Here are the examples of the python api numpy.eye taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
3862 Examples
5
Source : test_constraint_utilities.py
with MIT License
from TAMUparametric
with MIT License
from TAMUparametric
def test_facet_ball_elimination():
A = numpy.block([[numpy.eye(2)], [-numpy.eye(2)]])
b = numpy.array([[1], [1], [0], [0]])
A_t = numpy.block([[numpy.eye(2)], [-numpy.eye(2)], [numpy.array([[1, 1]])]])
b_t = numpy.array([[2], [2], [0], [0], [1]])
A_r = numpy.block([[A], [A_t]])
b_r = numpy.block([[b], [b_t]])
[_, _] = process_region_constraints(A_r, b_r)
5
Source : test_critical_region.py
with MIT License
from TAMUparametric
with MIT License
from TAMUparametric
def region() -> CriticalRegion:
"""A square critical region with predictable properties."""
A = numpy.eye(2)
b = numpy.zeros((2, 1))
C = numpy.eye(2)
d = numpy.zeros((2, 1))
E = numpy.block([[numpy.eye(2)], [-numpy.eye(2)]])
f = make_column([1, 1, 0, 0])
return CriticalRegion(A, b, C, d, E, f, [])
def test_docs(region):
5
Source : test_mp_program.py
with MIT License
from TAMUparametric
with MIT License
from TAMUparametric
def linear_program() -> MPLP_Program:
"""a simple mplp to test the dimensional correctness of its functions"""
A = numpy.eye(3)
b = numpy.zeros((3, 1))
F = numpy.ones((3, 10))
A_t = numpy.block([[-numpy.eye(5)], [numpy.eye(5)]])
b_t = numpy.ones((10, 1))
c = numpy.ones((3, 1))
H = numpy.zeros((A.shape[1], F.shape[1]))
return MPLP_Program(A, b, c, H, A_t, b_t, F, [0])
@pytest.fixture()
5
Source : test_fixtures.py
with MIT License
from TAMUparametric
with MIT License
from TAMUparametric
def region() -> CriticalRegion:
"""A square critical region with predictable properties."""
A = numpy.eye(2)
b = numpy.zeros((2, 1))
C = numpy.eye(2)
d = numpy.zeros((2, 1))
E = numpy.block([[numpy.eye(2)], [-numpy.eye(2)]])
f = make_column([1, 1, 0, 0])
return CriticalRegion(A, b, C, d, E, f, [])
@pytest.fixture()
5
Source : test_fixtures.py
with MIT License
from TAMUparametric
with MIT License
from TAMUparametric
def linear_program() -> MPLP_Program:
"""A simple mplp to test the dimensional correctness of its functions."""
A = numpy.eye(3)
b = numpy.zeros((3, 1))
F = numpy.ones((3, 10))
A_t = numpy.block([[-numpy.eye(5)], [numpy.eye(5)]])
b_t = numpy.ones((10, 1))
c = numpy.ones((3, 1))
H = numpy.zeros((A.shape[1], F.shape[1]))
return MPLP_Program(A, b, c, H, A_t, b_t, F, [0])
@pytest.fixture()
3
Source : m_gpflow.py
with MIT License
from AaltoML
with MIT License
from AaltoML
def inv(K):
K_chol = sp.linalg.cholesky(K + jit * np.eye(M), lower=True)
return sp.linalg.cho_solve((K_chol, True), np.eye(K.shape[0]))
# manual q(u) decompositin
nat1 = np.zeros([M, 1])
3
Source : ppbo_numerical_main.py
with MIT License
from AaltoPML
with MIT License
from AaltoPML
def hartmann6d(traj):
PPBO_settings_ = PPBO_settings(D=6,bounds=((0, 1),)*6,
xi_acquisition_function=traj.xi_acquisition_function,m=traj.m,
theta_initial=[0.001,0.26,0.1],alpha_grid_distribution='TGN') #[0.001,0.26,0.1], [1,0.1,8]
initial_queries_xi = np.eye(PPBO_settings_.D)
np.random.seed(traj.initialization_seed)
initial_queries_x = np.random.uniform([PPBO_settings_.original_bounds[i][0] for i in range(PPBO_settings_.D)], [PPBO_settings_.original_bounds[i][1] for i in range(PPBO_settings_.D)], (len(initial_queries_xi), PPBO_settings_.D))
results,xstar_results,mustar_results,GP_model = run_ppbo_loop('hartmann6d',initial_queries_xi,initial_queries_x,traj.number_of_actual_queries,PPBO_settings_)
traj.f_add_result('xstar',xstar_results)
traj.f_add_result('mustar',mustar_results)
return GP_model
''' Run experimetns '''
3
Source : acquisition.py
with MIT License
from AaltoPML
with MIT License
from AaltoPML
def EId_xstar(GP_model,mc_samples):
''' Returns the dimension that maximizes EI given x=xstar '''
xstar = GP_model.xstar.copy()
EIvals = [0]*GP_model.D
xis = np.eye(GP_model.D)
for d in range(GP_model.D):
xi = xis[d]
xstar_ = xstar.copy()
xstar_[d] = 0
EIvals[d] = EI(xi,xstar_,GP_model,mc_samples)
dstar = np.argmax(EIvals)
xistar = xis[dstar] #best standard unit vector
xstar[dstar] = 0
return xistar
def EId_integrate(GP_model,mc_samples):
3
