def d2_log_rho(beta, mk, K, N):
"""
Second derivate of the logarithm of the Dirichlet multinomial likelihood.
"""
return K ** 2 * (mp.psi(1, K * beta) - mp.psi(1, K * beta + N)) - K * mp.psi(1, beta) \
+ np.sum((mk[n] * mp.psi(1, n + beta) for n in mk))
def _dlogrho(
beta, mk, K, N
): # Derivative of the logarithm of the Dirichlet-multinomial likelihood
db = K * (mp.psi(0, K * beta) - mp.psi(0, K * beta + N)) - \
K * mp.psi(0, beta)
db += np.array([mk[n] * mp.psi(0, n + beta) for n in mk]).sum()
return db
def d_xi(beta, K):
"""
First derivative of xi(beta).
xi(beta) is the entropy of the system when no data has been observed.
d_xi is the prior for the nsb estimator
"""
return K * mp.psi(1, K * beta + 1.) - mp.psi(1, beta + 1.)
def _S2i_nondiag(x1, x2, nxkx, beta, N, kappa):
psNK2 = psi(0, N + kappa + 2)
ps1NK2 = psi(1, N + kappa + 2)
x1beta = x1 + beta
x2beta = x2 + beta
s1 = (psi(0, x1beta + 1) - psNK2) * (psi(0, x2beta + 1) - psNK2) - ps1NK2
s1 = s1 * nxkx[x1] * nxkx[x2] * x1beta * x2beta
return s1
def H1(beta, mk, K, N):
"""
Compute the first moment (expectation value) of the entropy H.
H is the entropy one obtains with a symmetric Dirichlet prior
with concentration parameter beta and a multinomial likelihood.
"""
norm = N + beta * K
return mp.psi(0, norm + 1) - np.sum(
(mk[n] * (n + beta) * mp.psi(0, n + beta + 1) for n in mk)) / norm
def kurtosis(k, theta):
"""
Excess kurtosis of the log-gamma distribution.
k is the shape parameter of the gamma distribution.
theta is the scale parameter of the log-gamma distribution.
"""
with mpmath.extradps(5):
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
return mpmath.psi(3, k) / mpmath.psi(1, k)**2
def skewness(k, theta):
"""
Variance of the log-gamma distribution.
k is the shape parameter of the gamma distribution.
theta is the scale parameter of the log-gamma distribution.
"""
with mpmath.extradps(5):
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
return mpmath.psi(2, k) / mpmath.psi(1, k)**1.5
def _H2(beta, mk, K, K1, N): # Computes 2nd moment of entropy
norm = N + beta * K
psi0norm = mp.psi(0, norm + 2)
psi1norm = mp.psi(1, norm + 2)
H2 = 0
index = 0
for k in mk:
index += 1
H2 += _H2summand(index, k, mk, beta, psi0norm, psi1norm)
H2 = H2 / (norm + 1) / norm
return H2
def get_x_y(omega):
Ts = []
ys = []
for T in np.linspace(0.01, 2, 200):
x = omega * 1j / (2 * np.pi * T)
Ts.append(T)
ys.append(-omega / (4 * np.pi * T) *
(psi(1, 0.5 + x) - psi(1, 0.5 - x)).imag)
return np.array(Ts), np.array(ys)
def _S2i_diag(x, nxkx, beta, N, kappa):
xbeta = x + beta
Nkappa2 = N + kappa + 2
psNK2 = psi(0, Nkappa2)
ps1NK2 = psi(1, Nkappa2)
s1 = (psi(0, xbeta + 2) - psNK2)**2 + psi(1, xbeta + 2) - ps1NK2
s1 *= nxkx[x] * xbeta * (xbeta + 1)
s2 = (psi(0, xbeta + 1) - psNK2)**2 - ps1NK2
s2 *= nxkx[x] * (nxkx[x] - 1) * xbeta * xbeta
return s1 + s2
def nsb_integrand_variance_term1(beta,nxkx,N,m):
'''computer variance of the nsb esimator, with term 1,2
names refering to eq 3.34 from kinney thesis, the third term,
which is simply the square of the expectation value of the
entropy, is subtracted off at the end'''
summand1 = sp.array(
[nxkx[x]*(x + beta+1)*(x+beta)/((N+m*beta+1)*(N+m*beta))*
psi(1,x + beta + 1) for x in nxkx])
summand2 = sp.array(
[nxkx[x]*(x+beta)/((N+m*beta+1)*(N+m*beta))*psi(0,x+beta+1)**2
for x in nxkx])
summand3 = sp.array(
[nxkx[x]*(x+beta)/(N+m*beta)*psi(0,x+beta+1) for x in nxkx])
return (
sp.sum(summand1) - psi(1,N + beta*m+1) + sp.sum(summand2)-1/
(N+m*beta+1)*sp.sum(summand3)**2)
def f88(x):
# psi
if abs(x) > 1e100:
return None
try:
return mpmath.psi(0, x)
except:
return None
def f90(x):
# psi_1
if abs(x) > 1e100:
return None
try:
return mpmath.psi(1, x)
except:
return None
def mle(x, k=None, theta=None):
"""
Gamma distribution maximum likelihood parameter estimation.
