def house_add_userid(request, userid):
try:
url = Url.from_string(request.url)
naidom.check_url(url)
naidom.house_add(request, userid)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0, text=u"Добавлен в базу URL:" + request.url)
def ft_update_by_url_userid(url, userid):
"""
Возвращает информацию о доме по URL и userid (ключи)
"""
try:
naidom.check_url(url)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0)
def house_add_userid(request, userid):
try:
url = Url.from_string(request.url)
naidom.check_url(url)
naidom.house_add(request, userid)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0,
text=u"Добавлен в базу URL:" + request.url)
def ft_update_by_url_userid(url, userid):
"""
Возвращает информацию о доме по URL и userid (ключи)
"""
try:
naidom.check_url(url)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0)
def house_delete_userid(request, userid):
try:
url = Url.from_string(request.url)
naidom.check_url(url)
#naidom.url_user_kind(request.url, userid)
naidom.house_delete(url, userid)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0, text=u"Удалён из базы URL:" + request.url)
def house_delete_userid(request, userid):
try:
url = Url.from_string(request.url)
naidom.check_url(url)
#naidom.url_user_kind(request.url, userid)
naidom.house_delete(url, userid)
FT.ft_update_by_url(url)
except NaiDomEx as ex:
return ApiErrorMessage(code=-1, text=ex.message)
return ApiErrorMessage(code=0,
text=u"Удалён из базы URL:" + request.url)
def __init__(self):
self.Vocabulary_Size = 0
self.l = tf.placeholder(tf.float32, [],
name='l') # Gradient reversal scaler
self.dataset = Combined_Data_Processor.Model()
self.bi_dataset = BiCorpus_Data_Processor.Data_holder()
self.SE = Sentence_Representation.Conv_Rep()
self.Fea_GEN = FT.Feature_Translator(length=50)
self.Word_Embedding_Dimension = 100
self.Y_ = tf.placeholder(dtype=tf.int32, shape=[None])
self.Y_2 = tf.placeholder(dtype=tf.int32, shape=[None])
self.Y = tf.placeholder(dtype=tf.float32, shape=[None, 1])
self.X_P = tf.placeholder(
dtype=tf.float32,
shape=[None, None, self.Word_Embedding_Dimension])
self.X_Q = tf.placeholder(
dtype=tf.float32,
shape=[None, None, self.Word_Embedding_Dimension])
self.X_Eng = tf.placeholder(
dtype=tf.float32,
shape=[None, None, self.Word_Embedding_Dimension])
self.X_Kor = tf.placeholder(
dtype=tf.float32,
shape=[None, None, self.Word_Embedding_Dimension])
def __init__(self):
self.dataset = Combined_Data_Processor.Model()
self.bi_dataset = BiCorpus_Data_Processor.Data_holder()
self.SE = Sentence_Representation.Conv_Rep()
self.Fea_GEN = FT.Feature_Translator(length=50)
self.Word_Embedding_Dimension = 100
self.Y = tf.placeholder(dtype=tf.float32, shape=[None, 2])
self.X_P = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Q = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Eng = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Kor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
return wp_E
# In[4]
if __name__ == '__main__':
# gmode: using simulation; gmode=1: using csv file
t, s = getSignal(sRate=1000, gmode=0)
wp_(t, s, level=4) # calculate wavelet packages with level 3
# In[5]: using actual data
sRate = 2000 # sampling rate
t, s = getSignal(source_path, sRate, gmode=1) # retreieve data from file
r = [0, len(t)] # specify the analysis range
# r = [0,100000]
ft.fft_(s[r[0]:r[1]], sRate) # call fft function
wp_(t[r[0]:r[1]], s[r[0]:r[1]], waveletFun='db3',
level=4) # calculate wavelet packages with level n
# In[5]: apply low pass filter by wden with threshold
ds = de.lowpassfilter(s, thresh=0.3, wavelet="db4") #
wp_(t, ds, waveletFun='db3', level=4)
# In[6]: apply wden filter with soft wavelets
# wden(X,TPTR,SORH,SCAL,N,'wname')
# TPTR: threshold selection rule, {rigrsure','heursure','sqtwolog', 'minimaxi'}
# SORH: ('soft' or 'hard') is for soft or hard thresholding
# SCAL: defines multiplicative threshold rescaling
# 'one' for no rescaling
# 'sln' for rescaling using a single estimation of level noise based on first-level coefficients
# 'mln' for rescaling done using level-dependent estimation of level noise
dft_time = []
fft_time = []
# Reading data of diff lengths
for i in range(len(path)):
with open(path[i], 'r') as f:
for line in f.readlines():
data_list[i].append(int(line))
for i in range(len(data_list)):
length.append(len(data_list[i]))
# Calling DFT
dft_start = time.time()
dft_array = FT.DFT(data_list[i])
dft_end = time.time()
dft_time.append(dft_end - dft_start)
#Calling FFT
fft_start = time.time()
fft_array = FT.FFT(data_list[i])
fft_end = time.time()
fft_time.append(fft_end - fft_start)
#calculating the mean squared error
meanSquaredError.append(
np.abs(np.square(np.subtract(dft_array, fft_array)).mean()))
project_name = "test_FSI"
