def test_inv_lowtr():
A = numpy.random.rand(5, 5)
L = numpy.zeros([5, 5])
for j in range(0, 5):
for i in range(j + 1, 5):
L[i, j] = A[i, j]
L = MPFMatrix(L) + MPFMatrix(mpmath.eye(5))
Linv = L.inv_lowtr()
assert (Linv * L).almost_close(MPFMatrix(mpmath.eye(5)))
def test_mul_exact():
Impmath = mpmath.eye(5)
A = numpy.random.rand(5, 5)
A = MPFMatrix(A)
C0 = A.mul_exact(MPFMatrix(Impmath))
assert C0.equal(A)
def rotate_3D_mp(x, y):
xnorm = mpm.norm(x)
ynorm = mpm.norm(y)
cos = mpm.fdot(x,y)/(xnorm*ynorm)
sin = mpm.sqrt(1.-cos**2)
K = (y.T*x-x.T*y)/(xnorm*ynorm*sin)
return mpm.eye(3) + sin*K + (1.-cos)*(K*K)
def abcd(self):
d = self._distance
T = mp.eye(2)
T[0, 1] = d
return T
def abcd(self):
f = self._focal_length
T = mp.eye(2)
T[1, 0] = -1 / f
return T
def full_transfer_matrix(self, Energy):
# Функция, создающая полную матрицу переноса через всю заданную гетероструктуру
M = []
Mt = []
N = []
E = Energy
T = mp.eye(2)
a = np.zeros(len(self.structure_width_vector) + 1)
# Полная матрица переноса через всю структуру
for number in range(self.number_of_layers - 1):
if number == max(range(self.number_of_layers - 1)):
T = self.matrix_invers(
self.matrix_M(number + 1, E,
self.a[number + 2])) * self.matrix_M(
number, E, self.a[number + 1]) * T
else:
T = self.matrix_N(
number + 1, E, self.a[number + 2]) * self.matrix_invers(
self.matrix_M(number + 1, E,
self.a[number + 2])) * self.matrix_M(
number, E, self.a[number + 1]) * T
# Возвращает элемент Т(2,2) матрицы Т
return T[1, 1]
def full_transfer_matrix(self, Energy):
# Функция, создающая полную матрицу переноса через всю заданную гетероструктуру
M = []
Mt = []
N = []
T = mp.eye(2)
for number in range(self.number_of_layers):
M.append(self.matrix_M(number, Energy))
Mt.append(self.matrix_invers(self.matrix_M(number, Energy)))
if not number == min(range(
self.number_of_layers)) and not number == max(
range(self.number_of_layers)):
N.append(self.matrix_N(number, Energy))
else:
N.append(0)
# Полная матрица переноса через всю структуру
for number in range(self.number_of_layers - 1):
if number == max(range(self.number_of_layers - 1)):
T = Mt[number + 1] * M[number] * T
else:
T = N[number + 1] * Mt[number + 1] * M[number] * T
# Возвращает элемент Т(2,2) матрицы Т
return T[1, 1]
def abcd(self):
n1 = self._n1
n2 = self._n2
T = mp.eye(2)
T[1, 1] = n1 / n2
return T
def cocycle(word, mathcalA):
#computes A^(n)(x) for x a periodic point
d = mathcalA('0').rows
A = mpmath.eye(d)
for i in range(len(word)):
A = A * mathcalA(word)
word = shift(word)
return A
def rotate_mp(pts, x, y):
out = mpm.zeros(pts.rows, pts.cols)
v = x/mpm.norm(x)
cos = mpm.fdot(x,y) / (mpm.norm(x), mpm.norm(y))
sin = mpm.sqrt(1.-cos**2)
u = y - mpm.fdot(v, y) * v
mat = mpm.eye(x.cols) - u.T * u - v.T * v \
+ cos * u.T * u - sin * v.T * u + sin * u.T * v + cos * v.T * v
return mat
def test_create_hex_grid(self):
b = CoherentBasis.create_hexagonal_grid(0, 1.2, 1)
self.assertEqual(len(b.states), 7)
trafo = b.trafo
inv_trafo = b.inv_trafo
self.assertIsInstance(trafo, numpy.ndarray)
self.assertIsInstance(inv_trafo, numpy.ndarray)
I1 = numpy.abs(numpy.dot(trafo, inv_trafo))
I2 = numpy.array(mpmath.eye(7).tolist())
self.assertTrue(((I1 - I2) < 1e-14).all())
