def test_random_append_bottom(self):
"""`linalg.append_bottom`: Randomized Validator.
Tests the behavior of `append_bottom` by feeding it randomly generated
arguments.
Raises:
AssertionError: If `append_bottom` needs debugging.
"""
for i in range(self.n_tests):
X = random_matrix(self.data_shape)
"""np.matrix: Random-valued feature set."""
v = random_matrix((1, self.data_shape[1]))
"""np.matrix: Random-valued row vector."""
result = append_bottom(X, v)
"""np.matrix: Test input."""
# Result should be a matrix.
self.assertIsInstance(result, _np.matrix)
to_norm = lambda (A, axis): _np.linalg.norm(A, axis=axis)
"""callable: Takes in a matrix returns the norm along the specified
axis."""
norm_normalizer = lambda n: [n] if type(n) == _np.float64 else n
"""callable: Make sure that all norms are lists. In particular,
treats the row vector norm as a single row of a regular matrix."""
norms = map(to_norm, [(X, 1), (v, None), (result, 1)])
"""list: Contains the row norms of both the input and the
augmented result."""
# Change the sign of the augmented matrix's norm to compute norm
# deltas and infer errors from there.
norms[2] *= -1.0
delta = compose(abs, sum, map)(sum, map(norm_normalizer, norms))
"""float: Absolute difference between row norms of input and those
of the augmented matrix."""
# The row norms of input should match those of the augmented matrix.
self.assertLessEqual(delta, self.zero_cutoff)
# The vector norm of `v` should match that of the bottommost row
# vector in the augmented matrix.
self.assertAlmostEqual(_np.linalg.norm(v),
_np.linalg.norm(result[-1, :]))
def test_invalid_args_append_helper(self):
"""`linalg.append_helper`: Argument Validator.
Tests the behavior of `append_helper` with invalid argument counts and
values.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
with self.assertRaises(ValueError):
# Invalid position argument.
_append_helper(random_matrix(self.data_shape),
random_matrix((self.data_shape[0], 1)),
position="corner")
def test_edge_cases_append_right(self):
"""`linalg.append_right`: Edge Case Validator.
Tests the behavior of `append_right` with edge cases.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
# Empty matrix `X`.
with self.assertRaises(_InvalidFeatureSetError):
append_right(_np.matrix([[]]), random_matrix(self.data_shape))
# Empty vector `v`.
with self.assertRaises(_InvalidFeatureSetError):
append_right(random_matrix(self.data_shape), _np.matrix([[]]))
def test_random_random_matrix(self):
"""`linalg.random_matrix`: Randomized Validator.
Tests the behavior of `random_matrix` by feeding it randomly generated
arguments.
Raises:
AssertionError: If `random_matrix` needs debugging.
"""
prev = None
"""float: Holds the norm of the random matrix of the previous
iteration."""
for i in range(self.n_tests):
min_val = _np.random.uniform(0.0, float(self.max_val))
"""float: Lower bound for random matrix."""
max_val = _np.random.uniform(min_val + 1.0, float(self.max_val))
"""float: Upper bound for random matrix."""
result = random_matrix(self.data_shape, min_val, max_val)
"""np.matrix: Test input."""
# Result should be a matrix of the specified dimensions.
self.assertIsInstance(result, _np.matrix)
self.assertEqual(result.shape, self.data_shape)
curr = _np.linalg.norm(result)
"""float: Holds the norm of the newly generated random matrix."""
# Current norm has virtually no chance of being equal to zero.
self.assertGreater(curr, self.zero_cutoff)
# The norm of this iteration's result has virutally no chance of
# being equal to that of the previous one.
self.assertNotEqual(prev, curr)
prev = curr
def test_invalid_args_random_matrix(self):
"""`linalg.random_matrix`: Argument Validator.
