def test_seed_same():
"""Verifies that two MNDs initialized with the same
seed produce the same samples """
skip_if_no_scipy()
rng = np.random.RandomState([1,2,3])
#the number in the argument here is the limit on
#seed value
seed = rng.randint(2147462579)
dim = 3
mu = rng.randn(dim)
rank = dim
X = rng.randn(rank,dim)
cov = np.dot(X.T,X)
mnd1 = MND( sigma = cov, mu = mu, seed = seed)
num_samples = 5
rd1 = mnd1.random_design_matrix(num_samples)
rd1 = function([],rd1)()
mnd2 = MND( sigma = cov, mu = mu, seed = seed)
rd2 = mnd2.random_design_matrix(num_samples)
rd2 = function([],rd2)()
assert np.all(rd1 == rd2)
def test_seed_same():
"""Verifies that two MNDs initialized with the same
seed produce the same samples """
skip_if_no_scipy()
rng = np.random.RandomState([1, 2, 3])
#the number in the argument here is the limit on
#seed value
seed = rng.randint(2147462579)
dim = 3
mu = rng.randn(dim)
rank = dim
X = rng.randn(rank, dim)
cov = np.dot(X.T, X)
mnd1 = MND(sigma=cov, mu=mu, seed=seed)
num_samples = 5
rd1 = mnd1.random_design_matrix(num_samples)
rd1 = function([], rd1)()
mnd2 = MND(sigma=cov, mu=mu, seed=seed)
rd2 = mnd2.random_design_matrix(num_samples)
rd2 = function([], rd2)()
assert np.all(rd1 == rd2)
def test_dbm_loader():
""" Loads an example model and some data and makes
sure that inference gets the same result as Ruslan
Salakhutdino's demo code. """
skip_if_no_scipy()
pylearn2_path = pylearn2.__path__[0]
dbm_path = pylearn2_path + '/models/tests/dbm.mat'
data_path = pylearn2_path + '/models/tests/dbm_data.mat'
dbm = load_matlab_dbm(dbm_path)
d = io.loadmat( data_path )
for key in d:
try:
d[key] = np.cast[config.floatX](d[key])
except:
pass
ip = InferenceProcedure( layer_schedule = [0,1] * 10 )
ip.register_model(dbm)
V = T.matrix()
if config.compute_test_value != 'off':
V.tag.test_value = d['data']
h1_theano, h2_theano = ip.infer(V)['H_hat']
f = function([V],[h1_theano,h2_theano])
data = d['data']
h1_numpy, h2_numpy = f(data)
assert np.allclose(d['h1'],h1_numpy)
assert np.allclose(d['h2'],h2_numpy)
def test_dbm_loader():
""" Loads an example model and some data and makes
sure that inference gets the same result as Ruslan
Salakhutdino's demo code. """
skip_if_no_scipy()
pylearn2_path = pylearn2.__path__[0]
dbm_path = pylearn2_path + '/models/tests/dbm.mat'
data_path = pylearn2_path + '/models/tests/dbm_data.mat'
dbm = load_matlab_dbm(dbm_path)
d = io.loadmat(data_path)
for key in d:
try:
d[key] = np.cast[config.floatX](d[key])
except:
pass
ip = InferenceProcedure(layer_schedule=[0, 1] * 10)
ip.register_model(dbm)
V = T.matrix()
if config.compute_test_value != 'off':
V.tag.test_value = d['data']
h1_theano, h2_theano = ip.infer(V)['H_hat']
f = function([V], [h1_theano, h2_theano])
data = d['data']
h1_numpy, h2_numpy = f(data)
assert np.allclose(d['h1'], h1_numpy)
assert np.allclose(d['h2'], h2_numpy)
def test_linear_cg():
rng = numpy.random.RandomState([1,2,3])
n = 5
M = rng.randn(2*n,n)
M = numpy.dot(M.T,M).astype(config.floatX)
b = rng.randn(n).astype(config.floatX)
c = rng.randn(1).astype(config.floatX)[0]
x = theano.tensor.vector('x')
f = 0.5 * tensor.dot(x,tensor.dot(M,x)) - tensor.dot(b,x) + c
sol = linear_cg.linear_cg(f,[x])
fn_sol = theano.function([x], sol)
start = time.time()
sol = fn_sol( rng.randn(n).astype(config.floatX))[0]
