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python-diamond/Diamond | src/diamond/handler/sentry.py | SentryHandler.process | def process(self, metric):
"""
process a single metric
@type metric: diamond.metric.Metric
@param metric: metric to process
@rtype None
"""
for rule in self.rules:
rule.process(metric, self) | python | def process(self, metric):
"""
process a single metric
@type metric: diamond.metric.Metric
@param metric: metric to process
@rtype None
"""
for rule in self.rules:
rule.process(metric, self) | [
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python-diamond/Diamond | src/collectors/network/network.py | NetworkCollector.collect | def collect(self):
"""
Collect network interface stats.
"""
# Initialize results
results = {}
if os.access(self.PROC, os.R_OK):
# Open File
file = open(self.PROC)
# Build Regular Expression
greed = ''
if str_t... | python | def collect(self):
"""
Collect network interface stats.
"""
# Initialize results
results = {}
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# Open File
file = open(self.PROC)
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python-diamond/Diamond | src/collectors/conntrack/conntrack.py | ConnTrackCollector.get_default_config_help | def get_default_config_help(self):
"""
Return help text for collector configuration
"""
config_help = super(ConnTrackCollector, self).get_default_config_help()
config_help.update({
"dir": "Directories with files of interest, comma seperated",
"files":... | python | def get_default_config_help(self):
"""
Return help text for collector configuration
"""
config_help = super(ConnTrackCollector, self).get_default_config_help()
config_help.update({
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python-diamond/Diamond | src/collectors/netapp/netapp.py | NetAppCollector._replace_and_publish | def _replace_and_publish(self, path, prettyname, value, device):
"""
Inputs a complete path for a metric and a value.
Replace the metric name and publish.
"""
if value is None:
return
newpath = path
# Change metric name before publish if needed.
... | python | def _replace_and_publish(self, path, prettyname, value, device):
"""
Inputs a complete path for a metric and a value.
Replace the metric name and publish.
"""
if value is None:
return
newpath = path
# Change metric name before publish if needed.
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python-diamond/Diamond | src/collectors/netapp/netapp.py | NetAppCollector._gen_delta_depend | def _gen_delta_depend(self, path, derivative, multiplier, prettyname,
device):
"""
For some metrics we need to divide the delta for one metric
with the delta of another.
Publishes a metric if the convertion goes well.
"""
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"""
For some metrics we need to divide the delta for one metric
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Publishes a metric if the convertion goes well.
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python-diamond/Diamond | src/collectors/netapp/netapp.py | NetAppCollector._gen_delta_per_sec | def _gen_delta_per_sec(self, path, value_delta, time_delta, multiplier,
prettyname, device):
"""
Calulates the difference between to point, and scales is to per second.
"""
if time_delta < 0:
return
value = (value_delta / time_delta) * multi... | python | def _gen_delta_per_sec(self, path, value_delta, time_delta, multiplier,
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"""
Calulates the difference between to point, and scales is to per second.
"""
if time_delta < 0:
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python-diamond/Diamond | src/diamond/handler/datadog.py | DatadogHandler._send | def _send(self):
"""
Take metrics from queue and send it to Datadog API
"""
while len(self.queue) > 0:
metric = self.queue.popleft()
path = '%s.%s.%s' % (
metric.getPathPrefix(),
metric.getCollectorPath(),
metric.g... | python | def _send(self):
"""
Take metrics from queue and send it to Datadog API
"""
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python-diamond/Diamond | setup.py | get_version | def get_version():
"""
Read the version.txt file to get the new version string
Generate it if version.txt is not available. Generation
is required for pip installs
"""
try:
f = open('version.txt')
except IOError:
os.system("./version.sh > version.txt")
f =... | python | def get_version():
"""
Read the version.txt file to get the new version string
Generate it if version.txt is not available. Generation
is required for pip installs
"""
try:
f = open('version.txt')
except IOError:
os.system("./version.sh > version.txt")
f =... | [
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python-diamond/Diamond | setup.py | pkgPath | def pkgPath(root, path, rpath="/"):
"""
Package up a path recursively
"""
global data_files
if not os.path.exists(path):
return
files = []
for spath in os.listdir(path):
# Ignore test directories
if spath == 'test':
continue
subpath = os.path.j... | python | def pkgPath(root, path, rpath="/"):
"""
Package up a path recursively
"""
global data_files
if not os.path.exists(path):
return
files = []
for spath in os.listdir(path):
# Ignore test directories
if spath == 'test':
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python-diamond/Diamond | src/diamond/handler/statsite.py | StatsiteHandler._send | def _send(self, data):
"""
Send data to statsite. Data that can not be sent will be queued.
