def test_levin_3():
mp.dps = 17
z = mp.mpf(2)
eps = mp.mpf(mp.eps)
with mp.extraprec(
7 * mp.prec
): # we need copious amount of precision to sum this highly divergent series
L = mp.levin(method="levin", variant="t")
n, s = 0, 0
while 1:
s += (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4**n))
n += 1
v, e = L.step_psum(s)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.8 * mp.log(eps))
exact = mp.quad(lambda x: mp.exp(-x * x / 2 - z * x**4),
[0, mp.inf]) * 2 / mp.sqrt(2 * mp.pi)
# there is also a symbolic expression for the integral:
# exact = mp.exp(mp.one / (32 * z)) * mp.besselk(mp.one / 4, mp.one / (32 * z)) / (4 * mp.sqrt(z * mp.pi))
err = abs(v - exact)
assert err < eps
w = mp.nsum(lambda n: (-z)**n * mp.fac(4 * n) /
(mp.fac(n) * mp.fac(2 * n) * (4**n)), [0, mp.inf],
method="levin",
levin_variant="t",
workprec=8 * mp.prec,
steps=[2] + [1 for x in xrange(1000)])
err = abs(v - w)
assert err < eps
def test_gauss_quadrature_dynamic(verbose = False):
n = 5
A = mp.randmatrix(2 * n, 1)
def F(x):
r = 0
for i in xrange(len(A) - 1, -1, -1):
r = r * x + A[i]
return r
def run(qtype, FW, R, alpha = 0, beta = 0):
X, W = mp.gauss_quadrature(n, qtype, alpha = alpha, beta = beta)
a = 0
for i in xrange(len(X)):
a += W[i] * F(X[i])
b = mp.quad(lambda x: FW(x) * F(x), R)
c = mp.fabs(a - b)
if verbose:
print(qtype, c, a, b)
assert c < 1e-5
run("legendre", lambda x: 1, [-1, 1])
run("legendre01", lambda x: 1, [0, 1])
run("hermite", lambda x: mp.exp(-x*x), [-mp.inf, mp.inf])
run("laguerre", lambda x: mp.exp(-x), [0, mp.inf])
run("glaguerre", lambda x: mp.sqrt(x)*mp.exp(-x), [0, mp.inf], alpha = 1 / mp.mpf(2))
run("chebyshev1", lambda x: 1/mp.sqrt(1-x*x), [-1, 1])
run("chebyshev2", lambda x: mp.sqrt(1-x*x), [-1, 1])
run("jacobi", lambda x: (1-x)**(1/mp.mpf(3)) * (1+x)**(1/mp.mpf(5)), [-1, 1], alpha = 1 / mp.mpf(3), beta = 1 / mp.mpf(5) )
def test_levin_2():
# [2] A. Sidi - "Pratical Extrapolation Methods" p.373
mp.dps = 17
z = mp.mpf(10)
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method="sidi", variant="t")
n = 0
while 1:
s = (-1)**n * mp.fac(n) * z**(-n)
v, e = L.step(s)
n += 1
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
exact = mp.quad(lambda x: mp.exp(-x) / (1 + x / z), [0, mp.inf])
# there is also a symbolic expression for the integral:
# exact = z * mp.exp(z) * mp.expint(1,z)
err = abs(v - exact)
assert err < eps
w = mp.nsum(lambda n: (-1)**n * mp.fac(n) * z**(-n), [0, mp.inf],
method="sidi",
levin_variant="t")
assert err < eps
def test_svd_test_case():