my_lcg = time.time() -start
eval_f = theano.function([x],f)
cgf = eval_f(sol)
print "conjugate gradient's value of f:", str(cgf), 'time (s)', my_lcg
skip_if_no_scipy()
spf = eval_f( scipy.linalg.solve(M,b) )
print "scipy.linalg.solve's value of f: "+str(spf)
abs_diff = abs(cgf - spf)
if not (abs_diff < 1e-5):
raise AssertionError("Expected abs_diff < 1e-5, got abs_diff of " +
str(abs_diff))
def test_pooling_1d_topology_tuples():
mat = pooling_matrix((3, ), (4, ), (2, ))
assert mat.shape == (3, 8)
expected = np.array([[1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1]])
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix(3, 4, 2, sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_no_topology():
mat = pooling_matrix(4, 5)
assert mat.shape == (4, 20)
expected = np.array([[1] * 5 + [0] * 15, [0] * 5 + [1] * 5 + [0] * 10,
[0] * 10 + [1] * 5 + [0] * 5, [0] * 15 + [1] * 5])
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix(4, 5, sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_1d_topology_tuples():
mat = pooling_matrix((3,), (4,), (2,))
assert mat.shape == (3, 8)
expected = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1]])
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix(3, 4, 2, sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_no_topology():
mat = pooling_matrix(4, 5)
assert mat.shape == (4, 20)
expected = np.array([[1] * 5 + [0] * 15,
[0] * 5 + [1] * 5 + [0] * 10,
[0] * 10 + [1] * 5 + [0] * 5,
[0] * 15 + [1] * 5])
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix(4, 5, sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_topology_stride2():
mat = pooling_matrix((3, 3), (3, 3), (2, 2))
expected = np.zeros((9, 49))
maps = expected.reshape((3, 3, 7, 7))
maps[0, 0, 0:3, 0:3] = 1.
maps[0, 1, 0:3, 2:5] = 1.
maps[0, 2, 0:3, 4:7] = 1.
maps[1, 0, 2:5, 0:3] = 1.
maps[1, 1, 2:5, 2:5] = 1.
maps[1, 2, 2:5, 4:7] = 1.
maps[2, 0, 4:7, 0:3] = 1.
maps[2, 1, 4:7, 2:5] = 1.
maps[2, 2, 4:7, 4:7] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (3, 3), (2, 2), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_topology_stride2():
mat = pooling_matrix((3, 3), (3, 3), (2, 2))
expected = np.zeros((9, 49))
maps = expected.reshape((3, 3, 7, 7))
maps[0, 0, 0:3, 0:3] = 1.
maps[0, 1, 0:3, 2:5] = 1.
maps[0, 2, 0:3, 4:7] = 1.
maps[1, 0, 2:5, 0:3] = 1.
maps[1, 1, 2:5, 2:5] = 1.
maps[1, 2, 2:5, 4:7] = 1.
maps[2, 0, 4:7, 0:3] = 1.
maps[2, 1, 4:7, 2:5] = 1.
maps[2, 2, 4:7, 4:7] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (3, 3), (2, 2), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_non_overlapping():
mat = pooling_matrix((3, 3), (3, 3), (3, 3))
assert mat.shape == (9, 81)
expected = np.zeros((9, 81))
maps = expected.reshape((3, 3, 9, 9))
maps[0, 0, 0:3, 0:3] = 1.
maps[0, 1, 0:3, 3:6] = 1.
maps[0, 2, 0:3, 6:9] = 1.
maps[1, 0, 3:6, 0:3] = 1.
maps[1, 1, 3:6, 3:6] = 1.
maps[1, 2, 3:6, 6:9] = 1.
maps[2, 0, 6:9, 0:3] = 1.
maps[2, 1, 6:9, 3:6] = 1.
maps[2, 2, 6:9, 6:9] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (3, 3), (3, 3), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_topology():
mat = pooling_matrix((3, 3), (2, 2), (1, 1))
assert mat.shape == (9, 16)
expected = np.zeros((9, 16))
maps = expected.reshape((3, 3, 4, 4))
maps[0, 0, 0:2, 0:2] = 1.
