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.newaxis], numpy.ones(X.shape[0]), numpy.ones(1), 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, 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)
def test_orthonormal_update():
forwardPasser = ForwardPasser(
X, 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))) < 0.0000001)
class TestForwardPasser(object):
def __init__(self):
numpy.random.seed(0)
self.basis = Basis(10)
constant = ConstantBasisFunction()
self.basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
self.basis.append(bf1)
self.basis.append(bf2)
self.basis.append(bf3)
self.X = numpy.random.normal(size=(100, 10))
self.B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
self.basis.transform(self.X, self.B)
self.beta = numpy.random.normal(size=4)
self.y = numpy.empty(shape=100, dtype=numpy.float64)
self.y[:] = numpy.dot(
self.B, self.beta) + numpy.random.normal(size=100)
self.forwardPasser = ForwardPasser(
self.X, self.y, numpy.ones(self.y.shape), max_terms=1000, penalty=1)
def test_orthonormal_update(self):
numpy.set_printoptions(precision=4)
m, n = self.X.shape
B_orth = self.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_
self.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(self):
self.forwardPasser.run()
res = str(self.forwardPasser.get_basis()) + \
'\n' + str(self.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 __init__(self):
numpy.random.seed(0)
self.basis = Basis(10)
constant = ConstantBasisFunction()
self.basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
self.basis.append(bf1)
self.basis.append(bf2)
self.basis.append(bf3)
self.X = numpy.random.normal(size=(100, 10))
self.B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
self.basis.transform(self.X, self.B)
self.beta = numpy.random.normal(size=4)
self.y = numpy.empty(shape=100, dtype=numpy.float64)
self.y[:] = numpy.dot(
self.B, self.beta) + numpy.random.normal(size=100)
self.forwardPasser = ForwardPasser(
self.X, self.y, numpy.ones(self.y.shape), max_terms=1000, penalty=1)
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)