Tests the behavior of `random_matrix` with invalid argument counts and
values.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
with self.assertRaises(TypeError):
# No arguments.
random_matrix()
with self.assertRaises(TypeError):
# Only optional argument given.
random_matrix(min_val=0.2)
with self.assertRaises(TypeError):
# More arguments than needed.
random_matrix(self.data_shape, 1.0, 100.0, "extra")
with self.assertRaises(TypeError):
# Invalid *kwarg.
random_matrix(self.data_shape, key="val")
with self.assertRaises(TypeError):
# `None` instead of integer tuple `size`.
random_matrix(None)
with self.assertRaises(TypeError):
# Integer instead of integer tuple `size`.
random_matrix(1)
with self.assertRaises(TypeError):
# Float instead of integer tuple `size`.
random_matrix(1e-6)
with self.assertRaises(ValueError):
# Float tuple instead of integer tuple `size`.
random_matrix((100.5, 5.34))
with self.assertRaises(ValueError):
# Empty integer tuple.
random_matrix(())
with self.assertRaises(ValueError):
# Insufficient integer tuple length.
random_matrix((100, ))
with self.assertRaises(ValueError):
# Exceeding integer tuple length.
random_matrix((100, 5, 123))
with self.assertRaises(ValueError):
# Zero-valued row number.
random_matrix((0, self.data_shape[1]))
with self.assertRaises(ValueError):
# Negative row number.
random_matrix((-12, self.data_shape[1]))
with self.assertRaises(ValueError):
# Zero-valued column number.
random_matrix((self.data_shape[0], 0))
with self.assertRaises(ValueError):
# Negative column number.
random_matrix((self.data_shape[0], -12))
with self.assertRaises(TypeError):
# String instead of optional float `min_val`.
random_matrix(self.data_shape, "val")
with self.assertRaises(TypeError):
# `None` instead of optional float `min_val`.
random_matrix(self.data_shape, None)
with self.assertRaises(TypeError):
# Float tuple instead of optional float `min_val`.
random_matrix(self.data_shape, (0.2123, ))
with self.assertRaises(TypeError):
# String instead of optional float `max_val`.
random_matrix(self.data_shape, max_val="val")
with self.assertRaises(TypeError):
# `None` instead of optional float `max_val`.
random_matrix(self.data_shape, max_val=None)
with self.assertRaises(TypeError):
# Float tuple instead of optional float `max_val`.
random_matrix(self.data_shape, max_val=(0.2123, ))
with self.assertRaises(ValueError):
# Incompatible bounds.
random_matrix(self.data_shape, min_val=10, max_val=5)
def test_invalid_args_append_top(self):
"""`linalg.append_top`: Argument Validator.
Tests the behavior of `append_top` with invalid argument counts and
values.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
with self.assertRaises(TypeError):
# No arguments.
append_top()
with self.assertRaises(TypeError):
# Only one argument given.
append_top(random_matrix(self.data_shape))
with self.assertRaises(TypeError):
# More arguments than needed.
append_top(random_matrix(self.data_shape),
random_matrix((1, self.data_shape[1])),
random_matrix(self.data_shape))
with self.assertRaises(TypeError):
# With **kwargs.
append_top(random_matrix(self.data_shape),
random_matrix((1, self.data_shape[1])),
key="value")
with self.assertRaises(_InvalidFeatureSetError):
# String instead of matrix `X`.
append_top("string", random_matrix((1, self.data_shape[1])))
with self.assertRaises(_InvalidFeatureSetError):
# `None` instead of matrix `X`.
append_top(None, random_matrix((1, self.data_shape[1])))
with self.assertRaises(_InvalidFeatureSetError):
# ndarray instead of matrix `X`.
append_top(_np.random.uniform(size=self.data_shape[1]),
random_matrix((1, self.data_shape[1])))
with self.assertRaises(_InvalidFeatureSetError):
# String instead of vector `v`.
append_top(random_matrix(self.data_shape), "string")
with self.assertRaises(_InvalidFeatureSetError):
# `None` instead of vector `v`.
append_top(random_matrix(self.data_shape), None)
with self.assertRaises(_InvalidFeatureSetError):
# ndarray instead of vector `v`.
append_top(random_matrix(self.data_shape),
_np.random.uniform(size=(1, self.data_shape[1])))
with self.assertRaises(_IncompatibleDataSetsError):
# Incompatible matrices `X` and `v`.
append_top(random_matrix(self.data_shape),
random_matrix((self.data_shape[1], 1)))
with self.assertRaises(_IncompatibleDataSetsError):
# Incompatible matrices `X` and `v`.
append_top(random_matrix((self.data_shape[1], self.data_shape[0])),
random_matrix((1, self.data_shape[1])))