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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.dimension | def dimension(self):
"""Compute the dimension of the sampling space and identify the slices
belonging to each stochastic.
"""
self.dim = 0
self._slices = {}
for stochastic in self.stochastics:
if isinstance(stochastic.value, np.matrix):
p_len =... | python | def dimension(self):
"""Compute the dimension of the sampling space and identify the slices
belonging to each stochastic.
"""
self.dim = 0
self._slices = {}
for stochastic in self.stochastics:
if isinstance(stochastic.value, np.matrix):
p_len =... | [
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.update_cov | def update_cov(self):
"""Recursively compute the covariance matrix for the multivariate normal
proposal distribution.
This method is called every self.interval once self.delay iterations
have been performed.
"""
scaling = (2.4) ** 2 / self.dim # Gelman et al. 1996.
... | python | def update_cov(self):
"""Recursively compute the covariance matrix for the multivariate normal
proposal distribution.
This method is called every self.interval once self.delay iterations
have been performed.
"""
scaling = (2.4) ** 2 / self.dim # Gelman et al. 1996.
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.recursive_cov | def recursive_cov(self, cov, length, mean, chain, scaling=1, epsilon=0):
r"""Compute the covariance recursively.
Return the new covariance and the new mean.
.. math::
C_k & = \frac{1}{k-1} (\sum_{i=1}^k x_i x_i^T - k\bar{x_k}\bar{x_k}^T)
C_n & = \frac{1}{n-1} (\sum_{i=1... | python | def recursive_cov(self, cov, length, mean, chain, scaling=1, epsilon=0):
r"""Compute the covariance recursively.
Return the new covariance and the new mean.
.. math::
C_k & = \frac{1}{k-1} (\sum_{i=1}^k x_i x_i^T - k\bar{x_k}\bar{x_k}^T)
C_n & = \frac{1}{n-1} (\sum_{i=1... | [
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.recursive_mean | def recursive_mean(self, mean, length, chain):
r"""Compute the chain mean recursively.
Instead of computing the mean :math:`\bar{x_n}` of the entire chain,
use the last computed mean :math:`bar{x_j}` and the tail of the chain
to recursively estimate the mean.
.. math::
... | python | def recursive_mean(self, mean, length, chain):
r"""Compute the chain mean recursively.
Instead of computing the mean :math:`\bar{x_n}` of the entire chain,
use the last computed mean :math:`bar{x_j}` and the tail of the chain
to recursively estimate the mean.
.. math::
... | [
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.propose | 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.... | python | 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,
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.step | def step(self):
"""
Perform a Metropolis step.
Stochastic parameters are block-updated using a multivariate normal
distribution whose covariance is updated every self.interval once
self.delay steps have been performed.
The AM instance keeps a local copy of the stochasti... | python | def step(self):
"""
Perform a Metropolis step.
Stochastic parameters are block-updated using a multivariate normal
distribution whose covariance is updated every self.interval once
self.delay steps have been performed.
The AM instance keeps a local copy of the stochasti... | [
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.internal_tally | def internal_tally(self):
"""Store the trace of stochastics for the computation of the covariance.
This trace is completely independent from the backend used by the
sampler to store the samples."""
chain = []
for stochastic in self.stochastics:
chain.append(np.ravel(s... | python | def internal_tally(self):
"""Store the trace of stochastics for the computation of the covariance.
This trace is completely independent from the backend used by the
sampler to store the samples."""
chain = []
for stochastic in self.stochastics:
chain.append(np.ravel(s... | [
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.trace2array | def trace2array(self, sl):
"""Return an array with the trace of all stochastics, sliced by sl."""
chain = []
for stochastic in self.stochastics:
tr = stochastic.trace.gettrace(slicing=sl)
if tr is None:
raise AttributeError
chain.append(tr)
... | python | def trace2array(self, sl):
"""Return an array with the trace of all stochastics, sliced by sl."""
chain = []
for stochastic in self.stochastics:
tr = stochastic.trace.gettrace(slicing=sl)
if tr is None:
raise AttributeError
chain.append(tr)
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pymc-devs/pymc | pymc/StepMethods.py | AdaptiveMetropolis.stoch2array | def stoch2array(self):
"""Return the stochastic objects as an array."""
a = np.empty(self.dim)
for stochastic in self.stochastics:
a[self._slices[stochastic]] = stochastic.value
return a | python | def stoch2array(self):
"""Return the stochastic objects as an array."""
