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ask/carrot | carrot/messaging.py | ConsumerSet._declare_consumer | def _declare_consumer(self, consumer, nowait=False):
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if consumer.queue not in self._open_consumers:
# Use the ConsumerSet's consumer by default, but if the
# child consumer has a callbac... | python | def _declare_consumer(self, consumer, nowait=False):
"""Declare consumer so messages can be received from it using
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ask/carrot | carrot/messaging.py | ConsumerSet.consume | def consume(self):
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head = self.consumers[:-1]
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[self._declare_consumer(consumer, nowait=True)
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self._declare_consumer(tail, nowait=False) | python | def consume(self):
"""Declare consumers."""
head = self.consumers[:-1]
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ask/carrot | carrot/messaging.py | ConsumerSet.iterconsume | def iterconsume(self, limit=None):
"""Cycle between all consumers in consume mode.
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"""
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ask/carrot | carrot/messaging.py | ConsumerSet.cancel | def cancel(self):
"""Cancel a running :meth:`iterconsume` session."""
for consumer_tag in self._open_consumers.values():
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self.backend.cancel(consumer_tag)
except KeyError:
pass
self._open_consumers.clear() | python | def cancel(self):
"""Cancel a running :meth:`iterconsume` session."""
for consumer_tag in self._open_consumers.values():
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self.backend.cancel(consumer_tag)
except KeyError:
pass
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hosford42/xcs | build_readme.py | convert_md_to_rst | def convert_md_to_rst(source, destination=None, backup_dir=None):
"""Try to convert the source, an .md (markdown) file, to an .rst
(reStructuredText) file at the destination. If the destination isn't
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hosford42/xcs | build_readme.py | build_readme | def build_readme(base_path=None):
"""Call the conversion routine on README.md to generate README.rst.
Why do all this? Because pypi requires reStructuredText, but markdown
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"""Call the conversion routine on README.md to generate README.rst.
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hosford42/xcs | xcs/scenarios.py | MUXProblem.sense | def sense(self):
"""Return a situation, encoded as a bit string, which represents
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Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
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Usage:
situation = scenario.sense()
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hosford42/xcs | xcs/scenarios.py | MUXProblem.execute | def execute(self, action):
"""Execute the indicated action within the environment and
return the resulting immediate reward dictated by the reward
program.
Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | python | def execute(self, action):
"""Execute the indicated action within the environment and
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immediate_reward = scenario.execute(selected_action)
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hosford42/xcs | xcs/scenarios.py | HaystackProblem.reset | def reset(self):
"""Reset the scenario, starting it over for a new run.
Usage:
if not scenario.more():
scenario.reset()
Arguments: None
Return: None
"""
self.remaining_cycles = self.initial_training_cycles
self.needle_index = random.r... | python | def reset(self):
"""Reset the scenario, starting it over for a new run.
Usage:
if not scenario.more():
scenario.reset()
Arguments: None
Return: None
"""
self.remaining_cycles = self.initial_training_cycles
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hosford42/xcs | xcs/scenarios.py | HaystackProblem.sense | def sense(self):
"""Return a situation, encoded as a bit string, which represents
the observable state of the environment.
Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
Arguments: None
Return:
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hosford42/xcs | xcs/scenarios.py | HaystackProblem.execute | def execute(self, action):
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return the resulting immediate reward dictated by the reward
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Usage:
immediate_reward = scenario.execute(selected_action)
Arguments:
action: The action to be ex... | python | def execute(self, action):
"""Execute the indicated action within the environment and
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hosford42/xcs | xcs/scenarios.py | ScenarioObserver.get_possible_actions | def get_possible_actions(self):
"""Return a sequence containing the possible actions that can be
executed within the environment.
Usage:
possible_actions = scenario.get_possible_actions()
Arguments: None
Return:
A sequence containing the possible actions... | python | def get_possible_actions(self):
"""Return a sequence containing the possible actions that can be
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Usage:
possible_actions = scenario.get_possible_actions()
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hosford42/xcs | xcs/scenarios.py | ScenarioObserver.sense | def sense(self):
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Usage:
situation = scenario.sense()
assert isinstance(situation, BitString)
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hosford42/xcs | xcs/scenarios.py | ScenarioObserver.execute | def execute(self, action):
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return the resulting immediate reward dictated by the reward
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Usage:
immediate_reward = scenario.execute(selected_action)
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hosford42/xcs | xcs/scenarios.py | ScenarioObserver.more | def more(self):
"""Return a Boolean indicating whether additional actions may be
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Usage:
while scenario.more():
situation = scenario.sense()
selected_action = choice(possible_actions)
reward = scenario.... | python | def more(self):
"""Return a Boolean indicating whether additional actions may be
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hosford42/xcs | xcs/scenarios.py | PreClassifiedData.execute | def execute(self, action):
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Usage:
immediate_reward = scenario.execute(selected_action)
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hosford42/xcs | xcs/scenarios.py | UnclassifiedData.get_classifications | def get_classifications(self):
"""Return the classifications made by the algorithm for this
scenario.
