def main():
# Read the list of files from the command line arguments
# Make sure they are ordered in alphabetical order
files = sorted(sys.argv[1:])
file_input = fileinput.FileInput(files)
running_median = median.RunningMedian()
for line in file_input:
words = list(tokenize.word_tokenize(line))
running_median.add(len(words))
print ('%.1f' % running_median.get_median())
def main():
# Read the list of files from the command line arguments
# Make sure they are ordered in alphabetical order
files = sorted(sys.argv[1:])
file_input = fileinput.FileInput(files)
running_median = median.RunningMedian()
for line in file_input:
words = list(tokenize.word_tokenize(line))
running_median.add(len(words))
print('%.1f' % running_median.get_median())
def main():
# Read the list of files from the command line arguments
# Make sure they are ordered in alphabetical order
files = sorted(sys.argv[1:])
file_input = fileinput.FileInput(files)
# Count frequencies using a Counter object based on Python's dict
# For more memory efficient implementation we may use Trie data structure
word_counter = collections.Counter()
for line in file_input:
for word in tokenize.word_tokenize(line):
word_counter[word] += 1
for word in sorted(word_counter.keys()):
print('%s\t%s' % (word, word_counter[word]))
def main():
# Read the list of files from the command line arguments
# Make sure they are ordered in alphabetical order
files = sorted(sys.argv[1:])
file_input = fileinput.FileInput(files)
# Count frequencies using a Counter object based on Python's dict
# For more memory efficient implementation we may use Trie data structure
word_counter = collections.Counter()
for line in file_input:
for word in tokenize.word_tokenize(line):
word_counter[word] += 1
for word in sorted(word_counter.keys()):
print ('%s\t%s' % (word, word_counter[word]))