a = np.empty(self.dim)
for stochastic in self.stochastics:
a[self._slices[stochastic]] = stochastic.value
return a | [
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pymc-devs/pymc | pymc/StepMethods.py | TWalk.walk | def walk(self):
"""Walk proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Walk proposal kernel')
# Mask for values to move
phi = self.phi
theta = self.walk_theta
u = random(len(phi))
z = (theta / (1 + theta)) * (theta * u *... | python | def walk(self):
"""Walk proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Walk proposal kernel')
# Mask for values to move
phi = self.phi
theta = self.walk_theta
u = random(len(phi))
z = (theta / (1 + theta)) * (theta * u *... | [
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pymc-devs/pymc | pymc/StepMethods.py | TWalk.traverse | def traverse(self):
"""Traverse proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Traverse proposal kernel')
# Mask for values to move
phi = self.phi
theta = self.traverse_theta
# Calculate beta
if (random() < (theta - 1) /... | python | def traverse(self):
"""Traverse proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Traverse proposal kernel')
# Mask for values to move
phi = self.phi
theta = self.traverse_theta
# Calculate beta
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pymc-devs/pymc | pymc/StepMethods.py | TWalk.blow | def blow(self):
"""Blow proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Blow proposal kernel')
# Mask for values to move
phi = self.phi
if self._prime:
xp, x = self.values
else:
x, xp = self.values
... | python | def blow(self):
"""Blow proposal kernel"""
if self.verbose > 1:
print_('\t' + self._id + ' Running Blow proposal kernel')
# Mask for values to move
phi = self.phi
if self._prime:
xp, x = self.values
else:
x, xp = self.values
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pymc-devs/pymc | pymc/StepMethods.py | TWalk._g | def _g(self, h, xp, s):
"""Density function for blow and hop moves"""
nphi = sum(self.phi)
return (nphi / 2.0) * log(2 * pi) + nphi * \
log(s) + 0.5 * sum((h - xp) ** 2) / (s ** 2) | python | def _g(self, h, xp, s):
"""Density function for blow and hop moves"""
nphi = sum(self.phi)
return (nphi / 2.0) * log(2 * pi) + nphi * \
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pymc-devs/pymc | pymc/StepMethods.py | TWalk.reject | def reject(self):
"""Sets current s value to the last accepted value"""
self.stochastic.revert()
# Increment rejected count
self.rejected[self.current_kernel] += 1
if self.verbose > 1:
print_(
self._id,
"rejected, reverting to value =... | python | def reject(self):
"""Sets current s value to the last accepted value"""
self.stochastic.revert()
# Increment rejected count
self.rejected[self.current_kernel] += 1
if self.verbose > 1:
print_(
self._id,
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pymc-devs/pymc | pymc/StepMethods.py | TWalk.step | def step(self):
"""Single iteration of t-walk algorithm"""
valid_proposal = False
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if self._prime:
#... | python | def step(self):
"""Single iteration of t-walk algorithm"""
valid_proposal = False
# Use x or xprime as pivot
self._prime = (random() < 0.5)
if self.verbose > 1:
print_("\n\nUsing x%s as pivot" % (" prime" * self._prime or ""))
if self._prime:
#... | [
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pymc-devs/pymc | pymc/StepMethods.py | Slicer.step | def step(self):
"""
Slice step method
From Neal 2003 (doi:10.1214/aos/1056562461)
"""
logy = self.loglike - rexponential(1)
L = self.stochastic.value - runiform(0, self.w)
R = L + self.w
if self.doubling:
# Doubling procedure
K =... | python | def step(self):
"""
Slice step method
From Neal 2003 (doi:10.1214/aos/1056562461)
"""
logy = self.loglike - rexponential(1)
L = self.stochastic.value - runiform(0, self.w)
R = L + self.w
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"""
Returns loglike of value
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self.stochastic.value = value
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pymc-devs/pymc | pymc/StepMethods.py | Slicer.tune | def tune(self, verbose=None):
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Tuning initial slice width parameter
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"""
Tuning initial slice width parameter
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if not self._tune:
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pymc-devs/pymc | pymc/database/ram.py | Trace.tally | def tally(self, chain):
"""Store the object's current value to a chain.
:Parameters:
chain : integer
Chain index.
"""
value = self._getfunc()
try:
self._trace[chain][self._index[chain]] = value.copy()
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sel... | python | def tally(self, chain):
"""Store the object's current value to a chain.
:Parameters:
chain : integer
Chain index.
"""
value = self._getfunc()
try:
self._trace[chain][self._index[chain]] = value.copy()
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pymc-devs/pymc | pymc/database/ram.py | Trace.truncate | def truncate(self, index, chain):
"""
Truncate the trace array to some index.
:Parameters:
index : int
The index within the chain after which all values will be removed.
chain : int
The chain index (>=0).
"""
self._trace[chain] = self._trace[c... | python | def truncate(self, index, chain):
"""
Truncate the trace array to some index.
:Parameters:
index : int
The index within the chain after which all values will be removed.
chain : int
The chain index (>=0).
"""
self._trace[chain] = self._trace[c... | [
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pymc-devs/pymc | pymc/database/ram.py | Trace.gettrace | def gettrace(self, burn=0, thin=1, chain=-1, slicing=None):
"""Return the trace.
:Stochastics:
- burn (int): The number of transient steps to skip.
- thin (int): Keep one in thin.
- chain (int): The index of the chain to fetch. If None, return all chains.
- slici... | python | def gettrace(self, burn=0, thin=1, chain=-1, slicing=None):
"""Return the trace.
:Stochastics:
- burn (int): The number of transient steps to skip.
- thin (int): Keep one in thin.
- chain (int): The index of the chain to fetch. If None, return all chains.
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pymc-devs/pymc | pymc/database/hdf5ea.py | load | def load(dbname, dbmode='a'):
"""Load an existing hdf5 database.
Return a Database instance.
:Parameters:
filename : string
Name of the hdf5 database to open.
mode : 'a', 'r'
File mode : 'a': append, 'r': read-only.
"""
if dbmode == 'w':
raise AttributeError("db... | python | def load(dbname, dbmode='a'):
"""Load an existing hdf5 database.
Return a Database instance.
:Parameters:
filename : string
Name of the hdf5 database to open.
mode : 'a', 'r'
File mode : 'a': append, 'r': read-only.
"""
if dbmode == 'w':
raise AttributeError("db... | [
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pymc-devs/pymc | pymc/database/txt.py | load | def load(dirname):
"""Create a Database instance from the data stored in the directory."""
if not os.path.exists(dirname):
raise AttributeError('No txt database named %s' % dirname)
db = Database(dirname, dbmode='a')
chain_folders = [os.path.join(dirname, c) for c in db.get_chains()]
db.cha... | python | def load(dirname):
"""Create a Database instance from the data stored in the directory."""
if not os.path.exists(dirname):
raise AttributeError('No txt database named %s' % dirname)
db = Database(dirname, dbmode='a')
chain_folders = [os.path.join(dirname, c) for c in db.get_chains()]
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pymc-devs/pymc | pymc/database/txt.py | funname | def funname(file):
"""Return variable names from file names."""
if isinstance(file, str):
files = [file]
else:
files = file
bases = [os.path.basename(f) for f in files]
names = [os.path.splitext(b)[0] for b in bases]
if isinstance(file, str):
return names[0]
else:
... | python | def funname(file):
"""Return variable names from file names."""
if isinstance(file, str):
files = [file]
else:
files = file
bases = [os.path.basename(f) for f in files]
names = [os.path.splitext(b)[0] for b in bases]
if isinstance(file, str):
return names[0]
else:
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pymc-devs/pymc | pymc/database/txt.py | Trace._finalize | def _finalize(self, chain):
"""Write the trace to an ASCII file.