Usage:
model.run(scenario, learn=False)
classifications = scenario.get_classifications()
Arguments: None
Return:
An indexable sequence conta... | python | def get_classifications(self):
"""Return the classifications made by the algorithm for this
scenario.
Usage:
model.run(scenario, learn=False)
classifications = scenario.get_classifications()
Arguments: None
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hosford42/xcs | xcs/framework.py | LCSAlgorithm.new_model | def new_model(self, scenario):
"""Create and return a new classifier set initialized for handling
the given scenario.
Usage:
scenario = MUXProblem()
model = algorithm.new_model(scenario)
model.run(scenario, learn=True)
Arguments:
scenario... | python | def new_model(self, scenario):
"""Create and return a new classifier set initialized for handling
the given scenario.
Usage:
scenario = MUXProblem()
model = algorithm.new_model(scenario)
model.run(scenario, learn=True)
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hosford42/xcs | xcs/framework.py | LCSAlgorithm.run | def run(self, scenario):
"""Run the algorithm, utilizing a classifier set to choose the
most appropriate action for each situation produced by the
scenario. Improve the situation/action mapping on each reward
cycle to maximize reward. Return the classifier set that was
created.
... | python | def run(self, scenario):
"""Run the algorithm, utilizing a classifier set to choose the
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hosford42/xcs | xcs/framework.py | ActionSet._compute_prediction | def _compute_prediction(self):
"""Compute the combined prediction and prediction weight for this
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hosford42/xcs | xcs/framework.py | MatchSet.best_prediction | def best_prediction(self):
"""The highest value from among the predictions made by the action
sets in this match set."""
if self._best_prediction is None and self._action_sets:
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"""The highest value from among the predictions made by the action
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hosford42/xcs | xcs/framework.py | MatchSet.best_actions | def best_actions(self):
"""A tuple containing the actions whose action sets have the best
prediction."""
if self._best_actions is None:
best_prediction = self.best_prediction
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"""A tuple containing the actions whose action sets have the best
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hosford42/xcs | xcs/framework.py | MatchSet.select_action | def select_action(self):
"""Select an action according to the action selection strategy of
the associated algorithm. If an action has already been selected,
raise a ValueError instead.
Usage:
if match_set.selected_action is None:
match_set.select_action()
... | python | def select_action(self):
"""Select an action according to the action selection strategy of
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Usage:
if match_set.selected_action is None:
match_set.select_action()
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hosford42/xcs | xcs/framework.py | MatchSet._set_selected_action | def _set_selected_action(self, action):
"""Setter method for the selected_action property."""
assert action in self._action_sets
if self._selected_action is not None:
raise ValueError("The action has already been selected.")
self._selected_action = action | python | def _set_selected_action(self, action):
"""Setter method for the selected_action property."""
assert action in self._action_sets
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raise ValueError("The action has already been selected.")
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hosford42/xcs | xcs/framework.py | MatchSet._set_payoff | def _set_payoff(self, payoff):
"""Setter method for the payoff property."""
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hosford42/xcs | xcs/framework.py | MatchSet.pay | def pay(self, predecessor):
"""If the predecessor is not None, gives the appropriate amount of
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match set's expected future payoff. The predecessor argument should
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hosford42/xcs | xcs/framework.py | MatchSet.apply_payoff | def apply_payoff(self):
"""Apply the payoff that has been accumulated from immediate
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hosford42/xcs | xcs/framework.py | ClassifierSet.discard | def discard(self, rule, count=1):
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if rule in... | python | def discard(self, rule, count=1):
"""Remove one or more instances of a rule from the classifier set.
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hosford42/xcs | xcs/framework.py | ClassifierSet.get | def get(self, rule, default=None):
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hosford42/xcs | xcs/framework.py | ClassifierSet.run | def run(self, scenario, learn=True):
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | ls | def ls(system, user, local, include_missing):
"""List configuration files detected (and/or examined paths)."""
# default action is to list *all* auto-detected files
if not (system or user or local):
system = user = local = True
for path in get_configfile_paths(system=system, user=user, local=l... | python | def ls(system, user, local, include_missing):
"""List configuration files detected (and/or examined paths)."""