def test_mev():
output("""\
reim:{$[0>type x;1 0*x;2=count x;x;'`]};
mc:{((x[0]*y 0)-x[1]*y 1;(x[0]*y 1)+x[1]*y 0)};
mmc:{((.qml.mm[x 0]y 0)-.qml.mm[x 1]y 1;(.qml.mm[x 0]y 1)+.qml.mm[x 1]y 0)};
mev_:{[b;x]
if[2<>count wv:.qml.mev x;'`length];
if[not all over prec>=abs
mmc[flip vc;flip(flip')(reim'')flip x]-
flip(w:reim'[wv 0])mc'vc:(flip')(reim'')(v:wv 1);'`check];
/ Normalize sign; LAPACK already normalized to real
v*:1-2*0>{x a?max a:abs x}each vc[;0];
(?'[prec>=abs w[;1];w[;0];w];?'[b;v;0n])};""")
for A in eigenvalue_subjects:
if A.rows <= 3:
V = []
for w, n, r in A.eigenvects():
w = sp.simplify(sp.expand_complex(w))
if len(r) == 1:
r = r[0]
r = sp.simplify(sp.expand_complex(r))
r = r.normalized() / sp.sign(max(r, key=abs))
r = sp.simplify(sp.expand_complex(r))
else:
r = None
V.extend([(w, r)] * n)
V.sort(key=lambda (x, _): (-abs(x), -sp.im(x)))
else:
Am = mp.matrix(A)
# extra precision for complex pairs to be equal in sort
with mp.extradps(mp.mp.dps):
W, R = mp.eig(Am)
V = []
for w, r in zip(W, (R.column(i) for i in range(R.cols))):
w = mp.chop(w)
with mp.extradps(mp.mp.dps):
_, S, _ = mp.svd(Am - w * mp.eye(A.rows))
if sum(x == 0 for x in mp.chop(S)) == 1:
# nullity 1, so normalized eigenvector is unique
r /= mp.norm(r) * mp.sign(max(r, key=abs))
r = mp.chop(r)
else:
r = None
V.append((w, r))
V.sort(key=lambda (x, _): (-abs(x), -x.imag))
W, R = zip(*V)
test("mev_[%sb" % "".join("0" if r is None else "1" for r in R),
A, (W, [r if r is None else list(r) for r in R]),
complex_pair=True)
def test_mev():
output("""\
reim:{$[0>type x;1 0*x;2=count x;x;'`]};
mc:{((x[0]*y 0)-x[1]*y 1;(x[0]*y 1)+x[1]*y 0)};
mmc:{((.qml.mm[x 0]y 0)-.qml.mm[x 1]y 1;(.qml.mm[x 0]y 1)+.qml.mm[x 1]y 0)};
mev_:{[b;x]
if[2<>count wv:.qml.mev x;'`length];
if[not all over prec>=abs
mmc[flip vc;flip(flip')(reim'')flip x]-
flip(w:reim'[wv 0])mc'vc:(flip')(reim'')(v:wv 1);'`check];
/ Normalize sign; LAPACK already normalized to real
v*:1-2*0>{x a?max a:abs x}each vc[;0];
(?'[prec>=abs w[;1];w[;0];w];?'[b;v;0n])};""")
for A in eigenvalue_subjects:
if A.rows <= 3:
V = []
for w, n, r in A.eigenvects():
w = sp.simplify(sp.expand_complex(w))
if len(r) == 1:
r = r[0]
r = sp.simplify(sp.expand_complex(r))
r = r.normalized() / sp.sign(max(r, key=abs))
r = sp.simplify(sp.expand_complex(r))
else:
r = None
V.extend([(w, r)]*n)
V.sort(key=lambda (x, _): (-abs(x), -sp.im(x)))
else:
Am = mp.matrix(A)
# extra precision for complex pairs to be equal in sort
with mp.extradps(mp.mp.dps):
W, R = mp.eig(Am)
V = []
for w, r in zip(W, (R.column(i) for i in range(R.cols))):
w = mp.chop(w)
with mp.extradps(mp.mp.dps):
_, S, _ = mp.svd(Am - w*mp.eye(A.rows))
if sum(x == 0 for x in mp.chop(S)) == 1:
# nullity 1, so normalized eigenvector is unique
r /= mp.norm(r) * mp.sign(max(r, key=abs))
r = mp.chop(r)
else:
r = None
V.append((w, r))
V.sort(key=lambda (x, _): (-abs(x), -x.imag))
W, R = zip(*V)
test("mev_[%sb" % "".join("0" if r is None else "1" for r in R), A,
(W, [r if r is None else list(r) for r in R]), complex_pair=True)
def q_eit_standing_wave(Delta, Deltac, Omega, g1d, periodLength, phaseShift):
if Delta == Deltac:
return 0
gprime = 1 - g1d
