repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
pymc-devs/pymc | pymc/NormalApproximation.py | MAP.i_logp | def i_logp(self, index):
"""
Evaluates the log-probability of the Markov blanket of
a stochastic owning a particular index.
"""
all_relevant_stochastics = set()
p, i = self.stochastic_indices[index]
try:
return p.logp + logp_of_set(p.extended_children)... | python | def i_logp(self, index):
"""
Evaluates the log-probability of the Markov blanket of
a stochastic owning a particular index.
"""
all_relevant_stochastics = set()
p, i = self.stochastic_indices[index]
try:
return p.logp + logp_of_set(p.extended_children)... | [
"def",
"i_logp",
"(",
"self",
",",
"index",
")",
":",
"all_relevant_stochastics",
"=",
"set",
"(",
")",
"p",
",",
"i",
"=",
"self",
".",
"stochastic_indices",
"[",
"index",
"]",
"try",
":",
"return",
"p",
".",
"logp",
"+",
"logp_of_set",
"(",
"p",
".... | Evaluates the log-probability of the Markov blanket of
a stochastic owning a particular index. | [
"Evaluates",
"the",
"log",
"-",
"probability",
"of",
"the",
"Markov",
"blanket",
"of",
"a",
"stochastic",
"owning",
"a",
"particular",
"index",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/NormalApproximation.py#L430-L440 | train |
pymc-devs/pymc | pymc/NormalApproximation.py | MAP.grad_and_hess | def grad_and_hess(self):
"""
Computes self's gradient and Hessian. Used if the
optimization method for a NormApprox doesn't
use gradients and hessians, for instance fmin.
"""
for i in xrange(self.len):
di = self.diff(i)
self.grad[i] = di
... | python | def grad_and_hess(self):
"""
Computes self's gradient and Hessian. Used if the
optimization method for a NormApprox doesn't
use gradients and hessians, for instance fmin.
"""
for i in xrange(self.len):
di = self.diff(i)
self.grad[i] = di
... | [
"def",
"grad_and_hess",
"(",
"self",
")",
":",
"for",
"i",
"in",
"xrange",
"(",
"self",
".",
"len",
")",
":",
"di",
"=",
"self",
".",
"diff",
"(",
"i",
")",
"self",
".",
"grad",
"[",
"i",
"]",
"=",
"di",
"self",
".",
"hess",
"[",
"i",
",",
... | Computes self's gradient and Hessian. Used if the
optimization method for a NormApprox doesn't
use gradients and hessians, for instance fmin. | [
"Computes",
"self",
"s",
"gradient",
"and",
"Hessian",
".",
"Used",
"if",
"the",
"optimization",
"method",
"for",
"a",
"NormApprox",
"doesn",
"t",
"use",
"gradients",
"and",
"hessians",
"for",
"instance",
"fmin",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/NormalApproximation.py#L487-L505 | train |
pymc-devs/pymc | pymc/NormalApproximation.py | MAP.hessfunc | def hessfunc(self, p):
"""
The Hessian function that will be passed to the optimizer,
if needed.
"""
self._set_stochastics(p)
for i in xrange(self.len):
di = self.diff(i)
self.hess[i, i] = self.diff(i, 2)
if i < self.len - 1:
... | python | def hessfunc(self, p):
"""
The Hessian function that will be passed to the optimizer,
if needed.
"""
self._set_stochastics(p)
for i in xrange(self.len):
di = self.diff(i)
self.hess[i, i] = self.diff(i, 2)
if i < self.len - 1:
... | [
"def",
"hessfunc",
"(",
"self",
",",
"p",
")",
":",
"self",
".",
"_set_stochastics",
"(",
"p",
")",
"for",
"i",
"in",
"xrange",
"(",
"self",
".",
"len",
")",
":",
"di",
"=",
"self",
".",
"diff",
"(",
"i",
")",
"self",
".",
"hess",
"[",
"i",
"... | The Hessian function that will be passed to the optimizer,
if needed. | [
"The",
"Hessian",
"function",
"that",
"will",
"be",
"passed",
"to",
"the",
"optimizer",
"if",
"needed",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/NormalApproximation.py#L507-L526 | train |
pymc-devs/pymc | pymc/threadpool.py | makeRequests | def makeRequests(callable_, args_list, callback=None,
exc_callback=_handle_thread_exception):
"""Create several work requests for same callable with different arguments.
Convenience function for creating several work requests for the same
callable where each invocation of the callable rece... | python | def makeRequests(callable_, args_list, callback=None,
exc_callback=_handle_thread_exception):
"""Create several work requests for same callable with different arguments.
Convenience function for creating several work requests for the same
callable where each invocation of the callable rece... | [
"def",
"makeRequests",
"(",
"callable_",
",",
"args_list",
",",
"callback",
"=",
"None",
",",
"exc_callback",
"=",
"_handle_thread_exception",
")",
":",
"requests",
"=",
"[",
"]",
"for",
"item",
"in",
"args_list",
":",
"if",
"isinstance",
"(",
"item",
",",
... | Create several work requests for same callable with different arguments.
Convenience function for creating several work requests for the same
callable where each invocation of the callable receives different values
for its arguments.
``args_list`` contains the parameters for each invocation of callabl... | [
"Create",
"several",
"work",
"requests",
"for",
"same",
"callable",
"with",
"different",
"arguments",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L95-L124 | train |
pymc-devs/pymc | pymc/threadpool.py | thread_partition_array | def thread_partition_array(x):
"Partition work arrays for multithreaded addition and multiplication"
n_threads = get_threadpool_size()
if len(x.shape) > 1:
maxind = x.shape[1]
else:
maxind = x.shape[0]
bounds = np.array(np.linspace(0, maxind, n_threads + 1), dtype='int')
cmin = b... | python | def thread_partition_array(x):
"Partition work arrays for multithreaded addition and multiplication"
n_threads = get_threadpool_size()
if len(x.shape) > 1:
maxind = x.shape[1]
else:
maxind = x.shape[0]
bounds = np.array(np.linspace(0, maxind, n_threads + 1), dtype='int')
cmin = b... | [
"def",
"thread_partition_array",
"(",
"x",
")",
":",
"n_threads",
"=",
"get_threadpool_size",
"(",
")",
"if",
"len",
"(",
"x",
".",
"shape",
")",
">",
"1",
":",
"maxind",
"=",
"x",
".",
"shape",
"[",
"1",
"]",
"else",
":",
"maxind",
"=",
"x",
".",
... | Partition work arrays for multithreaded addition and multiplication | [
"Partition",
"work",
"arrays",
"for",
"multithreaded",
"addition",
"and",
"multiplication"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L402-L412 | train |
pymc-devs/pymc | pymc/threadpool.py | WorkerThread.run | def run(self):
"""Repeatedly process the job queue until told to exit."""
while True:
if self._dismissed.isSet():
# we are dismissed, break out of loop
break
# get next work request.
request = self._requests_queue.get()
# p... | python | def run(self):
"""Repeatedly process the job queue until told to exit."""
while True:
if self._dismissed.isSet():
# we are dismissed, break out of loop
break
# get next work request.
request = self._requests_queue.get()
# p... | [
"def",
"run",
"(",
"self",
")",
":",
"while",
"True",
":",
"if",
"self",
".",
"_dismissed",
".",
"isSet",
"(",
")",
":",
"# we are dismissed, break out of loop",
"break",
"# get next work request.",
"request",
"=",
"self",
".",
"_requests_queue",
".",
"get",
"... | Repeatedly process the job queue until told to exit. | [
"Repeatedly",
"process",
"the",
"job",
"queue",
"until",
"told",
"to",
"exit",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L152-L179 | train |
pymc-devs/pymc | pymc/threadpool.py | ThreadPool.createWorkers | def createWorkers(self, num_workers):
"""Add num_workers worker threads to the pool.
``poll_timout`` sets the interval in seconds (int or float) for how
ofte threads should check whether they are dismissed, while waiting for
requests.
"""
for i in range(num_workers):
... | python | def createWorkers(self, num_workers):
"""Add num_workers worker threads to the pool.
``poll_timout`` sets the interval in seconds (int or float) for how
ofte threads should check whether they are dismissed, while waiting for
requests.
"""
for i in range(num_workers):
... | [
"def",
"createWorkers",
"(",
"self",
",",
"num_workers",
")",
":",
"for",
"i",
"in",
"range",
"(",
"num_workers",
")",
":",
"self",
".",
"workers",
".",
"append",
"(",
"WorkerThread",
"(",
"self",
".",
"_requests_queue",
")",
")"
] | Add num_workers worker threads to the pool.
``poll_timout`` sets the interval in seconds (int or float) for how
ofte threads should check whether they are dismissed, while waiting for
requests. | [
"Add",
"num_workers",
"worker",
"threads",
"to",
"the",
"pool",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L287-L296 | train |
pymc-devs/pymc | pymc/threadpool.py | ThreadPool.dismissWorkers | def dismissWorkers(self, num_workers):
"""Tell num_workers worker threads to quit after their current task."""
for i in range(min(num_workers, len(self.workers))):
worker = self.workers.pop()
worker.dismiss() | python | def dismissWorkers(self, num_workers):
"""Tell num_workers worker threads to quit after their current task."""
for i in range(min(num_workers, len(self.workers))):
worker = self.workers.pop()
worker.dismiss() | [
"def",
"dismissWorkers",
"(",
"self",
",",
"num_workers",
")",
":",
"for",
"i",
"in",
"range",
"(",
"min",
"(",
"num_workers",
",",
"len",
"(",
"self",
".",
"workers",
")",
")",
")",
":",
"worker",
"=",
"self",
".",
"workers",
".",
"pop",
"(",
")",... | Tell num_workers worker threads to quit after their current task. | [
"Tell",
"num_workers",
"worker",
"threads",
"to",
"quit",
"after",
"their",
"current",
"task",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L298-L302 | train |
pymc-devs/pymc | pymc/threadpool.py | ThreadPool.setNumWorkers | def setNumWorkers(self, num_workers):
"""Set number of worker threads to num_workers"""
cur_num = len(self.workers)
if cur_num > num_workers:
self.dismissWorkers(cur_num - num_workers)
else:
self.createWorkers(num_workers - cur_num) | python | def setNumWorkers(self, num_workers):
"""Set number of worker threads to num_workers"""
cur_num = len(self.workers)
if cur_num > num_workers:
self.dismissWorkers(cur_num - num_workers)
else:
self.createWorkers(num_workers - cur_num) | [
"def",
"setNumWorkers",
"(",
"self",
",",
"num_workers",
")",
":",
"cur_num",
"=",
"len",
"(",
"self",
".",
"workers",
")",
"if",
"cur_num",
">",
"num_workers",
":",
"self",
".",
"dismissWorkers",
"(",
"cur_num",
"-",
"num_workers",
")",
"else",
":",
"se... | Set number of worker threads to num_workers | [
"Set",
"number",
"of",
"worker",
"threads",
"to",
"num_workers"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L304-L310 | train |
pymc-devs/pymc | pymc/threadpool.py | ThreadPool.putRequest | def putRequest(self, request, block=True, timeout=0):
"""Put work request into work queue and save its id for later."""
# don't reuse old work requests
# print '\tthread pool putting work request %s'%request
self._requests_queue.put(request, block, timeout)
self.workRequests[requ... | python | def putRequest(self, request, block=True, timeout=0):
"""Put work request into work queue and save its id for later."""
# don't reuse old work requests
# print '\tthread pool putting work request %s'%request
self._requests_queue.put(request, block, timeout)
self.workRequests[requ... | [
"def",
"putRequest",
"(",
"self",
",",
"request",
",",
"block",
"=",
"True",
",",
"timeout",
"=",
"0",
")",
":",
"# don't reuse old work requests",
"# print '\\tthread pool putting work request %s'%request",
"self",
".",
"_requests_queue",
".",
"put",
"(",
"request",
... | Put work request into work queue and save its id for later. | [
"Put",
"work",
"request",
"into",
"work",
"queue",
"and",
"save",
"its",
"id",
"for",
"later",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/threadpool.py#L312-L317 | train |
pymc-devs/pymc | pymc/examples/zip.py | zip | def zip(value=data, mu=mu, psi=psi):
""" Zero-inflated Poisson likelihood """
# Initialize likeihood
like = 0.0
# Loop over data
for x in value:
if not x:
# Zero values
like += np.log((1. - psi) + psi * np.exp(-mu))
else:
# Non-zero values
... | python | def zip(value=data, mu=mu, psi=psi):
""" Zero-inflated Poisson likelihood """
# Initialize likeihood
like = 0.0
# Loop over data
for x in value:
if not x:
# Zero values
like += np.log((1. - psi) + psi * np.exp(-mu))
else:
# Non-zero values
... | [
"def",
"zip",
"(",
"value",
"=",
"data",
",",
"mu",
"=",
"mu",
",",
"psi",
"=",
"psi",
")",
":",
"# Initialize likeihood",
"like",
"=",
"0.0",
"# Loop over data",
"for",
"x",
"in",
"value",
":",
"if",
"not",
"x",
":",
"# Zero values",
"like",
"+=",
"... | Zero-inflated Poisson likelihood | [
"Zero",
"-",
"inflated",
"Poisson",
"likelihood"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/examples/zip.py#L26-L43 | train |
pymc-devs/pymc | pymc/Matplot.py | plot | def plot(
data, name, format='png', suffix='', path='./', common_scale=True, datarange=(None, None), fontmap=None, verbose=1,
new=True, last=True, rows=1, num=1):
"""
Generates summary plots for nodes of a given PyMC object.
:Arguments:
data: PyMC object, trace or array
A tr... | python | def plot(
data, name, format='png', suffix='', path='./', common_scale=True, datarange=(None, None), fontmap=None, verbose=1,
new=True, last=True, rows=1, num=1):
"""
Generates summary plots for nodes of a given PyMC object.
:Arguments:
data: PyMC object, trace or array
A tr... | [
"def",
"plot",
"(",
"data",
",",
"name",
",",
"format",
"=",
"'png'",
",",
"suffix",
"=",
"''",
",",
"path",
"=",
"'./'",
",",
"common_scale",
"=",
"True",
",",
"datarange",
"=",
"(",
"None",
",",
"None",
")",
",",
"fontmap",
"=",
"None",
",",
"v... | Generates summary plots for nodes of a given PyMC object.