Source : acquisition.py
with MIT License
from AaltoPML
with MIT License
from AaltoPML
def PCD_next_xi(PPBO_settings):
I = np.eye(PPBO_settings.D)
d = int(PPBO_settings.dim_query_prev_iter + 1)
if d > PPBO_settings.D:
d = 1
PPBO_settings.dim_query_prev_iter = d
return I[:,d-1]
def EXT_next_xi(PPBO_settings,GP_model):
3
Source : align.py
with MIT License
from abel-research
with MIT License
from abel-research
def __init__(self, moving, static, method = 'linPoint2Plane',
inverse=False, *args, **kwargs):
mData = dict(zip(['vert', 'faces', 'values'],
[moving.vert, moving.faces, moving.values]))
alData = copy.deepcopy(mData)
self.setMoving(AmpObject(alData, stype='reg'))
self.setStatic(static)
self.R = np.eye(3)
self.T = np.zeros(3)
self.tForm = np.eye(4)
self.rmse = 0
if inverse:
self.inverse(method=method, *args, **kwargs)
else:
self.runICP(method=method, *args, **kwargs)
def setStatic(self, amp):
3
Source : test_transform.py
with Apache License 2.0
from Accenture
with Apache License 2.0
from Accenture
def assert_is_translation(transform, min, max):
assert transform.shape == (3, 3)
assert np.array_equal(transform[:, 0:2], np.eye(3, 2))
assert transform[2, 2] == 1
assert np.greater_equal(transform[0:2, 2], min).all()
assert np.less( transform[0:2, 2], max).all()
def test_random_translation():
3
Source : test_numpy_routines.py
with GNU General Public License v3.0
from ad12
with GNU General Public License v3.0
from ad12
def test_clip(self):
# Clip
shape = (10, 20, 30)
mv = MedicalVolume(np.random.rand(*shape), np.eye(4))
mv2 = np.clip(mv, 0.4, 0.6)
assert np.all((mv2.volume >= 0.4) & (mv2.volume < = 0.6))
mv_lower = MedicalVolume(np.ones(mv.shape) * 0.4, mv.affine)
mv_upper = MedicalVolume(np.ones(mv.shape) * 0.6, mv.affine)
mv2 = np.clip(mv, mv_lower, mv_upper)
assert np.all((mv2.volume >= 0.4) & (mv2.volume < = 0.6))
def test_array_like(self):
3
Source : test_numpy_routines.py
with GNU General Public License v3.0
from ad12
with GNU General Public License v3.0
from ad12
def test_array_like(self):
shape = (10, 20, 30)
mv = MedicalVolume(np.random.rand(*shape), np.eye(4))
mv2 = np.zeros_like(mv)
assert np.all(mv2.volume == 0)
mv2 = np.ones_like(mv)
assert np.all(mv2.volume == 1)
def test_shares_memory(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_keepdims(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for axis in [None, 0, 1]:
tgt = rf(mat, axis=axis, keepdims=True)
res = nf(mat, axis=axis, keepdims=True)
assert_(res.ndim == tgt.ndim)
def test_out(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_out(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
resout = np.zeros(3)
tgt = rf(mat, axis=1)
res = nf(mat, axis=1, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
def test_dtype_from_input(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dtype_from_input(self):
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
mat = np.eye(3, dtype=c)
tgt = rf(mat, axis=1).dtype.type
res = nf(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, axis=None).dtype.type
res = nf(mat, axis=None).dtype.type
assert_(res is tgt)
def test_result_values(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_matrices(self):
# Check that it works and that type and
# shape are preserved
mat = np.matrix(np.eye(3))
for f in self.nanfuncs:
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 1))
res = f(mat)
assert_(np.isscalar(res))
class TestNanFunctions_IntTypes(object):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_out(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
resout = np.zeros(3)
tgt = rf(mat, axis=1)
res = nf(mat, axis=1, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
def test_dtype_from_dtype(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
def test_dtype_from_char(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
def test_dtype_from_input(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dtype_from_input(self):
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
mat = np.eye(3, dtype=c)
tgt = rf(mat, axis=1).dtype.type
res = nf(mat, axis=1).dtype.type
assert_(res is tgt, "res %s, tgt %s" % (res, tgt))
# scalar case
tgt = rf(mat, axis=None).dtype.type