:Parameter:
chain : int
The chain index.
"""
path = os.path.join(
self.db._directory,
self.db.get_chains()[chain],
self.name + '.txt')
arr = self.gettrace(chain... | python | def _finalize(self, chain):
"""Write the trace to an ASCII file.
:Parameter:
chain : int
The chain index.
"""
path = os.path.join(
self.db._directory,
self.db.get_chains()[chain],
self.name + '.txt')
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pymc-devs/pymc | pymc/database/txt.py | Database._initialize | def _initialize(self, funs_to_tally, length):
"""Create folder to store simulation results."""
dir = os.path.join(self._directory, CHAIN_NAME % self.chains)
os.mkdir(dir)
base.Database._initialize(self, funs_to_tally, length) | python | def _initialize(self, funs_to_tally, length):
"""Create folder to store simulation results."""
dir = os.path.join(self._directory, CHAIN_NAME % self.chains)
os.mkdir(dir)
base.Database._initialize(self, funs_to_tally, length) | [
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pymc-devs/pymc | pymc/database/txt.py | Database.savestate | def savestate(self, state):
"""Save the sampler's state in a state.txt file."""
oldstate = np.get_printoptions()
np.set_printoptions(threshold=1e6)
try:
with open(os.path.join(self._directory, 'state.txt'), 'w') as f:
print_(state, file=f)
finally:
... | python | def savestate(self, state):
"""Save the sampler's state in a state.txt file."""
oldstate = np.get_printoptions()
np.set_printoptions(threshold=1e6)
try:
with open(os.path.join(self._directory, 'state.txt'), 'w') as f:
print_(state, file=f)
finally:
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pymc-devs/pymc | pymc/examples/disaster_model_missing.py | rate | def rate(s=switch, e=early_mean, l=late_mean):
"""Allocate appropriate mean to time series"""
out = np.empty(len(disasters_array))
# Early mean prior to switchpoint
out[:s] = e
# Late mean following switchpoint
out[s:] = l
return out | python | def rate(s=switch, e=early_mean, l=late_mean):
"""Allocate appropriate mean to time series"""
out = np.empty(len(disasters_array))
# Early mean prior to switchpoint
out[:s] = e
# Late mean following switchpoint
out[s:] = l
return out | [
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pymc-devs/pymc | pymc/gp/GPutils.py | fast_matrix_copy | def fast_matrix_copy(f, t=None, n_threads=1):
"""
Not any faster than a serial copy so far.
"""
if not f.flags['F_CONTIGUOUS']:
raise RuntimeError(
'This will not be fast unless input array f is Fortran-contiguous.')
if t is None:
t = asmatrix(empty(f.shape, order='F'))
... | python | def fast_matrix_copy(f, t=None, n_threads=1):
"""
Not any faster than a serial copy so far.
"""
if not f.flags['F_CONTIGUOUS']:
raise RuntimeError(
'This will not be fast unless input array f is Fortran-contiguous.')
if t is None:
t = asmatrix(empty(f.shape, order='F'))
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pymc-devs/pymc | pymc/gp/GPutils.py | vecs_to_datmesh | def vecs_to_datmesh(x, y):
"""
Converts input arguments x and y to a 2d meshgrid,
suitable for calling Means, Covariances and Realizations.
"""
x, y = meshgrid(x, y)
out = zeros(x.shape + (2,), dtype=float)
out[:, :, 0] = x
out[:, :, 1] = y
return out | python | def vecs_to_datmesh(x, y):
"""
Converts input arguments x and y to a 2d meshgrid,
suitable for calling Means, Covariances and Realizations.
"""
x, y = meshgrid(x, y)
out = zeros(x.shape + (2,), dtype=float)
out[:, :, 0] = x
out[:, :, 1] = y
return out | [
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pymc-devs/pymc | pymc/database/base.py | batchsd | def batchsd(trace, batches=5):
"""
Calculates the simulation standard error, accounting for non-independent
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"""
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# ttrace ... | python | def batchsd(trace, batches=5):
"""
Calculates the simulation standard error, accounting for non-independent
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pymc-devs/pymc | pymc/database/base.py | Database._initialize | def _initialize(self, funs_to_tally, length=None):
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pymc-devs/pymc | pymc/database/base.py | Database._finalize | def _finalize(self, chain=-1):
"""Finalize the chain for all tallyable objects."""
chain = range(self.chains)[chain]
for name in self.trace_names[chain]:
self._traces[name]._finalize(chain)
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chain = range(self.chains)[chain]
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self._traces[name]._finalize(chain)
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pymc-devs/pymc | pymc/database/base.py | Database.truncate | def truncate(self, index, chain=-1):
"""Tell the traces to truncate themselves at the given index."""
chain = range(self.chains)[chain]
for name in self.trace_names[chain]:
self._traces[name].truncate(index, chain) | python | def truncate(self, index, chain=-1):
"""Tell the traces to truncate themselves at the given index."""
chain = range(self.chains)[chain]
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pymc-devs/pymc | pymc/gp/Mean.py | Mean.observe | def observe(self, C, obs_mesh_new, obs_vals_new, mean_under=None):
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Synchronizes self's observation status with C's.
