:
winning_idx = idx
enemies += friends_lots
return my_keys[winning_idx]
if __name__ == '__main__':
my_file = str(sys.argv[1])
my_histogram = word_frequency.histogram(my_file)
# random_word = histogram_word(my_histogram)
# print(random_word)
# random_word = random_word_frequency(my_histogram)
# print(random_word)
my_results = dan_hoang_helper.test_results_parameter(10000, random_word_colorbonus, my_histogram)
print(my_results)
'''Dictionary-type histogram for stochatic sampling
Stochastic sampling takes an element from a given collection at random
based on weight
Then returns a random word
'''
word_count = 0
word_count += sum(
histogram[word] for word in
histogram.keys()) # Calculates total word count in given text
word_range = 0
histogram = {}
random_integer = random.random()
for key in histogram.keys():
histogram[key] = histogram[key] / word_count
if random_integer > word_range and random_integer <= word_range + histogram[
key]:
return key
word_range += histogram[key]
if __name__ == '__main__':
filename = "words.txt"
word_histogram = {}
with open(filename, 'r') as f:
words = f.read().split(' ')
histogram = histogram(words)
print(sample_dict(histogram))