text: import pandas as pd
  import numpy as np
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.manifold import TSNE
  import matplotlib.pyplot as plt
  from keras.utils import np_utils
  from DEC import DeepEmbeddingClustering
  from sklearn.model_selection import train_test_split
  from keras.models import load_model
  from sklearn.metrics import accuracy_score
  import keras.backend as K
  from scipy.stats import t
  from scipy.stats import norm
  from SKNN import Semi_Supervised_KNN
  from DAE_KNN import DAEKNN
  
  sca = MinMaxScaler()
  tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)
  
  dados = pd.read_csv('c:/basedados/agricultura.csv')
  X = sca.fit_transform(dados.drop(['classe'], axis=1).values)
  Y = dados['classe'].values
  dados = pd.DataFrame(X)
  dados['classe'] = Y
  
  
  L, U, y, yu = train_test_split(X,Y, train_size=0.05, test_size=0.95, stratify=Y)
  
  DK = DAEKNN(3, np.size(L, axis=1))
  DK.reducaoZ(U)
  
  PL = DK.encoder.predict(L)
  PU = DK.encoder.predict(U)
  
  knn = Semi_Supervised_KNN()
  rotulos = knn.classificar(PL, PU, y, k=5)
  acuracia = accuracy_score(yu, rotulos)