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Synchronizes self's observation status with C's.
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pymc-devs/pymc | pymc/InstantiationDecorators.py | _extract | def _extract(__func__, kwds, keys, classname, probe=True):
"""
Used by decorators stochastic and deterministic to inspect declarations
"""
# Add docs and name
kwds['doc'] = __func__.__doc__
if not 'name' in kwds:
kwds['name'] = __func__.__name__
# kwds.update({'doc':__func__.__doc__... | python | def _extract(__func__, kwds, keys, classname, probe=True):
"""
Used by decorators stochastic and deterministic to inspect declarations
"""
# Add docs and name
kwds['doc'] = __func__.__doc__
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pymc-devs/pymc | pymc/InstantiationDecorators.py | observed | def observed(obj=None, **kwds):
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@stochastic(observed=True)
... | python | def observed(obj=None, **kwds):
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pymc-devs/pymc | pymc/InstantiationDecorators.py | robust_init | def robust_init(stochclass, tries, *args, **kwds):
"""Robust initialization of a Stochastic.
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a val... | python | def robust_init(stochclass, tries, *args, **kwds):
"""Robust initialization of a Stochastic.
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pymc-devs/pymc | pymc/examples/gp/more_examples/MKMsalmon/salmon.py | salmon.plot | def plot(self):
"""
Plot posterior from simple nonstochetric regression.
"""
figure()
plot_envelope(self.M, self.C, self.xplot)
for i in range(3):
f = Realization(self.M, self.C)
plot(self.xplot,f(self.xplot))
plot(self.abundance, self.fry... | python | def plot(self):
"""
Plot posterior from simple nonstochetric regression.
"""
figure()
plot_envelope(self.M, self.C, self.xplot)
for i in range(3):
f = Realization(self.M, self.C)
plot(self.xplot,f(self.xplot))
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pymc-devs/pymc | pymc/examples/melanoma.py | survival | def survival(value=t, lam=lam, f=failure):
"""Exponential survival likelihood, accounting for censoring"""
return sum(f * log(lam) - lam * value) | python | def survival(value=t, lam=lam, f=failure):
"""Exponential survival likelihood, accounting for censoring"""
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pymc-devs/pymc | pymc/examples/disaster_model_gof.py | disasters_sim | def disasters_sim(early_mean=early_mean,
late_mean=late_mean,
switchpoint=switchpoint):
"""Coal mining disasters sampled from the posterior predictive distribution"""
return concatenate((pm.rpoisson(early_mean, size=switchpoint), pm.rpoisson(
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"""Coal mining disasters sampled from the posterior predictive distribution"""
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pymc-devs/pymc | pymc/examples/disaster_model_gof.py | expected_values | def expected_values(early_mean=early_mean,
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# Sample size
n = len(disasters_array)
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# Sample size
n = len(disasters_array)
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pymc-devs/pymc | pymc/database/pickle.py | load | def load(filename):
"""Load a pickled database.
Return a Database instance.
"""
file = open(filename, 'rb')
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file.close()
db = Database(file.name)
chains = 0
funs = set()
for k, v in six.iteritems(container):
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... | python | def load(filename):
"""Load a pickled database.
Return a Database instance.
"""
file = open(filename, 'rb')
container = std_pickle.load(file)
file.close()
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pymc-devs/pymc | pymc/database/pickle.py | Database._finalize | def _finalize(self):
"""Dump traces using cPickle."""
container = {}
try:
for name in self._traces:
container[name] = self._traces[name]._trace
container['_state_'] = self._state_
file = open(self.filename, 'w+b')
std_pickle.dump(c... | python | def _finalize(self):
"""Dump traces using cPickle."""
container = {}
try:
for name in self._traces:
container[name] = self._traces[name]._trace
container['_state_'] = self._state_
file = open(self.filename, 'w+b')
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pymc-devs/pymc | pymc/diagnostics.py | geweke | def geweke(x, first=.1, last=.5, intervals=20, maxlag=20):
"""Return z-scores for convergence diagnostics.
Compare the mean of the first % of series with the mean of the last % of
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pymc-devs/pymc | pymc/diagnostics.py | raftery_lewis | def raftery_lewis(x, q, r, s=.95, epsilon=.001, verbose=1):
"""
Return the number of iterations needed to achieve a given
precision.
:Parameters:
x : sequence
Sampled series.
q : float
Quantile.
r : float
Accuracy requested for quantile.
... | python | def raftery_lewis(x, q, r, s=.95, epsilon=.001, verbose=1):
"""
Return the number of iterations needed to achieve a given
precision.
:Parameters:
x : sequence
Sampled series.
q : float
Quantile.
r : float
Accuracy requested for quantile.
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pymc-devs/pymc | pymc/diagnostics.py | effective_n | def effective_n(x):
""" Returns estimate of the effective sample size of a set of traces.
Parameters
----------
x : array-like
An array containing the 2 or more traces of a stochastic parameter. That is, an array of dimension m x n x k, where m is the number of traces, n the number of samples, an... | python | def effective_n(x):
""" Returns estimate of the effective sample size of a set of traces.
Parameters
----------
x : array-like
An array containing the 2 or more traces of a stochastic parameter. That is, an array of dimension m x n x k, where m is the number of traces, n the number of samples, an... | [
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pymc-devs/pymc | pymc/diagnostics.py | gelman_rubin | def gelman_rubin(x, return_var=False):
""" Returns estimate of R for a set of traces.
The Gelman-Rubin diagnostic tests for lack of convergence by comparing
the variance between multiple chains to the variance within each chain.