"""
retry = self.RETRY
# Attempt to send any data in the queue
while retry > 0:
# Check socket
if not self.socket:
# Log Error
... | python | def _send(self, data):
"""
Send data to statsite. Data that can not be sent will be queued.
"""
retry = self.RETRY
# Attempt to send any data in the queue
while retry > 0:
# Check socket
if not self.socket:
# Log Error
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python-diamond/Diamond | src/diamond/handler/statsite.py | StatsiteHandler._connect | def _connect(self):
"""
Connect to the statsite server
"""
# Create socket
if self.udpport > 0:
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.port = self.udpport
elif self.tcpport > 0:
self.socket = socket.socket(s... | python | def _connect(self):
"""
Connect to the statsite server
"""
# Create socket
if self.udpport > 0:
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.port = self.udpport
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python-diamond/Diamond | src/diamond/utils/classes.py | load_include_path | def load_include_path(paths):
"""
Scan for and add paths to the include path
"""
for path in paths:
# Verify the path is valid
if not os.path.isdir(path):
continue
# Add path to the system path, to avoid name clashes
# with mysql-connector for example ...
... | python | def load_include_path(paths):
"""
Scan for and add paths to the include path
"""
for path in paths:
# Verify the path is valid
if not os.path.isdir(path):
continue
# Add path to the system path, to avoid name clashes
# with mysql-connector for example ...
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python-diamond/Diamond | src/diamond/utils/classes.py | load_dynamic_class | def load_dynamic_class(fqn, subclass):
"""
Dynamically load fqn class and verify it's a subclass of subclass
"""
if not isinstance(fqn, basestring):
return fqn
cls = load_class_from_name(fqn)
if cls == subclass or not issubclass(cls, subclass):
raise TypeError("%s is not a vali... | python | def load_dynamic_class(fqn, subclass):
"""
Dynamically load fqn class and verify it's a subclass of subclass
"""
if not isinstance(fqn, basestring):
return fqn
cls = load_class_from_name(fqn)
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python-diamond/Diamond | src/diamond/utils/classes.py | load_collectors_from_paths | def load_collectors_from_paths(paths):
"""
Scan for collectors to load from path
"""
# Initialize return value
collectors = {}
if paths is None:
return
if isinstance(paths, basestring):
paths = paths.split(',')
paths = map(str.strip, paths)
load_include_path(pa... | python | def load_collectors_from_paths(paths):
"""
Scan for collectors to load from path
"""
# Initialize return value
collectors = {}
if paths is None:
return
if isinstance(paths, basestring):
paths = paths.split(',')
paths = map(str.strip, paths)
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python-diamond/Diamond | src/diamond/utils/classes.py | load_collectors_from_entry_point | def load_collectors_from_entry_point(path):
"""
Load collectors that were installed into an entry_point.
"""
collectors = {}
for ep in pkg_resources.iter_entry_points(path):
try:
mod = ep.load()
except Exception:
logger.error('Failed to import entry_point: %s.... | python | def load_collectors_from_entry_point(path):
"""
Load collectors that were installed into an entry_point.
"""
collectors = {}
for ep in pkg_resources.iter_entry_points(path):
try:
mod = ep.load()
except Exception:
logger.error('Failed to import entry_point: %s.... | [
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python-diamond/Diamond | src/diamond/utils/classes.py | get_collectors_from_module | def get_collectors_from_module(mod):
"""
Locate all of the collector classes within a given module
"""
for attrname in dir(mod):
attr = getattr(mod, attrname)
# Only attempt to load classes that are infact classes
# are Collectors but are not the base Collector class
if (... | python | def get_collectors_from_module(mod):
"""
Locate all of the collector classes within a given module
"""
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python-diamond/Diamond | src/diamond/handler/mysql.py | MySQLHandler._send | def _send(self, data):
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python-diamond/Diamond | src/collectors/nvidia_gpu/nvidia_gpu.py | NvidiaGPUCollector.collect | def collect(self):
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"""
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"""
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python-diamond/Diamond | src/collectors/jcollectd/collectd_network.py | decode_network_values | def decode_network_values(ptype, plen, buf):
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python-diamond/Diamond | src/collectors/jcollectd/collectd_network.py | Reader.receive | def receive(self, poll_interval):
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for s in readable:
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python-diamond/Diamond | src/collectors/jcollectd/collectd_network.py | Reader.decode | def decode(self, poll_interval, buf=None):
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"""
if buf is None:
buf = self.receive(poll_interval)
if buf is None:
return None
return decode_network_packet(buf) | python | def decode(self, poll_interval, buf=None):
"""Decodes a given buffer or the next received packet.
