old_contents: import numpy
  
  
  def linear_cca(H1, H2, outdim_size):
      """
      An implementation of linear CCA
      # Arguments:
          H1 and H2: the matrices containing the data for view 1 and view 2. Each row is a sample.
          outdim_size: specifies the number of new features
      # Returns
          A and B: the linear transformation matrices 
          mean1 and mean2: the means of data for both views
      """
      r1 = 1e-4
      r2 = 1e-4
  
      m = H1.shape[0]
      o = H1.shape[1]
  
      mean1 = numpy.mean(H1, axis=0)
      mean2 = numpy.mean(H2, axis=0)
      H1bar = H1 - numpy.tile(mean1, (m, 1))
      H2bar = H2 - numpy.tile(mean2, (m, 1))
  
      SigmaHat12 = (1.0 / (m - 1)) * numpy.dot(H1bar.T, H2bar)
      SigmaHat11 = (1.0 / (m - 1)) * numpy.dot(H1bar.T, H1bar) + r1 * numpy.identity(o)
      SigmaHat22 = (1.0 / (m - 1)) * numpy.dot(H2bar.T, H2bar) + r2 * numpy.identity(o)
  
      [D1, V1] = numpy.linalg.eig(SigmaHat11)
      [D2, V2] = numpy.linalg.eig(SigmaHat22)
      SigmaHat11RootInv = numpy.dot(numpy.dot(V1, numpy.diag(D1 ** -0.5)), V1.T)
      SigmaHat22RootInv = numpy.dot(numpy.dot(V2, numpy.diag(D2 ** -0.5)), V2.T)
  
      Tval = numpy.dot(numpy.dot(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv)
  
      [U, D, V] = numpy.linalg.svd(Tval)
      V = V.T
      A = numpy.dot(SigmaHat11RootInv, U[:, 0:outdim_size])
      B = numpy.dot(SigmaHat22RootInv, V[:, 0:outdim_size])
      D = D[0:outdim_size]
  
      return A, B, mean1, mean2
  
new_contents: import numpy
  
  
  def linear_cca(H1, H2, outdim_size):
      """
      An implementation of linear CCA
      # Arguments:
          H1 and H2: the matrices containing the data for view 1 and view 2. Each row is a sample.
          outdim_size: specifies the number of new features
      # Returns
          A and B: the linear transformation matrices 
          mean1 and mean2: the means of data for both views
      """
      r1 = 1e-4
      r2 = 1e-4
  
      m = H1.shape[0]
      o = H1.shape[1]
  
      mean1 = numpy.mean(H1, axis=0)
      mean2 = numpy.mean(H2, axis=0)
      H1bar = H1 - numpy.tile(mean1, (m, 1))
      H2bar = H2 - numpy.tile(mean2, (m, 1))
  
      SigmaHat12 = (1.0 / (m - 1)) * numpy.dot(H1bar.T, H2bar)
      SigmaHat11 = (1.0 / (m - 1)) * numpy.dot(H1bar.T, H1bar) + r1 * numpy.identity(o)
      SigmaHat22 = (1.0 / (m - 1)) * numpy.dot(H2bar.T, H2bar) + r2 * numpy.identity(o)
  
      [D1, V1] = numpy.linalg.eigh(SigmaHat11)
      [D2, V2] = numpy.linalg.eigh(SigmaHat22)
      SigmaHat11RootInv = numpy.dot(numpy.dot(V1, numpy.diag(D1 ** -0.5)), V1.T)
      SigmaHat22RootInv = numpy.dot(numpy.dot(V2, numpy.diag(D2 ** -0.5)), V2.T)
  
      Tval = numpy.dot(numpy.dot(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv)
  
      [U, D, V] = numpy.linalg.svd(Tval)
      V = V.T
      A = numpy.dot(SigmaHat11RootInv, U[:, 0:outdim_size])
      B = numpy.dot(SigmaHat22RootInv, V[:, 0:outdim_size])
      D = D[0:outdim_size]
  
      return A, B, mean1, mean2
  
subject: Update linear_cca.py