If convergence has been achieved, the between-chain and within-chain
v... | python | def gelman_rubin(x, return_var=False):
""" Returns estimate of R for a set of traces.
The Gelman-Rubin diagnostic tests for lack of convergence by comparing
the variance between multiple chains to the variance within each chain.
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pymc-devs/pymc | pymc/diagnostics.py | _find_max_lag | def _find_max_lag(x, rho_limit=0.05, maxmaxlag=20000, verbose=0):
"""Automatically find an appropriate maximum lag to calculate IAT"""
# Fetch autocovariance matrix
acv = autocov(x)
# Calculate rho
rho = acv[0, 1] / acv[0, 0]
lam = -1. / np.log(abs(rho))
# Initial guess at 1.5 times lambd... | python | def _find_max_lag(x, rho_limit=0.05, maxmaxlag=20000, verbose=0):
"""Automatically find an appropriate maximum lag to calculate IAT"""
# Fetch autocovariance matrix
acv = autocov(x)
# Calculate rho
rho = acv[0, 1] / acv[0, 0]
lam = -1. / np.log(abs(rho))
# Initial guess at 1.5 times lambd... | [
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pymc-devs/pymc | pymc/diagnostics.py | ppp_value | def ppp_value(simdata, trueval, round=3):
"""
Calculates posterior predictive p-values on data simulated from the posterior
predictive distribution, returning the quantile of the observed data relative to
simulated.
The posterior predictive p-value is computed by:
.. math:: Pr(T(y^{\text... | python | def ppp_value(simdata, trueval, round=3):
"""
Calculates posterior predictive p-values on data simulated from the posterior
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pymc-devs/pymc | pymc/Model.py | check_valid_object_name | def check_valid_object_name(sequence):
"""Check that the names of the objects are all different."""
names = []
for o in sequence:
if o.__name__ in names:
raise ValueError(
'A tallyable PyMC object called %s already exists. This will cause problems for some database backen... | python | def check_valid_object_name(sequence):
"""Check that the names of the objects are all different."""
names = []
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pymc-devs/pymc | pymc/Model.py | Model.seed | def seed(self):
"""
Seed new initial values for the stochastics.
"""
for generation in self.generations:
for s in generation:
try:
if s.rseed is not None:
value = s.random(**s.parents.value)
except:
... | python | def seed(self):
"""
Seed new initial values for the stochastics.
"""
for generation in self.generations:
for s in generation:
try:
if s.rseed is not None:
value = s.random(**s.parents.value)
except:
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pymc-devs/pymc | pymc/Model.py | Model.get_node | def get_node(self, node_name):
"""Retrieve node with passed name"""
for node in self.nodes:
if node.__name__ == node_name:
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"""Retrieve node with passed name"""
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pymc-devs/pymc | pymc/Model.py | Sampler.sample | def sample(self, iter, length=None, verbose=0):
"""
Draws iter samples from the posterior.
"""
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self.max_trace_length = iter
self._iter = iter
self.verbose = verbose or 0
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# Assign Trace instances to tallyable o... | python | def sample(self, iter, length=None, verbose=0):
"""
Draws iter samples from the posterior.
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pymc-devs/pymc | pymc/Model.py | Sampler._finalize | def _finalize(self):
"""Reset the status and tell the database to finalize the traces."""
if self.status in ['running', 'halt']:
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print_('\nSampling finished normally.')
self.status = 'ready'
self.save_state()
self.db._finalize... | python | def _finalize(self):
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print_('\nSampling finished normally.')
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pymc-devs/pymc | pymc/Model.py | Sampler.stats | def stats(self, variables=None, alpha=0.05, start=0,
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"""
Statistical output for variables.
:Parameters:
variables : iterable
List or array of variables for which statistics are to be
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"""
Statistical output for variables.
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variables : iterable
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pymc-devs/pymc | pymc/Model.py | Sampler.write_csv | def write_csv(
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Save summary statistics to a csv table.
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filename : string
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pymc-devs/pymc | pymc/Model.py | Sampler._csv_str | def _csv_str(self, param, stats, quantiles, index=None):
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else:
buffer += '_' + '_'.join([str(i) for i in index]) + ', '
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"""Support function for write_csv"""
buffer = param
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pymc-devs/pymc | pymc/Model.py | Sampler.summary | def summary(self, variables=None, alpha=0.05, start=0, batches=100,
chain=None, roundto=3):
"""
Generate a pretty-printed summary of the model's variables.
:Parameters:
alpha : float
The alpha level for generating posterior intervals. Defaults to
0.05... | python | def summary(self, variables=None, alpha=0.05, start=0, batches=100,
chain=None, roundto=3):
"""
Generate a pretty-printed summary of the model's variables.
:Parameters:
alpha : float
The alpha level for generating posterior intervals. Defaults to
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pymc-devs/pymc | pymc/Model.py | Sampler._assign_database_backend | def _assign_database_backend(self, db):
"""Assign Trace instance to stochastics and deterministics and Database instance
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- `db` : string, Database instance
The name of the database module (see below), or a Database instance.
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"""Assign Trace instance to stochastics and deterministics and Database instance
to self.
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- `db` : string, Database instance
The name of the database module (see below), or a Database instance.
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pymc-devs/pymc | pymc/Model.py | Sampler.pause | def pause(self):
"""Pause the sampler. Sampling can be resumed by calling `icontinue`.
"""
self.status = 'paused'
# The _loop method will react to 'paused' status and stop looping.
if hasattr(
self, '_sampling_thread') and self._sampling_thread.isAlive():
... | python | def pause(self):
"""Pause the sampler. Sampling can be resumed by calling `icontinue`.