kd = pi / periodLength
Mcell = eye(2)
for i in range(periodLength):
OmegaAtThisSite = Omega * cos(kd * i + pi * phaseShift)
beta3 = (g1d * (Delta - Deltac)) / (
(-2.0j * Delta + gprime) *
(Delta - Deltac) + 2.0j * OmegaAtThisSite**2)
M3 = matrix([[1 - beta3, -beta3], [beta3, 1 + beta3]])
Mf = matrix([[exp(1j * kd), 0], [0, exp(-1j * kd)]])
Mcell = Mf * M3 * Mcell
ret = (1.0 / periodLength) * acos(-0.5 * (Mcell[0, 0] + Mcell[1, 1]))
return ret
def compute_pw_coal_rates(Nes, ms, ts, popmaps):
"""Given a list of population sizes and the migration matrix,
one for each time slice, compute the expected coalescent rates
for all possible pairs of lines. Uses the comp_pw_coal_cont
function to do this for each time slice.
The lists ms and Nes must be ordered from most recent to most
ancient. Here ts is the list of time slice lengths - in the same
order. popmaps is a list which tells which pops merge back
in time at the beginning of this time slice. Each entry
of popmaps is a list of arrays or tuples with all pops that
merge into one
"""
numslices = len(ts)
numpops = len(Nes[0])
nr = numpops * (numpops + 1) / 2 + 1
exp_rates = mp.zeros(nr - 1, numslices)
P0 = mp.eye(nr)
for i in xrange(numslices):
Ne_inv = [ 1.0 / x for x in Nes[i] ]
mtemp = ms[i]
oldnumpops = numpops
numpops = len(Ne_inv)
m = np.zeros((numpops, numpops))
cnt = 0
if len(popmaps[i]) == numpops:
P0 = converge_pops(popmaps[i], P0)
elif len(popmaps[i]) > 0:
raise Exception('Population map and other parameters do not match ' + str(i))
for ii in xrange(numpops):
for jj in xrange(ii + 1, numpops):
m[ii, jj] = m[jj, ii] = mtemp[cnt]
cnt += 1
Q = comp_pw_coal_cont(m, Ne_inv)
eQ = expM(ts[i] * Q)
P = P0 * eQ
exp_rates[:, i] = P[0:nr - 1, P.cols - 1]
P0 = P0 * conv_scrambling_matrix(eQ)
for r in xrange(exp_rates.rows):
for c in xrange(exp_rates.cols):
if exp_rates[r, c] < 0:
exp_rates[r, c] = 0
return exp_rates
def test_mul():
A = numpy.random.rand(5, 5)
I = numpy.eye(5)
Z = numpy.zeros([5, 5])
Impmath = mpmath.eye(5)
Zmpmath = mpmath.zeros(5, 5)
A = MPFMatrix(A)
C0 = A * I
assert C0.almost_close(A)
C1 = A * Impmath
assert C1.almost_close(A)
C2 = MPFMatrix(I) * A
assert C2.almost_close(A)
C3 = A * Z
assert C3.almost_close(Z)
C4 = A * Zmpmath
assert C4.almost_close(Zmpmath)
def Han_algorithm(B, m, dim):
"""
From Insu Han, Dmitry Malioutov and Jinwoo Shin: Large-scale Log-determinant Computation
through Stochastic Chebyshev Expansions, Proceedings of the 32 nd International Conference on Machine
Learning, Lille, France, 2015. JMLR: W&CP volume 37
:param B: Square input Matrix
:param m: Order of chebyshev approximation
:param dim: Dimension of input Matrix
:return G: log-determinant of B for cases, where all eigenvalues of B lie within (0, 1)
"""
B = mp.eye(12) - B
G = 0
v = mp.matrix(dim, 1)
w = mp.matrix(dim, 3)
for i in range(m):
for j in range(dim):
v[j] = random.choice([-1, 1])
u = coeffs[0] * v
n = len(coeffs)
if m > 1:
w[:, 0] = v
w[:, 1] = B * v
u = u + coeffs[1] * v
for k in range(1, n, 1):
w[:, 2] = 2 * B * w[:, 1] - w[:, 0]
u = u + coeffs[k] * w[:, 2]
w[:, 0] = w[:, 1]
w[:, 1] = w[:, 2]
G = G + v.T * u/m
return G
def QSidentity(sz):
if QSMODE == MODE_NORM:
return np.matrix(np.identity(sz, dtype=np.complex128))
else:
return mpmath.eye(sz)
],
'agm': [
'primitive',
[lambda x, y: mp.agm(x) if y is None else mp.agm(x, y[0]), None]
],
#
'matrix': [
'primitive',
[
lambda x, y: mp.matrix(x) if isa(x, Vector) else mp.matrix(x)
if y is None else mp.matrix(x, y[0]), None
]
],
'matrix-ref': ['primitive', [lambda x, y: x[y[0], y[1], [0]], None]],
'matrix-set': [
'primitive',
[lambda x, y: matrix_set(x, y[0], y[1][0], y[1][1][0]), None]
],
'zeros': ['primitive', [lambda x, y: mp.zeros(x), None]],
'ones': ['primitive', [lambda x, y: mp.ones(x), None]],
'eye': ['primitive', [lambda x, y: mp.eye(x), None]],
'diag': ['primitive', [lambda x, y: mp.diag(x), None]],
'randmatrix': ['primitive', [lambda x, y: mp.randmatrix(x), None]],
'matrix-inv': ['primitive', [lambda x, y: x**(-1), None]],
'norm': [
'primitive',
[lambda x, y: mp.norm(x) if y is None else mp.norm(x, y[0]), None]
],
'mnorm': ['primitive', [lambda x, y: mp.mnorm(x, y[0]), None]],
}
def abcd(self):
return mp.eye(2)
def comp_N_m_mp(obs_rates, t, merge_threshold, useMigration):
"""This function estimates the N and m parameters for the various time slices. The
time slices are given in a vector form 't'. t specifies the length of the time slice, not
the time from present to end of time slice (not cumulative but atomic)
Also, the obs_rates are given, for each time slice are given in columns of the obs_rates
matrix. Both obs_rates and time slice lengths are given from present to past.
"""
FTOL = 10.0
PTOL = 1e-20
EPSILON = 1e-11
RESTARTS = 10
FLIMIT = 1e-20
numslices = len(t)
nr = obs_rates.rows + 1
numdemes = int(np.real(np.sqrt(8 * nr - 7)) / 2)
print 'Starting iterations'
P0 = mp.eye(nr)
xopts = []
pdlist = []
for i in xrange(numslices):
bestxopt = None
bestfval = 1e+200
print 'Running for slice ', i
if i > 0:
x0 = xopt[0:nr - 1].copy()
try:
xopt = mp.findroot(lambda x: compute_Frob_norm_mig(x, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist)), x0)
fun = compute_Frob_norm_mig(xopt, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist))
bestxopt = xopt
bestfval = fun
except ValueError as e:
print 'Error:', e.message
for lll in xrange(numdemes * (numdemes - 1) / 2):
x01 = x0[0:nr - 1].copy()
x01[numdemes + lll] = 0.0099
try:
xopt = mp.findroot(lambda x: compute_Frob_norm_mig(x, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist)), x01)
fun = compute_Frob_norm_mig(xopt, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist))
if fun < bestfval:
bestfval = fun
bestxopt = xopt
except ValueError as e:
print 'Error:', e.message
reestimate = True
while reestimate:
pdslice = make_merged_pd(pdlist)
print 'pdslice:', pdslice
N0_inv = np.random.uniform(5e-05, 0.001, numdemes)
m0 = np.random.uniform(0.008, 0.001, numdemes * (numdemes - 1) / 2)
x0 = [ mp.convert(rrr) for rrr in N0_inv ]
for zz in range(len(m0)):
x0.append(mp.convert(m0[zz]))
lims = [(1e-15, 0.1)] * numdemes
lims += [(1e-15, 0.1)] * (numdemes * (numdemes - 1) / 2)
try:
xopt = mp.findroot(lambda x: compute_Frob_norm_mig(x, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist)), x0)
fun = compute_Frob_norm_mig(xopt, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist))