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
format (optional): string
Graphic output f... | [
"Generates",
"summary",
"plots",
"for",
"nodes",
"of",
"a",
"given",
"PyMC",
"object",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Matplot.py#L385-L475 | train |
pymc-devs/pymc | pymc/Matplot.py | histogram | def histogram(
data, name, bins='sturges', datarange=(None, None), format='png', suffix='', path='./', rows=1,
columns=1, num=1, last=True, fontmap = None, verbose=1):
"""
Generates histogram from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC... | python | def histogram(
data, name, bins='sturges', datarange=(None, None), format='png', suffix='', path='./', rows=1,
columns=1, num=1, last=True, fontmap = None, verbose=1):
"""
Generates histogram from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC... | [
"def",
"histogram",
"(",
"data",
",",
"name",
",",
"bins",
"=",
"'sturges'",
",",
"datarange",
"=",
"(",
"None",
",",
"None",
")",
",",
"format",
"=",
"'png'",
",",
"suffix",
"=",
"''",
",",
"path",
"=",
"'./'",
",",
"rows",
"=",
"1",
",",
"colum... | Generates histogram from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC sample.
name: string
The name of the histogram.
bins: int or string
The number of bins, or a preferred binning method. Available methods include
... | [
"Generates",
"histogram",
"from",
"an",
"array",
"of",
"data",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Matplot.py#L486-L589 | train |
pymc-devs/pymc | pymc/Matplot.py | trace | def trace(
data, name, format='png', datarange=(None, None), suffix='', path='./', rows=1, columns=1,
num=1, last=True, fontmap = None, verbose=1):
"""
Generates trace plot from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC sample.
n... | python | def trace(
data, name, format='png', datarange=(None, None), suffix='', path='./', rows=1, columns=1,
num=1, last=True, fontmap = None, verbose=1):
"""
Generates trace plot from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC sample.
n... | [
"def",
"trace",
"(",
"data",
",",
"name",
",",
"format",
"=",
"'png'",
",",
"datarange",
"=",
"(",
"None",
",",
"None",
")",
",",
"suffix",
"=",
"''",
",",
"path",
"=",
"'./'",
",",
"rows",
"=",
"1",
",",
"columns",
"=",
"1",
",",
"num",
"=",
... | Generates trace plot from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC sample.
name: string
The name of the trace.
datarange: tuple or list
Preferred y-range of trace (defaults to (None,None)).
format (optional... | [
"Generates",
"trace",
"plot",
"from",
"an",
"array",
"of",
"data",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Matplot.py#L593-L655 | train |
pymc-devs/pymc | pymc/Matplot.py | gof_plot | def gof_plot(
simdata, trueval, name=None, bins=None, format='png', suffix='-gof', path='./',
fontmap=None, verbose=0):
"""
Plots histogram of replicated data, indicating the location of the observed data
:Arguments:
simdata: array or PyMC object
Trace of simulated data or t... | python | def gof_plot(
simdata, trueval, name=None, bins=None, format='png', suffix='-gof', path='./',
fontmap=None, verbose=0):
"""
Plots histogram of replicated data, indicating the location of the observed data
:Arguments:
simdata: array or PyMC object
Trace of simulated data or t... | [
"def",
"gof_plot",
"(",
"simdata",
",",
"trueval",
",",
"name",
"=",
"None",
",",
"bins",
"=",
"None",
",",
"format",
"=",
"'png'",
",",
"suffix",
"=",
"'-gof'",
",",
"path",
"=",
"'./'",
",",
"fontmap",
"=",
"None",
",",
"verbose",
"=",
"0",
")",
... | Plots histogram of replicated data, indicating the location of the observed data
:Arguments:
simdata: array or PyMC object
Trace of simulated data or the PyMC stochastic object containing trace.
trueval: numeric
True (observed) value of the data
bins: int or string... | [
"Plots",
"histogram",
"of",
"replicated",
"data",
"indicating",
"the",
"location",
"of",
"the",
"observed",
"data"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Matplot.py#L790-L894 | train |
pymc-devs/pymc | pymc/database/sqlite.py | load | def load(dbname):
"""Load an existing SQLite database.
Return a Database instance.
"""
db = Database(dbname)
# Get the name of the objects
tables = get_table_list(db.cur)
# Create a Trace instance for each object
chains = 0
for name in tables:
db._traces[name] = Trace(name... | python | def load(dbname):
"""Load an existing SQLite database.
Return a Database instance.
"""
db = Database(dbname)
# Get the name of the objects
tables = get_table_list(db.cur)
# Create a Trace instance for each object
chains = 0
for name in tables:
db._traces[name] = Trace(name... | [
"def",
"load",
"(",
"dbname",
")",
":",
"db",
"=",
"Database",
"(",
"dbname",
")",
"# Get the name of the objects",
"tables",
"=",
"get_table_list",
"(",
"db",
".",
"cur",
")",
"# Create a Trace instance for each object",
"chains",
"=",
"0",
"for",
"name",
"in",... | Load an existing SQLite database.
Return a Database instance. | [
"Load",
"an",
"existing",
"SQLite",
"database",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/database/sqlite.py#L233-L255 | train |
pymc-devs/pymc | pymc/database/sqlite.py | get_shape | def get_shape(cursor, name):
"""Return the shape of the table ``name``."""
cursor.execute('select * from [%s]' % name)
inds = cursor.description[-1][0][1:].split('_')
return tuple([int(i) for i in inds]) | python | def get_shape(cursor, name):
"""Return the shape of the table ``name``."""
cursor.execute('select * from [%s]' % name)
inds = cursor.description[-1][0][1:].split('_')
return tuple([int(i) for i in inds]) | [
"def",
"get_shape",
"(",
"cursor",
",",
"name",
")",
":",
"cursor",
".",
"execute",
"(",
"'select * from [%s]'",
"%",
"name",
")",
"inds",
"=",
"cursor",
".",
"description",
"[",
"-",
"1",
"]",
"[",
"0",
"]",
"[",
"1",
":",
"]",
".",
"split",
"(",
... | Return the shape of the table ``name``. | [
"Return",
"the",
"shape",
"of",
"the",
"table",
"name",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/database/sqlite.py#L270-L274 | train |
pymc-devs/pymc | pymc/database/sqlite.py | Database.close | def close(self, *args, **kwds):
"""Close database."""
self.cur.close()
self.commit()
self.DB.close() | python | def close(self, *args, **kwds):
"""Close database."""
self.cur.close()
self.commit()
self.DB.close() | [
"def",
"close",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwds",
")",
":",
"self",
".",
"cur",
".",
"close",
"(",
")",
"self",
".",
"commit",
"(",
")",
"self",
".",
"DB",
".",
"close",
"(",
")"
] | Close database. | [
"Close",
"database",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/database/sqlite.py#L204-L208 | train |
pymc-devs/pymc | pymc/CommonDeterministics.py | create_nonimplemented_method | def create_nonimplemented_method(op_name, klass):
"""
Creates a new method that raises NotImplementedError.
"""
def new_method(self, *args):
raise NotImplementedError(
'Special method %s has not been implemented for PyMC variables.' %
op_name)
new_method.__name__ = '... | python | def create_nonimplemented_method(op_name, klass):
"""
Creates a new method that raises NotImplementedError.
"""
def new_method(self, *args):
raise NotImplementedError(
'Special method %s has not been implemented for PyMC variables.' %
op_name)
new_method.__name__ = '... | [
"def",
"create_nonimplemented_method",
"(",
"op_name",
",",
"klass",
")",
":",
"def",
"new_method",
"(",
"self",
",",
"*",
"args",
")",
":",
"raise",
"NotImplementedError",
"(",
"'Special method %s has not been implemented for PyMC variables.'",
"%",
"op_name",
")",
"... | Creates a new method that raises NotImplementedError. | [
"Creates",
"a",
"new",
"method",
"that",
"raises",
"NotImplementedError",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/CommonDeterministics.py#L802-L818 | train |
pymc-devs/pymc | pymc/MCMC.py | MCMC.remove_step_method | def remove_step_method(self, step_method):
"""
Removes a step method.
"""
try:
for s in step_method.stochastics:
self.step_method_dict[s].remove(step_method)
if hasattr(self, "step_methods"):
self.step_methods.discard(step_method)
... | python | def remove_step_method(self, step_method):
"""
Removes a step method.
"""
try:
for s in step_method.stochastics:
self.step_method_dict[s].remove(step_method)
if hasattr(self, "step_methods"):
self.step_methods.discard(step_method)
... | [
"def",
"remove_step_method",
"(",
"self",
",",
"step_method",
")",
":",
"try",
":",
"for",
"s",
"in",
"step_method",
".",
"stochastics",
":",
"self",
".",
"step_method_dict",
"[",
"s",
"]",
".",
"remove",
"(",
"step_method",
")",
"if",
"hasattr",
"(",
"s... | Removes a step method. | [
"Removes",
"a",
"step",
"method",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/MCMC.py#L129-L141 | train |
pymc-devs/pymc | pymc/MCMC.py | MCMC.assign_step_methods | def assign_step_methods(self, verbose=-1, draw_from_prior_when_possible=True):
"""
Make sure every stochastic variable has a step method. If not,
assign a step method from the registry.
"""
if not self._sm_assigned:
if draw_from_prior_when_possible:
... | python | def assign_step_methods(self, verbose=-1, draw_from_prior_when_possible=True):
"""
Make sure every stochastic variable has a step method. If not,
assign a step method from the registry.
"""
if not self._sm_assigned:
if draw_from_prior_when_possible:
... | [
"def",
"assign_step_methods",
"(",
"self",
",",
"verbose",
"=",
"-",
"1",
",",
"draw_from_prior_when_possible",
"=",
"True",
")",
":",
"if",
"not",
"self",
".",
"_sm_assigned",
":",
"if",
"draw_from_prior_when_possible",
":",
"# Assign dataless stepper first",
"last... | Make sure every stochastic variable has a step method. If not,
assign a step method from the registry. | [
"Make",
"sure",
"every",
"stochastic",
"variable",
"has",
"a",
"step",
"method",
".",
"If",
"not",
"assign",
"a",
"step",
"method",
"from",
"the",
"registry",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/MCMC.py#L143-L204 | train |
pymc-devs/pymc | pymc/MCMC.py | MCMC.tune | def tune(self):
"""
Tell all step methods to tune themselves.
"""
if self.verbose > 0:
print_('\tTuning at iteration', self._current_iter)
# Initialize counter for number of tuning stochastics
tuning_count = 0
for step_method in self.step_methods:
... | python | def tune(self):
"""
Tell all step methods to tune themselves.
"""
if self.verbose > 0:
print_('\tTuning at iteration', self._current_iter)
# Initialize counter for number of tuning stochastics
tuning_count = 0
for step_method in self.step_methods:
... | [
"def",
"tune",
"(",
"self",
")",
":",
"if",
"self",
".",
"verbose",
">",
"0",
":",
"print_",
"(",
"'\\tTuning at iteration'",
",",
"self",
".",
"_current_iter",
")",
"# Initialize counter for number of tuning stochastics",
"tuning_count",
"=",
"0",
"for",
"step_me... | Tell all step methods to tune themselves. | [
"Tell",
"all",
"step",
"methods",
"to",
"tune",
"themselves",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/MCMC.py#L349-L387 | train |
pymc-devs/pymc | pymc/MCMC.py | MCMC.get_state | def get_state(self):
"""
Return the sampler and step methods current state in order to
restart sampling at a later time.
"""
self.step_methods = set()
for s in self.stochastics:
self.step_methods |= set(self.step_method_dict[s])
state = Sampler.get_s... | python | def get_state(self):
"""
Return the sampler and step methods current state in order to
restart sampling at a later time.
"""
self.step_methods = set()
for s in self.stochastics:
self.step_methods |= set(self.step_method_dict[s])
state = Sampler.get_s... | [
"def",
"get_state",
"(",
"self",
")",
":",
"self",
".",
"step_methods",
"=",
"set",
"(",
")",
"for",
"s",
"in",
"self",
".",
"stochastics",
":",
"self",
".",
"step_methods",
"|=",
"set",
"(",
"self",
".",
"step_method_dict",
"[",
"s",
"]",
")",
"stat... | Return the sampler and step methods current state in order to
restart sampling at a later time. | [
"Return",
"the",
"sampler",
"and",
"step",
"methods",
"current",
"state",
"in",
"order",
"to",
"restart",
"sampling",
"at",
"a",
"later",
"time",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/MCMC.py#L389-L406 | train |
pymc-devs/pymc | pymc/MCMC.py | MCMC._calc_dic | def _calc_dic(self):
"""Calculates deviance information Criterion"""
# Find mean deviance
mean_deviance = np.mean(self.db.trace('deviance')(), axis=0)
# Set values of all parameters to their mean
for stochastic in self.stochastics:
# Calculate mean of paramter
... | python | def _calc_dic(self):
"""Calculates deviance information Criterion"""
# Find mean deviance
mean_deviance = np.mean(self.db.trace('deviance')(), axis=0)
# Set values of all parameters to their mean
for stochastic in self.stochastics:
# Calculate mean of paramter
... | [
"def",
"_calc_dic",
"(",
"self",
")",
":",
"# Find mean deviance",
"mean_deviance",
"=",
"np",
".",
"mean",
"(",
"self",
".",
"db",
".",
"trace",
"(",
"'deviance'",
")",
"(",
")",
",",
"axis",
"=",
"0",
")",
"# Set values of all parameters to their mean",
"f... | Calculates deviance information Criterion | [
"Calculates",
"deviance",
"information",
"Criterion"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/MCMC.py#L419-L450 | train |
pymc-devs/pymc | pymc/distributions.py | stochastic_from_data | def stochastic_from_data(name, data, lower=-np.inf, upper=np.inf,
value=None, observed=False, trace=True, verbose=-1, debug=False):
"""
Return a Stochastic subclass made from arbitrary data.
The histogram for the data is fitted with Kernel Density Estimation.
:Parameters:
... | python | def stochastic_from_data(name, data, lower=-np.inf, upper=np.inf,
value=None, observed=False, trace=True, verbose=-1, debug=False):
"""
Return a Stochastic subclass made from arbitrary data.
The histogram for the data is fitted with Kernel Density Estimation.
:Parameters:
... | [
"def",
"stochastic_from_data",
"(",
"name",
",",
"data",
",",
"lower",
"=",
"-",
"np",
".",
"inf",
",",
"upper",
"=",
"np",
".",
"inf",
",",
"value",
"=",
"None",
",",
"observed",
"=",
"False",
",",
"trace",
"=",
"True",
",",
"verbose",
"=",
"-",
... | Return a Stochastic subclass made from arbitrary data.
The histogram for the data is fitted with Kernel Density Estimation.
:Parameters:
- `data` : An array with samples (e.g. trace[:])
- `lower` : Lower bound on possible outcomes
- `upper` : Upper bound on possible outcomes
:Example:
... | [
"Return",
"a",
"Stochastic",
"subclass",
"made",
"from",
"arbitrary",
"data",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L401-L458 | train |
pymc-devs/pymc | pymc/distributions.py | randomwrap | def randomwrap(func):
"""
Decorator for random value generators
Allows passing of sequence of parameters, as well as a size argument.
Convention:
- If size=1 and the parameters are all scalars, return a scalar.
- If size=1, the random variates are 1D.
- If the parameters are scalar... | python | def randomwrap(func):
"""
Decorator for random value generators
Allows passing of sequence of parameters, as well as a size argument.
Convention:
- If size=1 and the parameters are all scalars, return a scalar.
- If size=1, the random variates are 1D.
