2.5.2.2.2. List of Lists Format (LIL)

• row-based linked list

  • each row is a Python list (sorted) of column indices of non-zero elements
  • rows stored in a NumPy array (dtype=np.object)
  • non-zero values data stored analogously

• efficient for constructing sparse matrices incrementally

• constructor accepts:

  • dense matrix (array)
  • sparse matrix
  • shape tuple (create empty matrix)

• flexible slicing, changing sparsity structure is efficient

• slow arithmetics, slow column slicing due to being row-based

• use:

  • when sparsity pattern is not known apriori or changes
  • example: reading a sparse matrix from a text file

2.5.2.2.2.1. Examples

• create an empty LIL matrix:

  >>> mtx = sparse.lil_matrix((4, 5))
  

• prepare random data:

  >>> from numpy.random import rand
  >>> data = np.round(rand(2, 3))
  >>> data
  array([[ 1.,  1.,  1.],
         [ 1.,  0.,  1.]])
  

• assign the data using fancy indexing:

  >>> mtx[:2, [1, 2, 3]] = data
  >>> mtx  
  <4x5 sparse matrix of type '<type 'numpy.float64'>'
          with 5 stored elements in LInked List format>
  >>> print mtx   
    (0, 1)  1.0
    (0, 2)  1.0
    (0, 3)  1.0
    (1, 1)  1.0
    (1, 3)  1.0
  >>> mtx.todense()
  matrix([[ 0.,  1.,  1.,  1.,  0.],
          [ 0.,  1.,  0.,  1.,  0.],
          [ 0.,  0.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0.,  0.]])
  >>> mtx.toarray()
  array([[ 0.,  1.,  1.,  1.,  0.],
         [ 0.,  1.,  0.,  1.,  0.],
         [ 0.,  0.,  0.,  0.,  0.],
         [ 0.,  0.,  0.,  0.,  0.]])
  

• more slicing and indexing:

  >>> mtx = sparse.lil_matrix([[0, 1, 2, 0], [3, 0, 1, 0], [1, 0, 0, 1]])
  >>> mtx.todense()
  matrix([[0, 1, 2, 0],
          [3, 0, 1, 0],
          [1, 0, 0, 1]])
  >>> print mtx 
    (0, 1)    1
    (0, 2)    2
    (1, 0)    3
    (1, 2)    1
    (2, 0)    1
    (2, 3)    1
  >>> mtx[:2, :] 
  <2x4 sparse matrix of type '<type 'numpy.int64'>'
    with 4 stored elements in LInked List format>
  >>> mtx[:2, :].todense()
  matrix([[0, 1, 2, 0],
          [3, 0, 1, 0]])
  >>> mtx[1:2, [0,2]].todense()
  matrix([[3, 1]])
  >>> mtx.todense()
  matrix([[0, 1, 2, 0],
          [3, 0, 1, 0],
          [1, 0, 0, 1]])