#!/usr/bin/env python3
import pickle
import fileinput
import random
import math
import random
import re
from _collections import defaultdict

def define_vocabulary(file_to_learn_new_words):
     word_counts = {'count': defaultdict(int)}
     with open(file_to_learn_new_words, encoding='utf-8') as in_file:
         for line in in_file:
             text, timestamp = line.rstrip('\n').split('\t')
             tokens = text.lower().split(' ')
             for token in tokens:
                 word_counts['count'][token] += 1
     in_file.close()
     return word_counts

def tokenize_list(string_input):
    words=[]
    string=string_input.replace('\\n',' ')
    text=re.sub(r'\w+:\/{2}[\d\w-]+(\.[\d\w-]+)*(?:(?:\/[^\s/]*))*', '', string)
    string=''
    for word in text:
        string+=word
    words=re.split(';+|,+|\*+|\n+| +|\_+|\%+|\t+|\[+|\]+|\.+|\(+|\)+|\++|\\+|\/+|[0-9]+|\#+|\'+|\"+|\-+|\=+|\&+|\:+|\?+|\!+|\^+|\·+',string)
    regex=re.compile(r'http|^[a-zA-Z]$|org')
    filtered_values=[
        word
        for word in words if not regex.match(word)
    ]
    filtered_values[:] = (
        value.lower()
        for value in filtered_values if len(value)!=0
    )
    return filtered_values

def train(vocabulary,input_train,expected_train):
    learning_rate=0.001
    learning_precision=0.00001
    words_vocabulary={}
    with open(input_train,encoding='utf-8') as input_file, open(expected_train,encoding='utf-8') as expected_file:
        for line, exp in zip(input_file,expected_file):
            words_vocabulary[line]=int(exp)
    weights={}
    weight={}
    delta=1
    iteration=0
    loss_sum=0.0
    error=10.0
    max_iteration=len(vocabulary)
    for i in vocabulary['count'].keys():
        weights[i]=random.uniform(-0.01,0.01)
    while delta>learning_precision and iteration<max_iteration:
        d,y = random.choice(list(words_vocabulary.items()))
        y_hat=0
        tokens=tokenize_list(d)
        for token in tokens:
            if token in vocabulary['count'].keys():
                y_hat += weights[token] * tokens.count(token)
        delta=(y_hat-y) * learning_rate
        for word in tokens:
            if word in words_vocabulary:
                weights[word] -= (tokens.count(word)) * delta
        loss = (y_hat - y)**2.0
        loss_sum += loss
        if iteration%1000 == 0:
            if (error>(loss_sum/1000)):
                weight=weights
                error=loss_sum/1000
            loss_sum=0.0
        iteration += 1
    input_file.close()
    expected_file.close()
    return weight, vocabulary

def prediction(input,output,weights,vocabulary):
    with open(input,encoding='utf-8') as input_file, open(output,'w+',encoding='utf-8') as output:
        for line in input_file:
            y_hat=0
            tokens=tokenize_list(line)
            for token in tokens:
                if token in vocabulary['count'].keys():
                    y_hat += weights[token] * (token.count(token))
            if y_hat>0.0:
                output.write('1\n')
            else:
                output.write('0\n')
    output.close()
    input_file.close()

vocabulary=define_vocabulary('train/in.tsv');
weights, words = train(vocabulary,'train/in.tsv','train/expected.tsv')
prediction('dev-0/in.tsv','dev-0/out.tsv',weights,words)
prediction('test-A/in.tsv','test-A/out.tsv',weights,words)