def test_pseudo_inverse():
"Tests of the pseudo-inverse"
# Numerical version of the pseudo-inverse:
pinv_num = core.wrap_array_func(numpy.linalg.pinv)
##########
# Full rank rectangular matrix:
m = unumpy.matrix([[ufloat((10, 1)), -3.1],
[0, ufloat((3, 0))],
[1, -3.1]])
# Numerical and package (analytical) pseudo-inverses: they must be
# the same:
rcond = 1e-8 # Test of the second argument to pinv()
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
##########
# Example with a non-full rank rectangular matrix:
vector = [ufloat((10, 1)), -3.1, 11]
m = unumpy.matrix([vector, vector])
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
##########
# Example with a non-full-rank square matrix:
m = unumpy.matrix([[ufloat((10, 1)), 0], [3, 0]])
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
def test_pseudo_inverse():
"Tests of the pseudo-inverse"
# Numerical version of the pseudo-inverse:
pinv_num = core.wrap_array_func(numpy.linalg.pinv)
##########
# Full rank rectangular matrix:
m = unumpy.matrix([[ufloat((10, 1)), -3.1],
[0, ufloat((3, 0))],
[1, -3.1]])
# Numerical and package (analytical) pseudo-inverses: they must be
# the same:
rcond = 1e-8 # Test of the second argument to pinv()
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
##########
# Example with a non-full rank rectangular matrix:
vector = [ufloat((10, 1)), -3.1, 11]
m = unumpy.matrix([vector, vector])
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
##########
# Example with a non-full-rank square matrix:
m = unumpy.matrix([[ufloat((10, 1)), 0], [3, 0]])
m_pinv_num = pinv_num(m, rcond)
m_pinv_package = core._pinv(m, rcond)
assert matrices_close(m_pinv_num, m_pinv_package)
def test_wrap_array_func():
'''
Test of numpy.wrap_array_func(), with optional arguments and
keyword arguments.
'''
# Function that works with numbers with uncertainties in mat (if
# mat is an uncertainties.unumpy.matrix):
def f_unc(mat, *args, **kwargs):
return mat.I + args[0]*kwargs['factor']
# Test with optional arguments and keyword arguments:
def f(mat, *args, **kwargs):
# This function is wrapped: it should only be called with pure
# numbers:
assert not any(isinstance(v, uncert_core.UFloat) for v in mat.flat)
return f_unc(mat, *args, **kwargs)
# Wrapped function:
f_wrapped = core.wrap_array_func(f)
##########
# Full rank rectangular matrix:
m = unumpy.matrix([[ufloat(10, 1), -3.1],
[0, ufloat(3, 0)],
[1, -3.1]])
# Numerical and package (analytical) pseudo-inverses: they must be
# the same:
m_f_wrapped = f_wrapped(m, 2, factor=10)
m_f_unc = f_unc(m, 2, factor=10)
assert arrays_close(m_f_wrapped, m_f_unc)
def test_wrap_array_func():
'''
Test of numpy.wrap_array_func(), with optional arguments and
keyword arguments.
'''
# Function that works with numbers with uncertainties in mat (if
# mat is an uncertainties.unumpy.matrix):
def f_unc(mat, *args, **kwargs):
return mat.I + args[0] * kwargs['factor']
# Test with optional arguments and keyword arguments:
def f(mat, *args, **kwargs):
# This function is wrapped: it should only be called with pure
# numbers:
assert not any(isinstance(v, uncert_core.UFloat) for v in mat.flat)
return f_unc(mat, *args, **kwargs)
# Wrapped function:
f_wrapped = core.wrap_array_func(f)
##########
# Full rank rectangular matrix:
m = unumpy.matrix([[ufloat(10, 1), -3.1], [0, ufloat(3, 0)], [1, -3.1]])
# Numerical and package (analytical) pseudo-inverses: they must be
# the same:
m_f_wrapped = f_wrapped(m, 2, factor=10)
m_f_unc = f_unc(m, 2, factor=10)
assert arrays_close(m_f_wrapped, m_f_unc)