# default action is to list *all* auto-detected files
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | inspect | def inspect(config_file, profile):
"""Inspect existing configuration/profile."""
try:
section = load_profile_from_files(
[config_file] if config_file else None, profile)
click.echo("Configuration file: {}".format(config_file if config_file else "auto-detected"))
click.echo(... | python | def inspect(config_file, profile):
"""Inspect existing configuration/profile."""
try:
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click.echo("Configuration file: {}".format(config_file if config_file else "auto-detected"))
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | create | def create(config_file, profile):
"""Create and/or update cloud client configuration file."""
# determine the config file path
if config_file:
click.echo("Using configuration file: {}".format(config_file))
else:
# path not given, try to detect; or use default, but allow user to override... | python | def create(config_file, profile):
"""Create and/or update cloud client configuration file."""
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | _ping | def _ping(config_file, profile, solver_def, request_timeout, polling_timeout, output):
"""Helper method for the ping command that uses `output()` for info output
and raises `CLIError()` on handled errors.
This function is invariant to output format and/or error signaling mechanism.
"""
config = di... | python | def _ping(config_file, profile, solver_def, request_timeout, polling_timeout, output):
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | ping | def ping(config_file, profile, solver_def, json_output, request_timeout, polling_timeout):
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"""Ping the QPU by submitting a single-qubit problem."""
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | solvers | def solvers(config_file, profile, solver_def, list_solvers):
"""Get solver details.
Unless solver name/id specified, fetch and display details for
all online solvers available on the configured endpoint.
"""
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"""Get solver details.
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"""
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dwavesystems/dwave-cloud-client | dwave/cloud/cli.py | sample | def sample(config_file, profile, solver_def, biases, couplings, random_problem,
num_reads, verbose):
"""Submit Ising-formulated problem and return samples."""
# TODO: de-dup wrt ping
def echo(s, maxlen=100):
click.echo(s if verbose else strtrunc(s, maxlen))
try:
client = Cl... | python | def sample(config_file, profile, solver_def, biases, couplings, random_problem,
num_reads, verbose):
"""Submit Ising-formulated problem and return samples."""
# TODO: de-dup wrt ping
def echo(s, maxlen=100):
click.echo(s if verbose else strtrunc(s, maxlen))
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tuxu/python-samplerate | examples/play_modulation.py | get_input_callback | def get_input_callback(samplerate, params, num_samples=256):
"""Return a function that produces samples of a sine.
Parameters
----------
samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
num_samples : int, optional
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samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
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tuxu/python-samplerate | examples/play_modulation.py | get_playback_callback | def get_playback_callback(resampler, samplerate, params):
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resampler
The resampler from which samples are read.
samplerate : float
The sample rate.
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Parameters for FM generation.
"""
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resampler
The resampler from which samples are read.
samplerate : float
The sample rate.
params : dict
Parameters for FM generation.
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tuxu/python-samplerate | examples/play_modulation.py | main | def main(source_samplerate, target_samplerate, params, converter_type):
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver.max_num_reads | def max_num_reads(self, **params):
"""Returns the maximum number of reads for the given solver parameters.
Args:
**params:
Parameters for the sampling method. Relevant to num_reads:
- annealing_time
- readout_thermalization
- ... | python | def max_num_reads(self, **params):
"""Returns the maximum number of reads for the given solver parameters.
Args:
**params:
Parameters for the sampling method. Relevant to num_reads:
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- readout_thermalization
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver.sample_ising | def sample_ising(self, linear, quadratic, **params):
"""Sample from the specified Ising model.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver.sample_qubo | def sample_qubo(self, qubo, **params):
"""Sample from the specified QUBO.
Args:
qubo (dict of (int, int):float): Coefficients of a quadratic unconstrained binary
optimization (QUBO) model.
**params: Parameters for the sampling method, specified per solver.
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"""Sample from the specified QUBO.
Args:
qubo (dict of (int, int):float): Coefficients of a quadratic unconstrained binary
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**params: Parameters for the sampling method, specified per solver.
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver._sample | def _sample(self, type_, linear, quadratic, params):
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linear (list/dict): Linear terms of the model.
quadratic (dict of (int, int):float): Quadratic terms of the model.
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver._format_params | def _format_params(self, type_, params):
"""Reformat some of the parameters for sapi."""
if 'initial_state' in params:
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver.check_problem | def check_problem(self, linear, quadratic):
"""Test if an Ising model matches the graph provided by the solver.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
Returns:
boolean
... | python | def check_problem(self, linear, quadratic):
"""Test if an Ising model matches the graph provided by the solver.