- If the parameters are scalar... | [
"def",
"randomwrap",
"(",
"func",
")",
":",
"# Find the order of the arguments.",
"refargs",
",",
"defaults",
"=",
"utils",
".",
"get_signature",
"(",
"func",
")",
"# vfunc = np.vectorize(self.func)",
"npos",
"=",
"len",
"(",
"refargs",
")",
"-",
"len",
"(",
"de... | Decorator for random value generators
Allows passing of sequence of parameters, as well as a size argument.
Convention:
- If size=1 and the parameters are all scalars, return a scalar.
- If size=1, the random variates are 1D.
- If the parameters are scalars and size > 1, the random variate... | [
"Decorator",
"for",
"random",
"value",
"generators"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L469-L572 | train |
pymc-devs/pymc | pymc/distributions.py | constrain | def constrain(value, lower=-np.Inf, upper=np.Inf, allow_equal=False):
"""
Apply interval constraint on stochastic value.
"""
ok = flib.constrain(value, lower, upper, allow_equal)
if ok == 0:
raise ZeroProbability | python | def constrain(value, lower=-np.Inf, upper=np.Inf, allow_equal=False):
"""
Apply interval constraint on stochastic value.
"""
ok = flib.constrain(value, lower, upper, allow_equal)
if ok == 0:
raise ZeroProbability | [
"def",
"constrain",
"(",
"value",
",",
"lower",
"=",
"-",
"np",
".",
"Inf",
",",
"upper",
"=",
"np",
".",
"Inf",
",",
"allow_equal",
"=",
"False",
")",
":",
"ok",
"=",
"flib",
".",
"constrain",
"(",
"value",
",",
"lower",
",",
"upper",
",",
"allo... | Apply interval constraint on stochastic value. | [
"Apply",
"interval",
"constraint",
"on",
"stochastic",
"value",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L599-L606 | train |
pymc-devs/pymc | pymc/distributions.py | expand_triangular | def expand_triangular(X, k):
"""
Expand flattened triangular matrix.
"""
X = X.tolist()
# Unflatten matrix
Y = np.asarray(
[[0] * i + X[i * k - (i * (i - 1)) / 2: i * k + (k - i)] for i in range(k)])
# Loop over rows
for i in range(k):
# Loop over columns
for j i... | python | def expand_triangular(X, k):
"""
Expand flattened triangular matrix.
"""
X = X.tolist()
# Unflatten matrix
Y = np.asarray(
[[0] * i + X[i * k - (i * (i - 1)) / 2: i * k + (k - i)] for i in range(k)])
# Loop over rows
for i in range(k):
# Loop over columns
for j i... | [
"def",
"expand_triangular",
"(",
"X",
",",
"k",
")",
":",
"X",
"=",
"X",
".",
"tolist",
"(",
")",
"# Unflatten matrix",
"Y",
"=",
"np",
".",
"asarray",
"(",
"[",
"[",
"0",
"]",
"*",
"i",
"+",
"X",
"[",
"i",
"*",
"k",
"-",
"(",
"i",
"*",
"("... | Expand flattened triangular matrix. | [
"Expand",
"flattened",
"triangular",
"matrix",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L632-L646 | train |
pymc-devs/pymc | pymc/distributions.py | rarlognormal | def rarlognormal(a, sigma, rho, size=1):
R"""
Autoregressive normal random variates.
If a is a scalar, generates one series of length size.
If a is a sequence, generates size series of the same length
as a.
"""
f = utils.ar1
if np.isscalar(a):
r = f(rho, 0, sigma, size)
else... | python | def rarlognormal(a, sigma, rho, size=1):
R"""
Autoregressive normal random variates.
If a is a scalar, generates one series of length size.
If a is a sequence, generates size series of the same length
as a.
"""
f = utils.ar1
if np.isscalar(a):
r = f(rho, 0, sigma, size)
else... | [
"def",
"rarlognormal",
"(",
"a",
",",
"sigma",
",",
"rho",
",",
"size",
"=",
"1",
")",
":",
"f",
"=",
"utils",
".",
"ar1",
"if",
"np",
".",
"isscalar",
"(",
"a",
")",
":",
"r",
"=",
"f",
"(",
"rho",
",",
"0",
",",
"sigma",
",",
"size",
")",... | R"""
Autoregressive normal random variates.
If a is a scalar, generates one series of length size.
If a is a sequence, generates size series of the same length
as a. | [
"R",
"Autoregressive",
"normal",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L722-L738 | train |
pymc-devs/pymc | pymc/distributions.py | arlognormal_like | def arlognormal_like(x, a, sigma, rho):
R"""
Autoregressive lognormal log-likelihood.
.. math::
x_i & = a_i \exp(e_i) \\
e_i & = \rho e_{i-1} + \epsilon_i
where :math:`\epsilon_i \sim N(0,\sigma)`.
"""
return flib.arlognormal(x, np.log(a), sigma, rho, beta=1) | python | def arlognormal_like(x, a, sigma, rho):
R"""
Autoregressive lognormal log-likelihood.
.. math::
x_i & = a_i \exp(e_i) \\
e_i & = \rho e_{i-1} + \epsilon_i
where :math:`\epsilon_i \sim N(0,\sigma)`.
"""
return flib.arlognormal(x, np.log(a), sigma, rho, beta=1) | [
"def",
"arlognormal_like",
"(",
"x",
",",
"a",
",",
"sigma",
",",
"rho",
")",
":",
"return",
"flib",
".",
"arlognormal",
"(",
"x",
",",
"np",
".",
"log",
"(",
"a",
")",
",",
"sigma",
",",
"rho",
",",
"beta",
"=",
"1",
")"
] | R"""
Autoregressive lognormal log-likelihood.
.. math::
x_i & = a_i \exp(e_i) \\
e_i & = \rho e_{i-1} + \epsilon_i
where :math:`\epsilon_i \sim N(0,\sigma)`. | [
"R",
"Autoregressive",
"lognormal",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L741-L751 | train |
pymc-devs/pymc | pymc/distributions.py | rbeta | def rbeta(alpha, beta, size=None):
"""
Random beta variates.
"""
from scipy.stats.distributions import beta as sbeta
return sbeta.ppf(np.random.random(size), alpha, beta) | python | def rbeta(alpha, beta, size=None):
"""
Random beta variates.
"""
from scipy.stats.distributions import beta as sbeta
return sbeta.ppf(np.random.random(size), alpha, beta) | [
"def",
"rbeta",
"(",
"alpha",
",",
"beta",
",",
"size",
"=",
"None",
")",
":",
"from",
"scipy",
".",
"stats",
".",
"distributions",
"import",
"beta",
"as",
"sbeta",
"return",
"sbeta",
".",
"ppf",
"(",
"np",
".",
"random",
".",
"random",
"(",
"size",
... | Random beta variates. | [
"Random",
"beta",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L803-L808 | train |
pymc-devs/pymc | pymc/distributions.py | rbinomial | def rbinomial(n, p, size=None):
"""
Random binomial variates.
"""
if not size:
size = None
return np.random.binomial(np.ravel(n), np.ravel(p), size) | python | def rbinomial(n, p, size=None):
"""
Random binomial variates.
"""
if not size:
size = None
return np.random.binomial(np.ravel(n), np.ravel(p), size) | [
"def",
"rbinomial",
"(",
"n",
",",
"p",
",",
"size",
"=",
"None",
")",
":",
"if",
"not",
"size",
":",
"size",
"=",
"None",
"return",
"np",
".",
"random",
".",
"binomial",
"(",
"np",
".",
"ravel",
"(",
"n",
")",
",",
"np",
".",
"ravel",
"(",
"... | Random binomial variates. | [
"Random",
"binomial",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L858-L864 | train |
pymc-devs/pymc | pymc/distributions.py | rbetabin | def rbetabin(alpha, beta, n, size=None):
"""
Random beta-binomial variates.
"""
phi = np.random.beta(alpha, beta, size)
return np.random.binomial(n, phi) | python | def rbetabin(alpha, beta, n, size=None):
"""
Random beta-binomial variates.
"""
phi = np.random.beta(alpha, beta, size)
return np.random.binomial(n, phi) | [
"def",
"rbetabin",
"(",
"alpha",
",",
"beta",
",",
"n",
",",
"size",
"=",
"None",
")",
":",
"phi",
"=",
"np",
".",
"random",
".",
"beta",
"(",
"alpha",
",",
"beta",
",",
"size",
")",
"return",
"np",
".",
"random",
".",
"binomial",
"(",
"n",
","... | Random beta-binomial variates. | [
"Random",
"beta",
"-",
"binomial",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L904-L910 | train |
pymc-devs/pymc | pymc/distributions.py | rcategorical | def rcategorical(p, size=None):
"""
Categorical random variates.
"""
out = flib.rcat(p, np.random.random(size=size))
if sum(out.shape) == 1:
return out.squeeze()
else:
return out | python | def rcategorical(p, size=None):
"""
Categorical random variates.
"""
out = flib.rcat(p, np.random.random(size=size))
if sum(out.shape) == 1:
return out.squeeze()
else:
return out | [
"def",
"rcategorical",
"(",
"p",
",",
"size",
"=",
"None",
")",
":",
"out",
"=",
"flib",
".",
"rcat",
"(",
"p",
",",
"np",
".",
"random",
".",
"random",
"(",
"size",
"=",
"size",
")",
")",
"if",
"sum",
"(",
"out",
".",
"shape",
")",
"==",
"1"... | Categorical random variates. | [
"Categorical",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L957-L965 | train |
pymc-devs/pymc | pymc/distributions.py | categorical_like | def categorical_like(x, p):
R"""
Categorical log-likelihood. The most general discrete distribution.
.. math:: f(x=i \mid p) = p_i
for :math:`i \in 0 \ldots k-1`.
:Parameters:
- `x` : [int] :math:`x \in 0\ldots k-1`
- `p` : [float] :math:`p > 0`, :math:`\sum p = 1`
"""
p = ... | python | def categorical_like(x, p):
R"""
Categorical log-likelihood. The most general discrete distribution.
.. math:: f(x=i \mid p) = p_i
for :math:`i \in 0 \ldots k-1`.
:Parameters:
- `x` : [int] :math:`x \in 0\ldots k-1`
- `p` : [float] :math:`p > 0`, :math:`\sum p = 1`
"""
p = ... | [
"def",
"categorical_like",
"(",
"x",
",",
"p",
")",
":",
"p",
"=",
"np",
".",
"atleast_2d",
"(",
"p",
")",
"if",
"np",
".",
"any",
"(",
"abs",
"(",
"np",
".",
"sum",
"(",
"p",
",",
"1",
")",
"-",
"1",
")",
">",
"0.0001",
")",
":",
"print_",... | R"""
Categorical log-likelihood. The most general discrete distribution.
.. math:: f(x=i \mid p) = p_i
for :math:`i \in 0 \ldots k-1`.
:Parameters:
- `x` : [int] :math:`x \in 0\ldots k-1`
- `p` : [float] :math:`p > 0`, :math:`\sum p = 1` | [
"R",
"Categorical",
"log",
"-",
"likelihood",
".",
"The",
"most",
"general",
"discrete",
"distribution",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L968-L985 | train |
pymc-devs/pymc | pymc/distributions.py | rcauchy | def rcauchy(alpha, beta, size=None):
"""
Returns Cauchy random variates.
"""
return alpha + beta * np.tan(pi * random_number(size) - pi / 2.0) | python | def rcauchy(alpha, beta, size=None):
"""
Returns Cauchy random variates.
"""
return alpha + beta * np.tan(pi * random_number(size) - pi / 2.0) | [
"def",
"rcauchy",
"(",
"alpha",
",",
"beta",
",",
"size",
"=",
"None",
")",
":",
"return",
"alpha",
"+",
"beta",
"*",
"np",
".",
"tan",
"(",
"pi",
"*",
"random_number",
"(",
"size",
")",
"-",
"pi",
"/",
"2.0",
")"
] | Returns Cauchy random variates. | [
"Returns",
"Cauchy",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L990-L995 | train |
pymc-devs/pymc | pymc/distributions.py | degenerate_like | def degenerate_like(x, k):
R"""
Degenerate log-likelihood.
.. math::
f(x \mid k) = \left\{ \begin{matrix} 1 \text{ if } x = k \\ 0 \text{ if } x \ne k\end{matrix} \right.
:Parameters:
- `x` : Input value.
- `k` : Degenerate value.
"""
x = np.atleast_1d(x)
return sum(np... | python | def degenerate_like(x, k):
R"""
Degenerate log-likelihood.
.. math::
f(x \mid k) = \left\{ \begin{matrix} 1 \text{ if } x = k \\ 0 \text{ if } x \ne k\end{matrix} \right.
:Parameters:
- `x` : Input value.
- `k` : Degenerate value.
"""
x = np.atleast_1d(x)
return sum(np... | [
"def",
"degenerate_like",
"(",
"x",
",",
"k",
")",
":",
"x",
"=",
"np",
".",
"atleast_1d",
"(",
"x",
")",
"return",
"sum",
"(",
"np",
".",
"log",
"(",
"[",
"i",
"==",
"k",
"for",
"i",
"in",
"x",
"]",
")",
")"
] | R"""
Degenerate log-likelihood.
.. math::
f(x \mid k) = \left\{ \begin{matrix} 1 \text{ if } x = k \\ 0 \text{ if } x \ne k\end{matrix} \right.
:Parameters:
- `x` : Input value.
- `k` : Degenerate value. | [
"R",
"Degenerate",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1094-L1107 | train |
pymc-devs/pymc | pymc/distributions.py | rdirichlet | def rdirichlet(theta, size=1):
"""
Dirichlet random variates.
"""
gammas = np.vstack([rgamma(theta, 1) for i in xrange(size)])
if size > 1 and np.size(theta) > 1:
return (gammas.T / gammas.sum(1))[:-1].T
elif np.size(theta) > 1:
return (gammas[0] / gammas[0].sum())[:-1]
else:... | python | def rdirichlet(theta, size=1):
"""
Dirichlet random variates.
"""
gammas = np.vstack([rgamma(theta, 1) for i in xrange(size)])
if size > 1 and np.size(theta) > 1:
return (gammas.T / gammas.sum(1))[:-1].T
elif np.size(theta) > 1:
return (gammas[0] / gammas[0].sum())[:-1]
else:... | [
"def",
"rdirichlet",
"(",
"theta",
",",
"size",
"=",
"1",
")",
":",
"gammas",
"=",
"np",
".",
"vstack",
"(",
"[",
"rgamma",
"(",
"theta",
",",
"1",
")",
"for",
"i",
"in",
"xrange",
"(",
"size",
")",
"]",
")",
"if",
"size",
">",
"1",
"and",
"n... | Dirichlet random variates. | [
"Dirichlet",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1128-L1138 | train |
pymc-devs/pymc | pymc/distributions.py | dirichlet_like | def dirichlet_like(x, theta):
R"""
Dirichlet log-likelihood.
This is a multivariate continuous distribution.
.. math::
f(\mathbf{x}) = \frac{\Gamma(\sum_{i=1}^k \theta_i)}{\prod \Gamma(\theta_i)}\prod_{i=1}^{k-1} x_i^{\theta_i - 1}
\cdot\left(1-\sum_{i=1}^{k-1}x_i\right)^\theta_k
... | python | def dirichlet_like(x, theta):
R"""
Dirichlet log-likelihood.