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if buf is None:
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if buf is None:
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python-diamond/Diamond | src/collectors/jcollectd/collectd_network.py | Reader.interpret | def interpret(self, iterable=None, poll_interval=0.2):
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ncclient/ncclient | ncclient/manager.py | make_device_handler | def make_device_handler(device_params):
"""
Create a device handler object that provides device specific parameters and
functions, which are called in various places throughout our code.
If no device_params are defined or the "name" in the parameter dict is not
known then a default handler will be ... | python | def make_device_handler(device_params):
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ncclient/ncclient | ncclient/manager.py | Manager.take_notification | def take_notification(self, block=True, timeout=None):
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If timeout is a number greater than 0, the call will wait that
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ncclient/ncclient | ncclient/operations/third_party/hpcomware/rpc.py | DisplayCommand.request | def request(self, cmds):
"""
Single Execution element is permitted.
cmds can be a list or single command
"""
if isinstance(cmds, list):
cmd = '\n'.join(cmds)
elif isinstance(cmds, str) or isinstance(cmds, unicode):
cmd = cmds
node = etree.... | python | def request(self, cmds):
"""
Single Execution element is permitted.
cmds can be a list or single command
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ncclient/ncclient | ncclient/xml_.py | validated_element | def validated_element(x, tags=None, attrs=None):
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ncclient/ncclient | ncclient/xml_.py | NCElement.tostring | def tostring(self):
"""return a pretty-printed string output for rpc reply"""
parser = etree.XMLParser(remove_blank_text=True)
outputtree = etree.XML(etree.tostring(self.__doc), parser)
return etree.tostring(outputtree, pretty_print=True) | python | def tostring(self):
"""return a pretty-printed string output for rpc reply"""
parser = etree.XMLParser(remove_blank_text=True)
outputtree = etree.XML(etree.tostring(self.__doc), parser)
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ncclient/ncclient | ncclient/xml_.py | NCElement.remove_namespaces | def remove_namespaces(self, rpc_reply):
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"""remove xmlns attributes from rpc reply"""
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ncclient/ncclient | ncclient/operations/retrieve.py | GetSchema.request | def request(self, identifier, version=None, format=None):
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*identifier* name of the schema to be retrieved
*version* version of schema to get
*format* format of the schema to be retrieved, yang is the default
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ncclient/ncclient | ncclient/devices/default.py | DefaultDeviceHandler.is_rpc_error_exempt | def is_rpc_error_exempt(self, error_text):
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ncclient/ncclient | ncclient/devices/nexus.py | NexusDeviceHandler.get_ssh_subsystem_names | def get_ssh_subsystem_names(self):
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Different NXOS versions use different SSH subsystem names for netconf.