Args:
linear (list/dict): Linear terms of the model (h).
quadratic (dict of (int, int):float): Quadratic terms of the model (J).
Returns:
boolean
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dwavesystems/dwave-cloud-client | dwave/cloud/solver.py | Solver._retrieve_problem | def _retrieve_problem(self, id_):
"""Resume polling for a problem previously submitted.
Args:
id_: Identification of the query.
Returns:
:obj: `Future`
"""
future = Future(self, id_, self.return_matrix, None)
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ret... | python | def _retrieve_problem(self, id_):
"""Resume polling for a problem previously submitted.
Args:
id_: Identification of the query.
Returns:
:obj: `Future`
"""
future = Future(self, id_, self.return_matrix, None)
self.client._poll(future)
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tuxu/python-samplerate | samplerate/converters.py | _get_converter_type | def _get_converter_type(identifier):
"""Return the converter type for `identifier`."""
if isinstance(identifier, str):
return ConverterType[identifier]
if isinstance(identifier, ConverterType):
return identifier
return ConverterType(identifier) | python | def _get_converter_type(identifier):
"""Return the converter type for `identifier`."""
if isinstance(identifier, str):
return ConverterType[identifier]
if isinstance(identifier, ConverterType):
return identifier
return ConverterType(identifier) | [
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tuxu/python-samplerate | samplerate/converters.py | resample | def resample(input_data, ratio, converter_type='sinc_best', verbose=False):
"""Resample the signal in `input_data` at once.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
Input data with several channels is repre... | python | def resample(input_data, ratio, converter_type='sinc_best', verbose=False):
"""Resample the signal in `input_data` at once.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
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tuxu/python-samplerate | samplerate/converters.py | Resampler.set_ratio | def set_ratio(self, new_ratio):
"""Set a new conversion ratio immediately."""
from samplerate.lowlevel import src_set_ratio
return src_set_ratio(self._state, new_ratio) | python | def set_ratio(self, new_ratio):
"""Set a new conversion ratio immediately."""
from samplerate.lowlevel import src_set_ratio
return src_set_ratio(self._state, new_ratio) | [
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tuxu/python-samplerate | samplerate/converters.py | Resampler.process | def process(self, input_data, ratio, end_of_input=False, verbose=False):
"""Resample the signal in `input_data`.
Parameters
----------
input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
Input data with several chan... | python | def process(self, input_data, ratio, end_of_input=False, verbose=False):
"""Resample the signal in `input_data`.
Parameters
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input_data : ndarray
Input data. A single channel is provided as a 1D array of `num_frames` length.
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tuxu/python-samplerate | samplerate/converters.py | CallbackResampler._create | def _create(self):
"""Create new callback resampler."""
from samplerate.lowlevel import ffi, src_callback_new, src_delete
from samplerate.exceptions import ResamplingError
state, handle, error = src_callback_new(
self._callback, self._converter_type.value, self._channels)
... | python | def _create(self):
"""Create new callback resampler."""
from samplerate.lowlevel import ffi, src_callback_new, src_delete
from samplerate.exceptions import ResamplingError
state, handle, error = src_callback_new(
self._callback, self._converter_type.value, self._channels)
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tuxu/python-samplerate | samplerate/converters.py | CallbackResampler.set_starting_ratio | def set_starting_ratio(self, ratio):
""" Set the starting conversion ratio for the next `read` call. """
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if self._state is None:
self._create()
src_set_ratio(self._state, ratio)
self.ratio = ratio | python | def set_starting_ratio(self, ratio):
""" Set the starting conversion ratio for the next `read` call. """
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self._create()
src_set_ratio(self._state, ratio)
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tuxu/python-samplerate | samplerate/converters.py | CallbackResampler.reset | def reset(self):
"""Reset state."""
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
src_reset(self._state) | python | def reset(self):
"""Reset state."""
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
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tuxu/python-samplerate | samplerate/converters.py | CallbackResampler.read | def read(self, num_frames):
"""Read a number of frames from the resampler.
Parameters
----------
num_frames : int
Number of frames to read.
Returns
-------
output_data : ndarray
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"""Read a number of frames from the resampler.
Parameters
----------
num_frames : int
Number of frames to read.
Returns
-------
output_data : ndarray
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chrisspen/dtree | dtree.py | get_variance | def get_variance(seq):
"""
Batch variance calculation.
