130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
from collections import defaultdict
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import math
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import pickle
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import re
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from pip._vendor.msgpack.fallback import xrange
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import random
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vocabulary=[]
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file_to_save=open("test.tsv","w",encoding='utf-8')
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def define_vocabulary(file_to_learn_new_words):
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word_counts={'count': defaultdict(int)}
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with open(file_to_learn_new_words,encoding='utf-8') as in_file:
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for line in in_file:
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text, timestamp = line.rstrip('\n').split('\t')
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tokens = text.lower().split(' ')
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for token in tokens:
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word_counts['count'][token]+=1
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return word_counts
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def read_input(file_path):
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read_word_counts={'count': defaultdict(int)}
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with open(file_path, encoding='utf-8') as in_file:
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for line in in_file:
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text, timestamp = line.rstrip('\n').split('\t')
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tokens = text.lower().split(' ')
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for token in tokens:
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read_word_counts['count'][token]+=1
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return read_word_counts
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def training(vocabulary,read_input,expected):
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learning_rate=0.00001
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learning_precision=0.0000001
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weights=[]
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iteration=0
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loss_sum=0.0
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ix=1
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readed_words_values = []
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for word in read_input['count']:
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if word not in vocabulary['count']:
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read_input['count'][word]=0
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readed_words_values.append(read_input['count'][word])
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for ix in range(0,len(vocabulary['count'])+1):
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weights.append(random.uniform(-0.001,0.001))
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#max_iteration=len(vocabulary['count'])+1
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max_iteration=1000
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delta=1
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while (delta>learning_precision and iteration<max_iteration):
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d,y=random.choice(list(read_input['count'].items())) #d-word, y-value of
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y_hat=weights[0]
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i=0
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for word_d in d:
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if word_d in vocabulary['count'].keys():
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#print(vocabulary['count'][d])
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y_hat+=weights[vocabulary['count'][word_d]]*readed_words_values[i]
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delta=abs(y_hat-y)*learning_rate
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weights[0]=weights[0]-delta
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i+=i
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i=0
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for word_w in d:
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if word_w in vocabulary['count'].keys():
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weights[vocabulary['count'][word_w]]-=readed_words_values[i]*delta
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i+=1
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#print(weights)
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print(y_hat)
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print(y)
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loss=(y_hat-y)**2.0
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#loss=(y_hat-y)*(y_hat-y)
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loss_sum+=loss
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if(iteration%1000==0):
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print(loss_sum/1000)
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iteration=0
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loss_sum=0.0
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iteration+=1
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def main():
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vocabulary = define_vocabulary('train/in.tsv')
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readed_words=read_input('dev-0/in.tsv')
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training(vocabulary,readed_words,'test.tsv')
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#def cost_function(y_hat,y):
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# loss=(y_hat-y)**2.0
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# loss_sum+=loss
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# if loss_counter%1000==0:
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# print(loss_sum/1000)
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# loss_counter=0
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# loss_sum=0.0
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#def main():
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# --------------- initialization ---------------------------------
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# vocabulary = define_vocabulary('train/in.tsv')
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# readed_words=read_input('dev-0/in.tsv')
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# i=1;
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# weights=[]
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# readed_words_values=[]
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# rangeVocabulary=len(vocabulary['count'])+1
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# for i in range(rangeVocabulary):
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# weights.append(random.randrange(0,len(vocabulary['count'])+1))
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# for word in readed_words['count']:
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# if word not in vocabulary['count']:
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# readed_words['count'][word]=0
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# readed_words_values.append(readed_words['count'][word])
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# precision=0.00001
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# learning_rate=0.00001
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# delta=1
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# max_iterations=len(vocabulary['count'])+1
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# current_iteration=0
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# rangeReadedValues=len(readed_words['count'])+1
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# --------------- prediction -------------------------------------
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# while (delta>precision and current_iteration<max_iterations):
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# y=random.choice(readed_words_values)
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# y_hat=weights[0]
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# i=0
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# j=0
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# for i in range(rangeReadedValues):
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# y_hat+=weights[i]*y
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# delta=abs(y_hat-y)*learning_rate
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# weights[0]=weights[0]-delta
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# for j in range(rangeVocabulary):
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# weights[j]-=y*delta
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# print(delta)
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# current_iteration+=1
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main()
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