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ncclient/ncclient | ncclient/transport/session.py | Session.add_listener | def add_listener(self, listener):
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:type listener: :class:`SessionListener`
"""
self.logger.debug('installing listener %r', listener)
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ncclient/ncclient | ncclient/transport/session.py | Session.remove_listener | def remove_listener(self, listener):
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:type listener: :class:`SessionListener`
"""
self.logger.debug('discarding listener %r', listener)
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ncclient/ncclient | ncclient/operations/subscribe.py | CreateSubscription.request | def request(self, filter=None, stream_name=None, start_time=None, stop_time=None):
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ncclient/ncclient | ncclient/operations/edit.py | DeleteConfig.request | def request(self, target):
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ncclient/ncclient | ncclient/operations/edit.py | CopyConfig.request | def request(self, source, target):
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ncclient/ncclient | ncclient/operations/edit.py | Validate.request | def request(self, source="candidate"):
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ncclient/ncclient | ncclient/operations/lock.py | Lock.request | def request(self, target="candidate"):
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"""
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sub_ele(sub_ele(node, "target"), target)
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ncclient/ncclient | ncclient/transport/ssh.py | SSHSession._parse10 | def _parse10(self):
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ncclient/ncclient | ncclient/operations/util.py | one_of | def one_of(*args):
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for argh in args[i+1:]:
if argh is not None:
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return
... | python | def one_of(*args):
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RJT1990/pyflux | pyflux/arma/arimax.py | ARIMAX._get_scale_and_shape | def _get_scale_and_shape(self, transformed_lvs):
""" Obtains model scale, shape and skewness latent variables
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Transformed latent variable vector
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RJT1990/pyflux | pyflux/arma/arimax.py | ARIMAX._get_scale_and_shape_sim | def _get_scale_and_shape_sim(self, transformed_lvs):
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RJT1990/pyflux | pyflux/arma/arimax.py | ARIMAX._summarize_simulations | def _summarize_simulations(self, mean_values, sim_vector, date_index, h, past_values):
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... | python | def _summarize_simulations(self, mean_values, sim_vector, date_index, h, past_values):
""" Produces forecasted values to plot, along with prediction intervals
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RJT1990/pyflux | pyflux/arma/arimax.py | ARIMAX.normal_neg_loglik | def normal_neg_loglik(self, beta):
""" Calculates the negative log-likelihood of the model for Normal family
Parameters
----------
beta : np.ndarray
Contains untransformed starting values for latent variables
Returns
----------
The negative logliklih... | python | def normal_neg_loglik(self, beta):
""" Calculates the negative log-likelihood of the model for Normal family
Parameters
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beta : np.ndarray
Contains untransformed starting values for latent variables
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RJT1990/pyflux | pyflux/arma/arimax.py | ARIMAX.normal_mb_neg_loglik | def normal_mb_neg_loglik(self, beta, mini_batch):
""" Calculates the negative log-likelihood of the Normal model for a minibatch
Parameters
----------
beta : np.ndarray
Contains untransformed starting values for latent variables
mini_batch : int
Size of ... | python | def normal_mb_neg_loglik(self, beta, mini_batch):
""" Calculates the negative log-likelihood of the Normal model for a minibatch
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beta : np.ndarray
Contains untransformed starting values for latent variables
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.draw_variable | def draw_variable(loc, scale, shape, skewness, nsims):
""" Draws random variables from Skew t distribution
Parameters
----------
loc : float
location parameter for the distribution
scale : float
scale parameter for the distribution
shape : float... | python | def draw_variable(loc, scale, shape, skewness, nsims):
""" Draws random variables from Skew t distribution
Parameters
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loc : float
location parameter for the distribution
scale : float
scale parameter for the distribution
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.first_order_score | def first_order_score(y, mean, scale, shape, skewness):
""" GAS Skew t Update term using gradient only - native Python function
Parameters
----------
y : float
datapoint for the time series
mean : float
location parameter for the Skew t distribution
... | python | def first_order_score(y, mean, scale, shape, skewness):
""" GAS Skew t Update term using gradient only - native Python function
Parameters
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y : float
datapoint for the time series
mean : float
location parameter for the Skew t distribution
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.rvs | def rvs(df, gamma, n):
""" Generates random variables from a Skew t distribution
Parameters
----------
df : float
degrees of freedom parameter
gamma : float
skewness parameter
n : int or list
Number of simulations to perform; if li... | python | def rvs(df, gamma, n):
""" Generates random variables from a Skew t distribution
Parameters
----------
df : float
degrees of freedom parameter
gamma : float
skewness parameter
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.logpdf | def logpdf(self, mu):
"""
Log PDF for Skew t prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- log(p(mu))
"""
if self.transform is not None:
mu = s... | python | def logpdf(self, mu):
"""
Log PDF for Skew t prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- log(p(mu))
"""
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.markov_blanket | def markov_blanket(y, mean, scale, shape, skewness):
""" Markov blanket for each likelihood term
Parameters
----------
y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Skew t distribution
scale : float... | python | def markov_blanket(y, mean, scale, shape, skewness):
""" Markov blanket for each likelihood term
Parameters
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y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Skew t distribution
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.pdf_internal | def pdf_internal(x, df, loc=0.0, scale=1.0, gamma = 1.0):
"""
Raw PDF function for the Skew t distribution
"""