"""
m = get_mean(seq)
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"""
Batch variance calculation.
"""
m = get_mean(seq)
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chrisspen/dtree | dtree.py | mean_absolute_error | def mean_absolute_error(seq, correct):
"""
Batch mean absolute error calculation.
"""
assert len(seq) == len(correct)
diffs = [abs(a-b) for a, b in zip(seq, correct)]
return sum(diffs)/float(len(diffs)) | python | def mean_absolute_error(seq, correct):
"""
Batch mean absolute error calculation.
"""
assert len(seq) == len(correct)
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chrisspen/dtree | dtree.py | normalize | def normalize(seq):
"""
Scales each number in the sequence so that the sum of all numbers equals 1.
"""
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return [v/s for v in seq] | python | def normalize(seq):
"""
Scales each number in the sequence so that the sum of all numbers equals 1.
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s = float(sum(seq))
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chrisspen/dtree | dtree.py | normcdf | def normcdf(x, mu, sigma):
"""
Describes the probability that a real-valued random variable X with a given
probability distribution will be found at a value less than or equal to X
in a normal distribution.
http://en.wikipedia.org/wiki/Cumulative_distribution_function
"""
t = x-mu
y... | python | def normcdf(x, mu, sigma):
"""
Describes the probability that a real-valued random variable X with a given
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http://en.wikipedia.org/wiki/Cumulative_distribution_function
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chrisspen/dtree | dtree.py | normpdf | def normpdf(x, mu, sigma):
"""
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http://en.wikipedia.org/wiki/Probability_density_function
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Describes the relative likelihood that a real-valued random variable X will
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http://en.wikipedia.org/wiki/Probability_density_function
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chrisspen/dtree | dtree.py | entropy | def entropy(data, class_attr=None, method=DEFAULT_DISCRETE_METRIC):
"""
Calculates the entropy of the attribute attr in given data set data.
Parameters:
data<dict|list> :=
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"""
Calculates the entropy of the attribute attr in given data set data.
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data<dict|list> :=
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chrisspen/dtree | dtree.py | entropy_variance | def entropy_variance(data, class_attr=None,
method=DEFAULT_CONTINUOUS_METRIC):
"""
Calculates the variance fo a continuous class attribute, to be used as an
entropy metric.
"""
assert method in CONTINUOUS_METRICS, "Unknown entropy variance metric: %s" % (method,)
assert (class_attr is None a... | python | def entropy_variance(data, class_attr=None,
method=DEFAULT_CONTINUOUS_METRIC):
"""
Calculates the variance fo a continuous class attribute, to be used as an
entropy metric.
"""
assert method in CONTINUOUS_METRICS, "Unknown entropy variance metric: %s" % (method,)
assert (class_attr is None a... | [
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chrisspen/dtree | dtree.py | get_gain | def get_gain(data, attr, class_attr,
method=DEFAULT_DISCRETE_METRIC,
only_sub=0, prefer_fewer_values=False, entropy_func=None):
"""
Calculates the information gain (reduction in entropy) that would
result by splitting the data on the chosen attribute (attr).
Parameters:
prefer_fewe... | python | def get_gain(data, attr, class_attr,
method=DEFAULT_DISCRETE_METRIC,
only_sub=0, prefer_fewer_values=False, entropy_func=None):
"""
Calculates the information gain (reduction in entropy) that would
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chrisspen/dtree | dtree.py | majority_value | def majority_value(data, class_attr):
"""
Creates a list of all values in the target attribute for each record
in the data list object, and returns the value that appears in this list
the most frequently.
"""
if is_continuous(data[0][class_attr]):
return CDist(seq=[record[class_attr] for... | python | def majority_value(data, class_attr):
"""
Creates a list of all values in the target attribute for each record
in the data list object, and returns the value that appears in this list
the most frequently.
"""
if is_continuous(data[0][class_attr]):
return CDist(seq=[record[class_attr] for... | [
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chrisspen/dtree | dtree.py | most_frequent | def most_frequent(lst):
"""
Returns the item that appears most frequently in the given list.
"""
lst = lst[:]
highest_freq = 0
most_freq = None
for val in unique(lst):
if lst.count(val) > highest_freq:
most_freq = val
highest_freq = lst.count(val)
... | python | def most_frequent(lst):
"""
Returns the item that appears most frequently in the given list.
"""
lst = lst[:]
highest_freq = 0
most_freq = None
for val in unique(lst):
if lst.count(val) > highest_freq:
most_freq = val
highest_freq = lst.count(val)
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chrisspen/dtree | dtree.py | unique | def unique(lst):
"""
Returns a list made up of the unique values found in lst. i.e., it
removes the redundant values in lst.