This is a multivariate continuous distribution.
.. math::
f(\mathbf{x}) = \frac{\Gamma(\sum_{i=1}^k \theta_i)}{\prod \Gamma(\theta_i)}\prod_{i=1}^{k-1} x_i^{\theta_i - 1}
\cdot\left(1-\sum_{i=1}^{k-1}x_i\right)^\theta_k
... | [
"def",
"dirichlet_like",
"(",
"x",
",",
"theta",
")",
":",
"x",
"=",
"np",
".",
"atleast_2d",
"(",
"x",
")",
"theta",
"=",
"np",
".",
"atleast_2d",
"(",
"theta",
")",
"if",
"(",
"np",
".",
"shape",
"(",
"x",
")",
"[",
"-",
"1",
"]",
"+",
"1",... | R"""
Dirichlet log-likelihood.
This is a multivariate continuous distribution.
.. math::
f(\mathbf{x}) = \frac{\Gamma(\sum_{i=1}^k \theta_i)}{\prod \Gamma(\theta_i)}\prod_{i=1}^{k-1} x_i^{\theta_i - 1}
\cdot\left(1-\sum_{i=1}^{k-1}x_i\right)^\theta_k
:Parameters:
x : (n, k-1) ar... | [
"R",
"Dirichlet",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1148-L1175 | train |
pymc-devs/pymc | pymc/distributions.py | rexponweib | def rexponweib(alpha, k, loc=0, scale=1, size=None):
"""
Random exponentiated Weibull variates.
"""
q = np.random.uniform(size=size)
r = flib.exponweib_ppf(q, alpha, k)
return loc + r * scale | python | def rexponweib(alpha, k, loc=0, scale=1, size=None):
"""
Random exponentiated Weibull variates.
"""
q = np.random.uniform(size=size)
r = flib.exponweib_ppf(q, alpha, k)
return loc + r * scale | [
"def",
"rexponweib",
"(",
"alpha",
",",
"k",
",",
"loc",
"=",
"0",
",",
"scale",
"=",
"1",
",",
"size",
"=",
"None",
")",
":",
"q",
"=",
"np",
".",
"random",
".",
"uniform",
"(",
"size",
"=",
"size",
")",
"r",
"=",
"flib",
".",
"exponweib_ppf",... | Random exponentiated Weibull variates. | [
"Random",
"exponentiated",
"Weibull",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1225-L1232 | train |
pymc-devs/pymc | pymc/distributions.py | exponweib_like | def exponweib_like(x, alpha, k, loc=0, scale=1):
R"""
Exponentiated Weibull log-likelihood.
The exponentiated Weibull distribution is a generalization of the Weibull
family. Its value lies in being able to model monotone and non-monotone
failure rates.
.. math::
f(x \mid \alpha,k,loc,s... | python | def exponweib_like(x, alpha, k, loc=0, scale=1):
R"""
Exponentiated Weibull log-likelihood.
The exponentiated Weibull distribution is a generalization of the Weibull
family. Its value lies in being able to model monotone and non-monotone
failure rates.
.. math::
f(x \mid \alpha,k,loc,s... | [
"def",
"exponweib_like",
"(",
"x",
",",
"alpha",
",",
"k",
",",
"loc",
"=",
"0",
",",
"scale",
"=",
"1",
")",
":",
"return",
"flib",
".",
"exponweib",
"(",
"x",
",",
"alpha",
",",
"k",
",",
"loc",
",",
"scale",
")"
] | R"""
Exponentiated Weibull log-likelihood.
The exponentiated Weibull distribution is a generalization of the Weibull
family. Its value lies in being able to model monotone and non-monotone
failure rates.
.. math::
f(x \mid \alpha,k,loc,scale) & = \frac{\alpha k}{scale} (1-e^{-z^k})^{\alph... | [
"R",
"Exponentiated",
"Weibull",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1241-L1261 | train |
pymc-devs/pymc | pymc/distributions.py | rgamma | def rgamma(alpha, beta, size=None):
"""
Random gamma variates.
"""
return np.random.gamma(shape=alpha, scale=1. / beta, size=size) | python | def rgamma(alpha, beta, size=None):
"""
Random gamma variates.
"""
return np.random.gamma(shape=alpha, scale=1. / beta, size=size) | [
"def",
"rgamma",
"(",
"alpha",
",",
"beta",
",",
"size",
"=",
"None",
")",
":",
"return",
"np",
".",
"random",
".",
"gamma",
"(",
"shape",
"=",
"alpha",
",",
"scale",
"=",
"1.",
"/",
"beta",
",",
"size",
"=",
"size",
")"
] | Random gamma variates. | [
"Random",
"gamma",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1275-L1280 | train |
pymc-devs/pymc | pymc/distributions.py | gev_expval | def gev_expval(xi, mu=0, sigma=1):
"""
Expected value of generalized extreme value distribution.
"""
return mu - (sigma / xi) + (sigma / xi) * flib.gamfun(1 - xi) | python | def gev_expval(xi, mu=0, sigma=1):
"""
Expected value of generalized extreme value distribution.
"""
return mu - (sigma / xi) + (sigma / xi) * flib.gamfun(1 - xi) | [
"def",
"gev_expval",
"(",
"xi",
",",
"mu",
"=",
"0",
",",
"sigma",
"=",
"1",
")",
":",
"return",
"mu",
"-",
"(",
"sigma",
"/",
"xi",
")",
"+",
"(",
"sigma",
"/",
"xi",
")",
"*",
"flib",
".",
"gamfun",
"(",
"1",
"-",
"xi",
")"
] | Expected value of generalized extreme value distribution. | [
"Expected",
"value",
"of",
"generalized",
"extreme",
"value",
"distribution",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1333-L1337 | train |
pymc-devs/pymc | pymc/distributions.py | gev_like | def gev_like(x, xi, mu=0, sigma=1):
R"""
Generalized Extreme Value log-likelihood
.. math::
pdf(x \mid \xi,\mu,\sigma) = \frac{1}{\sigma}(1 + \xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi-1}\exp{-(1+\xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi}}
.. math::
\sigma & > 0,\\
x &... | python | def gev_like(x, xi, mu=0, sigma=1):
R"""
Generalized Extreme Value log-likelihood
.. math::
pdf(x \mid \xi,\mu,\sigma) = \frac{1}{\sigma}(1 + \xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi-1}\exp{-(1+\xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi}}
.. math::
\sigma & > 0,\\
x &... | [
"def",
"gev_like",
"(",
"x",
",",
"xi",
",",
"mu",
"=",
"0",
",",
"sigma",
"=",
"1",
")",
":",
"return",
"flib",
".",
"gev",
"(",
"x",
",",
"xi",
",",
"mu",
",",
"sigma",
")"
] | R"""
Generalized Extreme Value log-likelihood
.. math::
pdf(x \mid \xi,\mu,\sigma) = \frac{1}{\sigma}(1 + \xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi-1}\exp{-(1+\xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi}}
.. math::
\sigma & > 0,\\
x & > \mu-\sigma/\xi \text{ if } \xi > 0,\\... | [
"R",
"Generalized",
"Extreme",
"Value",
"log",
"-",
"likelihood"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1340-L1355 | train |
pymc-devs/pymc | pymc/distributions.py | rhalf_cauchy | def rhalf_cauchy(alpha, beta, size=None):
"""
Returns half-Cauchy random variates.
"""
return abs(alpha + beta * np.tan(pi * random_number(size) - pi / 2.0)) | python | def rhalf_cauchy(alpha, beta, size=None):
"""
Returns half-Cauchy random variates.
"""
return abs(alpha + beta * np.tan(pi * random_number(size) - pi / 2.0)) | [
"def",
"rhalf_cauchy",
"(",
"alpha",
",",
"beta",
",",
"size",
"=",
"None",
")",
":",
"return",
"abs",
"(",
"alpha",
"+",
"beta",
"*",
"np",
".",
"tan",
"(",
"pi",
"*",
"random_number",
"(",
"size",
")",
"-",
"pi",
"/",
"2.0",
")",
")"
] | Returns half-Cauchy random variates. | [
"Returns",
"half",
"-",
"Cauchy",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1403-L1408 | train |
pymc-devs/pymc | pymc/distributions.py | half_cauchy_like | def half_cauchy_like(x, alpha, beta):
R"""
Half-Cauchy log-likelihood. Simply the absolute value of Cauchy.
.. math::
f(x \mid \alpha, \beta) = \frac{2}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}
:Parameters:
- `alpha` : Location parameter.
- `beta` : Scale parameter (beta > 0).
... | python | def half_cauchy_like(x, alpha, beta):
R"""
Half-Cauchy log-likelihood. Simply the absolute value of Cauchy.
.. math::
f(x \mid \alpha, \beta) = \frac{2}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}
:Parameters:
- `alpha` : Location parameter.
- `beta` : Scale parameter (beta > 0).
... | [
"def",
"half_cauchy_like",
"(",
"x",
",",
"alpha",
",",
"beta",
")",
":",
"x",
"=",
"np",
".",
"atleast_1d",
"(",
"x",
")",
"if",
"sum",
"(",
"x",
".",
"ravel",
"(",
")",
"<",
"0",
")",
":",
"return",
"-",
"inf",
"return",
"flib",
".",
"cauchy"... | R"""
Half-Cauchy log-likelihood. Simply the absolute value of Cauchy.
.. math::
f(x \mid \alpha, \beta) = \frac{2}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}
:Parameters:
- `alpha` : Location parameter.
- `beta` : Scale parameter (beta > 0).
.. note::
- x must be non-negati... | [
"R",
"Half",
"-",
"Cauchy",
"log",
"-",
"likelihood",
".",
"Simply",
"the",
"absolute",
"value",
"of",
"Cauchy",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1421-L1439 | train |
pymc-devs/pymc | pymc/distributions.py | rhalf_normal | def rhalf_normal(tau, size=None):
"""
Random half-normal variates.
"""
return abs(np.random.normal(0, np.sqrt(1 / tau), size)) | python | def rhalf_normal(tau, size=None):
"""
Random half-normal variates.
"""
return abs(np.random.normal(0, np.sqrt(1 / tau), size)) | [
"def",
"rhalf_normal",
"(",
"tau",
",",
"size",
"=",
"None",
")",
":",
"return",
"abs",
"(",
"np",
".",
"random",
".",
"normal",
"(",
"0",
",",
"np",
".",
"sqrt",
"(",
"1",
"/",
"tau",
")",
",",
"size",
")",
")"
] | Random half-normal variates. | [
"Random",
"half",
"-",
"normal",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1445-L1450 | train |
pymc-devs/pymc | pymc/distributions.py | rhypergeometric | def rhypergeometric(n, m, N, size=None):
"""
Returns hypergeometric random variates.
"""
if n == 0:
return np.zeros(size, dtype=int)
elif n == N:
out = np.empty(size, dtype=int)
out.fill(m)
return out
return np.random.hypergeometric(n, N - n, m, size) | python | def rhypergeometric(n, m, N, size=None):
"""
Returns hypergeometric random variates.
"""
if n == 0:
return np.zeros(size, dtype=int)
elif n == N:
out = np.empty(size, dtype=int)
out.fill(m)
return out
return np.random.hypergeometric(n, N - n, m, size) | [
"def",
"rhypergeometric",
"(",
"n",
",",
"m",
",",
"N",
",",
"size",
"=",
"None",
")",
":",
"if",
"n",
"==",
"0",
":",
"return",
"np",
".",
"zeros",
"(",
"size",
",",
"dtype",
"=",
"int",
")",
"elif",
"n",
"==",
"N",
":",
"out",
"=",
"np",
... | Returns hypergeometric random variates. | [
"Returns",
"hypergeometric",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1483-L1493 | train |
pymc-devs/pymc | pymc/distributions.py | hypergeometric_like | def hypergeometric_like(x, n, m, N):
R"""
Hypergeometric log-likelihood.
Discrete probability distribution that describes the number of successes in
a sequence of draws from a finite population without replacement.
.. math::
f(x \mid n, m, N) = \frac{\left({ \begin{array}{c} {m} \\ {x} \\... | python | def hypergeometric_like(x, n, m, N):
R"""
Hypergeometric log-likelihood.
Discrete probability distribution that describes the number of successes in
a sequence of draws from a finite population without replacement.
.. math::
f(x \mid n, m, N) = \frac{\left({ \begin{array}{c} {m} \\ {x} \\... | [
"def",
"hypergeometric_like",
"(",
"x",
",",
"n",
",",
"m",
",",
"N",
")",
":",
"return",
"flib",
".",
"hyperg",
"(",
"x",
",",
"n",
",",
"m",
",",
"N",
")"
] | R"""
Hypergeometric log-likelihood.
Discrete probability distribution that describes the number of successes in
a sequence of draws from a finite population without replacement.
.. math::
f(x \mid n, m, N) = \frac{\left({ \begin{array}{c} {m} \\ {x} \\
\end{array} }\right)\left({ \beg... | [
"R",
"Hypergeometric",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1503-L1529 | train |
pymc-devs/pymc | pymc/distributions.py | rlogistic | def rlogistic(mu, tau, size=None):
"""
Logistic random variates.
"""
u = np.random.random(size)
return mu + np.log(u / (1 - u)) / tau | python | def rlogistic(mu, tau, size=None):
"""
Logistic random variates.
"""
u = np.random.random(size)
return mu + np.log(u / (1 - u)) / tau | [
"def",
"rlogistic",
"(",
"mu",
",",
"tau",
",",
"size",
"=",
"None",
")",
":",
"u",
"=",
"np",
".",
"random",
".",
"random",
"(",
"size",
")",
"return",
"mu",
"+",
"np",
".",
"log",
"(",
"u",
"/",
"(",
"1",
"-",
"u",
")",
")",
"/",
"tau"
] | Logistic random variates. | [
"Logistic",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1685-L1691 | train |
pymc-devs/pymc | pymc/distributions.py | rlognormal | def rlognormal(mu, tau, size=None):
"""
Return random lognormal variates.
"""
return np.random.lognormal(mu, np.sqrt(1. / tau), size) | python | def rlognormal(mu, tau, size=None):
"""
Return random lognormal variates.
"""
return np.random.lognormal(mu, np.sqrt(1. / tau), size) | [
"def",
"rlognormal",
"(",
"mu",
",",
"tau",
",",
"size",
"=",
"None",
")",
":",
"return",
"np",
".",
"random",
".",
"lognormal",
"(",
"mu",
",",
"np",
".",
"sqrt",
"(",
"1.",
"/",
"tau",
")",
",",
"size",
")"
] | Return random lognormal variates. | [
"Return",
"random",
"lognormal",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1726-L1731 | train |
pymc-devs/pymc | pymc/distributions.py | rmultinomial | def rmultinomial(n, p, size=None):
"""
Random multinomial variates.
"""
# Leaving size=None as the default means return value is 1d array
# if not specified-- nicer.
# Single value for p:
if len(np.shape(p)) == 1:
return np.random.multinomial(n, p, size)
# Multiple values for p... | python | def rmultinomial(n, p, size=None):
"""
Random multinomial variates.