result = np.zeros(x.shape[0])
result[x<0] = 2.0/(gamma + 1.0/gamma)*stats.t.pdf(x=gamma*x[(x-loc) < 0], loc=loc[(x-loc) < 0]*gamma,df=df, scale=scale)
result[x>=... | python | def pdf_internal(x, df, loc=0.0, scale=1.0, gamma = 1.0):
"""
Raw PDF function for the Skew t distribution
"""
result = np.zeros(x.shape[0])
result[x<0] = 2.0/(gamma + 1.0/gamma)*stats.t.pdf(x=gamma*x[(x-loc) < 0], loc=loc[(x-loc) < 0]*gamma,df=df, scale=scale)
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.pdf | def pdf(self, mu):
"""
PDF for Skew t prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- p(mu)
"""
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"""
PDF for Skew t prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
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----------
- p(mu)
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.cdf | def cdf(x, df, loc=0.0, scale=1.0, gamma = 1.0):
"""
CDF function for Skew t distribution
"""
result = np.zeros(x.shape[0])
result[x<0] = 2.0/(np.power(gamma,2) + 1.0)*ss.t.cdf(gamma*(x[x-loc < 0]-loc[x-loc < 0])/scale, df=df)
result[x>=0] = 1.0/(np.power(gamma,2) + 1.0) ... | python | def cdf(x, df, loc=0.0, scale=1.0, gamma = 1.0):
"""
CDF function for Skew t distribution
"""
result = np.zeros(x.shape[0])
result[x<0] = 2.0/(np.power(gamma,2) + 1.0)*ss.t.cdf(gamma*(x[x-loc < 0]-loc[x-loc < 0])/scale, df=df)
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RJT1990/pyflux | pyflux/families/skewt.py | Skewt.ppf | def ppf(q, df, loc=0.0, scale=1.0, gamma = 1.0):
"""
PPF function for Skew t distribution
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result = np.zeros(q.shape[0])
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"""
PPF function for Skew t distribution
"""
result = np.zeros(q.shape[0])
probzero = Skewt.cdf(x=np.zeros(1),loc=np.zeros(1),df=df,gamma=gamma)
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RJT1990/pyflux | pyflux/families/family.py | Family.transform_define | def transform_define(transform):
"""
This function links the user's choice of transformation with the associated numpy function
"""
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elif transform == 'exp':
return np.exp
elif transform == 'logit':
... | python | def transform_define(transform):
"""
This function links the user's choice of transformation with the associated numpy function
"""
if transform == 'tanh':
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elif transform == 'exp':
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RJT1990/pyflux | pyflux/families/family.py | Family.itransform_define | def itransform_define(transform):
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"""
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RJT1990/pyflux | pyflux/gpnarx/gpnarx.py | GPNARX._L | def _L(self, parm):
""" Creates cholesky decomposition of covariance matrix
Parameters
----------
parm : np.array
Contains transformed latent variables
Returns
----------
The cholesky decomposition (L) of K
"""
return np.linalg.chol... | python | def _L(self, parm):
""" Creates cholesky decomposition of covariance matrix
Parameters
----------
parm : np.array
Contains transformed latent variables
Returns
----------
The cholesky decomposition (L) of K
"""
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beta : np.array
Contains untransformed starting values for latent variables
intervals : Boolean
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Contains untransformed starting values for latent variables
intervals : Boolean
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parm : np.array
Transformed latent variable vector
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Plots draws from the posterior predictive density against the data
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nsims : int (default : 1000)
How many draws from the posterior predictive distribution
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Plots draws from the posterior predictive density against the data
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How many draws from the posterior predictive distribution
plot_data boolean
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RJT1990/pyflux | pyflux/arma/arma.py | ARIMA._ar_matrix | def _ar_matrix(self):
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""" Creates the Autoregressive matrix for the model
Returns
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X : np.ndarray
Autoregressive Matrix
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X : float
Covariance matrix for which the prior is being formed over
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- log(p(X))
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"""
Log PDF for Inverse Wishart prior
Parameters
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X : float
Covariance matrix for which the prior is being formed over
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- log(p(X))
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Parameters
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x : float
Covariance matrix for which the prior is being formed over
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----------
- p(x)
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PDF for Inverse Wishart prior
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x : float
Covariance matrix for which the prior is being formed over
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RJT1990/pyflux | pyflux/ssm/nllt.py | NLLT.neg_loglik | def neg_loglik(self, beta):
""" Creates negative loglikelihood of the model
Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
Returns
----------
- Negative loglikelihood
"""
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Parameters
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beta : np.array
Contains untransformed starting values for latent variables
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Parameters
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beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
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beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
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beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
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----------
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beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
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RJT1990/pyflux | pyflux/ssm/nllt.py | NLLT._general_approximating_model | def _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
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Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
T, Z, R, Q : np.array
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RJT1990/pyflux | pyflux/ssm/nllt.py | NLLT.fit | def fit(self, optimizer='RMSProp', iterations=1000, print_progress=True, start_diffuse=False, **kwargs):
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optimizer : string
Stochastic optimizer: either RMSProp or ADAM.