if fun < bestfval:
bestfval = fun
bestxopt = xopt
except ValueError as e:
print 'Error:', e.message
N0_inv = np.random.uniform(5e-05, 0.001, numdemes)
m0 = np.random.uniform(1e-08, 0.001, numdemes * (numdemes - 1) / 2)
x0 = [ mp.convert(rrr) for rrr in N0_inv ]
for zz in range(len(m0)):
x0.append(mp.convert(m0[zz]))
nrestarts = 0
try:
xopt = mp.findroot(lambda x: compute_Frob_norm_mig(x, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist)), x0)
fun = compute_Frob_norm_mig(xopt, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist))
if fun < bestfval:
bestxopt = xopt
bestfval = fun
except ValueError as e:
print 'Error:', e.message
print nrestarts
while nrestarts < RESTARTS and bestfval > FLIMIT:
N0_inv = np.random.uniform(5e-05, 0.001, numdemes)
m0 = np.random.uniform(1e-08, 0.001, numdemes * (numdemes - 1) / 2)
x0 = mp.zeros(len(N0_inv) + len(m0))
for zz in range(len(N0_inv)):
x0[zz] = N0_inv[zz]
for zz in range(len(m0)):
x0[zz + len(N0_inv)] = m0[i]
try:
xopt = mp.findroot(lambda x: compute_Frob_norm_mig(x, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist)), x0)
fun = compute_Frob_norm_mig(xopt, t[i], obs_rates[:, i], P0, make_merged_pd(pdlist))
if fun < bestfval:
bestfval = fun
bestxopt = xopt
except ValueError as e:
print 'Error:', e.message
print nrestarts
nrestarts += 1
print bestfval, i, bestxopt[0:numdemes], bestxopt[numdemes:]
Ne_inv = bestxopt[0:numdemes]
mtemp = bestxopt[numdemes:]
popdict = find_pop_merges(Ne_inv, mtemp, t[i], P0, merge_threshold, useMigration)
reestimate = False
if len(popdict) < numdemes:
print 'Merging populations and reestimating parameters:', popdict
print bestxopt
P0 = converge_pops(popdict, P0)
reestimate = True
pdlist.append(popdict)
numdemes = len(popdict)
nr = numdemes * (numdemes + 1) / 2 + 1
lims[:] = []
bestfval = 1e+200
bestxopt = None
else:
modXopt = [ 1.0 / x for x in bestxopt[0:numdemes] ]
cnt = 0
for ii in xrange(numdemes):
for jj in xrange(ii + 1, numdemes):
modXopt.append(bestxopt[numdemes + cnt])
cnt = cnt + 1
xopts.append(np.array(modXopt))
Ne_inv = bestxopt[0:numdemes]
mtemp = bestxopt[numdemes:]
m = np.zeros((numdemes, numdemes))
cnt = 0
for ii in xrange(numdemes):
for jj in xrange(ii + 1, numdemes):
m[ii, jj] = m[jj, ii] = mtemp[cnt]
cnt += 1
Q = comp_pw_coal_cont(m, Ne_inv)
P = expM(t[i] * Q)
print 'est rates', i
print np.real(P0 * P)[0:-1, -1]
print 'obs rates', i
print np.real(obs_rates[:, i])
print 'Min func value', bestfval
P0 = P0 * conv_scrambling_matrix(P)
ist = raw_input('Waiting for input...')
return (xopts, pdlist)
def get_unary(self, item):
return {
**{
k: calc2.UnaryOperator(s, f, 80)
for k, s, f in [
('abs', 'abs', abs),
('fac', 'fac', mpmath.factorial),
('sqrt', 'sqrt', mpmath.sqrt),
('_/', 'sqrt', mpmath.sqrt),
('√', 'sqrt', mpmath.sqrt),
('ln', 'ln', mpmath.ln),
('lg', 'log10', mpmath.log10),
('exp', 'e^', mpmath.exp),
('floor', 'floor', mpmath.floor),
('ceil', 'ceil', mpmath.ceil),
('det', 'det', mpmath.det),
]
}, 'pcn':
calc2.UnaryOperator('a%', lambda x: x / 100, 80),
'+':
calc2.UnaryOperator('(+)', lambda x: x, 0),
'-':
calc2.UnaryOperator('+/-', lambda x: -x, 80),
'conj':
calc2.UnaryOperator(
'conj', lambda x: x.conjugate()