maps[1, 0, 1:3, 0:2] = 1.
maps[2, 0, 2:4, 0:2] = 1.
maps[0, 1, 0:2, 1:3] = 1.
maps[1, 1, 1:3, 1:3] = 1.
maps[2, 1, 2:4, 1:3] = 1.
maps[0, 2, 0:2, 2:4] = 1.
maps[1, 2, 1:3, 2:4] = 1.
maps[2, 2, 2:4, 2:4] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (2, 2), (1, 1), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_topology():
mat = pooling_matrix((3, 3), (2, 2), (1, 1))
assert mat.shape == (9, 16)
expected = np.zeros((9, 16))
maps = expected.reshape((3, 3, 4, 4))
maps[0, 0, 0:2, 0:2] = 1.
maps[1, 0, 1:3, 0:2] = 1.
maps[2, 0, 2:4, 0:2] = 1.
maps[0, 1, 0:2, 1:3] = 1.
maps[1, 1, 1:3, 1:3] = 1.
maps[2, 1, 2:4, 1:3] = 1.
maps[0, 2, 0:2, 2:4] = 1.
maps[1, 2, 1:3, 2:4] = 1.
maps[2, 2, 2:4, 2:4] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (2, 2), (1, 1), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_pooling_2d_non_overlapping():
mat = pooling_matrix((3, 3), (3, 3), (3, 3))
assert mat.shape == (9, 81)
expected = np.zeros((9, 81))
maps = expected.reshape((3, 3, 9, 9))
maps[0, 0, 0:3, 0:3] = 1.
maps[0, 1, 0:3, 3:6] = 1.
maps[0, 2, 0:3, 6:9] = 1.
maps[1, 0, 3:6, 0:3] = 1.
maps[1, 1, 3:6, 3:6] = 1.
maps[1, 2, 3:6, 6:9] = 1.
maps[2, 0, 6:9, 0:3] = 1.
maps[2, 1, 6:9, 3:6] = 1.
maps[2, 2, 6:9, 6:9] = 1.
assert np.all(mat == expected)
skip_if_no_scipy()
spmat = pooling_matrix((3, 3), (3, 3), (3, 3), sparse='csr')
assert np.all(spmat.todense() == expected)
def test_seed_diff():
"""Verifies that two MNDs initialized with different
seeds produce samples that differ at least somewhat
(theoretically the samples could match even under
valid behavior but this is extremely unlikely)"""
skip_if_no_scipy()
rng = np.random.RandomState([1, 2, 3])
#the number in the argument here is the limit on
#seed value, and we subtract 1 so it will be
#possible to add 1 to it for the second MND
seed = rng.randint(2147462579) - 1
dim = 3
mu = rng.randn(dim)
rank = dim
X = rng.randn(rank, dim)
cov = np.dot(X.T, X)
mnd1 = MND(sigma=cov, mu=mu, seed=seed)
num_samples = 5
rd1 = mnd1.random_design_matrix(num_samples)
rd1 = function([], rd1)()
mnd2 = MND(sigma=cov, mu=mu, seed=seed + 1)
rd2 = mnd2.random_design_matrix(num_samples)
rd2 = function([], rd2)()
assert np.any(rd1 != rd2)
def test_seed_diff():
"""Verifies that two MNDs initialized with different
seeds produce samples that differ at least somewhat
(theoretically the samples could match even under
valid behavior but this is extremely unlikely)"""
skip_if_no_scipy()
rng = np.random.RandomState([1,2,3])
#the number in the argument here is the limit on
#seed value, and we subtract 1 so it will be
#possible to add 1 to it for the second MND
seed = rng.randint(2147462579) -1
dim = 3
mu = rng.randn(dim)
rank = dim
X = rng.randn(rank,dim)
cov = np.dot(X.T,X)
mnd1 = MND( sigma = cov, mu = mu, seed = seed)
num_samples = 5
rd1 = mnd1.random_design_matrix(num_samples)
rd1 = function([],rd1)()
mnd2 = MND( sigma = cov, mu = mu, seed = seed + 1)
rd2 = mnd2.random_design_matrix(num_samples)
rd2 = function([],rd2)()
assert np.any(rd1 != rd2)
