def doAll():
count = 0
for tag in language_tags.keys():
print('generating dictionary for ' + tag)
dic = functions.generate_dictionary(tag, max_letters)
for word in dic:
master_dic.append(word)
#print(word)
#print(len(master_dic))
vct = functions.convert_dic_to_vector(dic, max_letters)
for vector in vct:
word_data.append(vector)
output_vct = functions.create_output_vector(count, len(language_tags))
for i in range(len(vct)):
language_data.append(output_vct)
count += 1
arr = []
for i in range(len(word_data)):
entry = []
entry.append(master_dic[i])
for digit in language_data[i]:
entry.append(float(digit))
for digit in word_data[i]:
entry.append(float(digit))
arr.append(entry)
return arr
import pandas as pd
import config
word_data = []
language_data = []
master_dic = []
count = 0
#this function is used to generate dictionary for words in german and english language, and produces a final csv
#which contains each word transformed into a vector and its label.
for tag in config.language_tags.keys():
print('generating dictionary for ' + tag)
dic = functions.create_df(tag, max_letters)
for word in dic:
master_dic.append(word)
vct = functions.convert_dic_to_vector(dic, max_letters)
for vector in vct:
word_data.append(vector)
output_vct = functions.create_output_vector(count, len(language_tags))
for i in range(len(vct)):
language_data.append(output_vct)
count += 1
arr = []
for i in range(len(word_data)):
entry = []
entry.append(master_dic[i])
for digit in language_data[i]:
entry.append(float(digit))
for digit in word_data[i]:
entry.append(float(digit))
testString = ""
spacing = 3
for rchr in removechar:
text.replace(rchr, " ")
replaceString = text.split(" ")
i = 0
dic = []
print(replaceString)
formulae = []
for remove in replaceString:
dic = []
dic.append(remove)
vct_str = convert_dic_to_vector(dic, max_letters)
vct = np.zeros((1, 128 * max_letters - 1))
count = 0
for digit in vct_str[0]:
if count == 128 * (max_letters - 1):
break
vct[0, count] = int(digit)
count += 1
prediction_vct = network.predict(vct)
langs = list(language_tags.keys())
for i in range(len(language_tags)):
lang = langs[i]
score = prediction_vct[0][i]
print(remove + " " + lang + ': ' + str(round(100 * score, 2)) + '%')
if (lang == "en"):