"""
# Leaving size=None as the default means return value is 1d array
# if not specified-- nicer.
# Single value for p:
if len(np.shape(p)) == 1:
return np.random.multinomial(n, p, size)
# Multiple values for p... | [
"def",
"rmultinomial",
"(",
"n",
",",
"p",
",",
"size",
"=",
"None",
")",
":",
"# Leaving size=None as the default means return value is 1d array",
"# if not specified-- nicer.",
"# Single value for p:",
"if",
"len",
"(",
"np",
".",
"shape",
"(",
"p",
")",
")",
"=="... | Random multinomial variates. | [
"Random",
"multinomial",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1773-L1790 | train |
pymc-devs/pymc | pymc/distributions.py | multinomial_like | def multinomial_like(x, n, p):
R"""
Multinomial log-likelihood.
Generalization of the binomial
distribution, but instead of each trial resulting in "success" or
"failure", each one results in exactly one of some fixed finite number k
of possible outcomes over n independent trials. 'x[i]' indica... | python | def multinomial_like(x, n, p):
R"""
Multinomial log-likelihood.
Generalization of the binomial
distribution, but instead of each trial resulting in "success" or
"failure", each one results in exactly one of some fixed finite number k
of possible outcomes over n independent trials. 'x[i]' indica... | [
"def",
"multinomial_like",
"(",
"x",
",",
"n",
",",
"p",
")",
":",
"# flib expects 2d arguments. Do we still want to support multiple p",
"# values along realizations ?",
"x",
"=",
"np",
".",
"atleast_2d",
"(",
"x",
")",
"p",
"=",
"np",
".",
"atleast_2d",
"(",
"p"... | R"""
Multinomial log-likelihood.
Generalization of the binomial
distribution, but instead of each trial resulting in "success" or
"failure", each one results in exactly one of some fixed finite number k
of possible outcomes over n independent trials. 'x[i]' indicates the number
of times outcome... | [
"R",
"Multinomial",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1800-L1836 | train |
pymc-devs/pymc | pymc/distributions.py | rmultivariate_hypergeometric | def rmultivariate_hypergeometric(n, m, size=None):
"""
Random multivariate hypergeometric variates.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy.
"""
N = len(m)
urn = np.repeat(np.arange(N), m)
if size:
draw = np.array([[urn[i] for i in ... | python | def rmultivariate_hypergeometric(n, m, size=None):
"""
Random multivariate hypergeometric variates.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy.
"""
N = len(m)
urn = np.repeat(np.arange(N), m)
if size:
draw = np.array([[urn[i] for i in ... | [
"def",
"rmultivariate_hypergeometric",
"(",
"n",
",",
"m",
",",
"size",
"=",
"None",
")",
":",
"N",
"=",
"len",
"(",
"m",
")",
"urn",
"=",
"np",
".",
"repeat",
"(",
"np",
".",
"arange",
"(",
"N",
")",
",",
"m",
")",
"if",
"size",
":",
"draw",
... | Random multivariate hypergeometric variates.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy. | [
"Random",
"multivariate",
"hypergeometric",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1841-L1863 | train |
pymc-devs/pymc | pymc/distributions.py | multivariate_hypergeometric_expval | def multivariate_hypergeometric_expval(n, m):
"""
Expected value of multivariate hypergeometric distribution.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy.
"""
m = np.asarray(m, float)
return n * (m / m.sum()) | python | def multivariate_hypergeometric_expval(n, m):
"""
Expected value of multivariate hypergeometric distribution.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy.
"""
m = np.asarray(m, float)
return n * (m / m.sum()) | [
"def",
"multivariate_hypergeometric_expval",
"(",
"n",
",",
"m",
")",
":",
"m",
"=",
"np",
".",
"asarray",
"(",
"m",
",",
"float",
")",
"return",
"n",
"*",
"(",
"m",
"/",
"m",
".",
"sum",
"(",
")",
")"
] | Expected value of multivariate hypergeometric distribution.
Parameters:
- `n` : Number of draws.
- `m` : Number of items in each categoy. | [
"Expected",
"value",
"of",
"multivariate",
"hypergeometric",
"distribution",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1866-L1875 | train |
pymc-devs/pymc | pymc/distributions.py | mv_normal_like | def mv_normal_like(x, mu, tau):
R"""
Multivariate normal log-likelihood
.. math::
f(x \mid \pi, T) = \frac{|T|^{1/2}}{(2\pi)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}T(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location parameter sequence.
- `Tau` : (k,k) ... | python | def mv_normal_like(x, mu, tau):
R"""
Multivariate normal log-likelihood
.. math::
f(x \mid \pi, T) = \frac{|T|^{1/2}}{(2\pi)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}T(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location parameter sequence.
- `Tau` : (k,k) ... | [
"def",
"mv_normal_like",
"(",
"x",
",",
"mu",
",",
"tau",
")",
":",
"# TODO: Vectorize in Fortran",
"if",
"len",
"(",
"np",
".",
"shape",
"(",
"x",
")",
")",
">",
"1",
":",
"return",
"np",
".",
"sum",
"(",
"[",
"flib",
".",
"prec_mvnorm",
"(",
"r",... | R"""
Multivariate normal log-likelihood
.. math::
f(x \mid \pi, T) = \frac{|T|^{1/2}}{(2\pi)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}T(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location parameter sequence.
- `Tau` : (k,k) Positive definite precision matrix.
... | [
"R",
"Multivariate",
"normal",
"log",
"-",
"likelihood"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1939-L1958 | train |
pymc-devs/pymc | pymc/distributions.py | mv_normal_cov_like | def mv_normal_cov_like(x, mu, C):
R"""
Multivariate normal log-likelihood parameterized by a covariance
matrix.
.. math::
f(x \mid \pi, C) = \frac{1}{(2\pi|C|)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}C^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Locatio... | python | def mv_normal_cov_like(x, mu, C):
R"""
Multivariate normal log-likelihood parameterized by a covariance
matrix.
.. math::
f(x \mid \pi, C) = \frac{1}{(2\pi|C|)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}C^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Locatio... | [
"def",
"mv_normal_cov_like",
"(",
"x",
",",
"mu",
",",
"C",
")",
":",
"# TODO: Vectorize in Fortran",
"if",
"len",
"(",
"np",
".",
"shape",
"(",
"x",
")",
")",
">",
"1",
":",
"return",
"np",
".",
"sum",
"(",
"[",
"flib",
".",
"cov_mvnorm",
"(",
"r"... | R"""
Multivariate normal log-likelihood parameterized by a covariance
matrix.
.. math::
f(x \mid \pi, C) = \frac{1}{(2\pi|C|)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}C^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location parameter.
- `C` : (k,k) Posit... | [
"R",
"Multivariate",
"normal",
"log",
"-",
"likelihood",
"parameterized",
"by",
"a",
"covariance",
"matrix",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L1982-L2002 | train |
pymc-devs/pymc | pymc/distributions.py | mv_normal_chol_like | def mv_normal_chol_like(x, mu, sig):
R"""
Multivariate normal log-likelihood.
.. math::
f(x \mid \pi, \sigma) = \frac{1}{(2\pi)^{1/2}|\sigma|)} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}(\sigma \sigma^{\prime})^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location pa... | python | def mv_normal_chol_like(x, mu, sig):
R"""
Multivariate normal log-likelihood.
.. math::
f(x \mid \pi, \sigma) = \frac{1}{(2\pi)^{1/2}|\sigma|)} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}(\sigma \sigma^{\prime})^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location pa... | [
"def",
"mv_normal_chol_like",
"(",
"x",
",",
"mu",
",",
"sig",
")",
":",
"# TODO: Vectorize in Fortran",
"if",
"len",
"(",
"np",
".",
"shape",
"(",
"x",
")",
")",
">",
"1",
":",
"return",
"np",
".",
"sum",
"(",
"[",
"flib",
".",
"chol_mvnorm",
"(",
... | R"""
Multivariate normal log-likelihood.
.. math::
f(x \mid \pi, \sigma) = \frac{1}{(2\pi)^{1/2}|\sigma|)} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}(\sigma \sigma^{\prime})^{-1}(x-\mu) \right\}
:Parameters:
- `x` : (n,k)
- `mu` : (k) Location parameter.
- `sigma` : (k,k) Lower tr... | [
"R",
"Multivariate",
"normal",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2041-L2060 | train |
pymc-devs/pymc | pymc/distributions.py | rnegative_binomial | def rnegative_binomial(mu, alpha, size=None):
"""
Random negative binomial variates.
"""
# Using gamma-poisson mixture rather than numpy directly
# because numpy apparently rounds
mu = np.asarray(mu, dtype=float)
pois_mu = np.random.gamma(alpha, mu / alpha, size)
return np.random.poisson... | python | def rnegative_binomial(mu, alpha, size=None):
"""
Random negative binomial variates.
"""
# Using gamma-poisson mixture rather than numpy directly
# because numpy apparently rounds
mu = np.asarray(mu, dtype=float)
pois_mu = np.random.gamma(alpha, mu / alpha, size)
return np.random.poisson... | [
"def",
"rnegative_binomial",
"(",
"mu",
",",
"alpha",
",",
"size",
"=",
"None",
")",
":",
"# Using gamma-poisson mixture rather than numpy directly",
"# because numpy apparently rounds",
"mu",
"=",
"np",
".",
"asarray",
"(",
"mu",
",",
"dtype",
"=",
"float",
")",
... | Random negative binomial variates. | [
"Random",
"negative",
"binomial",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2065-L2073 | train |
pymc-devs/pymc | pymc/distributions.py | negative_binomial_like | def negative_binomial_like(x, mu, alpha):
R"""
Negative binomial log-likelihood.
The negative binomial
distribution describes a Poisson random variable whose rate
parameter is gamma distributed. PyMC's chosen parameterization is
based on this mixture interpretation.
.. math::
f(x \... | python | def negative_binomial_like(x, mu, alpha):
R"""
Negative binomial log-likelihood.
The negative binomial
distribution describes a Poisson random variable whose rate
parameter is gamma distributed. PyMC's chosen parameterization is
based on this mixture interpretation.
.. math::
f(x \... | [
"def",
"negative_binomial_like",
"(",
"x",
",",
"mu",
",",
"alpha",
")",
":",
"alpha",
"=",
"np",
".",
"array",
"(",
"alpha",
")",
"if",
"(",
"alpha",
">",
"1e10",
")",
".",
"any",
"(",
")",
":",
"if",
"(",
"alpha",
">",
"1e10",
")",
".",
"all"... | R"""
Negative binomial log-likelihood.
The negative binomial
distribution describes a Poisson random variable whose rate
parameter is gamma distributed. PyMC's chosen parameterization is
based on this mixture interpretation.
.. math::
f(x \mid \mu, \alpha) = \frac{\Gamma(x+\alpha)}{x! ... | [
"R",
"Negative",
"binomial",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2084-L2119 | train |
pymc-devs/pymc | pymc/distributions.py | rnormal | def rnormal(mu, tau, size=None):
"""
Random normal variates.
"""
return np.random.normal(mu, 1. / np.sqrt(tau), size) | python | def rnormal(mu, tau, size=None):
"""
Random normal variates.
"""
return np.random.normal(mu, 1. / np.sqrt(tau), size) | [
"def",
"rnormal",
"(",
"mu",
",",
"tau",
",",
"size",
"=",
"None",
")",
":",
"return",
"np",
".",
"random",
".",
"normal",
"(",
"mu",
",",
"1.",
"/",
"np",
".",
"sqrt",
"(",
"tau",
")",
",",
"size",
")"
] | Random normal variates. | [
"Random",
"normal",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2128-L2132 | train |
pymc-devs/pymc | pymc/distributions.py | rvon_mises | def rvon_mises(mu, kappa, size=None):
"""
Random von Mises variates.
"""
# TODO: Just return straight from numpy after release 1.3
return (np.random.mtrand.vonmises(
mu, kappa, size) + np.pi) % (2. * np.pi) - np.pi | python | def rvon_mises(mu, kappa, size=None):
"""
Random von Mises variates.
"""
# TODO: Just return straight from numpy after release 1.3
return (np.random.mtrand.vonmises(
mu, kappa, size) + np.pi) % (2. * np.pi) - np.pi | [
"def",
"rvon_mises",
"(",
"mu",
",",
"kappa",
",",
"size",
"=",
"None",
")",
":",
"# TODO: Just return straight from numpy after release 1.3",
"return",
"(",
"np",
".",
"random",
".",
"mtrand",
".",
"vonmises",
"(",
"mu",
",",
"kappa",
",",
"size",
")",
"+",... | Random von Mises variates. | [
"Random",
"von",
"Mises",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2191-L2197 | train |
pymc-devs/pymc | pymc/distributions.py | rtruncated_pareto | def rtruncated_pareto(alpha, m, b, size=None):
"""
Random bounded Pareto variates.
"""
u = random_number(size)
return (-(u * b ** alpha - u * m ** alpha - b ** alpha) /
(b ** alpha * m ** alpha)) ** (-1. / alpha) | python | def rtruncated_pareto(alpha, m, b, size=None):
"""
Random bounded Pareto variates.
"""
u = random_number(size)
return (-(u * b ** alpha - u * m ** alpha - b ** alpha) /
(b ** alpha * m ** alpha)) ** (-1. / alpha) | [
"def",
"rtruncated_pareto",
"(",
"alpha",
",",
"m",
",",
"b",
",",
"size",
"=",
"None",
")",
":",
"u",
"=",
"random_number",
"(",
"size",
")",
"return",
"(",
"-",
"(",
"u",
"*",
"b",
"**",
"alpha",
"-",
"u",
"*",
"m",
"**",
"alpha",
"-",
"b",
... | Random bounded Pareto variates. | [
"Random",
"bounded",
"Pareto",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2274-L2280 | train |
pymc-devs/pymc | pymc/distributions.py | truncated_pareto_expval | def truncated_pareto_expval(alpha, m, b):
"""
Expected value of truncated Pareto distribution.
"""
if alpha <= 1:
return inf
part1 = (m ** alpha) / (1. - (m / b) ** alpha)
part2 = 1. * alpha / (alpha - 1)
part3 = (1. / (m ** (alpha - 1)) - 1. / (b ** (alpha - 1.)))
return part1 ... | python | def truncated_pareto_expval(alpha, m, b):
"""
Expected value of truncated Pareto distribution.
"""
if alpha <= 1:
return inf
part1 = (m ** alpha) / (1. - (m / b) ** alpha)
part2 = 1. * alpha / (alpha - 1)
part3 = (1. / (m ** (alpha - 1)) - 1. / (b ** (alpha - 1.)))
return part1 ... | [
"def",
"truncated_pareto_expval",
"(",
"alpha",
",",
"m",
",",
"b",
")",
":",
"if",
"alpha",
"<=",
"1",
":",
"return",
"inf",
"part1",
"=",
"(",
"m",
"**",
"alpha",
")",
"/",
"(",
"1.",
"-",
"(",
"m",
"/",
"b",
")",
"**",
"alpha",
")",
"part2",... | Expected value of truncated Pareto distribution. | [
"Expected",
"value",
"of",
"truncated",
"Pareto",
"distribution",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2283-L2293 | train |
pymc-devs/pymc | pymc/distributions.py | rtruncated_poisson | def rtruncated_poisson(mu, k, size=None):
"""
Random truncated Poisson variates with minimum value k, generated
using rejection sampling.
