def free_energy(self, beta=0.0):
q_ = self.ppm[self.mask]
f = np.sum(q_*np.log(np.maximum(q_/self.ref_, TINY)))
if beta > 0.0:
print(' ... Concensus correction')
fc = _concensus(self.ppm, np.array(self.mask, dtype='int'))
print fc
f = f - .5*beta*fc
return f
def free_energy(self):
"""
Compute the free energy defined as:
F(q, theta) = int q(x) log q(x)/p(x,y/theta) dx
associated with input parameters mu,
sigma and beta (up to an ignored constant).
"""
q_ = self.ppm[self.mask]
# Entropy term
f = np.sum(q_*np.log(np.maximum(q_/self.ref_, TINY)))
# Interaction term
if self.beta > 0.0:
print(' ... Concensus correction')
fc = _concensus(self.ppm, np.array(self.mask, dtype='int'))
f -= .5*self.beta*fc
return f