iterations: int
How many iterations to run
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optimizer : string
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RJT1990/pyflux | pyflux/var/var.py | create_design_matrix_2 | def create_design_matrix_2(Z, data, Y_len, lag_no):
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row_count += 1
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RJT1990/pyflux | pyflux/var/var.py | VAR._create_B | def _create_B(self,Y):
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Parameters
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Y : np.array
The dependent variables Y
Returns
----------
The coefficient matrix B
"""
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The coefficient matrix B
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Y : np.array
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----------
The design matrix Z
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The design matrix Z
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RJT1990/pyflux | pyflux/var/var.py | VAR._forecast_mean | def _forecast_mean(self,h,t_params,Y,shock_type=None,shock_index=0,shock_value=None,shock_dir='positive',irf_intervals=False):
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Parameters
----------
h : int
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RJT1990/pyflux | pyflux/var/var.py | VAR._shock_create | def _shock_create(self, h, shock_type, shock_index, shock_value, shock_dir, irf_intervals):
""" Function creates shocks based on desired specification
Parameters
----------
h : int
How many steps ahead to forecast
shock_type : None or str
Type of shock; ... | python | def _shock_create(self, h, shock_type, shock_index, shock_value, shock_dir, irf_intervals):
""" Function creates shocks based on desired specification
Parameters
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h : int
How many steps ahead to forecast
shock_type : None or str
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RJT1990/pyflux | pyflux/var/var.py | VAR.estimator_cov | def estimator_cov(self,method):
""" Creates covariance matrix for the estimators
Parameters
----------
method : str
Estimation method
Returns
----------
A Covariance Matrix
"""
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Parameters
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method : str
Estimation method
Returns
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A Covariance Matrix
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RJT1990/pyflux | pyflux/var/var.py | VAR.ols_covariance | def ols_covariance(self):
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Returns
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The OLS estimate of the covariance matrix
"""
Y = np.array([reg[self.lags:reg.shape[0]] for reg in self.data])
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""" Creates OLS estimate of the covariance matrix
Returns
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The OLS estimate of the covariance matrix
"""
Y = np.array([reg[self.lags:reg.shape[0]] for reg in self.data])
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RJT1990/pyflux | pyflux/var/var.py | VAR.residuals | def residuals(self,Y):
""" Creates the model residuals
Parameters
----------
Y : np.array
The dependent variables Y
Returns
----------
The model residuals
"""
return (Y-np.dot(self._create_B(Y),self._create_Z(Y))) | python | def residuals(self,Y):
""" Creates the model residuals
Parameters
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Y : np.array
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Returns
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The model residuals
"""
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RJT1990/pyflux | pyflux/var/var.py | VAR.construct_wishart | def construct_wishart(self,v,X):
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Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
Returns
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beta : np.array
Contains untransformed starting values for latent variables
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Parameters
----------
data : np.array
Data to be smoothed
beta : np.array
Contains untransformed starting values for latent variables
Returns
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data : np.array
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beta : np.array
Contains untransformed starting values for latent variables
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RJT1990/pyflux | pyflux/tsm.py | TSM._laplace_fit | def _laplace_fit(self,obj_type):
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----------
obj_type : method
Whether a likelihood or a posterior
Returns
----------
None (plots posterior)
"""
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Parameters
