def buildtopicmodel(filename,topicnum):
pretreatment(filename)
#build the dictionary
dictionary = corpora.Dictionary(finitialwords)
# build the whole corpus words-frequency list
corpus = [dictionary.doc2bow(document) for document in finitialwords]
#print corpus
# calculate the tf-idf
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
# build the lda model
lda = gensim.models.ldamodel.LdaModel(corpus_tfidf,id2word=dictionary,num_topics=topicnum,update_every=0,passes=10)
index = similarities.MatrixSimilarity(lda[corpus])
lda.save(str(topicnum) + ".pkl")
#model = models.ldamodel.LdaModel.load('20topics.pkl')
print lda.print_topics
def buildtopicmodel(filename, topicnum):
pretreatment(filename)
#build the dictionary
dictionary = corpora.Dictionary(finitialwords)
# build the whole corpus words-frequency list
corpus = [dictionary.doc2bow(document) for document in finitialwords]
#print corpus
# calculate the tf-idf
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
# build the lda model
lda = gensim.models.ldamodel.LdaModel(corpus_tfidf,
id2word=dictionary,
num_topics=topicnum,
update_every=0,
passes=10)
index = similarities.MatrixSimilarity(lda[corpus])
lda.save(str(topicnum) + ".pkl")
#model = models.ldamodel.LdaModel.load('20topics.pkl')
print lda.print_topics
def test():
Buf = pretreatment()
code = ''
value = ''
i = 0
while(code != '#'):
while(Buf[i] == ' '):
i += 1
code,value,i = scanner(Buf,i)
print '(',code,',',value,')'
def main():
TNT = "{};,ia=+*()x-#PLVSXYZ"
M = [[2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,99,0,0,0,0,0,0,0],
[0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0],
[0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0],
[0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0],
[0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,-4,-4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,-2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,-3,-3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,19,0,0,0,0,17,0,20,18,0,0,0,0,0,14,15,16],
[0,-5,0,0,0,0,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,-7,0,0,0,0,0,-7,22,0,-7,0,0,0,0,0,0,0,0,0,0],
[0,-9,0,0,0,0,0,-9,-9,0,-9,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,19,0,0,0,0,17,0,20,18,0,0,0,0,0,23,15,16],
[0,0,0,0,19,0,0,0,0,17,0,20,18,0,0,0,0,0,0,0,24],
[0,-12,0,0,0,0,0,-12,-12,0,-12,0,0,0,0,0,0,0,0,0,0],
[0,-13,0,0,0,0,0,-13,-13,0,-13,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,19,0,0,0,0,17,0,20,18,0,0,0,0,0,0,25,16],
[0,0,0,0,19,0,0,0,0,17,0,20,18,0,0,0,0,0,0,0,26],
[0,0,0,0,0,0,0,21,0,0,27,0,0,0,0,0,0,0,0,0,0],
[0,-11,0,0,0,0,0,-11,-11,0,-11,0,0,0,0,0,0,0,0,0,0],
[0,-6,0,0,0,0,0,-6,22,0,-6,0,0,0,0,0,0,0,0,0,0],
[0,-8,0,0,0,0,0,-8,-8,0,-8,0,0,0,0,0,0,0,0,0,0],
[0,-10,0,0,0,0,0,-10,-10,0,-10,0,0,0,0,0,0,0,0,0,0]]
p = ["P'->P",
"P->{L}",
"L->V;S",
"V->V,i",
"V->ai",
"S->i=X",
"X->X+T",
"X->Y",
"Y->Y*Z",
"Y->Z",
"Z->(E)",
"Z->-Z",
"Z->i",
"Z->x"]
LIN = len(M)
PRO_NUM = len(p)
state = [0]*50
top = 0
symbol = ['#']*50
global i
code,value = '',''
Buf = pretreatment()
while(Buf[i] == ' '):
i += 1
code,value,i = scanner(Buf,i)
j = 0
print 'step','\t\t\t\t','状态栈','\t\t\t',' ','符号栈','\t\t\t',' ','输入串首字符'
while(1):
print j,')','\t\t\t\t',
j += 1
state_str = ''
for x in range(top+1):
state_str += str(state[x])
print state_str,' '*(20-len(state_str)),
print '\t\t',
symbol_str = ''
for y in range(top+1):
symbol_str += str(symbol[y])
print symbol_str,' '*(10-len(symbol_str)),
print '\t\t\t',code
action = M[state[top]][col(code,TNT)]
if(action >= 1 and action < LIN):
top += 1
state[top] = action
symbol[top] = code
while(Buf[i] == ' '):
i += 1
code,value,i = scanner(Buf,i)
elif action > -PRO_NUM and action <= -1:
top -= len(p[-action]) -3
state[top+1] = M[state[top]][col(p[-action][0],TNT)]
top += 1
symbol[top] = p[-action][0]
elif action == 99:
print '\t\t\t\t','Acc'
break
else:
print 'Err in main->',action
break
# plt.xlabel(nameCols[0])
# plt.ylabel(nameCols[1])
# plt.show()
def neuronalNetwork(self):
model = tf.keras.models.Sequential()
#model.add(tf.keras.layers.Dense(32, activation="relu")) #16 neurones en entrée
#model.add(tf.keras.layers.Dense(32,activation="relu")) #12 neurones suivant
#model.add(tf.keras.layers.Dense(32, activation="relu")) # dernier neuroens
#model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd')#, metrics=["accuracy"]) #fct couts, Stokastique gradient descent, //
model.add(tf.keras.layers.Dense(units=16, input_shape=[16]))
model.add(tf.keras.layers.Dense(units=5, input_shape=[16]))
model.compile(optimizer='sgd', loss='mean_squared_error')
history = model.fit(self.X, self.Y, epochs=10) #X, Y, nb d'itération
print(history)
modelOutput = model.predict(pre.dataXVector)
model.summary()
print(modelOutput, pre.dataYVector)
if __name__ == "__main__":
pretreat = pretreatment(importData('simulateGenData.csv'))
pretreat.shape()
pretreat.normalize()
treat = treatment([pretreat.dataXVector, pretreat.dataYVector])
treat.neuronalNetwork()
#treat.linearRegression()
#treat.plot(['windSpd', 'spd'])
#treat.plot(['windSpd'])
#treat.polynomialRegression()