res = nf(mat, axis=None).dtype.type
assert_(res is tgt)
def test_result_values(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_matrices(self):
# Check that it works and that type and
# shape are preserved
mat = np.matrix(np.eye(3))
for f in self.nanfuncs:
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 1))
res = f(mat)
assert_(np.isscalar(res))
class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_matrices(self):
# Check that it works and that type and
# shape are preserved
mat = np.matrix(np.eye(3))
for f in self.nanfuncs:
for axis in np.arange(2):
res = f(mat, axis=axis)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 3))
res = f(mat)
assert_(res.shape == (1, 3*3))
def test_result_values(self):
3
Source : test_nanfunctions.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_out(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
resout = np.eye(3)
for axis in (-2, -1, 0, 1):
tgt = rf(mat, axis=axis)
res = nf(mat, axis=axis, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
class TestNanFunctions_MeanVarStd(SharedNanFunctionsTestsMixin):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_basic(self):
assert_equal(eye(4),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]]))
assert_equal(eye(4, dtype='f'),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], 'f'))
assert_equal(eye(3) == 1,
eye(3, dtype=bool))
def test_diag(self):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_diag(self):
assert_equal(eye(4, k=1),
array([[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]]))
assert_equal(eye(4, k=-1),
array([[0, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]))
def test_2d(self):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_2d(self):
assert_equal(eye(4, 3),
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]]))
assert_equal(eye(3, 4),
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]))
def test_diag2d(self):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_diag2d(self):
assert_equal(eye(3, 4, k=2),
array([[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]]))
assert_equal(eye(4, 3, k=-2),
array([[0, 0, 0],
[0, 0, 0],
[1, 0, 0],
[0, 1, 0]]))
def test_eye_bounds(self):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_eye_bounds(self):
assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]])
assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]])
assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]])
assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]])
assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]])
assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]])
assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]])
assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]])
assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]])
def test_strings(self):
3
Source : test_twodim_base.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_order(self):
mat_c = eye(4, 3, k=-1)
mat_f = eye(4, 3, k=-1, order='F')
assert_equal(mat_c, mat_f)
assert mat_c.flags.c_contiguous
assert not mat_c.flags.f_contiguous
assert not mat_f.flags.c_contiguous
assert mat_f.flags.f_contiguous
class TestDiag(object):
3
Source : test_deprecations.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_qr_mode_full_future_warning():
"""Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
"""
a = np.eye(2)
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
if __name__ == "__main__":
3
Source : test_linalg.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_symmetric_rank(self):
yield assert_equal, 4, matrix_rank(np.eye(4), hermitian=True)
yield assert_equal, 1, matrix_rank(np.ones((4, 4)), hermitian=True)
yield assert_equal, 0, matrix_rank(np.zeros((4, 4)), hermitian=True)
# rank deficient matrix
I = np.eye(4)
I[-1, -1] = 0.
yield assert_equal, 3, matrix_rank(I, hermitian=True)
# manually supplied tolerance
I[-1, -1] = 1e-8
yield assert_equal, 4, matrix_rank(I, hermitian=True, tol=0.99e-8)
yield assert_equal, 3, matrix_rank(I, hermitian=True, tol=1.01e-8)
def test_reduced_rank():
3
Source : test_regression.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_large_svd_32bit(self):