"""
# Calculate m
try:
m = max(0, np.floor(k - mu))
except (TypeError, ValueError):
# More than one mu
return np.array([rtruncate... | python | def rtruncated_poisson(mu, k, size=None):
"""
Random truncated Poisson variates with minimum value k, generated
using rejection sampling.
"""
# Calculate m
try:
m = max(0, np.floor(k - mu))
except (TypeError, ValueError):
# More than one mu
return np.array([rtruncate... | [
"def",
"rtruncated_poisson",
"(",
"mu",
",",
"k",
",",
"size",
"=",
"None",
")",
":",
"# Calculate m",
"try",
":",
"m",
"=",
"max",
"(",
"0",
",",
"np",
".",
"floor",
"(",
"k",
"-",
"mu",
")",
")",
"except",
"(",
"TypeError",
",",
"ValueError",
"... | Random truncated Poisson variates with minimum value k, generated
using rejection sampling. | [
"Random",
"truncated",
"Poisson",
"variates",
"with",
"minimum",
"value",
"k",
"generated",
"using",
"rejection",
"sampling",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2365-L2404 | train |
pymc-devs/pymc | pymc/distributions.py | rtruncated_normal | def rtruncated_normal(mu, tau, a=-np.inf, b=np.inf, size=None):
"""
Random truncated normal variates.
"""
sigma = 1. / np.sqrt(tau)
na = utils.normcdf((a - mu) / sigma)
nb = utils.normcdf((b - mu) / sigma)
# Use the inverse CDF generation method.
U = np.random.mtrand.uniform(size=size)... | python | def rtruncated_normal(mu, tau, a=-np.inf, b=np.inf, size=None):
"""
Random truncated normal variates.
"""
sigma = 1. / np.sqrt(tau)
na = utils.normcdf((a - mu) / sigma)
nb = utils.normcdf((b - mu) / sigma)
# Use the inverse CDF generation method.
U = np.random.mtrand.uniform(size=size)... | [
"def",
"rtruncated_normal",
"(",
"mu",
",",
"tau",
",",
"a",
"=",
"-",
"np",
".",
"inf",
",",
"b",
"=",
"np",
".",
"inf",
",",
"size",
"=",
"None",
")",
":",
"sigma",
"=",
"1.",
"/",
"np",
".",
"sqrt",
"(",
"tau",
")",
"na",
"=",
"utils",
"... | Random truncated normal variates. | [
"Random",
"truncated",
"normal",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2447-L2462 | train |
pymc-devs/pymc | pymc/distributions.py | truncated_normal_expval | def truncated_normal_expval(mu, tau, a, b):
"""Expected value of the truncated normal distribution.
.. math::
E(X) =\mu + \frac{\sigma(\varphi_1-\varphi_2)}{T}
where
.. math::
T & =\Phi\left(\frac{B-\mu}{\sigma}\right)-\Phi
\left(\frac{A-\mu}{\sigma}\right)\text \\
\varph... | python | def truncated_normal_expval(mu, tau, a, b):
"""Expected value of the truncated normal distribution.
.. math::
E(X) =\mu + \frac{\sigma(\varphi_1-\varphi_2)}{T}
where
.. math::
T & =\Phi\left(\frac{B-\mu}{\sigma}\right)-\Phi
\left(\frac{A-\mu}{\sigma}\right)\text \\
\varph... | [
"def",
"truncated_normal_expval",
"(",
"mu",
",",
"tau",
",",
"a",
",",
"b",
")",
":",
"phia",
"=",
"np",
".",
"exp",
"(",
"normal_like",
"(",
"a",
",",
"mu",
",",
"tau",
")",
")",
"phib",
"=",
"np",
".",
"exp",
"(",
"normal_like",
"(",
"b",
",... | Expected value of the truncated normal distribution.
.. math::
E(X) =\mu + \frac{\sigma(\varphi_1-\varphi_2)}{T}
where
.. math::
T & =\Phi\left(\frac{B-\mu}{\sigma}\right)-\Phi
\left(\frac{A-\mu}{\sigma}\right)\text \\
\varphi_1 &=
\varphi\left(\frac{A-\mu}{\sigma}\rig... | [
"Expected",
"value",
"of",
"the",
"truncated",
"normal",
"distribution",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2467-L2501 | train |
pymc-devs/pymc | pymc/distributions.py | truncated_normal_like | def truncated_normal_like(x, mu, tau, a=None, b=None):
R"""
Truncated normal log-likelihood.
.. math::
f(x \mid \mu, \tau, a, b) = \frac{\phi(\frac{x-\mu}{\sigma})} {\Phi(\frac{b-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})},
where :math:`\sigma^2=1/\tau`, `\phi` is the standard normal PDF and `... | python | def truncated_normal_like(x, mu, tau, a=None, b=None):
R"""
Truncated normal log-likelihood.
.. math::
f(x \mid \mu, \tau, a, b) = \frac{\phi(\frac{x-\mu}{\sigma})} {\Phi(\frac{b-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})},
where :math:`\sigma^2=1/\tau`, `\phi` is the standard normal PDF and `... | [
"def",
"truncated_normal_like",
"(",
"x",
",",
"mu",
",",
"tau",
",",
"a",
"=",
"None",
",",
"b",
"=",
"None",
")",
":",
"x",
"=",
"np",
".",
"atleast_1d",
"(",
"x",
")",
"if",
"a",
"is",
"None",
":",
"a",
"=",
"-",
"np",
".",
"inf",
"a",
"... | R"""
Truncated normal log-likelihood.
.. math::
f(x \mid \mu, \tau, a, b) = \frac{\phi(\frac{x-\mu}{\sigma})} {\Phi(\frac{b-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})},
where :math:`\sigma^2=1/\tau`, `\phi` is the standard normal PDF and `\Phi` is the standard normal CDF.
:Parameters:
-... | [
"R",
"Truncated",
"normal",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2506-L2549 | train |
pymc-devs/pymc | pymc/distributions.py | rskew_normal | def rskew_normal(mu, tau, alpha, size=()):
"""
Skew-normal random variates.
"""
size_ = size or (1,)
len_ = np.prod(size_)
return flib.rskewnorm(
len_, mu, tau, alpha, np.random.normal(size=2 * len_)).reshape(size) | python | def rskew_normal(mu, tau, alpha, size=()):
"""
Skew-normal random variates.
"""
size_ = size or (1,)
len_ = np.prod(size_)
return flib.rskewnorm(
len_, mu, tau, alpha, np.random.normal(size=2 * len_)).reshape(size) | [
"def",
"rskew_normal",
"(",
"mu",
",",
"tau",
",",
"alpha",
",",
"size",
"=",
"(",
")",
")",
":",
"size_",
"=",
"size",
"or",
"(",
"1",
",",
")",
"len_",
"=",
"np",
".",
"prod",
"(",
"size_",
")",
"return",
"flib",
".",
"rskewnorm",
"(",
"len_"... | Skew-normal random variates. | [
"Skew",
"-",
"normal",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2557-L2564 | train |
pymc-devs/pymc | pymc/distributions.py | skew_normal_expval | def skew_normal_expval(mu, tau, alpha):
"""
Expectation of skew-normal random variables.
"""
delta = alpha / np.sqrt(1. + alpha ** 2)
return mu + np.sqrt(2 / pi / tau) * delta | python | def skew_normal_expval(mu, tau, alpha):
"""
Expectation of skew-normal random variables.
"""
delta = alpha / np.sqrt(1. + alpha ** 2)
return mu + np.sqrt(2 / pi / tau) * delta | [
"def",
"skew_normal_expval",
"(",
"mu",
",",
"tau",
",",
"alpha",
")",
":",
"delta",
"=",
"alpha",
"/",
"np",
".",
"sqrt",
"(",
"1.",
"+",
"alpha",
"**",
"2",
")",
"return",
"mu",
"+",
"np",
".",
"sqrt",
"(",
"2",
"/",
"pi",
"/",
"tau",
")",
... | Expectation of skew-normal random variables. | [
"Expectation",
"of",
"skew",
"-",
"normal",
"random",
"variables",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2567-L2572 | train |
pymc-devs/pymc | pymc/distributions.py | skew_normal_like | def skew_normal_like(x, mu, tau, alpha):
R"""
Azzalini's skew-normal log-likelihood
.. math::
f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)
where :math:\Phi is the normal CDF and :math: \phi is the normal PDF.
:Parameters:
- `x` : Input data.
... | python | def skew_normal_like(x, mu, tau, alpha):
R"""
Azzalini's skew-normal log-likelihood
.. math::
f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)
where :math:\Phi is the normal CDF and :math: \phi is the normal PDF.
:Parameters:
- `x` : Input data.
... | [
"def",
"skew_normal_like",
"(",
"x",
",",
"mu",
",",
"tau",
",",
"alpha",
")",
":",
"return",
"flib",
".",
"sn_like",
"(",
"x",
",",
"mu",
",",
"tau",
",",
"alpha",
")"
] | R"""
Azzalini's skew-normal log-likelihood
.. math::
f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)
where :math:\Phi is the normal CDF and :math: \phi is the normal PDF.
:Parameters:
- `x` : Input data.
- `mu` : Mean of the distribution.
- `t... | [
"R",
"Azzalini",
"s",
"skew",
"-",
"normal",
"log",
"-",
"likelihood"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2575-L2593 | train |
pymc-devs/pymc | pymc/distributions.py | rt | def rt(nu, size=None):
"""
Student's t random variates.
"""
return rnormal(0, 1, size) / np.sqrt(rchi2(nu, size) / nu) | python | def rt(nu, size=None):
"""
Student's t random variates.
"""
return rnormal(0, 1, size) / np.sqrt(rchi2(nu, size) / nu) | [
"def",
"rt",
"(",
"nu",
",",
"size",
"=",
"None",
")",
":",
"return",
"rnormal",
"(",
"0",
",",
"1",
",",
"size",
")",
"/",
"np",
".",
"sqrt",
"(",
"rchi2",
"(",
"nu",
",",
"size",
")",
"/",
"nu",
")"
] | Student's t random variates. | [
"Student",
"s",
"t",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2598-L2602 | train |
pymc-devs/pymc | pymc/distributions.py | t_like | def t_like(x, nu):
R"""
Student's T log-likelihood.
Describes a zero-mean normal variable
whose precision is gamma distributed. Alternatively, describes the
mean of several zero-mean normal random variables divided by their
sample standard deviation.
.. math::
f(x \mid \nu) = \frac... | python | def t_like(x, nu):
R"""
Student's T log-likelihood.
Describes a zero-mean normal variable
whose precision is gamma distributed. Alternatively, describes the
mean of several zero-mean normal random variables divided by their
sample standard deviation.
.. math::
f(x \mid \nu) = \frac... | [
"def",
"t_like",
"(",
"x",
",",
"nu",
")",
":",
"nu",
"=",
"np",
".",
"asarray",
"(",
"nu",
")",
"return",
"flib",
".",
"t",
"(",
"x",
",",
"nu",
")"
] | R"""
Student's T log-likelihood.
Describes a zero-mean normal variable
whose precision is gamma distributed. Alternatively, describes the
mean of several zero-mean normal random variables divided by their
sample standard deviation.
.. math::
f(x \mid \nu) = \frac{\Gamma(\frac{\nu+1}{2}... | [
"R",
"Student",
"s",
"T",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2612-L2630 | train |
pymc-devs/pymc | pymc/distributions.py | rnoncentral_t | def rnoncentral_t(mu, lam, nu, size=None):
"""
Non-central Student's t random variates.
"""
tau = rgamma(nu / 2., nu / (2. * lam), size)
return rnormal(mu, tau) | python | def rnoncentral_t(mu, lam, nu, size=None):
"""
Non-central Student's t random variates.
"""
tau = rgamma(nu / 2., nu / (2. * lam), size)
return rnormal(mu, tau) | [
"def",
"rnoncentral_t",
"(",
"mu",
",",
"lam",
",",
"nu",
",",
"size",
"=",
"None",
")",
":",
"tau",
"=",
"rgamma",
"(",
"nu",
"/",
"2.",
",",
"nu",
"/",
"(",
"2.",
"*",
"lam",
")",
",",
"size",
")",
"return",
"rnormal",
"(",
"mu",
",",
"tau"... | Non-central Student's t random variates. | [
"Non",
"-",
"central",
"Student",
"s",
"t",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2636-L2641 | train |
pymc-devs/pymc | pymc/distributions.py | noncentral_t_like | def noncentral_t_like(x, mu, lam, nu):
R"""
Non-central Student's T log-likelihood.
Describes a normal variable whose precision is gamma distributed.
.. math::
f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu +
1}{2})}{\Gamma(\frac{\nu}{2})}
\left(\frac{\lambda}{\pi\nu}\right)^{\f... | python | def noncentral_t_like(x, mu, lam, nu):
R"""
Non-central Student's T log-likelihood.
Describes a normal variable whose precision is gamma distributed.
.. math::
f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu +
1}{2})}{\Gamma(\frac{\nu}{2})}
\left(\frac{\lambda}{\pi\nu}\right)^{\f... | [
"def",
"noncentral_t_like",
"(",
"x",
",",
"mu",
",",
"lam",
",",
"nu",
")",
":",
"mu",
"=",
"np",
".",
"asarray",
"(",
"mu",
")",
"lam",
"=",
"np",
".",
"asarray",
"(",
"lam",
")",
"nu",
"=",
"np",
".",
"asarray",
"(",
"nu",
")",
"return",
"... | R"""
Non-central Student's T log-likelihood.
Describes a normal variable whose precision is gamma distributed.
.. math::
f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu +
1}{2})}{\Gamma(\frac{\nu}{2})}
\left(\frac{\lambda}{\pi\nu}\right)^{\frac{1}{2}}
\left[1+\frac{\lambda(x-... | [
"R",
"Non",
"-",
"central",
"Student",
"s",
"T",
"log",
"-",
"likelihood",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2655-L2677 | train |
pymc-devs/pymc | pymc/distributions.py | rdiscrete_uniform | def rdiscrete_uniform(lower, upper, size=None):
"""
Random discrete_uniform variates.
"""
return np.random.randint(lower, upper + 1, size) | python | def rdiscrete_uniform(lower, upper, size=None):
"""
Random discrete_uniform variates.
"""
return np.random.randint(lower, upper + 1, size) | [
"def",
"rdiscrete_uniform",
"(",
"lower",
",",
"upper",
",",
"size",
"=",
"None",
")",
":",
"return",
"np",
".",
"random",
".",
"randint",
"(",
"lower",
",",
"upper",
"+",
"1",
",",
"size",
")"
] | Random discrete_uniform variates. | [
"Random",
"discrete_uniform",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2691-L2695 | train |
pymc-devs/pymc | pymc/distributions.py | runiform | def runiform(lower, upper, size=None):
"""
Random uniform variates.
