old_contents: import numpy as np
  
  
  def svd_pca(data):
      """
      Run PCA using a singular value decomposition.
      
      `data` is a numpy array, n_samples by n_dimensions
      """
      U, S, V = np.linalg.svd(data)
      eigvec = V.T
      eigval = S ** 2. / (data.shape[0] - 1.)
      return eigvec, eigval
  
new_contents: import numpy as np
  
  
  def svd_pca(data):
      """
      Run PCA using a singular value decomposition.
      
      `data` is a numpy array, n_samples by n_dimensions
      """
      U, S, eigvec = np.linalg.svd(data)
      eigval = S ** 2. / (data.shape[0] - 1.)
      return eigvec, eigval
  
subject: reshape eigenvectors