text: # encoding=utf-8
  """
      Created on 21:29 2018/11/12 
      @author: Jindong Wang
  """
  import numpy as np
  import scipy.io
  import scipy.linalg
  import sklearn.metrics
  import sklearn.neighbors
  
  
  def kernel(ker, X, X2, gamma):
      if not ker or ker == 'primal':
          return X
      elif ker == 'linear':
          if not X2:
              K = np.dot(X.T, X)
          else:
              K = np.dot(X.T, X2)
      elif ker == 'rbf':
          n1sq = np.sum(X ** 2, axis=0)
          n1 = X.shape[1]
          if not X2:
              D = (np.ones((n1, 1)) * n1sq).T + np.ones((n1, 1)) * n1sq - 2 * np.dot(X.T, X)
          else:
              n2sq = np.sum(X2 ** 2, axis=0)
              n2 = X2.shape[1]
              D = (np.ones((n2, 1)) * n1sq).T + np.ones((n1, 1)) * n2sq - 2 * np.dot(X.T, X)
          K = np.exp(-gamma * D)
      elif ker == 'sam':
          if not X2:
              D = np.dot(X.T, X)
          else:
              D = np.dot(X.T, X2)
          K = np.exp(-gamma * np.arccos(D) ** 2)
      return K
  
  
  class JDA:
      def __init__(self, kernel_type='primal', dim=30, lamb=1, gamma=1, T=10):
          '''
          Init func
          :param kernel_type: kernel, values: 'primal' | 'linear' | 'rbf' | 'sam'
          :param dim: dimension after transfer
          :param lamb: lambda value in equation
          :param gamma: kernel bandwidth for rbf kernel
          :param T: iteration number
          '''
          self.kernel_type = kernel_type
          self.dim = dim
          self.lamb = lamb
          self.gamma = gamma
          self.T = T
  
      def fit_predict(self, Xs, Ys, Xt, Yt):
          '''
          Transform and Predict using 1NN as JDA paper did
          :param Xs: ns * n_feature, source feature
          :param Ys: ns * 1, source label
          :param Xt: nt * n_feature, target feature
          :param Yt: nt * 1, target label
          :return: acc, y_pred, list_acc
          '''
          list_acc = []
          X = np.hstack((Xs.T, Xt.T))
          X /= np.linalg.norm(X, axis=0)
          m, n = X.shape
          ns, nt = len(Xs), len(Xt)
          e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
          C = len(np.unique(Ys))
          H = np.eye(n) - 1 / n * np.ones((n, n))
  
          M = e * e.T * C
          Y_tar_pseudo = None
          for t in range(self.T):
              N = 0
              if Y_tar_pseudo is not None and len(Y_tar_pseudo) == nt:
                  for c in range(1, C + 1):
                      e = np.zeros((n, 1))
                      tt = Ys == c
                      e[np.where(tt == True)] = 1 / len(Ys[np.where(self.Ys == c)])
                      yy = Y_tar_pseudo == c
                      ind = np.where(yy == True)
                      inds = [item + ns for item in ind]
                      e[tuple(inds)] = -1 / len(Y_tar_pseudo[np.where(Y_tar_pseudo == c)])
                      e[np.isinf(e)] = 0
                      N = N + np.dot(e, e.T)
              M += N
              M = M / np.linalg.norm(M, 'fro')
              K = kernel(self.kernel_type, X, None, gamma=self.gamma)
              n_eye = m if self.kernel_type == 'primal' else n
              a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T])
              w, V = scipy.linalg.eig(a, b)
              ind = np.argsort(w)
              A = V[:, ind[:self.dim]]
              Z = np.dot(A.T, K)
              Z /= np.linalg.norm(Z, axis=0)
              Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T
  
              clf = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)
              clf.fit(Xs_new, Ys.ravel())
              Y_tar_pseudo = clf.predict(Xt_new)
              acc = sklearn.metrics.accuracy_score(Yt, Y_tar_pseudo)
              list_acc.append(acc)
              print('JDA iteration [{}/{}]: Acc: {:.4f}'.format(t + 1, self.T, acc))
          return acc, Y_tar_pseudo, list_acc
  
  
  if __name__ == '__main__':
      domains = ['caltech.mat', 'amazon.mat', 'webcam.mat', 'dslr.mat']
      for i in range(1):
          for j in range(2):
              if i != j:
                  src, tar = 'data/' + domains[i], 'data/' + domains[j]
                  src_domain, tar_domain = scipy.io.loadmat(src), scipy.io.loadmat(tar)
                  Xs, Ys, Xt, Yt = src_domain['feas'], src_domain['label'], tar_domain['feas'], tar_domain['label']
                  jda = JDA(kernel_type='primal', dim=30, lamb=1, gamma=1)
                  acc, ypre, list_acc = jda.fit_predict(Xs, Ys, Xt, Yt)
                  print(acc)
  