Maximum likelihood estimate for the k (shape) and theta (scale) parameters
of the gamma distribution.
x must be a sequence of values.
"""
meanx = mpmath.fsum(x) / len(x)
meanlnx = mpmath.fsum(mpmath.log(t) for t in x) / len(x)
if k is None:
if theta is None:
# Solve for k and theta
s = mpmath.log(meanx) - meanlnx
k_hat = (3 - s + mpmath.sqrt((s - 3)**2 + 24*s)) / (12*s)
# XXX This loop implements a "dumb" convergence criterion.
# It exits early if the old k equals the new k, but if that never
# happens, then whatever value k_hat has after the last iteration
# is the value that is returned.
for _ in range(10):
oldk = k_hat
delta = ((mpmath.log(k_hat) - mpmath.psi(0, k_hat) - s) /
(1/k_hat - mpmath.psi(1, k_hat)))
k_hat = k_hat - delta
if k_hat == oldk:
break
theta_hat = meanx / k_hat
else:
# theta is fixed, only solve for k
theta = mpmath.mpf(theta)
k_hat = digammainv(meanlnx - mpmath.log(theta))
theta_hat = theta
else:
if theta is None:
# Solve for theta, k is fixed.
k_hat = mpmath.mpf(k)
theta_hat = meanx / k_hat
else:
# Both k and theta are fixed.
k_hat = mpmath.mpf(k)
theta_hat = mpmath.mpf(theta)
return k_hat, theta_hat
def nsb_integrand_variance_term1(beta, nxkx, N, m):
'''computer variance of the nsb esimator, with term 1,2
names refering to eq 3.34 from kinney thesis, the third term,
which is simply the square of the expectation value of the
entropy, is subtracted off at the end'''
summand1 = sp.array([
nxkx[x] * (x + beta + 1) * (x + beta) /
((N + m * beta + 1) * (N + m * beta)) * psi(1, x + beta + 1)
for x in nxkx
])
summand2 = sp.array([
nxkx[x] * (x + beta) / ((N + m * beta + 1) * (N + m * beta)) *
psi(0, x + beta + 1)**2 for x in nxkx
])
summand3 = sp.array([
nxkx[x] * (x + beta) / (N + m * beta) * psi(0, x + beta + 1)
for x in nxkx
])
return (sp.sum(summand1) - psi(1, N + beta * m + 1) + sp.sum(summand2) -
1 / (N + m * beta + 1) * sp.sum(summand3)**2)
def mean(k, theta):
"""
Mean of the log-gamma distribution.
k is the shape parameter of the gamma distribution.
theta is the scale parameter of the log-gamma distribution.
"""
with mpmath.extradps(5):
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
return theta * mpmath.psi(0, k)
def var(k, theta):
"""
Variance of the log-gamma distribution.
k is the shape parameter of the gamma distribution.
theta is the scale parameter of the log-gamma distribution.
