Esempio n. 1
0
  svdX = []
  svdY = []
  u, s, vT = scipy.linalg.svd(train)
  for k in xrange(1, 100):
    low_s = [s[i] for i in xrange(k)]  # + (min(u.shape[0], vT.shape[1]) - k) * [0]
    print 'Exact SVD with low-rank approximation {}'.format(k)
    svdX.append(k)
    svdY.append(get_error(u, np.diag(low_s), vT, train, test))
  plt.plot(svdX, svdY, label="SVD", color='black', linewidth='2', linestyle='--')
  """

  print
  print 'Testing incremental SVD'
  for num in xrange(400, 1001, 300):
    print '... with block size of {}'.format(num)
    X, Y = [], []
    for k in xrange(1, 91, 10):
      print k
      u, s, vT = incremental_SVD(train, k, num)
      X.append(k)
      Y.append(get_error(u, s, vT, train, test, prod_avg))
    plt.plot(X, Y, label='iSVD u={}'.format(num))
  ##
  plt.title('Recommendation system RMSE on {}x{} matrix'.format(*train.shape))
  plt.xlabel('Low rank approximation (k)')
  plt.ylabel('Root Mean Squared Error')
  #plt.ylim(0, max(svdY))
  plt.legend(loc='best')
  plt.savefig('recommend_rmse_{}x{}.pdf'.format(*train.shape))
  plt.show(block=True)
Esempio n. 2
0
  svdY = []
  u, s, vT = scipy.linalg.svd(train)
  for k in xrange(1, 100):
    low_s = [s[i] for i in xrange(k)]  # + (min(u.shape[0], vT.shape[1]) - k) * [0]
    print 'Exact SVD with low-rank approximation {}'.format(k)
    svdX.append(k)
    svdY.append(get_error(u, np.diag(low_s), vT, train, test))
  plt.plot(svdX, svdY, label="SVD", color='black', linewidth='2', linestyle='--')
  """

    print
    print 'Testing incremental SVD'
    for num in xrange(400, 1001, 300):
        print '... with block size of {}'.format(num)
        X, Y = [], []
        for k in xrange(1, 91, 10):
            print k
            u, s, vT = incremental_SVD(train, k, num)
            X.append(k)
            Y.append(get_error(u, s, vT, train, test, prod_avg))
        plt.plot(X, Y, label='iSVD u={}'.format(num))
    ##
    plt.title(
        'Recommendation system RMSE on {}x{} matrix'.format(*train.shape))
    plt.xlabel('Low rank approximation (k)')
    plt.ylabel('Root Mean Squared Error')
    #plt.ylim(0, max(svdY))
    plt.legend(loc='best')
    plt.savefig('recommend_rmse_{}x{}.pdf'.format(*train.shape))
    plt.show(block=True)
Esempio n. 3
0
    #print err
    #print
    svdX.append(k)
    svdY.append(err)
    orthoX.append(k)
    orthoY.append(check_orthogonality(u))
  plt.plot(svdX, svdY, label="SVD", color='black', linewidth='2', linestyle='--')

  print
  print 'Testing incremental SVD'
  incr_ortho = []
  for num in xrange(100, 1001, 300):
    print '... with block size of {}'.format(num)
    X, Y = [], []
    incr_orthoY = []
    uL, sL, vTL = incremental_SVD(train, range(1, 101), num)
    for i in xrange(len(uL)):
      reconstruct = uL[i].dot(sL[i].dot(vTL[i]))
      err = np.linalg.norm(train - reconstruct, 'fro')
      X.append(i + 1)
      Y.append(err)
      incr_orthoY.append(check_orthogonality(uL[i]))
    incr_ortho.append(['iSVD u={}'.format(num), X, incr_orthoY])
    plt.plot(X, Y, label='iSVD u={}'.format(num))
  """
  print 'Testing raw SVD => exact reconstruction'
  svT = scipy.linalg.diagsvd(s, u.shape[0], vT.shape[1]).dot(vT)
  for y in xrange(train.shape[0]):
    for x in xrange(train.shape[1]):
      colU = u[y, :]
      rowV = svT[:, x]