"""
lst = lst[:]
unique_lst = []
# Cycle through the list and add each value to the unique list only once.
for item in lst:
if unique_lst.count(item) <= ... | python | def unique(lst):
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Returns a list made up of the unique values found in lst. i.e., it
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"""
lst = lst[:]
unique_lst = []
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chrisspen/dtree | dtree.py | choose_attribute | def choose_attribute(data, attributes, class_attr, fitness, method):
"""
Cycles through all the attributes and returns the attribute with the
highest information gain (or lowest entropy).
"""
best = (-1e999999, None)
for attr in attributes:
if attr == class_attr:
continue
... | python | def choose_attribute(data, attributes, class_attr, fitness, method):
"""
Cycles through all the attributes and returns the attribute with the
highest information gain (or lowest entropy).
"""
best = (-1e999999, None)
for attr in attributes:
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chrisspen/dtree | dtree.py | create_decision_tree | def create_decision_tree(data, attributes, class_attr, fitness_func, wrapper, **kwargs):
"""
Returns a new decision tree based on the examples given.
"""
split_attr = kwargs.get('split_attr', None)
split_val = kwargs.get('split_val', None)
assert class_attr not in attributes
node =... | python | def create_decision_tree(data, attributes, class_attr, fitness_func, wrapper, **kwargs):
"""
Returns a new decision tree based on the examples given.
"""
split_attr = kwargs.get('split_attr', None)
split_val = kwargs.get('split_val', None)
assert class_attr not in attributes
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chrisspen/dtree | dtree.py | DDist.add | def add(self, k, count=1):
"""
Increments the count for the given element.
"""
self.counts[k] += count
self.total += count | python | def add(self, k, count=1):
"""
Increments the count for the given element.
"""
self.counts[k] += count
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chrisspen/dtree | dtree.py | DDist.best | def best(self):
"""
Returns the element with the highest probability.
"""
b = (-1e999999, None)
for k, c in iteritems(self.counts):
b = max(b, (c, k))
return b[1] | python | def best(self):
"""
Returns the element with the highest probability.
"""
b = (-1e999999, None)
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b = max(b, (c, k))
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chrisspen/dtree | dtree.py | DDist.probs | def probs(self):
"""
Returns a list of probabilities for all elements in the form
[(value1,prob1),(value2,prob2),...].
"""
return [
(k, self.counts[k]/float(self.total))
for k in iterkeys(self.counts)
] | python | def probs(self):
"""
Returns a list of probabilities for all elements in the form
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return [
(k, self.counts[k]/float(self.total))
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chrisspen/dtree | dtree.py | DDist.update | def update(self, dist):
"""
Adds the given distribution's counts to the current distribution.
"""
assert isinstance(dist, DDist)
for k, c in iteritems(dist.counts):
self.counts[k] += c
self.total += dist.total | python | def update(self, dist):
"""
Adds the given distribution's counts to the current distribution.
"""
assert isinstance(dist, DDist)
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self.counts[k] += c
self.total += dist.total | [
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chrisspen/dtree | dtree.py | CDist.probability_lt | def probability_lt(self, x):
"""
Returns the probability of a random variable being less than the
given value.
"""
if self.mean is None:
return
return normdist(x=x, mu=self.mean, sigma=self.standard_deviation) | python | def probability_lt(self, x):
"""
Returns the probability of a random variable being less than the
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"""
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return
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chrisspen/dtree | dtree.py | CDist.probability_in | def probability_in(self, a, b):
"""
Returns the probability of a random variable falling between the given
values.
"""
if self.mean is None:
return
p1 = normdist(x=a, mu=self.mean, sigma=self.standard_deviation)
p2 = normdist(x=b, mu=self.mean, sigma=s... | python | def probability_in(self, a, b):
"""
Returns the probability of a random variable falling between the given
values.
"""
if self.mean is None:
return
p1 = normdist(x=a, mu=self.mean, sigma=self.standard_deviation)
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chrisspen/dtree | dtree.py | CDist.probability_gt | def probability_gt(self, x):
"""
Returns the probability of a random variable being greater than the
given value.
"""
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return
p = normdist(x=x, mu=self.mean, sigma=self.standard_deviation)
return 1-p | python | def probability_gt(self, x):
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Returns the probability of a random variable being greater than the
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"""
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chrisspen/dtree | dtree.py | Data.copy_no_data | def copy_no_data(self):
"""
Returns a copy of the object without any data.
