def test_unit_norm(self):
""" Test that using std_bias = 0.0 and use_norm = True
results in vectors having unit norm """
tol = 1e-5
num_examples = 5
num_features = 10
rng = np.random.RandomState([1,2,3])
X = as_floatX(rng.randn(5,10))
dataset = DenseDesignMatrix( X = X )
#the setting of subtract_mean is not relevant to the test
#the test only applies when std_bias = 0.0 and use_norm = True
preprocessor = GlobalContrastNormalization( subtract_mean = False,
std_bias = 0.0,
use_norm = True)
dataset.apply(preprocessor)
result = dataset.get_design_matrix()
norms = np.sqrt(np.square(result).sum(axis=1))
max_norm_error = np.abs(norms-1.).max()
tol = 3e-5
assert max_norm_error < tol
def test_unit_norm(self):
""" Test that using std_bias = 0.0 and use_norm = True
results in vectors having unit norm """
tol = 1e-5
num_examples = 5
num_features = 10
rng = np.random.RandomState([1, 2, 3])
X = as_floatX(rng.randn(5, 10))
dataset = DenseDesignMatrix(X=X)
#the setting of subtract_mean is not relevant to the test
#the test only applies when std_bias = 0.0 and use_norm = True
preprocessor = GlobalContrastNormalization(subtract_mean=False,
std_bias=0.0,
use_norm=True)
dataset.apply(preprocessor)
result = dataset.get_design_matrix()
norms = np.sqrt(np.square(result).sum(axis=1))
max_norm_error = np.abs(norms - 1.).max()
tol = 3e-5
assert max_norm_error < tol
def test_zero_vector(self):
""" Test that passing in the zero vector does not result in
a divide by 0 """
dataset = DenseDesignMatrix(X = as_floatX(np.zeros(())))
#the settings of subtract_mean and use_norm are not relevant to
#the test
#std_bias = 0.0 is the only value for which there should be a risk
#of failure occurring
preprocessor = GlobalContrastNormalization( subtract_mean = True,
std_bias = 0.0,
use_norm = False)
dataset.apply(preprocessor)
result = dataset.get_design_matrix()
assert not np.any(np.isnan(result))
assert not np.any(np.isinf(result))
def test_zero_vector(self):
""" Test that passing in the zero vector does not result in
a divide by 0 """
dataset = DenseDesignMatrix(X=as_floatX(np.zeros(())))
#the settings of subtract_mean and use_norm are not relevant to
#the test
#std_bias = 0.0 is the only value for which there should be a risk
#of failure occurring
preprocessor = GlobalContrastNormalization(subtract_mean=True,
std_bias=0.0,
use_norm=False)
dataset.apply(preprocessor)
result = dataset.get_design_matrix()
assert not np.any(np.isnan(result))
assert not np.any(np.isinf(result))