"""
return np.random.uniform(lower, upper, size) | python | def runiform(lower, upper, size=None):
"""
Random uniform variates.
"""
return np.random.uniform(lower, upper, size) | [
"def",
"runiform",
"(",
"lower",
",",
"upper",
",",
"size",
"=",
"None",
")",
":",
"return",
"np",
".",
"random",
".",
"uniform",
"(",
"lower",
",",
"upper",
",",
"size",
")"
] | Random uniform variates. | [
"Random",
"uniform",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2723-L2727 | train |
pymc-devs/pymc | pymc/distributions.py | rweibull | def rweibull(alpha, beta, size=None):
"""
Weibull random variates.
"""
tmp = -np.log(runiform(0, 1, size))
return beta * (tmp ** (1. / alpha)) | python | def rweibull(alpha, beta, size=None):
"""
Weibull random variates.
"""
tmp = -np.log(runiform(0, 1, size))
return beta * (tmp ** (1. / alpha)) | [
"def",
"rweibull",
"(",
"alpha",
",",
"beta",
",",
"size",
"=",
"None",
")",
":",
"tmp",
"=",
"-",
"np",
".",
"log",
"(",
"runiform",
"(",
"0",
",",
"1",
",",
"size",
")",
")",
"return",
"beta",
"*",
"(",
"tmp",
"**",
"(",
"1.",
"/",
"alpha",... | Weibull random variates. | [
"Weibull",
"random",
"variates",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2761-L2766 | train |
pymc-devs/pymc | pymc/distributions.py | rwishart_cov | def rwishart_cov(n, C):
"""
Return a Wishart random matrix.
:Parameters:
n : int
Degrees of freedom, > 0.
C : matrix
Symmetric and positive definite
"""
# return rwishart(n, np.linalg.inv(C))
p = np.shape(C)[0]
# Need cholesky decomposition of precision matrix C... | python | def rwishart_cov(n, C):
"""
Return a Wishart random matrix.
:Parameters:
n : int
Degrees of freedom, > 0.
C : matrix
Symmetric and positive definite
"""
# return rwishart(n, np.linalg.inv(C))
p = np.shape(C)[0]
# Need cholesky decomposition of precision matrix C... | [
"def",
"rwishart_cov",
"(",
"n",
",",
"C",
")",
":",
"# return rwishart(n, np.linalg.inv(C))",
"p",
"=",
"np",
".",
"shape",
"(",
"C",
")",
"[",
"0",
"]",
"# Need cholesky decomposition of precision matrix C^-1?",
"sig",
"=",
"np",
".",
"linalg",
".",
"cholesky"... | Return a Wishart random matrix.
:Parameters:
n : int
Degrees of freedom, > 0.
C : matrix
Symmetric and positive definite | [
"Return",
"a",
"Wishart",
"random",
"matrix",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2866-L2893 | train |
pymc-devs/pymc | pymc/distributions.py | valuewrapper | def valuewrapper(f, arguments=None):
"""Return a likelihood accepting value instead of x as a keyword argument.
This is specifically intended for the instantiator above.
"""
def wrapper(**kwds):
value = kwds.pop('value')
return f(value, **kwds)
if arguments is None:
wrapper.... | python | def valuewrapper(f, arguments=None):
"""Return a likelihood accepting value instead of x as a keyword argument.
This is specifically intended for the instantiator above.
"""
def wrapper(**kwds):
value = kwds.pop('value')
return f(value, **kwds)
if arguments is None:
wrapper.... | [
"def",
"valuewrapper",
"(",
"f",
",",
"arguments",
"=",
"None",
")",
":",
"def",
"wrapper",
"(",
"*",
"*",
"kwds",
")",
":",
"value",
"=",
"kwds",
".",
"pop",
"(",
"'value'",
")",
"return",
"f",
"(",
"value",
",",
"*",
"*",
"kwds",
")",
"if",
"... | Return a likelihood accepting value instead of x as a keyword argument.
This is specifically intended for the instantiator above. | [
"Return",
"a",
"likelihood",
"accepting",
"value",
"instead",
"of",
"x",
"as",
"a",
"keyword",
"argument",
".",
"This",
"is",
"specifically",
"intended",
"for",
"the",
"instantiator",
"above",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2968-L2981 | train |
pymc-devs/pymc | pymc/distributions.py | local_decorated_likelihoods | def local_decorated_likelihoods(obj):
"""
New interface likelihoods
"""
for name, like in six.iteritems(likelihoods):
obj[name + '_like'] = gofwrapper(like, snapshot) | python | def local_decorated_likelihoods(obj):
"""
New interface likelihoods
"""
for name, like in six.iteritems(likelihoods):
obj[name + '_like'] = gofwrapper(like, snapshot) | [
"def",
"local_decorated_likelihoods",
"(",
"obj",
")",
":",
"for",
"name",
",",
"like",
"in",
"six",
".",
"iteritems",
"(",
"likelihoods",
")",
":",
"obj",
"[",
"name",
"+",
"'_like'",
"]",
"=",
"gofwrapper",
"(",
"like",
",",
"snapshot",
")"
] | New interface likelihoods | [
"New",
"interface",
"likelihoods"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L2994-L3000 | train |
pymc-devs/pymc | pymc/distributions.py | _inject_dist | def _inject_dist(distname, kwargs={}, ns=locals()):
"""
Reusable function to inject Stochastic subclasses into module
namespace
"""
dist_logp, dist_random, grad_logp = name_to_funcs(distname, ns)
classname = capitalize(distname)
ns[classname] = stochastic_from_dist(distname, dist_logp,
... | python | def _inject_dist(distname, kwargs={}, ns=locals()):
"""
Reusable function to inject Stochastic subclasses into module
namespace
"""
dist_logp, dist_random, grad_logp = name_to_funcs(distname, ns)
classname = capitalize(distname)
ns[classname] = stochastic_from_dist(distname, dist_logp,
... | [
"def",
"_inject_dist",
"(",
"distname",
",",
"kwargs",
"=",
"{",
"}",
",",
"ns",
"=",
"locals",
"(",
")",
")",
":",
"dist_logp",
",",
"dist_random",
",",
"grad_logp",
"=",
"name_to_funcs",
"(",
"distname",
",",
"ns",
")",
"classname",
"=",
"capitalize",
... | Reusable function to inject Stochastic subclasses into module
namespace | [
"Reusable",
"function",
"to",
"inject",
"Stochastic",
"subclasses",
"into",
"module",
"namespace"
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L3009-L3019 | train |
pymc-devs/pymc | pymc/distributions.py | mod_categorical_expval | def mod_categorical_expval(p):
"""
Expected value of categorical distribution with parent p of length k-1.
An implicit k'th category is assumed to exist with associated
probability 1-sum(p).
"""
p = extend_dirichlet(p)
return np.sum([p * i for i, p in enumerate(p)]) | python | def mod_categorical_expval(p):
"""
Expected value of categorical distribution with parent p of length k-1.
An implicit k'th category is assumed to exist with associated
probability 1-sum(p).
"""
p = extend_dirichlet(p)
return np.sum([p * i for i, p in enumerate(p)]) | [
"def",
"mod_categorical_expval",
"(",
"p",
")",
":",
"p",
"=",
"extend_dirichlet",
"(",
"p",
")",
"return",
"np",
".",
"sum",
"(",
"[",
"p",
"*",
"i",
"for",
"i",
",",
"p",
"in",
"enumerate",
"(",
"p",
")",
"]",
")"
] | Expected value of categorical distribution with parent p of length k-1.
An implicit k'th category is assumed to exist with associated
probability 1-sum(p). | [
"Expected",
"value",
"of",
"categorical",
"distribution",
"with",
"parent",
"p",
"of",
"length",
"k",
"-",
"1",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L3104-L3112 | train |
pymc-devs/pymc | pymc/distributions.py | Impute | def Impute(name, dist_class, imputable, **parents):
"""
This function accomodates missing elements for the data of simple
Stochastic distribution subclasses. The masked_values argument is an
object of type numpy.ma.MaskedArray, which contains the raw data and
a boolean mask indicating missing values... | python | def Impute(name, dist_class, imputable, **parents):
"""
This function accomodates missing elements for the data of simple
Stochastic distribution subclasses. The masked_values argument is an
object of type numpy.ma.MaskedArray, which contains the raw data and
a boolean mask indicating missing values... | [
"def",
"Impute",
"(",
"name",
",",
"dist_class",
",",
"imputable",
",",
"*",
"*",
"parents",
")",
":",
"dims",
"=",
"np",
".",
"shape",
"(",
"imputable",
")",
"masked_values",
"=",
"np",
".",
"ravel",
"(",
"imputable",
")",
"if",
"not",
"isinstance",
... | This function accomodates missing elements for the data of simple
Stochastic distribution subclasses. The masked_values argument is an
object of type numpy.ma.MaskedArray, which contains the raw data and
a boolean mask indicating missing values. The resulting list contains
a list of stochastics of type ... | [
"This",
"function",
"accomodates",
"missing",
"elements",
"for",
"the",
"data",
"of",
"simple",
"Stochastic",
"distribution",
"subclasses",
".",
"The",
"masked_values",
"argument",
"is",
"an",
"object",
"of",
"type",
"numpy",
".",
"ma",
".",
"MaskedArray",
"whic... | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/distributions.py#L3269-L3335 | train |
pymc-devs/pymc | pymc/Node.py | logp_gradient_of_set | def logp_gradient_of_set(variable_set, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to all the variables in variable_set.
Calculation of the log posterior is restricted to the variables in calculation_set.
Returns a dictionary of the gradients.
"""
... | python | def logp_gradient_of_set(variable_set, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to all the variables in variable_set.
Calculation of the log posterior is restricted to the variables in calculation_set.
Returns a dictionary of the gradients.
"""
... | [
"def",
"logp_gradient_of_set",
"(",
"variable_set",
",",
"calculation_set",
"=",
"None",
")",
":",
"logp_gradients",
"=",
"{",
"}",
"for",
"variable",
"in",
"variable_set",
":",
"logp_gradients",
"[",
"variable",
"]",
"=",
"logp_gradient",
"(",
"variable",
",",
... | Calculates the gradient of the joint log posterior with respect to all the variables in variable_set.
Calculation of the log posterior is restricted to the variables in calculation_set.
Returns a dictionary of the gradients. | [
"Calculates",
"the",
"gradient",
"of",
"the",
"joint",
"log",
"posterior",
"with",
"respect",
"to",
"all",
"the",
"variables",
"in",
"variable_set",
".",
"Calculation",
"of",
"the",
"log",
"posterior",
"is",
"restricted",
"to",
"the",
"variables",
"in",
"calcu... | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Node.py#L42-L54 | train |
pymc-devs/pymc | pymc/Node.py | logp_gradient | def logp_gradient(variable, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to variable.
Calculation of the log posterior is restricted to the variables in calculation_set.
"""
return variable.logp_partial_gradient(variable, calculation_set) + sum(
... | python | def logp_gradient(variable, calculation_set=None):
"""
Calculates the gradient of the joint log posterior with respect to variable.
Calculation of the log posterior is restricted to the variables in calculation_set.
"""
return variable.logp_partial_gradient(variable, calculation_set) + sum(
... | [
"def",
"logp_gradient",
"(",
"variable",
",",
"calculation_set",
"=",
"None",
")",
":",
"return",
"variable",
".",
"logp_partial_gradient",
"(",
"variable",
",",
"calculation_set",
")",
"+",
"sum",
"(",
"[",
"child",
".",
"logp_partial_gradient",
"(",
"variable"... | Calculates the gradient of the joint log posterior with respect to variable.
Calculation of the log posterior is restricted to the variables in calculation_set. | [
"Calculates",
"the",
"gradient",
"of",
"the",
"joint",
"log",
"posterior",
"with",
"respect",
"to",
"variable",
".",
"Calculation",
"of",
"the",
"log",
"posterior",
"is",
"restricted",
"to",
"the",
"variables",
"in",
"calculation_set",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Node.py#L57-L63 | train |
pymc-devs/pymc | pymc/Node.py | Variable.summary | def summary(self, alpha=0.05, start=0, batches=100, chain=None, roundto=3):
"""
Generate a pretty-printed summary of the node.
:Parameters:
alpha : float
The alpha level for generating posterior intervals. Defaults to
0.05.
start : int
The starting... | python | def summary(self, alpha=0.05, start=0, batches=100, chain=None, roundto=3):
"""
Generate a pretty-printed summary of the node.
:Parameters:
alpha : float
The alpha level for generating posterior intervals. Defaults to
0.05.
start : int
The starting... | [
"def",
"summary",
"(",
"self",
",",
"alpha",
"=",
"0.05",
",",
"start",
"=",
"0",
",",
"batches",
"=",
"100",
",",
"chain",
"=",
"None",
",",
"roundto",
"=",
"3",
")",
":",
"# Calculate statistics for Node",
"statdict",
"=",
"self",
".",
"stats",
"(",
... | Generate a pretty-printed summary of the node.
:Parameters:
alpha : float
The alpha level for generating posterior intervals. Defaults to
0.05.
start : int
The starting index from which to summarize (each) chain. Defaults
to zero.
batches : int
... | [
"Generate",
"a",
"pretty",
"-",
"printed",
"summary",
"of",
"the",
"node",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Node.py#L267-L358 | train |
pymc-devs/pymc | pymc/Container.py | file_items | def file_items(container, iterable):
"""
Files away objects into the appropriate attributes of the container.
"""
# container._value = copy(iterable)
container.nodes = set()
container.variables = set()
container.deterministics = set()
container.stochastics = set()
container.potenti... | python | def file_items(container, iterable):
"""
Files away objects into the appropriate attributes of the container.
"""
# container._value = copy(iterable)
container.nodes = set()
container.variables = set()
container.deterministics = set()
container.stochastics = set()
container.potenti... | [
"def",
"file_items",
"(",
"container",
",",
"iterable",
")",
":",
"# container._value = copy(iterable)",
"container",
".",
"nodes",
"=",
"set",
"(",
")",
"container",
".",
"variables",
"=",
"set",
"(",
")",
"container",
".",
"deterministics",
"=",
"set",
"(",
... | Files away objects into the appropriate attributes of the container. | [
"Files",
"away",
"objects",
"into",
"the",
"appropriate",
"attributes",
"of",
"the",
"container",
"."