# a test case from Golub and Reinsch
# (see wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971).)
eps = mp.exp(0.8 * mp.log(mp.eps))
a = [[22, 10, 2, 3, 7],
[14, 7, 10, 0, 8],
[-1, 13, -1, -11, 3],
[-3, -2, 13, -2, 4],
[ 9, 8, 1, -2, 4],
[ 9, 1, -7, 5, -1],
[ 2, -6, 6, 5, 1],
[ 4, 5, 0, -2, 2]]
a = mp.matrix(a)
b = mp.matrix([mp.sqrt(1248), 20, mp.sqrt(384), 0, 0])
S = mp.svd_r(a, compute_uv = False)
S -= b
assert mp.mnorm(S) < eps
S = mp.svd_c(a, compute_uv = False)
S -= b
assert mp.mnorm(S) < eps
def run_eigsy(A, verbose=False):
if verbose:
print("original matrix:\n", str(A))
D, Q = mp.eigsy(A)
B = Q * mp.diag(D) * Q.transpose()
C = A - B
E = Q * Q.transpose() - mp.eye(A.rows)
if verbose:
print("eigenvalues:\n", D)
print("eigenvectors:\n", Q)
NC = mp.mnorm(C)
NE = mp.mnorm(E)
if verbose:
print("difference:", NC, "\n", C, "\n")
print("difference:", NE, "\n", E, "\n")
eps = mp.exp(0.8 * mp.log(mp.eps))
assert NC < eps
assert NE < eps
return NC
def run_eigsy(A, verbose = False):
if verbose:
print("original matrix:\n", str(A))
D, Q = mp.eigsy(A)
B = Q * mp.diag(D) * Q.transpose()
C = A - B
E = Q * Q.transpose() - mp.eye(A.rows)
if verbose:
print("eigenvalues:\n", D)
print("eigenvectors:\n", Q)
NC = mp.mnorm(C)
NE = mp.mnorm(E)
if verbose:
print("difference:", NC, "\n", C, "\n")
print("difference:", NE, "\n", E, "\n")
eps = mp.exp( 0.8 * mp.log(mp.eps))
assert NC < eps
assert NE < eps
return NC
def run_eig(A, verbose=0):
if verbose > 1:
print("original matrix (eig):\n", A)
n = A.rows
E, EL, ER = mp.eig(A, left=True, right=True)
if verbose > 1:
print("E:\n", E)
print("EL:\n", EL)
print("ER:\n", ER)
eps = mp.exp(0.8 * mp.log(mp.eps))
err0 = 0
for i in xrange(n):
B = A * ER[:, i] - E[i] * ER[:, i]
err0 = max(err0, mp.mnorm(B))
B = EL[i, :] * A - EL[i, :] * E[i]
err0 = max(err0, mp.mnorm(B))
err0 /= n * n
if verbose > 0:
print("difference (E):", err0)
assert err0 < eps
def run_hessenberg(A, verbose=0):
if verbose > 1:
print("original matrix (hessenberg):\n", A)
n = A.rows
Q, H = mp.hessenberg(A)
if verbose > 1:
print("Q:\n", Q)
print("H:\n", H)
B = Q * H * Q.transpose_conj()
eps = mp.exp(0.8 * mp.log(mp.eps))
err0 = 0
for x in xrange(n):
for y in xrange(n):
err0 += abs(A[y, x] - B[y, x])
err0 /= n * n
err1 = 0
for x in xrange(n):
for y in xrange(x + 2, n):
err1 += abs(H[y, x])
if verbose > 0:
print("difference (H):", err0, err1)
if verbose > 1:
print("B:\n", B)
assert err0 < eps
assert err1 == 0
def test_gauss_quadrature_dynamic(verbose=False):
n = 5
A = mp.randmatrix(2 * n, 1)
def F(x):
r = 0
for i in xrange(len(A) - 1, -1, -1):
r = r * x + A[i]
return r
def run(qtype, FW, R, alpha=0, beta=0):
X, W = mp.gauss_quadrature(n, qtype, alpha=alpha, beta=beta)
a = 0
for i in xrange(len(X)):
a += W[i] * F(X[i])
b = mp.quad(lambda x: FW(x) * F(x), R)
c = mp.fabs(a - b)
if verbose:
print(qtype, c, a, b)
assert c < 1e-5
run("legendre", lambda x: 1, [-1, 1])
run("legendre01", lambda x: 1, [0, 1])
run("hermite", lambda x: mp.exp(-x * x), [-mp.inf, mp.inf])
run("laguerre", lambda x: mp.exp(-x), [0, mp.inf])
run("glaguerre",
lambda x: mp.sqrt(x) * mp.exp(-x), [0, mp.inf],
alpha=1 / mp.mpf(2))