"""
self.status = 'paused'
# The _loop method will react to 'paused' status and stop looping.
if hasattr(
self, '_sampling_thread') and self._sampling_thread.isAlive():
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pymc-devs/pymc | pymc/Model.py | Sampler.halt | def halt(self):
"""Halt a sampling running in another thread."""
self.status = 'halt'
# The _halt method is called by _loop.
if hasattr(
self, '_sampling_thread') and self._sampling_thread.isAlive():
print_('Waiting for current iteration to finish...')
... | python | def halt(self):
"""Halt a sampling running in another thread."""
self.status = 'halt'
# The _halt method is called by _loop.
if hasattr(
self, '_sampling_thread') and self._sampling_thread.isAlive():
print_('Waiting for current iteration to finish...')
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pymc-devs/pymc | pymc/Model.py | Sampler.isample | def isample(self, *args, **kwds):
"""
Samples in interactive mode. Main thread of control stays in this function.
"""
self._exc_info = None
out = kwds.pop('out', sys.stdout)
kwds['progress_bar'] = False
def samp_targ(*args, **kwds):
try:
... | python | def isample(self, *args, **kwds):
"""
Samples in interactive mode. Main thread of control stays in this function.
"""
self._exc_info = None
out = kwds.pop('out', sys.stdout)
kwds['progress_bar'] = False
def samp_targ(*args, **kwds):
try:
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pymc-devs/pymc | pymc/Model.py | Sampler.icontinue | def icontinue(self):
"""
Restarts thread in interactive mode
"""
if self.status != 'paused':
print_(
"No sampling to continue. Please initiate sampling with isample.")
return
def sample_and_finalize():
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... | python | def icontinue(self):
"""
Restarts thread in interactive mode
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if self.status != 'paused':
print_(
"No sampling to continue. Please initiate sampling with isample.")
return
def sample_and_finalize():
self._loop()
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pymc-devs/pymc | pymc/Model.py | Sampler.get_state | def get_state(self):
"""
Return the sampler's current state in order to
restart sampling at a later time.
"""
state = dict(sampler={}, stochastics={})
# The state of the sampler itself.
for s in self._state:
state['sampler'][s] = getattr(self, s)
... | python | def get_state(self):
"""
Return the sampler's current state in order to
restart sampling at a later time.
"""
state = dict(sampler={}, stochastics={})
# The state of the sampler itself.
for s in self._state:
state['sampler'][s] = getattr(self, s)
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pymc-devs/pymc | pymc/Model.py | Sampler.save_state | def save_state(self):
"""
Tell the database to save the current state of the sampler.
"""
try:
self.db.savestate(self.get_state())
except:
print_('Warning, unable to save state.')
print_('Error message:')
traceback.print_exc() | python | def save_state(self):
"""
Tell the database to save the current state of the sampler.
"""
try:
self.db.savestate(self.get_state())
except:
print_('Warning, unable to save state.')
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pymc-devs/pymc | pymc/Model.py | Sampler.restore_sampler_state | def restore_sampler_state(self):
"""
Restore the state of the sampler and to
the state stored in the database.
"""
state = self.db.getstate() or {}
# Restore sampler's state
sampler_state = state.get('sampler', {})
self.__dict__.update(sampler_state)
... | python | def restore_sampler_state(self):
"""
Restore the state of the sampler and to
the state stored in the database.
"""
state = self.db.getstate() or {}
# Restore sampler's state
sampler_state = state.get('sampler', {})
self.__dict__.update(sampler_state)
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pymc-devs/pymc | pymc/utils.py | normcdf | def normcdf(x, log=False):
"""Normal cumulative density function."""
y = np.atleast_1d(x).copy()
flib.normcdf(y)
if log:
if (y>0).all():
return np.log(y)
return -np.inf
return y | python | def normcdf(x, log=False):
"""Normal cumulative density function."""
y = np.atleast_1d(x).copy()
flib.normcdf(y)
if log:
if (y>0).all():
return np.log(y)
return -np.inf
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pymc-devs/pymc | pymc/utils.py | lognormcdf | def lognormcdf(x, mu, tau):
"""Log-normal cumulative density function"""
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[0.5 * (1 - flib.derf(-(np.sqrt(tau / 2)) * (np.log(y) - mu))) for y in x]) | python | def lognormcdf(x, mu, tau):
"""Log-normal cumulative density function"""
x = np.atleast_1d(x)
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pymc-devs/pymc | pymc/utils.py | invcdf | def invcdf(x):
"""Inverse of normal cumulative density function."""
x_flat = np.ravel(x)
x_trans = np.array([flib.ppnd16(y, 1) for y in x_flat])
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"""Inverse of normal cumulative density function."""
x_flat = np.ravel(x)
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pymc-devs/pymc | pymc/utils.py | trace_generator | def trace_generator(trace, start=0, stop=None, step=1):
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T.next()
for t in T:...
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pymc-devs/pymc | pymc/utils.py | draw_random | def draw_random(obj, **kwds):
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Ex:
R = draw_random(theta, beta=pymc.utils.trace_generator(beta.trace))
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"""
while True:
for k,... | python | def draw_random(obj, **kwds):
"""Draw random variates from obj.random method.
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pymc-devs/pymc | pymc/utils.py | rec_setattr | def rec_setattr(obj, attr, value):
"""Set object's attribute. May use dot notation.
>>> class C(object): pass
>>> a = C()
>>> a.b = C()
>>> a.b.c = 4
>>> rec_setattr(a, 'b.c', 2)
>>> a.b.c
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setattr(reduce(getattr, attrs[:-1], obj), attrs[-1], val... | python | def rec_setattr(obj, attr, value):
"""Set object's attribute. May use dot notation.
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pymc-devs/pymc | pymc/utils.py | calc_min_interval | def calc_min_interval(x, alpha):
"""Internal method to determine the minimum interval of
a given width
Assumes that x is sorted numpy array.
