import sys
sys.path.append('..')

import corpus as cp
import distributed_representation as dr

import utility

#data download
dl = utility.data_loader()
dl.dataload()

corpus = cp.Corpus(data = 'data/simple-examples/data/ptb.train.txt', mode = "l", 
                max_vocabulary_size = 5000, max_line = 10, 
               minimum_freq = 5)

window_size = 1
embedding_dims = 100
batch_size = 128

import time
start = time.time()

dr_sgns = dr.DistributedRepresentation(corpus, embedding_dims, window_size, batch_size, model_type = "skip-gram", 
                                ns = 0, trace = True)
dr_sgns.train(num_epochs = 11, learning_rate = 0.01)

process_time = time.time() - start
print(process_time)
import sys
sys.path.append('..')

import corpus as cp
import distributed_representation as dr

import utility

#data download
dl = utility.data_loader()
dl.dataload()

corpus = cp.Corpus(data = 'data/simple-examples/data/ptb.train.txt', mode = "l", 
                max_vocabulary_size = 5000, max_line = 10, 
               minimum_freq = 5)

window_size = 1
embedding_dims = 100
batch_size = 128

import time
start = time.time()

dr_sgns = dr.DistributedRepresentation(corpus, embedding_dims, window_size, batch_size, model_type = "skip-gram", 
                                ns = 0, trace = True)
dr_sgns.train(num_epochs = 11, learning_rate = 0.01)

process_time = time.time() - start
print(process_time)
#data download