def test_orthonormal_update():
forwardPasser = ForwardPasser(X,
missing,
y[:, numpy.newaxis],
numpy.ones(X.shape[0]),
numpy.ones(1),
max_terms=1000,
penalty=1)
numpy.set_printoptions(precision=4)
m, n = X.shape
B_orth = forwardPasser.get_B_orth()
v = numpy.random.normal(size=m)
for i in range(1, 10):
v_ = numpy.random.normal(size=m)
B_orth[:, i] = 10 * v_ + v
v = v_
forwardPasser.orthonormal_update(i)
B_orth_dot_B_orth_T = numpy.dot(B_orth[:, 0:i + 1].transpose(),
B_orth[:, 0:i + 1])
assert_true(
numpy.max(numpy.abs(B_orth_dot_B_orth_T -
numpy.eye(i + 1))) < .0000001)
def test_run():
forwardPasser = ForwardPasser(X,
y,
numpy.ones(y.shape),
max_terms=1000,
penalty=1)
forwardPasser.run()
res = str(forwardPasser.get_basis()) + \
'\n' + str(forwardPasser.trace())
filename = os.path.join(os.path.dirname(__file__), 'forward_regress.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_run():
forwardPasser = ForwardPasser(X,
missing,
y[:, numpy.newaxis],
sample_weight,
max_terms=1000,
penalty=1)
forwardPasser.run()
res = str(forwardPasser.get_basis()) + \
'\n' + str(forwardPasser.trace())
filename = os.path.join(os.path.dirname(__file__), 'forward_regress.txt')
# with open(filename, 'w') as fl:
# fl.write(res)
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