run("chebyshev1", lambda x: 1 / mp.sqrt(1 - x * x), [-1, 1])
run("chebyshev2", lambda x: mp.sqrt(1 - x * x), [-1, 1])
run("jacobi",
lambda x: (1 - x)**(1 / mp.mpf(3)) * (1 + x)**(1 / mp.mpf(5)), [-1, 1],
alpha=1 / mp.mpf(3),
beta=1 / mp.mpf(5))
def test_levin_3():
mp.dps = 17
z=mp.mpf(2)
eps = mp.mpf(mp.eps)
with mp.extraprec(7*mp.prec): # we need copious amount of precision to sum this highly divergent series
L = mp.levin(method = "levin", variant = "t")
n, s = 0, 0
while 1:
s += (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4 ** n))
n += 1
v, e = L.step_psum(s)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.8 * mp.log(eps))
exact = mp.quad(lambda x: mp.exp( -x * x / 2 - z * x ** 4), [0,mp.inf]) * 2 / mp.sqrt(2 * mp.pi)
# there is also a symbolic expression for the integral:
# exact = mp.exp(mp.one / (32 * z)) * mp.besselk(mp.one / 4, mp.one / (32 * z)) / (4 * mp.sqrt(z * mp.pi))
err = abs(v - exact)
assert err < eps
w = mp.nsum(lambda n: (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4 ** n)), [0, mp.inf], method = "levin", levin_variant = "t", workprec = 8*mp.prec, steps = [2] + [1 for x in xrange(1000)])
err = abs(v - w)
assert err < eps
def test_levin_2():
# [2] A. Sidi - "Pratical Extrapolation Methods" p.373
mp.dps = 17
z=mp.mpf(10)
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method = "sidi", variant = "t")
n = 0
while 1:
s = (-1)**n * mp.fac(n) * z ** (-n)
v, e = L.step(s)
n += 1
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
exact = mp.quad(lambda x: mp.exp(-x)/(1+x/z),[0,mp.inf])
# there is also a symbolic expression for the integral:
# exact = z * mp.exp(z) * mp.expint(1,z)
err = abs(v - exact)
assert err < eps
w = mp.nsum(lambda n: (-1) ** n * mp.fac(n) * z ** (-n), [0, mp.inf], method = "sidi", levin_variant = "t")
assert err < eps
def test_cohen_alt_0():
mp.dps = 17
AC = mp.cohen_alt()
S, s, n = [], 0, 1
while 1:
s += -((-1)**n) * mp.one / (n * n)
n += 1
S.append(s)
v, e = AC.update_psum(S)
if e < mp.eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(mp.eps))
err = abs(v - mp.pi**2 / 12)
assert err < eps
def test_cohen_alt_0():
mp.dps = 17
AC = mp.cohen_alt()
S, s, n = [], 0, 1
while 1:
s += -((-1) ** n) * mp.one / (n * n)
n += 1
S.append(s)
v, e = AC.update_psum(S)
if e < mp.eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(mp.eps))
err = abs(v - mp.pi ** 2 / 12)
assert err < eps
def test_cohen_alt_1():
mp.dps = 17
A = []
AC = mp.cohen_alt()
n = 1
while 1:
A.append(mp.loggamma(1 + mp.one / (2 * n - 1)))
A.append(-mp.loggamma(1 + mp.one / (2 * n)))
n += 1
v, e = AC.update(A)
if e < mp.eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
v = mp.exp(v)
err = abs(v - 1.06215090557106)
assert err < 1e-12
def test_cohen_alt_1():
mp.dps = 17
A = []
AC = mp.cohen_alt()
n = 1
while 1:
A.append( mp.loggamma(1 + mp.one / (2 * n - 1)))
A.append(-mp.loggamma(1 + mp.one / (2 * n)))
n += 1
v, e = AC.update(A)
if e < mp.eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
v = mp.exp(v)
err = abs(v - 1.06215090557106)
assert err < 1e-12
def run_schur(A, verbose=0):
if verbose > 1:
print("original matrix (schur):\n", A)