if isinstance(x, mpmath.matrix) else mpmath.conj(x), 80),
'~':
calc2.UnaryOperator(
'conj', lambda x: x.conjugate()
if isinstance(x, mpmath.matrix) else mpmath.conj(x), 80),
'O':
calc2.UnaryOperator(
'[O]', lambda x: mpmath.zeros(*OpList.__analyse_as_pair(x)),
80),
'I':
calc2.UnaryOperator(
'[I]', lambda x: mpmath.ones(*OpList.__analyse_as_pair(x)),
80),
'E':
calc2.UnaryOperator('[E]', lambda x: mpmath.eye(int(x)), 80),
'diag':
calc2.UnaryOperator(
'[diag]', lambda x: mpmath.diag(OpList.__analyse_list(x)), 80),
'log':
calc2.UnaryOperator(
'logbA', lambda x: OpList.__log(*OpList.__analyse_pair(x)),
80),
'tran':
calc2.UnaryOperator(
'[T]', lambda x: x.transpose()
if isinstance(x, mpmath.matrix) else x, 80),
**{
k: calc2.UnaryOperator(k, (lambda u: lambda x: u(self._drg2r(x)))(v), 80)
for k, v in {
'sin': mpmath.sinpi,
'cos': mpmath.cospi,
'tan': lambda x: mpmath.sinpi(x) / mpmath.cospi(x),
'cot': lambda x: mpmath.cospi(x) / mpmath.sinpi(x),
'sec': lambda x: 1 / mpmath.cospi(x),
'csc': lambda x: 1 / mpmath.sinpi(x),
}.items()
},
**{
k: calc2.UnaryOperator(k, (lambda u: lambda x: self._r2drg(1 / v(x)))(v), 80)
for k, v in {
'asin': mpmath.asin,
'acos': mpmath.acos,
'atan': mpmath.atan,
'acot': mpmath.acot,
'asec': mpmath.asec,
'acsc': mpmath.acsc
}.items()
},
**{
k: calc2.UnaryOperator(k, v, 80)
for k, v in {
'sinh': mpmath.sinh,
'cosh': mpmath.cosh,
'tanh': mpmath.tanh,
'coth': mpmath.coth,
'sech': mpmath.sech,
'csch': mpmath.csch,
'asinh': mpmath.asinh,
'acosh': mpmath.acosh,
'atanh': mpmath.atanh,
'acoth': mpmath.acoth,
'asech': mpmath.asech,
'acsch': mpmath.acsch
}.items()
}
}[item]
def identity(sz):
if mode == mode_python:
return np.matrix(np.identity(sz, dtype=np.complex128))
else:
return mpmath.eye(sz)
def QSidentity(sz):
if QSMODE == MODE_NORM:
return np.matrix(np.identity(sz, dtype=np.complex128))
else:
return mpmath.eye(sz)
import numpy as np from mpmath import eye array = np.array([[1, 31, 4], [64, 78, 16]]) print(array) print(array.ndim) print(array.shape) print(array.size) array1 = eye(4) print(array1) print(array1)
A = iv_matrix_mid_to_numpy_ndarray(A)
M2 = iv_matrix_mid_to_numpy_ndarray(M2)
# coerce tau to maximum
tau = float(mp.mpf(abs(iv.mpf(tau)).b))
max_norm = 0
for t in numpy.linspace(-tau, tau, 100):
matrix = numpy.matmul(M1, numpy.matmul(scipy.linalg.expm(A*t) - numpy.eye(len(A)), M2))
max_norm = max(max_norm, approx_P_norm(M=matrix, P_sqrt_T=P_sqrt_T))
return max_norm
if __name__ == "__main__":
random.seed(1234565567)
print("Example: random matrix")
for i in range(1):
c = 2
A = mp.randmatrix(20) - 0.5
eigv_A, _= mp.eig(iv_matrix_mid_as_mp(A))
A = 0.5 * A / max([abs(i) for i in eigv_A])
eigv_A, _= mp.eig(iv_matrix_mid_as_mp(A))
#print('eigenvalues(A) = ', eigv_A)
print('spectral radius(A) = ', max([abs(i) for i in eigv_A]))
print('interval spectral_norm(A) = ', iv_spectral_norm(A))
P_sqrt_T = approx_P_sqrt_T(A)
print('interval P_norm(A) = ',iv_P_norm(A, P_sqrt_T))
print('interval P_norm(...expm(...)) = ', iv_P_norm_expm(P_sqrt_T, M1=mp.eye(len(A)), A=A, M2=mp.eye(len(A)), tau=0.01))
print('sampled P_norm(...expm(...)) = ', approx_P_norm_expm(P_sqrt_T, M1=mp.eye(len(A)), A=A, M2=mp.eye(len(A)), tau=0.01))
print('')