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obj_type : method
Whether a likelihood or a posterior
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RJT1990/pyflux | pyflux/tsm.py | TSM._optimize_fit | def _optimize_fit(self, obj_type=None, **kwargs):
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"""
preopt_search = kwargs.get('preopt_search', True) # If user supplied
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"""
preopt_search = kwargs.get('preopt_search', True) # If user supplied
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RJT1990/pyflux | pyflux/tsm.py | TSM.multivariate_neg_logposterior | def multivariate_neg_logposterior(self,beta):
""" Returns negative log posterior, for a model with a covariance matrix
Parameters
----------
beta : np.array
Contains untransformed starting values for latent_variables
Returns
----------
Negative log ... | python | def multivariate_neg_logposterior(self,beta):
""" Returns negative log posterior, for a model with a covariance matrix
Parameters
----------
beta : np.array
Contains untransformed starting values for latent_variables
Returns
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RJT1990/pyflux | pyflux/tsm.py | TSM.shift_dates | def shift_dates(self,h):
""" Auxiliary function for creating dates for forecasts
Parameters
----------
h : int
How many steps to forecast
Returns
----------
A transformed date_index object
"""
date_index = copy.deepcopy(self.index)
... | python | def shift_dates(self,h):
""" Auxiliary function for creating dates for forecasts
Parameters
----------
h : int
How many steps to forecast
Returns
----------
A transformed date_index object
"""
date_index = copy.deepcopy(self.index)
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RJT1990/pyflux | pyflux/tsm.py | TSM.plot_z | def plot_z(self, indices=None,figsize=(15,5),**kwargs):
""" Plots latent variables by calling latent parameters object
Returns
----------
Pretty plot
"""
self.latent_variables.plot_z(indices=indices,figsize=figsize,**kwargs) | python | def plot_z(self, indices=None,figsize=(15,5),**kwargs):
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Returns
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Pretty plot
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RJT1990/pyflux | pyflux/ssm/ndynlin.py | NDynReg.evo_blanket | def evo_blanket(self, beta, alpha):
""" Creates Markov blanket for the variance latent variables
Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
A vector of states
Returns
... | python | def evo_blanket(self, beta, alpha):
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Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
alpha : np.array
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RJT1990/pyflux | pyflux/ssm/ndynlin.py | NDynReg.log_p_blanket | def log_p_blanket(self, beta):
""" Creates complete Markov blanket for latent variables
Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
Returns
----------
Markov blanket for latent variables
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""" Creates complete Markov blanket for latent variables
Parameters
----------
beta : np.array
Contains untransformed starting values for latent variables
Returns
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Markov blanket for latent variables
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RJT1990/pyflux | pyflux/families/exponential.py | Exponential.logpdf | def logpdf(self, mu):
"""
Log PDF for Exponential prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- log(p(mu))
"""
if self.transform is not None:
m... | python | def logpdf(self, mu):
"""
Log PDF for Exponential prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- log(p(mu))
"""
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RJT1990/pyflux | pyflux/families/exponential.py | Exponential.markov_blanket | def markov_blanket(y, mean, scale, shape, skewness):
""" Markov blanket for the Exponential distribution
Parameters
----------
y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Exponential distribution
... | python | def markov_blanket(y, mean, scale, shape, skewness):
""" Markov blanket for the Exponential distribution
Parameters
----------
y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Exponential distribution
... | [
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RJT1990/pyflux | pyflux/families/exponential.py | Exponential.pdf | def pdf(self, mu):
"""
PDF for Exponential prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- p(mu)
"""
if self.transform is not None:
mu = self.tra... | python | def pdf(self, mu):
"""
PDF for Exponential prior
Parameters
----------
mu : float
Latent variable for which the prior is being formed over
Returns
----------
- p(mu)
"""
if self.transform is not None:
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVI.change_parameters | def change_parameters(self,params):