"""
n = len(x)
cred_mass = 1.0 - alpha
interval_idx_inc = int(np.floor(cred_mass * n))
n_intervals = n - interval_idx_inc
interval_width = x... | python | def calc_min_interval(x, alpha):
"""Internal method to determine the minimum interval of
a given width
Assumes that x is sorted numpy array.
"""
n = len(x)
cred_mass = 1.0 - alpha
interval_idx_inc = int(np.floor(cred_mass * n))
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pymc-devs/pymc | pymc/utils.py | quantiles | def quantiles(x, qlist=(2.5, 25, 50, 75, 97.5)):
"""Returns a dictionary of requested quantiles from array
:Arguments:
x : Numpy array
An array containing MCMC samples
qlist : tuple or list
A list of desired quantiles (defaults to (2.5, 25, 50, 75, 97.5))
"""
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"""Returns a dictionary of requested quantiles from array
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x : Numpy array
An array containing MCMC samples
qlist : tuple or list
A list of desired quantiles (defaults to (2.5, 25, 50, 75, 97.5))
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pymc-devs/pymc | pymc/utils.py | coda_output | def coda_output(pymc_object, name=None, chain=-1):
"""Generate output files that are compatible with CODA
:Arguments:
pymc_object : Model or Node
A PyMC object containing MCMC output.
"""
print_()
print_("Generating CODA output")
print_('=' * 50)
if name is None:
... | python | def coda_output(pymc_object, name=None, chain=-1):
"""Generate output files that are compatible with CODA
:Arguments:
pymc_object : Model or Node
A PyMC object containing MCMC output.
"""
print_()
print_("Generating CODA output")
print_('=' * 50)
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pymc-devs/pymc | pymc/utils.py | getInput | def getInput():
"""Read the input buffer without blocking the system."""
input = ''
if sys.platform == 'win32':
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if msvcrt.kbhit(): # Check for a keyboard hit.
input += msvcrt.getch()
print_(input)
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"""Read the input buffer without blocking the system."""
input = ''
if sys.platform == 'win32':
import msvcrt
if msvcrt.kbhit(): # Check for a keyboard hit.
input += msvcrt.getch()
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pymc-devs/pymc | pymc/utils.py | find_generations | def find_generations(container, with_data=False):
"""
A generation is the set of stochastic variables that only has parents in
previous generations.
"""
generations = []
# Find root generation
generations.append(set())
all_children = set()
if with_data:
stochastics_to_itera... | python | def find_generations(container, with_data=False):
"""
A generation is the set of stochastic variables that only has parents in
previous generations.
"""
generations = []
# Find root generation
generations.append(set())
all_children = set()
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pymc-devs/pymc | pymc/utils.py | append | def append(nodelist, node, label=None, sep='_'):
"""
Append function to automate the naming of list elements in Containers.
:Arguments:
- `nodelist` : List containing nodes for Container.
- `node` : Node to be added to list.
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"""
Append function to automate the naming of list elements in Containers.
:Arguments:
- `nodelist` : List containing nodes for Container.
- `node` : Node to be added to list.
- `label` : Label to be appended to list (If not passed,
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pymc-devs/pymc | pymc/PyMCObjects.py | Deterministic.logp_partial_gradient | def logp_partial_gradient(self, variable, calculation_set=None):
"""
gets the logp gradient of this deterministic with respect to variable
"""
if self.verbose > 0:
print_('\t' + self.__name__ + ': logp_partial_gradient accessed.')
if not (datatypes.is_continuous(vari... | python | def logp_partial_gradient(self, variable, calculation_set=None):
"""
gets the logp gradient of this deterministic with respect to variable
"""
if self.verbose > 0:
print_('\t' + self.__name__ + ': logp_partial_gradient accessed.')
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pymc-devs/pymc | pymc/PyMCObjects.py | Stochastic.gen_lazy_function | def gen_lazy_function(self):
"""
Will be called by Node at instantiation.
"""
# If value argument to __init__ was None, draw value from random
# method.
if self._value is None:
# Use random function if provided
if self._random is not None:
... | python | def gen_lazy_function(self):
"""
Will be called by Node at instantiation.
"""
# If value argument to __init__ was None, draw value from random
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if self._value is None:
# Use random function if provided
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pymc-devs/pymc | pymc/PyMCObjects.py | Stochastic.logp_gradient_contribution | def logp_gradient_contribution(self, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to self.
Calculation of the log posterior is restricted to the variables in calculation_set.
"""
# NEED some sort of check to see if the log p calculati... | python | def logp_gradient_contribution(self, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to self.
Calculation of the log posterior is restricted to the variables in calculation_set.
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pymc-devs/pymc | pymc/PyMCObjects.py | Stochastic.logp_partial_gradient | def logp_partial_gradient(self, variable, calculation_set=None):
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pymc-devs/pymc | pymc/PyMCObjects.py | Stochastic.random | def random(self):
"""
Draws a new value for a stoch conditional on its parents
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Raises an error if no 'random' argument was passed to __init__.
"""
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# Get current values of parents for use as arguments for _random()
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"""
Draws a new value for a stoch conditional on its parents
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pymc-devs/pymc | pymc/database/hdf5.py | save_sampler | def save_sampler(sampler):
"""
Dumps a sampler into its hdf5 database.
"""
db = sampler.db
fnode = tables.filenode.newnode(db._h5file, where='/', name='__sampler__')
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pickle.dump(sampler, fnode) | python | def save_sampler(sampler):
"""
Dumps a sampler into its hdf5 database.
"""
db = sampler.db
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pymc-devs/pymc | pymc/database/hdf5.py | restore_sampler | def restore_sampler(fname):
"""
Creates a new sampler from an hdf5 database.
