# Main Function
if __name__ == '__main__':
# Get set of all movies
all_movies = list(load_all_movies("plot.list.gz"))
random.shuffle(all_movies)
years, plots, titles = [], [], []
for movie in all_movies:
years.append(movie['year'])
plots.append(movie['summary'])
titles.append(movie['title'])
min_year, max_year, bin_num = P2.year_stats(years)
# Get uniform subset of movies
years_train, plots_train, titles_train = [], [], []
years_test, plots_test, titles_test = [], [], []
year_count_train = [0]*bin_num
year_count_test = [0]*bin_num
train_sample_size = 5000
test_sample_size = 1000
# Create uniformly distributed training and test sets
for i, year in enumerate(years):
bin = int((year - min_year)/10)
if year_count_train[bin] < train_sample_size:
year_count_train[bin] += 1
years_train.append(year)