# See gh-4442, 64bit would require very large/slow matrices.
x = np.eye(1000, 66)
np.linalg.svd(x)
def test_svd_no_uv(self):
3
Source : test_core.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_asarray_default_order(self):
# See Issue #6646
m = np.eye(3).T
assert_(not m.flags.c_contiguous)
new_m = asarray(m)
assert_(new_m.flags.c_contiguous)
def test_asarray_enforce_order(self):
3
Source : test_core.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_asarray_enforce_order(self):
# See Issue #6646
m = np.eye(3).T
assert_(not m.flags.c_contiguous)
new_m = asarray(m, order='C')
assert_(new_m.flags.c_contiguous)
def test_fix_invalid(self):
3
Source : test_extras.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dot_returns_maskedarray(self):
# See gh-6611
a = np.eye(3)
b = array(a)
assert_(type(dot(a, a)) is MaskedArray)
assert_(type(dot(a, b)) is MaskedArray)
assert_(type(dot(b, a)) is MaskedArray)
assert_(type(dot(b, b)) is MaskedArray)
def test_dot_out(self):
3
Source : test_extras.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_dot_out(self):
a = array(np.eye(3))
out = array(np.zeros((3, 3)))
res = dot(a, a, out=out)
assert_(res is out)
assert_equal(a, res)
class TestApplyAlongAxis(object):
3
Source : test_defmatrix.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_row_column_indexing(self):
x = asmatrix(np.eye(2))
assert_array_equal(x[0,:], [[1, 0]])
assert_array_equal(x[1,:], [[0, 1]])
assert_array_equal(x[:, 0], [[1], [0]])
assert_array_equal(x[:, 1], [[0], [1]])
def test_boolean_indexing(self):
3
Source : test_regression.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_matrix_multiply_by_1d_vector(self):
# Ticket #473
def mul():
np.mat(np.eye(2))*np.ones(2)
assert_raises(ValueError, mul)
def test_matrix_std_argmax(self):
3
Source : rbf.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def A(self):
# this only exists for backwards compatibility: self.A was available
# and, at least technically, public.
r = self._call_norm(self.xi, self.xi)
return self._init_function(r) - np.eye(self.N)*self.smooth
def _call_norm(self, x1, x2):
3
Source : test_mio.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_sparse_in_struct():
# reproduces bug found by DC where Cython code was insisting on
# ndarray return type, but getting sparse matrix
st = {'sparsefield': SP.coo_matrix(np.eye(4))}
stream = BytesIO()
savemat(stream, {'a':st})
d = loadmat(stream, struct_as_record=True)
assert_array_equal(d['a'][0,0]['sparsefield'].todense(), np.eye(4))
def test_mat_struct_squeeze():
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_empty_rhs(self):
a = np.eye(2)
b = [[], []]
x = solve(a, b)
assert_(x.size == 0, 'Returned array is not empty')
assert_(x.shape == (2, 0), 'Returned empty array shape is wrong')
def test_multiple_rhs(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_multiple_rhs(self):
a = np.eye(2)
b = np.random.rand(2, 3, 4)
x = solve(a, b)
assert_array_almost_equal(x, b)
def test_transposed_keyword(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_transposed_notimplemented(self):
a = np.eye(3).astype(complex)
with assert_raises(NotImplementedError):
solve(a, a, transposed=True)
def test_nonsquare_a(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_simple(self):
a = [[1, 2], [3, 4]]
a_inv = inv(a)
assert_array_almost_equal(dot(a, a_inv), np.eye(2))
a = [[1, 2, 3], [4, 5, 6], [7, 8, 10]]
a_inv = inv(a)
assert_array_almost_equal(dot(a, a_inv), np.eye(3))
def test_random(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_simple_real(self):
a = array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=float)
a_pinv = pinv(a)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
a_pinv = pinv2(a)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
def test_simple_complex(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_simple_complex(self):
a = (array([[1, 2, 3], [4, 5, 6], [7, 8, 10]],
dtype=float) + 1j * array([[10, 8, 7], [6, 5, 4], [3, 2, 1]],
dtype=float))
a_pinv = pinv(a)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
a_pinv = pinv2(a)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
def test_simple_singular(self):
3
Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_check_finite(self):
a = array([[1, 2, 3], [4, 5, 6.], [7, 8, 10]])
a_pinv = pinv(a, check_finite=False)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
a_pinv = pinv2(a, check_finite=False)
assert_array_almost_equal(dot(a, a_pinv), np.eye(3))
def test_native_list_argument(self):
3
Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_simple3(self):
a = np.eye(3)
a[-1, 0] = 2
h, q = hessenberg(a, calc_q=1)
assert_array_almost_equal(dot(transp(q), dot(a, q)), h)
def test_random(self):
3
Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_2x2(self):
a = [[2, 1], [7, 12]]
h, q = hessenberg(a, calc_q=1)
assert_array_almost_equal(q, np.eye(2))
assert_array_almost_equal(h, a)
b = [[2-7j, 1+2j], [7+3j, 12-2j]]
h2, q2 = hessenberg(b, calc_q=1)
assert_array_almost_equal(q2, np.eye(2))
assert_array_almost_equal(h2, b)
class TestQZ(object):
See More Examples