"""
return type(self)(
[],
order=list(self.header_modes),
types=self.header_types.copy(),
modes=self.header_modes.copy()) | python | def copy_no_data(self):
"""
Returns a copy of the object without any data.
"""
return type(self)(
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chrisspen/dtree | dtree.py | Data.is_valid | def is_valid(self, name, value):
"""
Returns true if the given value matches the type for the given name
according to the schema.
Returns false otherwise.
"""
if name not in self.header_types:
return False
t = self.header_types[name]
if t == AT... | python | def is_valid(self, name, value):
"""
Returns true if the given value matches the type for the given name
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Returns false otherwise.
"""
if name not in self.header_types:
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chrisspen/dtree | dtree.py | Data._read_header | def _read_header(self):
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"""
if not self.filename or self.header_types:
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"""
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... | python | def validate_row(self, row):
"""
Ensure each element in the row matches the schema.
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if isinstance(row, (tuple, list)):
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chrisspen/dtree | dtree.py | Data.split | def split(self, ratio=0.5, leave_one_out=False):
"""
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The first instance will contain the ratio of data specified.
The second instance will contain the remaining r... | python | def split(self, ratio=0.5, leave_one_out=False):
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chrisspen/dtree | dtree.py | Node._get_attribute_value_for_node | def _get_attribute_value_for_node(self, record):
"""
Gets the closest value for the current node's attribute matching the
given record.
"""
# Abort if this node has not get split on an attribute.
if self.attr_name is None:
return
# O... | python | def _get_attribute_value_for_node(self, record):
"""
Gets the closest value for the current node's attribute matching the
given record.
"""
# Abort if this node has not get split on an attribute.
if self.attr_name is None:
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chrisspen/dtree | dtree.py | Node.get_values | def get_values(self, attr_name):
"""
Retrieves the unique set of values seen for the given attribute
at this node.
"""
ret = list(self._attr_value_cdist[attr_name].keys()) \
+ list(self._attr_value_counts[attr_name].keys()) \
+ list(self._branches.keys())
... | python | def get_values(self, attr_name):
"""
Retrieves the unique set of values seen for the given attribute
at this node.
"""
ret = list(self._attr_value_cdist[attr_name].keys()) \
+ list(self._attr_value_counts[attr_name].keys()) \
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chrisspen/dtree | dtree.py | Node.get_best_splitting_attr | def get_best_splitting_attr(self):
"""
Returns the name of the attribute with the highest gain.
"""
best = (-1e999999, None)
for attr in self.attributes:
best = max(best, (self.get_gain(attr), attr))
best_gain, best_attr = best
return best_attr | python | def get_best_splitting_attr(self):
"""
Returns the name of the attribute with the highest gain.
"""
best = (-1e999999, None)
for attr in self.attributes:
best = max(best, (self.get_gain(attr), attr))
best_gain, best_attr = best
return best_attr | [
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chrisspen/dtree | dtree.py | Node.get_entropy | def get_entropy(self, attr_name=None, attr_value=None):
"""
Calculates the entropy of a specific attribute/value combination.
"""
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# Calculate variance of class attribute.
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chrisspen/dtree | dtree.py | Node.get_gain | def get_gain(self, attr_name):
"""
Calculates the information gain from splitting on the given attribute.
"""
subset_entropy = 0.0
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chrisspen/dtree | dtree.py | Node.get_value_ddist | def get_value_ddist(self, attr_name, attr_value):
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Returns the class value probability distribution of the given
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"""
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chrisspen/dtree | dtree.py | Node.get_value_prob | def get_value_prob(self, attr_name, value):
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chrisspen/dtree | dtree.py | Node.predict | def predict(self, record, depth=0):
"""
Returns the estimated value of the class attribute for the given
record.
"""
# Check if we're ready to predict.
if not self.ready_to_predict:
raise NodeNotReadyToPredict
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Returns the estimated value of the class attribute for the given
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chrisspen/dtree | dtree.py | Node.ready_to_split | def ready_to_split(self):
"""
Returns true if this node is ready to branch off additional nodes.
Returns false otherwise.
"""
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threshold = self._tree.leaf_threshold
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... | python | def ready_to_split(self):
"""
Returns true if this node is ready to branch off additional nodes.
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chrisspen/dtree | dtree.py | Node.set_leaf_dist | def set_leaf_dist(self, attr_value, dist):
"""
Sets the probability distribution at a leaf node.
"""
assert self.attr_name
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"""
Sets the probability distribution at a leaf node.