n = A.rows
Q, R = mp.schur(A)
if verbose > 1:
print("Q:\n", Q)
print("R:\n", R)
B = Q * R * Q.transpose_conj()
C = Q * Q.transpose_conj()
eps = mp.exp(0.8 * mp.log(mp.eps))
err0 = 0
for x in xrange(n):
for y in xrange(n):
err0 += abs(A[y, x] - B[y, x])
err0 /= n * n
err1 = 0
for x in xrange(n):
for y in xrange(n):
if x == y:
C[y, x] -= 1
err1 += abs(C[y, x])
err1 /= n * n
err2 = 0
for x in xrange(n):
for y in xrange(x + 1, n):
err2 += abs(R[y, x])
if verbose > 0:
print("difference (S):", err0, err1, err2)
if verbose > 1:
print("B:\n", B)
assert err0 < eps
assert err1 < eps
assert err2 == 0
def test_levin_nsum():
mp.dps = 17
with mp.extraprec(mp.prec):
z = mp.mpf(10) ** (-10)
a = mp.nsum(lambda n: n**(-(1+z)), [1, mp.inf], method = "l") - 1 / z
assert abs(a - mp.euler) < 1e-10
eps = mp.exp(0.8 * mp.log(mp.eps))
a = mp.nsum(lambda n: (-1)**(n-1) / n, [1, mp.inf], method = "sidi")
assert abs(a - mp.log(2)) < eps
z = 2 + 1j
f = lambda n: mp.rf(2 / mp.mpf(3), n) * mp.rf(4 / mp.mpf(3), n) * z**n / (mp.rf(1 / mp.mpf(3), n) * mp.fac(n))
v = mp.nsum(f, [0, mp.inf], method = "levin", steps = [10 for x in xrange(1000)])
exact = mp.hyp2f1(2 / mp.mpf(3), 4 / mp.mpf(3), 1 / mp.mpf(3), z)
assert abs(exact - v) < eps
def test_levin_0():
mp.dps = 17
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method = "levin", variant = "u")
S, s, n = [], 0, 1
while 1:
s += mp.one / (n * n)
n += 1
S.append(s)
v, e = L.update_psum(S)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
err = abs(v - mp.pi ** 2 / 6)
assert err < eps
w = mp.nsum(lambda n: 1/(n * n), [1, mp.inf], method = "levin", levin_variant = "u")
err = abs(v - w)
assert err < eps
def test_svd_test_case():
# a test case from Golub and Reinsch
# (see wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971).)
eps = mp.exp(0.8 * mp.log(mp.eps))
a = [[22, 10, 2, 3, 7], [14, 7, 10, 0, 8], [-1, 13, -1, -11, 3],
[-3, -2, 13, -2, 4], [9, 8, 1, -2, 4], [9, 1, -7, 5, -1],
[2, -6, 6, 5, 1], [4, 5, 0, -2, 2]]
a = mp.matrix(a)
b = mp.matrix([mp.sqrt(1248), 20, mp.sqrt(384), 0, 0])
S = mp.svd_r(a, compute_uv=False)
S -= b
assert mp.mnorm(S) < eps
S = mp.svd_c(a, compute_uv=False)
S -= b
assert mp.mnorm(S) < eps
def test_levin_1():
mp.dps = 17
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method = "levin", variant = "v")
A, n = [], 1
while 1:
s = mp.mpf(n) ** (2 + 3j)
n += 1
A.append(s)
v, e = L.update(A)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
err = abs(v - mp.zeta(-2-3j))
assert err < eps
w = mp.nsum(lambda n: n ** (2 + 3j), [1, mp.inf], method = "levin", levin_variant = "v")
err = abs(v - w)
assert err < eps
def test_levin_nsum():
mp.dps = 17
with mp.extraprec(mp.prec):
z = mp.mpf(10)**(-10)
a = mp.nsum(lambda n: n**(-(1 + z)), [1, mp.inf], method="l") - 1 / z
assert abs(a - mp.euler) < 1e-10
eps = mp.exp(0.8 * mp.log(mp.eps))
a = mp.nsum(lambda n: (-1)**(n - 1) / n, [1, mp.inf], method="sidi")
assert abs(a - mp.log(2)) < eps
z = 2 + 1j
f = lambda n: mp.rf(2 / mp.mpf(3), n) * mp.rf(4 / mp.mpf(3), n) * z**n / (
mp.rf(1 / mp.mpf(3), n) * mp.fac(n))
v = mp.nsum(f, [0, mp.inf],
method="levin",
steps=[10 for x in xrange(1000)])
