From 2f46e045b149397d79875378330cb21559e3ee0e Mon Sep 17 00:00:00 2001
From: s152483 <lukszy1@st.amu.edu.pl>
Date: Fri, 10 Apr 2020 22:16:59 +0000
Subject: [PATCH] =?UTF-8?q?Usu=C5=84=20'program=5Flr.py'?=
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---
 program_lr.py | 98 ---------------------------------------------------
 1 file changed, 98 deletions(-)
 delete mode 100644 program_lr.py

diff --git a/program_lr.py b/program_lr.py
deleted file mode 100644
index 7c938ee..0000000
--- a/program_lr.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/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)
-