"""
assert self.attr_name
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chrisspen/dtree | dtree.py | Node.train | def train(self, record):
"""
Incrementally update the statistics at this node.
"""
self.n += 1
class_attr = self.tree.data.class_attribute_name
class_value = record[class_attr]
# Update class statistics.
is_con = self.tree.data.is_continuous_class... | python | def train(self, record):
"""
Incrementally update the statistics at this node.
"""
self.n += 1
class_attr = self.tree.data.class_attribute_name
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chrisspen/dtree | dtree.py | Tree.build | def build(cls, data, *args, **kwargs):
"""
Constructs a classification or regression tree in a single batch by
analyzing the given data.
"""
assert isinstance(data, Data)
if data.is_continuous_class:
fitness_func = gain_variance
else:
fitne... | python | def build(cls, data, *args, **kwargs):
"""
Constructs a classification or regression tree in a single batch by
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chrisspen/dtree | dtree.py | Tree.out_of_bag_mae | def out_of_bag_mae(self):
"""
Returns the mean absolute error for predictions on the out-of-bag
samples.
"""
if not self._out_of_bag_mae_clean:
try:
self._out_of_bag_mae = self.test(self.out_of_bag_samples)
self._out_of_bag_mae_clean = ... | python | def out_of_bag_mae(self):
"""
Returns the mean absolute error for predictions on the out-of-bag
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"""
if not self._out_of_bag_mae_clean:
try:
self._out_of_bag_mae = self.test(self.out_of_bag_samples)
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] | train | https://github.com/chrisspen/dtree/blob/9e9c9992b22ad9a7e296af7e6837666b05db43ef/dtree.py#L1331-L1342 |
chrisspen/dtree | dtree.py | Tree.out_of_bag_samples | def out_of_bag_samples(self):
"""
Returns the out-of-bag samples list, inside a wrapper to keep track
of modifications.
"""
#TODO:replace with more a generic pass-through wrapper?
class O(object):
def __init__(self, tree):
self.tree = tree
... | python | def out_of_bag_samples(self):
"""
Returns the out-of-bag samples list, inside a wrapper to keep track
of modifications.
"""
#TODO:replace with more a generic pass-through wrapper?
class O(object):
def __init__(self, tree):
self.tree = tree
... | [
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chrisspen/dtree | dtree.py | Tree.set_missing_value_policy | def set_missing_value_policy(self, policy, target_attr_name=None):
"""
Sets the behavior for one or all attributes to use when traversing the
tree using a query vector and it encounters a branch that does not
exist.
"""
assert policy in MISSING_VALUE_POLICIES, \
... | python | def set_missing_value_policy(self, policy, target_attr_name=None):
"""
Sets the behavior for one or all attributes to use when traversing the
tree using a query vector and it encounters a branch that does not
exist.
"""
assert policy in MISSING_VALUE_POLICIES, \
... | [
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chrisspen/dtree | dtree.py | Tree.train | def train(self, record):
"""
Incrementally updates the tree with the given sample record.
"""
assert self.data.class_attribute_name in record, \
"The class attribute must be present in the record."
record = record.copy()
self.sample_count += 1
self.tre... | python | def train(self, record):
"""
Incrementally updates the tree with the given sample record.
"""
assert self.data.class_attribute_name in record, \
"The class attribute must be present in the record."
record = record.copy()
self.sample_count += 1
self.tre... | [
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chrisspen/dtree | dtree.py | Forest._fell_trees | def _fell_trees(self):
"""
Removes trees from the forest according to the specified fell method.
"""
if callable(self.fell_method):
for tree in self.fell_method(list(self.trees)):
self.trees.remove(tree) | python | def _fell_trees(self):
"""
Removes trees from the forest according to the specified fell method.
"""
if callable(self.fell_method):
for tree in self.fell_method(list(self.trees)):
self.trees.remove(tree) | [
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chrisspen/dtree | dtree.py | Forest._get_best_prediction | def _get_best_prediction(self, record, train=True):
"""
Gets the prediction from the tree with the lowest mean absolute error.
"""
if not self.trees:
return
best = (+1e999999, None)
for tree in self.trees:
best = min(best, (tree.mae.mean, tree))
... | python | def _get_best_prediction(self, record, train=True):
"""
Gets the prediction from the tree with the lowest mean absolute error.
"""
if not self.trees:
return
best = (+1e999999, None)
for tree in self.trees:
best = min(best, (tree.mae.mean, tree))
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] | train | https://github.com/chrisspen/dtree/blob/9e9c9992b22ad9a7e296af7e6837666b05db43ef/dtree.py#L1478-L1489 |
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