exact = mp.hyp2f1(2 / mp.mpf(3), 4 / mp.mpf(3), 1 / mp.mpf(3), z)
assert abs(exact - v) < eps
def test_levin_0():
mp.dps = 17
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method="levin", variant="u")
S, s, n = [], 0, 1
while 1:
s += mp.one / (n * n)
n += 1
S.append(s)
v, e = L.update_psum(S)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
err = abs(v - mp.pi**2 / 6)
assert err < eps
w = mp.nsum(lambda n: 1 / (n * n), [1, mp.inf],
method="levin",
levin_variant="u")
err = abs(v - w)
assert err < eps
def test_levin_1():
mp.dps = 17
eps = mp.mpf(mp.eps)
with mp.extraprec(2 * mp.prec):
L = mp.levin(method="levin", variant="v")
A, n = [], 1
while 1:
s = mp.mpf(n)**(2 + 3j)
n += 1
A.append(s)
v, e = L.update(A)
if e < eps:
break
if n > 1000: raise RuntimeError("iteration limit exceeded")
eps = mp.exp(0.9 * mp.log(eps))
err = abs(v - mp.zeta(-2 - 3j))
assert err < eps
w = mp.nsum(lambda n: n**(2 + 3j), [1, mp.inf],
method="levin",
levin_variant="v")
err = abs(v - w)
assert err < eps
def run_svd_r(A, full_matrices=False, verbose=True):
m, n = A.rows, A.cols
eps = mp.exp(0.8 * mp.log(mp.eps))
if verbose:
print("original matrix:\n", str(A))
print("full", full_matrices)
U, S0, V = mp.svd_r(A, full_matrices=full_matrices)
S = mp.zeros(U.cols, V.rows)
for j in xrange(min(m, n)):
S[j, j] = S0[j]
if verbose:
print("U:\n", str(U))
print("S:\n", str(S0))
print("V:\n", str(V))
C = U * S * V - A
err = mp.mnorm(C)
if verbose:
print("C\n", str(C), "\n", err)
assert err < eps
D = V * V.transpose() - mp.eye(V.rows)
err = mp.mnorm(D)
if verbose:
print("D:\n", str(D), "\n", err)
assert err < eps
E = U.transpose() * U - mp.eye(U.cols)
err = mp.mnorm(E)
if verbose:
print("E:\n", str(E), "\n", err)
assert err < eps
def run_svd_r(A, full_matrices = False, verbose = True):
m, n = A.rows, A.cols
eps = mp.exp(0.8 * mp.log(mp.eps))
if verbose:
print("original matrix:\n", str(A))
print("full", full_matrices)
U, S0, V = mp.svd_r(A, full_matrices = full_matrices)
S = mp.zeros(U.cols, V.rows)
for j in xrange(min(m, n)):
S[j,j] = S0[j]
if verbose:
print("U:\n", str(U))
print("S:\n", str(S0))
print("V:\n", str(V))
C = U * S * V - A
err = mp.mnorm(C)
if verbose:
print("C\n", str(C), "\n", err)
assert err < eps
D = V * V.transpose() - mp.eye(V.rows)
err = mp.mnorm(D)
if verbose:
print("D:\n", str(D), "\n", err)
assert err < eps
E = U.transpose() * U - mp.eye(U.cols)
err = mp.mnorm(E)
if verbose:
print("E:\n", str(E), "\n", err)
assert err < eps
def pdf_gauss_mp(x, sigma, mean):
return mp.mpf(1.) / mp.sqrt(mp.mpf("2.") * sigma**2 * mp.pi) * mp.exp(
-(x - mean)**2 / (mp.mpf("2.") * sigma**2))
def pdf_gauss_mp(x, sigma, mean):
return mp.mpf(1.) / mp.sqrt(mp.mpf("2.") * sigma ** 2 * mp.pi) * mp.exp(
- (x - mean) ** 2 / (mp.mpf("2.") * sigma ** 2))
def exp(self):
"""
Exponencial de un intervalo: 'self.exp()'
"""
return Intervalo(mp.exp(self.lo), mp.exp(self.hi))
def exp(a):
try:
return a.exp()
except:
return mp.exp(a)
def exp(a):
try:
return a.exp()
except:
return mp.exp(a)
def exp(self):
"""
Exponencial de un intervalo: 'self.exp()'
"""
return Intervalo( mp.exp(self.lo), mp.exp(self.hi) )
