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()