"""
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fnode = hf.root.__sampler__
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sampler = pickle.load(fnode)
return sampler | python | def restore_sampler(fname):
"""
Creates a new sampler from an hdf5 database.
"""
hf = tables.open_file(fname)
fnode = hf.root.__sampler__
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sampler = pickle.load(fnode)
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pymc-devs/pymc | pymc/database/hdf5.py | Trace.tally | def tally(self, chain):
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pymc-devs/pymc | pymc/database/hdf5.py | Trace.hdf5_col | def hdf5_col(self, chain=-1):
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chain : integer
The index of the chain.
.. note::
This method is specific to the ``hdf5`` backend.
"""
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chain : integer
The index of the chain.
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This method is specific to the ``hdf5`` backend. | [
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pymc-devs/pymc | pymc/database/hdf5.py | Database.savestate | def savestate(self, state, chain=-1):
"""Store a dictionnary containing the state of the Model and its
StepMethods."""
cur_chain = self._chains[chain]
if hasattr(cur_chain, '_state_'):
cur_chain._state_[0] = state
else:
s = self._h5file.create_vlarray(
... | python | def savestate(self, state, chain=-1):
"""Store a dictionnary containing the state of the Model and its
StepMethods."""
cur_chain = self._chains[chain]
if hasattr(cur_chain, '_state_'):
cur_chain._state_[0] = state
else:
s = self._h5file.create_vlarray(
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pymc-devs/pymc | pymc/database/hdf5.py | Database._model_trace_description | def _model_trace_description(self):
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:Returns:
table_description : dict
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pymc-devs/pymc | pymc/database/hdf5.py | Database._check_compatibility | def _check_compatibility(self):
"""Make sure the next objects to be tallied are compatible with the
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stored_descr = self._file_trace_description()
try:
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exce... | python | def _check_compatibility(self):
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try:
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pymc-devs/pymc | pymc/database/hdf5.py | Database._gettables | def _gettables(self):
"""Return a list of hdf5 tables name PyMCsamples.
"""
groups = self._h5file.list_nodes("/")
if len(groups) == 0:
return []
else:
return [
gr.PyMCsamples for gr in groups if gr._v_name[:5] == 'chain'] | python | def _gettables(self):
"""Return a list of hdf5 tables name PyMCsamples.
"""
groups = self._h5file.list_nodes("/")
if len(groups) == 0:
return []
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return [
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pymc-devs/pymc | pymc/database/hdf5.py | Database.add_attr | def add_attr(self, name, object, description='', chain=-1, array=False):
"""Add an attribute to the chain.
description may not be supported for every date type.
if array is true, create an Array object.
"""
if not np.isscalar(chain):
raise TypeError("chain must be a... | python | def add_attr(self, name, object, description='', chain=-1, array=False):
"""Add an attribute to the chain.
description may not be supported for every date type.
if array is true, create an Array object.
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if not np.isscalar(chain):
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pymc-devs/pymc | pymc/examples/disaster_model.py | rate | def rate(s=switchpoint, e=early_mean, l=late_mean):
''' Concatenate Poisson means '''
out = empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out | python | def rate(s=switchpoint, e=early_mean, l=late_mean):
''' Concatenate Poisson means '''
out = empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out | [
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pymc-devs/pymc | pymc/gp/cov_funs/cov_utils.py | regularize_array | def regularize_array(A):
"""
Takes an np.ndarray as an input.
- If the array is one-dimensional, it's assumed to be an array of input values.
- If the array is more than one-dimensional, its last index is assumed to curse
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"""
Takes an np.ndarray as an input.
- If the array is one-dimensional, it's assumed to be an array of input values.
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pymc-devs/pymc | pymc/gp/cov_funs/cov_utils.py | import_item | def import_item(name):
"""
Useful for importing nested modules such as pymc.gp.cov_funs.isotropic_cov_funs.
Updated with code copied from IPython under a BSD license.
"""
package = '.'.join(name.split('.')[0:-1])
obj = name.split('.')[-1]
if package:
module = __import__(package,fro... | python | def import_item(name):
"""
Useful for importing nested modules such as pymc.gp.cov_funs.isotropic_cov_funs.
Updated with code copied from IPython under a BSD license.
"""
package = '.'.join(name.split('.')[0:-1])
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pymc-devs/pymc | pymc/gp/cov_funs/cov_utils.py | covariance_function_bundle.add_distance_metric | def add_distance_metric(self, distance_fun_name, distance_fun_module, with_x):
"""
Takes a function that computes a distance matrix for
points in some coordinate system and returns self's
covariance function wrapped to use that distance function.
Uses function apply_distance, w... | python | def add_distance_metric(self, distance_fun_name, distance_fun_module, with_x):
"""
Takes a function that computes a distance matrix for
points in some coordinate system and returns self's
covariance function wrapped to use that distance function.
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pymc-devs/pymc | pymc/NormalApproximation.py | MAP.func | def func(self, p):
"""
The function that gets passed to the optimizers.
"""
self._set_stochastics(p)
try:
return -1. * self.logp
except ZeroProbability:
return Inf | python | def func(self, p):
"""
The function that gets passed to the optimizers.
"""
self._set_stochastics(p)
try:
return -1. * self.logp
except ZeroProbability:
return Inf | [
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pymc-devs/pymc | pymc/NormalApproximation.py | MAP.gradfunc | def gradfunc(self, p):
"""
The gradient-computing function that gets passed to the optimizers,
if needed.
"""
self._set_stochastics(p)
for i in xrange(self.len):
self.grad[i] = self.diff(i)
return -1 * self.grad | python | def gradfunc(self, p):
"""
The gradient-computing function that gets passed to the optimizers,
if needed.
"""
self._set_stochastics(p)
for i in xrange(self.len):
self.grad[i] = self.diff(i)
return -1 * self.grad | [
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