"""
Utility function for changing the approximate distribution parameters
"""
no_of_params = 0
for core_param in range(len(self.q)):
for approx_param in range(self.q[core_param].param_no):
self.q[core_param].vi_chang... | python | def change_parameters(self,params):
"""
Utility function for changing the approximate distribution parameters
"""
no_of_params = 0
for core_param in range(len(self.q)):
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVI.current_parameters | def current_parameters(self):
"""
Obtains an array with the current parameters
"""
current = []
for core_param in range(len(self.q)):
for approx_param in range(self.q[core_param].param_no):
current.append(self.q[core_param].vi_return_param(approx_param... | python | def current_parameters(self):
"""
Obtains an array with the current parameters
"""
current = []
for core_param in range(len(self.q)):
for approx_param in range(self.q[core_param].param_no):
current.append(self.q[core_param].vi_return_param(approx_param... | [
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVI.cv_gradient | def cv_gradient(self,z):
"""
The control variate augmented Monte Carlo gradient estimate
"""
gradient = np.zeros(np.sum(self.approx_param_no))
z_t = z.T
log_q = self.normal_log_q(z.T)
log_p = self.log_p(z.T)
grad_log_q = self.grad_log_q(z)
gr... | python | def cv_gradient(self,z):
"""
The control variate augmented Monte Carlo gradient estimate
"""
gradient = np.zeros(np.sum(self.approx_param_no))
z_t = z.T
log_q = self.normal_log_q(z.T)
log_p = self.log_p(z.T)
grad_log_q = self.grad_log_q(z)
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVI.draw_variables | def draw_variables(self):
"""
Draw parameters from the approximating distributions
"""
z = self.q[0].draw_variable_local(self.sims)
for i in range(1,len(self.q)):
z = np.vstack((z,self.q[i].draw_variable_local(self.sims)))
return z | python | def draw_variables(self):
"""
Draw parameters from the approximating distributions
"""
z = self.q[0].draw_variable_local(self.sims)
for i in range(1,len(self.q)):
z = np.vstack((z,self.q[i].draw_variable_local(self.sims)))
return z | [
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVI.grad_log_q | def grad_log_q(self,z):
"""
The gradients of the approximating distributions
"""
param_count = 0
grad = np.zeros((np.sum(self.approx_param_no),self.sims))
for core_param in range(len(self.q)):
for approx_param in range(self.q[core_param].param_no):
... | python | def grad_log_q(self,z):
"""
The gradients of the approximating distributions
"""
param_count = 0
grad = np.zeros((np.sum(self.approx_param_no),self.sims))
for core_param in range(len(self.q)):
for approx_param in range(self.q[core_param].param_no):
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVIM.print_progress | def print_progress(self, i, current_params):
"""
Prints the current ELBO at every decile of total iterations
"""
for split in range(1,11):
if i == (round(self.iterations/10*split)-1):
post = -self.full_neg_posterior(current_params)
approx = sel... | python | def print_progress(self, i, current_params):
"""
Prints the current ELBO at every decile of total iterations
"""
for split in range(1,11):
if i == (round(self.iterations/10*split)-1):
post = -self.full_neg_posterior(current_params)
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RJT1990/pyflux | pyflux/inference/bbvi.py | BBVIM.run | def run(self):
"""
The core BBVI routine - draws Monte Carlo gradients and uses a stochastic optimizer.
"""
# Initialization assumptions
z = self.draw_normal_initial()
gradient = self.cv_gradient_initial(z)
gradient[np.isnan(gradient)] = 0
variance = np.p... | python | def run(self):
"""
The core BBVI routine - draws Monte Carlo gradients and uses a stochastic optimizer.
"""
# Initialization assumptions
z = self.draw_normal_initial()
gradient = self.cv_gradient_initial(z)
gradient[np.isnan(gradient)] = 0
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RJT1990/pyflux | pyflux/garch/segarchm.py | logpdf | def logpdf(x, shape, loc=0.0, scale=1.0, skewness=1.0):
"""
Log PDF for the Skew-t distribution
Parameters
----------
x : np.array
random variables
shape : float
The degrees of freedom for the skew-t distribution
loc : np.array
The location parameter for the skew-t... | python | def logpdf(x, shape, loc=0.0, scale=1.0, skewness=1.0):
"""
Log PDF for the Skew-t distribution
Parameters
----------
x : np.array
random variables
shape : float
The degrees of freedom for the skew-t distribution
loc : np.array
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Parameters
----------
x : np.array
random variables
shape : float
The degrees of freedom for the skew-t distribution
loc : np.array
The location parameter for the skew-t distribution
scale : float
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