#!/usr/bin/python3
import re, sys, pickle, nltk, math, random
from nltk.corpus import stopwords

def clear_post(post):
    post = post.replace('\\n', ' ')
    post = post.lower()
    post = re.sub(r'(\(|)(http|https|www)[a-zA-Z0-9\.\:\/\_\=\&\;\?\+\-\%]+(\)|)', ' internetlink ', post)
    post = re.sub(r'[\.\,\/\~]+', ' ', post)
    post = re.sub(r'(&lt|&gt|\@[a-zA-Z0-9]+)','',post)
    post = re.sub(r'[\'\(\)\?\*\"\`\;0-9\[\]\:\%\|\–\”\!\=\^]+', '', post)
    post = re.sub(r'( \- |\-\-+)', ' ', post)
    post = re.sub(r' +', ' ', post)
    post = post.rstrip(' ')
    post = post.split(' ')
    stop_words = set(stopwords.words('english'))
    post_no_stop = [w for w in post if not w in stop_words]
    return post_no_stop

# czy słowa musza byc setem?
def create_vocabulary_and_documents(in_file, expected_file):
    vocabulary = set()
    posts = {}
    with open(in_file) as in_f, open(expected_file) as exp_f:
        for line, exp in zip(in_f, exp_f):
            text, timestap = line.rstrip('\n').split('\t')
            post = clear_post(text)
            posts[" ".join(post)] = int(exp)
            for word in post:
                vocabulary.add(word)
    return vocabulary, posts

def create_mappings(vocabulary):
    word_to_index_mapping = {}
    index_to_word_mapping = {}
    xi = 1
    for word in vocabulary:
        word_to_index_mapping[word] = xi
        index_to_word_mapping[xi] = word
        xi += 1
    return word_to_index_mapping, index_to_word_mapping

def main():
    if len(sys.argv) != 4:
        print("syntax ./train.py model expected_file in_file")
        return
    model = str(sys.argv[1])
    expected_file = str(sys.argv[2])
    in_file = str(sys.argv[3])
    vocabulary, posts = create_vocabulary_and_documents(in_file, expected_file)
    word_to_index_mapping, index_to_word_mapping = create_mappings(vocabulary)

    weights = []
    for xi in range(0, len(vocabulary) + 1):
        weights.append(random.uniform(-0.01,0.01))

    learning_rate = 0.000001
    loss_sum = 0.0
    loss_sum_counter = 0
    lowest_loss_sum_weights = []
    lowest_loss_sum = 10000.0

    print(f"len of vocabulary {len(vocabulary)}")
    # mozna ustawić na bardzo bardzo duzo
    while True: #loss_sum_counter != 10:
        try:
            d, y = random.choice(list(posts.items()))
            y_hat = weights[0]
            tokens = d.split(' ')
            for word in tokens:
                # mozna tez cos pomyslec z count aby lepiej dzialalo
                #print(f"{d.count(word)} : {word}")
                y_hat += weights[word_to_index_mapping[word]] * tokens.count(word)

            loss = (y_hat - y)**2
            loss_sum += loss
            delta = (y_hat - y) * learning_rate
            if loss_sum_counter % 100 == 0:
                print(f"{loss_sum /1000} : {loss_sum_counter} : {y_hat} : {delta}")
                loss_sum_counter = 0
                loss_sum = 0

            weights[0] -= delta
            for word in tokens:
                weights[word_to_index_mapping[word]] -= tokens.count(word) * delta

            if lowest_loss_sum > loss_sum and loss_sum != 0:
                print("it happened")
                lowest_loss_sum = loss_sum
                lowest_loss_sum_weights = weights

            loss_sum_counter +=1
        except KeyboardInterrupt:
            break
    print(lowest_loss_sum_weights)
main()