#
# Is RBF the mesh deformation technique used?
#
RBF = True
if RBF: print "Mesh deformation is RBF"
#
############## END INPUT DATA ##################################################
#
#
############## MAIN BODY #######################################################
#
#
# Open FINE/Turbo project
#
FT.open_project(path + project_name + ".iec")
#
# ++++++ Save new project +++++++++++++++++++++++++++++++++++++++++++++++++++++
#
#FT.save_project_as("test_Py_FSI",0)
#
# ++++++ Get the number of blades (needs to be used for the IBPA defintion ++++
#
# Get the row object in order to find the number of blades on the row
# 0 signifies the first row, 1 the second etc
#
row_object = FT.get_row(0)
#
# Get the number of blades on the found row object
#
nb_blades = row_object.get_nb_blades()
def __init__(self):
"""
추가해야 할 것:
word2vec 읽어와서 tensor로 변환하여 넘겨주기
"""
"""
english glove
"""
in_path_glove = "C:\\Users\\Administrator\\Desktop\\qadataset\\glove6B100d.txt"
glove_f = codecs.open(in_path_glove, 'r', 'utf-8')
self.words = []
self.vectors = []
arr = []
for i in range(100):
pm = 1
if i % 2 == 0:
pm = -1
arr.append(0.002 * pm * i)
self.words.append('#END')
self.vectors.append(arr)
arr = []
for i in range(100):
pm = 1
if i % 2 == 0:
pm = 0.1
elif i % 3 == 0:
pm = -1
arr.append(0.1 * pm)
self.words.append('#START')
self.vectors.append(arr)
for line in glove_f:
tokens = line.split(' ')
self.words.append(tokens.pop(0))
self.vectors.append(tokens)
self.vectors = numpy.array((self.vectors), 'f').reshape((-1, self.Word_Embedding_Dimension))
self.dictionary = numpy.array(self.words)
self.glove_arg_index = self.dictionary.argsort()
self.dictionary.sort()
###############
self.word_embedding_eng_tensor = tf.convert_to_tensor(self.vectors, dtype=tf.float32, name='eng_embedding')
del self.vectors
"""
korean embedding
"""
word2vec_kor = codecs.open('C:\\Users\\Administrator\\Desktop\\qadataset\\kor_word2vec_100d', 'r', 'utf-8')
self.kor_words = []
self.kor_vectors = []
arr = []
for i in range(100):
pm = 1
if i % 2 == 0:
pm = -1
arr.append(0.002 * pm * i)
self.kor_words.append('#END')
self.kor_vectors.append(arr)
arr = []
for i in range(100):
pm = 1
if i % 2 == 0:
pm = 0.1
elif i % 3 == 0:
pm = -1
arr.append(0.1 * pm)
self.kor_words.append('#START')
self.kor_vectors.append(arr)
for line in word2vec_kor:
tokens = line.split('\t')
self.kor_words.append(tokens.pop(0))
self.kor_vectors.append(tokens)
print(self.kor_words[0])
print(self.kor_words[1])
self.kor_dictionary = numpy.array(self.kor_words)
self.word2vec_arg_index = self.kor_dictionary.argsort()
self.kor_dictionary.sort()
###################
self.word_embedding_kor_tensor = tf.convert_to_tensor(self.kor_vectors, dtype=tf.float32, name='kor_embedding')
del self.kor_vectors
self.l = tf.placeholder(tf.float32, [], name='l') # Gradient reversal scaler
self.dataset = Combined_Data_Processor.Model()
self.bi_dataset = BiCorpus_Data_Processor.Data_holder()
self.SE = Sentence_Representation.Conv_Rep()
self.Fea_GEN = FT.Feature_Translator(length=50)
self.Word_Embedding_Dimension = 100
self.Y_ = tf.placeholder(dtype=tf.int32, shape=[None])
self.Y_2 = tf.placeholder(dtype=tf.int32, shape=[None])
self.Y = tf.placeholder(dtype=tf.float32, shape=[None, 2])
self.X_P = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Q = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Eng = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.X_Kor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.Word_Embedding_Dimension])
self.eng_vocab_size = self.dictionary.shape[0]
self.kor_vocab_size = self.kor_dictionary.shape[0]
self.eng_start_token = tf.placeholder(dtype=tf.int32, shape=[None, 1])
self.eng_end_token = tf.placeholder(dtype=tf.int32, shape=[None, 1])
self.kor_start_token = tf.placeholder(dtype=tf.int32, shape=[None, 1])
self.kor_end_token = tf.placeholder(dtype=tf.int32, shape=[None, 1])
self.encoder_inputs_eng = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.encoder_inputs_eng_q = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.encoder_inputs_kor = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.shared_inputs_eng = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.shared_inputs_kor = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.decoder_inputs_eng = tf.concat([self.eng_start_token, enco])
self.decoder_inputs_kor = tf.placeholder(dtype=tf.int32, shape=[None, None])
self.class_label = tf.placeholder(dtype=tf.float32, shape=[None, None])
self.domain_label = tf.placeholder(dtype=tf.float32, shape=[None, None])
self.hidden_size = 200
self.keep_prob = 0.8
self.encoder_eng_length = seq_length(self.encoder_inputs_eng)
self.encoder_kor_length = seq_length(self.encoder_inputs_kor)
self.shared_length_eng = seq_length(self.shared_inputs_eng)
self.shared_length_kor = seq_length(self.shared_inputs_kor)
self.attention_hidden_size = 400
self.batch_size = 64
self.max_decoder_length = 50
self.embedding_size = 100
self.word_embedding_eng = tf.get_variable("encoder_embeddings",
shape=[self.eng_vocab_size, self.embedding_size],
dtype=tf.float32, trainable=True,
initializer=tf.constant_initializer(self.word_embedding_eng_tensor))
self.word_embedding_kor = tf.get_variable("decoder_embeddings",
shape=[self.kor_vocab_size, self.embedding_size],
dtype=tf.float32, trainable=True,
initializer=tf.constant_initializer(self.word_embedding_kor_tensor))