"""
with mpmath.extradps(5):
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
return theta**2 * mpmath.psi(1, k)
def nll_hess(x, k, theta):
"""
Gamma distribution hessian of the negative log-likelihood function.
"""
_validate_k_theta(k, theta)
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
N = len(x)
sumx = mpmath.fsum(x)
# sumlnx = mpmath.fsum(mpmath.log(t) for t in x)
dk2 = -N*mpmath.psi(1, k)
dkdtheta = -N/theta
dtheta2 = -2*sumx/theta**3 + N*k/theta**2
return mpmath.matrix([[-dk2, -dkdtheta], [-dkdtheta, -dtheta2]])
def nll_invhess(x, k, theta):
"""
Gamma distribution inverse of the hessian of the negative log-likelihood.
"""
_validate_k_theta(k, theta)
k = mpmath.mpf(k)
theta = mpmath.mpf(theta)
N = len(x)
sumx = mpmath.fsum(x)
# sumlnx = mpmath.fsum(mpmath.log(t) for t in x)
dk2 = -N*mpmath.psi(1, k)
dkdtheta = -N/theta
dtheta2 = -2*sumx/theta**3 + N*k/theta**2
det = dk2*dtheta2 - dkdtheta**2
return mpmath.matrix([[-dtheta2/det, dkdtheta/det],
[dkdtheta/det, -dk2/det]])
def _dxi(beta, K): #this is d<H_q|B>/dB return K*psi(1, K*beta+1)-psi(1, beta+1)
def _xi(beta, K): # This is <H_q|B> return psi(0, K*beta+1)-psi(0, beta+1)
def nsb_integrand_variance_term2(beta,nxkx,N,m):
summand1 = sp.array(
[nxkx[x]*((x+beta)/(N+m*beta)*psi(0,x+beta+1)) for x in nxkx])
return (psi(0,N+beta*m+1)-sp.sum(summand1))**2
def _dxi(beta, K):
return K * mp.psi(1, K * beta + 1.) - mp.psi(1, beta + 1.)
def _d2xi(beta, K):
return K**2 * mp.psi(2, K * beta + 1) - mp.psi(2, beta + 1)
def _d3xi(beta, K):
return K**3 * mp.psi(3, K * beta + 1) - mp.psi(3, beta + 1)
def _h2(beta, K, N, n): # 2nd moment of Shannon information content
return (mp.psi(0, N + beta * K) - mp.psi(0, n + beta))**2 - mp.psi(
1, N + beta * K) + mp.psi(1, n + beta)
def _h1(beta, K, N, n): # mean of Shannon information content
return mp.psi(0, N + beta * K) - mp.psi(0, n + beta)
def _dh(a):
return mp.psi(1, a + 1)
def _J(nk, psi0norm, psi1norm):
return (mp.psi(0, nk + 2) - psi0norm) * (
mp.psi(0, nk + 2) - psi0norm) - psi1norm + mp.psi(1, nk + 2)
def _I(nj, nk, psi0norm, psi1norm):
return (mp.psi(0, nj + 1) - psi0norm) * (mp.psi(0, nk + 1) -
psi0norm) - psi1norm
def _S1i(x, nxkx, beta, N, kappa): return nxkx[x] * (x+beta)/(N+kappa) * (psi(0, x+beta+1)-psi(0, N+kappa+1))
def d2_xi(beta, K):
"""
Second derivative of xi(beta) (cf d_xi).
"""
return K**2 * mp.psi(2, K * beta + 1) - mp.psi(2, beta + 1)
def d3_xi(beta, K):
"""
Third derivative of xi(beta) (cf d_xi).
"""
return K**3 * mp.psi(3, K * beta + 1) - mp.psi(3, beta + 1)
def _H1(beta, mk, K, K1, N): # Computes expression from eq. (78)
norm = N + beta * K
H1 = mp.psi(0, norm + 1)
for n in mk:
H1 += -mk[n] * (n + beta) * mp.psi(0, n + beta + 1) / norm
return H1
def _logvarrho_h_DP(a, mk, K1, N):
return _logvarrho_DP(a, mk, K1, N) + mp.log(mp.psi(1, a + 1))
