In [1]:

    

import numpy as np
import scipy.sparse as sp


    

In [2]:

    

import scipy.sparse.linalg as splin


    

In [3]:

    

from least_squares import ridge


    

In [4]:

    

# try the code used in the scikit-learn example. 
test_X = sp.csc_matrix([[0, 1], [1, 2], [2, 3]])
test_Y = sp.csc_matrix([[2, 5, 8]]).T


    

In [5]:

    

tests_true_ans =sp.csc_matrix([ [1, 2]]).T
test_X.dot(tests_true_ans).toarray()


    

    
Out[5]:





array([[2],
       [5],
       [8]], dtype=int64)

In [14]:

    

sol = ridge(X = test_X, y = test_Y, lam = 0.001)
print(sol)


    

    




  (0, 0)	1.00033211491
  (1, 0)	1.99966738666






    




/Users/janet/miniconda3/envs/mlpy3/lib/python3.5/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:247: SparseEfficiencyWarning: splu requires CSC matrix format
  warn('splu requires CSC matrix format', SparseEfficiencyWarning)
/Users/janet/miniconda3/envs/mlpy3/lib/python3.5/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:165: SparseEfficiencyWarning: spsolve is more efficient when sparse b is in the CSC matrix format
  'is in the CSC matrix format', SparseEfficiencyWarning)

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