def propose_second(self):
"""
This method proposes values for stochastics based on the empirical
covariance of the values sampled so far.
The proposal jumps are drawn from a multivariate normal distribution.
"""
arrayjump = self.drscale*np.dot( self.proposal_sd,\
np.random.normal(size=self.proposal_sd.shape[0]))
if self.verbose > 2: print('Second jump:', arrayjump)
# Update each stochastic individually.
for stochastic in self.stochastics:
jump = arrayjump[self._slices[stochastic]].squeeze()
if np.iterable(stochastic.value):
jump = np.reshape( arrayjump[ self._slices[stochastic]],\
np.shape(stochastic.value))
if self.isdiscrete[stochastic]:
jump = round_array(jump)
stochastic.value = stochastic.value + jump
arrayjump1 = self.arrayjump1
arrayjump2 = arrayjump
self.q = np.exp(-0.5 * (np.linalg.norm(
np.dot(arrayjump2 - arrayjump1,
self.proposal_sd_inv), ord=2)**2 - np.linalg.norm(
np.dot(-arrayjump1, self.proposal_sd_inv), ord=2)**2))
def propose(self):
"""
This method proposes values for stochastics based on the empirical
covariance of the values sampled so far.
The proposal jumps are drawn from a multivariate normal distribution.
"""
# fill_stdnormal(self._proposal_deviate)
# arrayjump = np.inner(self._proposal_deviate, self._sig)
arrayjump = np.dot(self._sig, np.random.normal(size=self._sig.shape[0]))
# 4. Update each stochastic individually.
for stochastic in self.stochastics:
jump = np.reshape(arrayjump[self._slices[stochastic]], np.shape(stochastic.value))
if self.isdiscrete[stochastic]:
stochastic.value = stochastic.value + round_array(jump)
else:
stochastic.value = stochastic.value + jump
def propose(self):
"""
This method proposes values for stochastics based on the empirical
covariance of the values sampled so far.
The proposal jumps are drawn from a multivariate normal distribution.
"""
#fill_stdnormal(self._proposal_deviate)
#arrayjump = np.inner(self._proposal_deviate, self._sig)
arrayjump = np.dot(self._sig, np.random.normal(size=self._sig.shape[0]))
# 4. Update each stochastic individually.
for stochastic in self.stochastics:
jump = np.reshape(arrayjump[self._slices[stochastic]],np.shape(stochastic.value))
if self.isdiscrete[stochastic]:
stochastic.value = stochastic.value + round_array(jump)
else:
stochastic.value = stochastic.value + jump
def propose(self):
"""
This method proposes values for stochastics based on the empirical
covariance of the values sampled so far.
The proposal jumps are drawn from a multivariate normal distribution.
"""
arrayjump = np.dot(self.proposal_sd,
np.random.normal(size=self.proposal_sd.shape[0]))
if self.verbose > 2:
print('Jump :', arrayjump)
# Update each stochastic individually.
for stochastic in self.stochastics:
jump = arrayjump[self._slices[stochastic]].squeeze()
if np.iterable(stochastic.value):
jump = np.reshape(arrayjump[self._slices[stochastic]],
np.shape(stochastic.value))
if self.isdiscrete[stochastic]:
jump = round_array(jump)
stochastic.value = stochastic.value + jump