] | c6e530210bff4c0d7189b35b2c971bc53f93f7cd | https://github.com/pymc-devs/pymc/blob/c6e530210bff4c0d7189b35b2c971bc53f93f7cd/pymc/Container.py#L168-L248 | train |
ethereum/web3.py | web3/middleware/gas_price_strategy.py | gas_price_strategy_middleware | def gas_price_strategy_middleware(make_request, web3):
"""
Includes a gas price using the gas price strategy
"""
def middleware(method, params):
if method == 'eth_sendTransaction':
transaction = params[0]
if 'gasPrice' not in transaction:
generated_gas_pri... | python | def gas_price_strategy_middleware(make_request, web3):
"""
Includes a gas price using the gas price strategy
"""
def middleware(method, params):
if method == 'eth_sendTransaction':
transaction = params[0]
if 'gasPrice' not in transaction:
generated_gas_pri... | [
"def",
"gas_price_strategy_middleware",
"(",
"make_request",
",",
"web3",
")",
":",
"def",
"middleware",
"(",
"method",
",",
"params",
")",
":",
"if",
"method",
"==",
"'eth_sendTransaction'",
":",
"transaction",
"=",
"params",
"[",
"0",
"]",
"if",
"'gasPrice'"... | Includes a gas price using the gas price strategy | [
"Includes",
"a",
"gas",
"price",
"using",
"the",
"gas",
"price",
"strategy"
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/middleware/gas_price_strategy.py#L6-L19 | train |
ethereum/web3.py | web3/_utils/transactions.py | fill_transaction_defaults | def fill_transaction_defaults(web3, transaction):
"""
if web3 is None, fill as much as possible while offline
"""
defaults = {}
for key, default_getter in TRANSACTION_DEFAULTS.items():
if key not in transaction:
if callable(default_getter):
if web3 is not None:
... | python | def fill_transaction_defaults(web3, transaction):
"""
if web3 is None, fill as much as possible while offline
"""
defaults = {}
for key, default_getter in TRANSACTION_DEFAULTS.items():
if key not in transaction:
if callable(default_getter):
if web3 is not None:
... | [
"def",
"fill_transaction_defaults",
"(",
"web3",
",",
"transaction",
")",
":",
"defaults",
"=",
"{",
"}",
"for",
"key",
",",
"default_getter",
"in",
"TRANSACTION_DEFAULTS",
".",
"items",
"(",
")",
":",
"if",
"key",
"not",
"in",
"transaction",
":",
"if",
"c... | if web3 is None, fill as much as possible while offline | [
"if",
"web3",
"is",
"None",
"fill",
"as",
"much",
"as",
"possible",
"while",
"offline"
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/_utils/transactions.py#L49-L64 | train |
ethereum/web3.py | web3/gas_strategies/time_based.py | _compute_probabilities | def _compute_probabilities(miner_data, wait_blocks, sample_size):
"""
Computes the probabilities that a txn will be accepted at each of the gas
prices accepted by the miners.
"""
miner_data_by_price = tuple(sorted(
miner_data,
key=operator.attrgetter('low_percentile_gas_price'),
... | python | def _compute_probabilities(miner_data, wait_blocks, sample_size):
"""
Computes the probabilities that a txn will be accepted at each of the gas
prices accepted by the miners.
"""
miner_data_by_price = tuple(sorted(
miner_data,
key=operator.attrgetter('low_percentile_gas_price'),
... | [
"def",
"_compute_probabilities",
"(",
"miner_data",
",",
"wait_blocks",
",",
"sample_size",
")",
":",
"miner_data_by_price",
"=",
"tuple",
"(",
"sorted",
"(",
"miner_data",
",",
"key",
"=",
"operator",
".",
"attrgetter",
"(",
"'low_percentile_gas_price'",
")",
","... | Computes the probabilities that a txn will be accepted at each of the gas
prices accepted by the miners. | [
"Computes",
"the",
"probabilities",
"that",
"a",
"txn",
"will",
"be",
"accepted",
"at",
"each",
"of",
"the",
"gas",
"prices",
"accepted",
"by",
"the",
"miners",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/gas_strategies/time_based.py#L76-L91 | train |
ethereum/web3.py | web3/gas_strategies/time_based.py | _compute_gas_price | def _compute_gas_price(probabilities, desired_probability):
"""
Given a sorted range of ``Probability`` named-tuples returns a gas price
computed based on where the ``desired_probability`` would fall within the
range.
:param probabilities: An iterable of `Probability` named-tuples sorted in reverse... | python | def _compute_gas_price(probabilities, desired_probability):
"""
Given a sorted range of ``Probability`` named-tuples returns a gas price
computed based on where the ``desired_probability`` would fall within the
range.
:param probabilities: An iterable of `Probability` named-tuples sorted in reverse... | [
"def",
"_compute_gas_price",
"(",
"probabilities",
",",
"desired_probability",
")",
":",
"first",
"=",
"probabilities",
"[",
"0",
"]",
"last",
"=",
"probabilities",
"[",
"-",
"1",
"]",
"if",
"desired_probability",
">=",
"first",
".",
"prob",
":",
"return",
"... | Given a sorted range of ``Probability`` named-tuples returns a gas price
computed based on where the ``desired_probability`` would fall within the
range.
:param probabilities: An iterable of `Probability` named-tuples sorted in reverse order.
:param desired_probability: An floating point representation... | [
"Given",
"a",
"sorted",
"range",
"of",
"Probability",
"named",
"-",
"tuples",
"returns",
"a",
"gas",
"price",
"computed",
"based",
"on",
"where",
"the",
"desired_probability",
"would",
"fall",
"within",
"the",
"range",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/gas_strategies/time_based.py#L94-L136 | train |
ethereum/web3.py | web3/gas_strategies/time_based.py | construct_time_based_gas_price_strategy | def construct_time_based_gas_price_strategy(max_wait_seconds,
sample_size=120,
probability=98):
"""
A gas pricing strategy that uses recently mined block data to derive a gas
price for which a transaction is likely to be... | python | def construct_time_based_gas_price_strategy(max_wait_seconds,
sample_size=120,
probability=98):
"""
A gas pricing strategy that uses recently mined block data to derive a gas
price for which a transaction is likely to be... | [
"def",
"construct_time_based_gas_price_strategy",
"(",
"max_wait_seconds",
",",
"sample_size",
"=",
"120",
",",
"probability",
"=",
"98",
")",
":",
"def",
"time_based_gas_price_strategy",
"(",
"web3",
",",
"transaction_params",
")",
":",
"avg_block_time",
"=",
"_get_a... | A gas pricing strategy that uses recently mined block data to derive a gas
price for which a transaction is likely to be mined within X seconds with
probability P.
:param max_wait_seconds: The desired maxiumum number of seconds the
transaction should take to mine.
:param sample_size: The number... | [
"A",
"gas",
"pricing",
"strategy",
"that",
"uses",
"recently",
"mined",
"block",
"data",
"to",
"derive",
"a",
"gas",
"price",
"for",
"which",
"a",
"transaction",
"is",
"likely",
"to",
"be",
"mined",
"within",
"X",
"seconds",
"with",
"probability",
"P",
"."... | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/gas_strategies/time_based.py#L140-L170 | train |
ethereum/web3.py | web3/manager.py | RequestManager.default_middlewares | def default_middlewares(web3):
"""
List the default middlewares for the request manager.
Leaving ens unspecified will prevent the middleware from resolving names.
"""
return [
(request_parameter_normalizer, 'request_param_normalizer'),
(gas_price_strategy_... | python | def default_middlewares(web3):
"""
List the default middlewares for the request manager.
Leaving ens unspecified will prevent the middleware from resolving names.
"""
return [
(request_parameter_normalizer, 'request_param_normalizer'),
(gas_price_strategy_... | [
"def",
"default_middlewares",
"(",
"web3",
")",
":",
"return",
"[",
"(",
"request_parameter_normalizer",
",",
"'request_param_normalizer'",
")",
",",
"(",
"gas_price_strategy_middleware",
",",
"'gas_price_strategy'",
")",
",",
"(",
"name_to_address_middleware",
"(",
"we... | List the default middlewares for the request manager.
Leaving ens unspecified will prevent the middleware from resolving names. | [
"List",
"the",
"default",
"middlewares",
"for",
"the",
"request",
"manager",
".",
"Leaving",
"ens",
"unspecified",
"will",
"prevent",
"the",
"middleware",
"from",
"resolving",
"names",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/manager.py#L57-L71 | train |
ethereum/web3.py | web3/manager.py | RequestManager.request_blocking | def request_blocking(self, method, params):
"""
Make a synchronous request using the provider
"""
response = self._make_request(method, params)
if "error" in response:
raise ValueError(response["error"])
return response['result'] | python | def request_blocking(self, method, params):
"""
Make a synchronous request using the provider
"""
response = self._make_request(method, params)
if "error" in response:
raise ValueError(response["error"])
return response['result'] | [
"def",
"request_blocking",
"(",
"self",
",",
"method",
",",
"params",
")",
":",
"response",
"=",
"self",
".",
"_make_request",
"(",
"method",
",",
"params",
")",
"if",
"\"error\"",
"in",
"response",
":",
"raise",
"ValueError",
"(",
"response",
"[",
"\"erro... | Make a synchronous request using the provider | [
"Make",
"a",
"synchronous",
"request",
"using",
"the",
"provider"
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/manager.py#L90-L99 | train |
ethereum/web3.py | web3/manager.py | RequestManager.coro_request | async def coro_request(self, method, params):
"""
Couroutine for making a request using the provider
"""
response = await self._coro_make_request(method, params)
if "error" in response:
raise ValueError(response["error"])
if response['result'] is None:
... | python | async def coro_request(self, method, params):
"""
Couroutine for making a request using the provider
"""
response = await self._coro_make_request(method, params)
if "error" in response:
raise ValueError(response["error"])
if response['result'] is None:
... | [
"async",
"def",
"coro_request",
"(",
"self",
",",
"method",
",",
"params",
")",
":",
"response",
"=",
"await",
"self",
".",
"_coro_make_request",
"(",
"method",
",",
"params",
")",
"if",
"\"error\"",
"in",
"response",
":",
"raise",
"ValueError",
"(",
"resp... | Couroutine for making a request using the provider | [
"Couroutine",
"for",
"making",
"a",
"request",
"using",
"the",
"provider"
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/manager.py#L101-L113 | train |
ethereum/web3.py | web3/middleware/fixture.py | construct_fixture_middleware | def construct_fixture_middleware(fixtures):
"""
Constructs a middleware which returns a static response for any method
which is found in the provided fixtures.
"""
def fixture_middleware(make_request, web3):
def middleware(method, params):
if method in fixtures:
r... | python | def construct_fixture_middleware(fixtures):
"""
Constructs a middleware which returns a static response for any method
which is found in the provided fixtures.
"""
def fixture_middleware(make_request, web3):
def middleware(method, params):
if method in fixtures:
r... | [
"def",
"construct_fixture_middleware",
"(",
"fixtures",
")",
":",
"def",
"fixture_middleware",
"(",
"make_request",
",",
"web3",
")",
":",
"def",
"middleware",
"(",
"method",
",",
"params",
")",
":",
"if",
"method",
"in",
"fixtures",
":",
"result",
"=",
"fix... | Constructs a middleware which returns a static response for any method
which is found in the provided fixtures. | [
"Constructs",
"a",
"middleware",
"which",
"returns",
"a",
"static",
"response",
"for",
"any",
"method",
"which",
"is",
"found",
"in",
"the",
"provided",
"fixtures",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/middleware/fixture.py#L1-L14 | train |
ethereum/web3.py | web3/middleware/__init__.py | combine_middlewares | def combine_middlewares(middlewares, web3, provider_request_fn):
"""
Returns a callable function which will call the provider.provider_request
function wrapped with all of the middlewares.
"""
return functools.reduce(
lambda request_fn, middleware: middleware(request_fn, web3),
rever... | python | def combine_middlewares(middlewares, web3, provider_request_fn):
"""
Returns a callable function which will call the provider.provider_request
function wrapped with all of the middlewares.
"""
return functools.reduce(
lambda request_fn, middleware: middleware(request_fn, web3),
rever... | [
"def",
"combine_middlewares",
"(",
"middlewares",
",",
"web3",
",",
"provider_request_fn",
")",
":",
"return",
"functools",
".",
"reduce",
"(",
"lambda",
"request_fn",
",",
"middleware",
":",
"middleware",
"(",
"request_fn",
",",
"web3",
")",
",",
"reversed",
... | Returns a callable function which will call the provider.provider_request
function wrapped with all of the middlewares. | [
"Returns",
"a",
"callable",
"function",
"which",
"will",
"call",
"the",
"provider",
".",
"provider_request",
"function",
"wrapped",
"with",
"all",
"of",
"the",
"middlewares",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/middleware/__init__.py#L67-L76 | train |
ethereum/web3.py | web3/_utils/events.py | get_event_data | def get_event_data(event_abi, log_entry):
"""
Given an event ABI and a log entry for that event, return the decoded
event data
"""
if event_abi['anonymous']:
log_topics = log_entry['topics']
elif not log_entry['topics']:
raise MismatchedABI("Expected non-anonymous event to have 1... | python | def get_event_data(event_abi, log_entry):
"""
Given an event ABI and a log entry for that event, return the decoded
event data
"""
if event_abi['anonymous']:
log_topics = log_entry['topics']
elif not log_entry['topics']:
raise MismatchedABI("Expected non-anonymous event to have 1... | [
"def",
"get_event_data",
"(",
"event_abi",
",",
"log_entry",
")",
":",
"if",
"event_abi",
"[",
"'anonymous'",
"]",
":",
"log_topics",
"=",
"log_entry",
"[",
"'topics'",
"]",
"elif",
"not",
"log_entry",
"[",
"'topics'",
"]",
":",
"raise",
"MismatchedABI",
"("... | Given an event ABI and a log entry for that event, return the decoded
event data | [
"Given",
"an",
"event",
"ABI",
"and",
"a",
"log",
"entry",
"for",
"that",
"event",
"return",
"the",
"decoded",
"event",
"data"
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/_utils/events.py#L159-L233 | train |
ethereum/web3.py | web3/middleware/exception_retry_request.py | exception_retry_middleware | def exception_retry_middleware(make_request, web3, errors, retries=5):
"""
Creates middleware that retries failed HTTP requests. Is a default
middleware for HTTPProvider.
"""
def middleware(method, params):
if check_if_retry_on_failure(method):
for i in range(retries):
... | python | def exception_retry_middleware(make_request, web3, errors, retries=5):
"""
Creates middleware that retries failed HTTP requests. Is a default
middleware for HTTPProvider.
"""
def middleware(method, params):
if check_if_retry_on_failure(method):
for i in range(retries):
... | [
"def",
"exception_retry_middleware",
"(",
"make_request",
",",
"web3",
",",
"errors",
",",
"retries",
"=",
"5",
")",
":",
"def",
"middleware",
"(",
"method",
",",
"params",
")",
":",
"if",
"check_if_retry_on_failure",
"(",
"method",
")",
":",
"for",
"i",
"... | Creates middleware that retries failed HTTP requests. Is a default
middleware for HTTPProvider. | [
"Creates",
"middleware",
"that",
"retries",
"failed",
"HTTP",
"requests",
".",
"Is",
"a",
"default",
"middleware",
"for",
"HTTPProvider",
"."
] | 71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab | https://github.com/ethereum/web3.py/blob/71b8bf03dc6d332dd97d8902a38ffab6f8b5a5ab/web3/middleware/exception_retry_request.py#L71-L88 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.