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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Solver._format_params | Reformat some of the parameters for sapi. | dwave/cloud/solver.py | def _format_params(self, type_, params):
"""Reformat some of the parameters for sapi."""
if 'initial_state' in params:
# NB: at this moment the error raised when initial_state does not match lin/quad (in
# active qubits) is not very informative, but there is also no clean way to ... | def _format_params(self, type_, params):
"""Reformat some of the parameters for sapi."""
if 'initial_state' in params:
# NB: at this moment the error raised when initial_state does not match lin/quad (in
# active qubits) is not very informative, but there is also no clean way to ... | [
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train | Solver.check_problem | 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
Examples:
This example creates a c... | dwave/cloud/solver.py | 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
... | 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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train | Solver._retrieve_problem | Resume polling for a problem previously submitted.
Args:
id_: Identification of the query.
Returns:
:obj: `Future` | dwave/cloud/solver.py | 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)
ret... | 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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train | _get_converter_type | Return the converter type for `identifier`. | samplerate/converters.py | 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) | 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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train | resample | 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 represented as a 2D array of shape
(`num_frames`, `num_channels`). For use with ... | samplerate/converters.py | 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... | 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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train | Resampler.set_ratio | Set a new conversion ratio immediately. | samplerate/converters.py | 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) | 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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train | Resampler.process | 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 channels is represented as a 2D array of shape
(`num_frames`, `num_channels`... | samplerate/converters.py | 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... | 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.
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train | CallbackResampler._create | Create new callback resampler. | samplerate/converters.py | 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)
... | 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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train | CallbackResampler.set_starting_ratio | Set the starting conversion ratio for the next `read` call. | samplerate/converters.py | def set_starting_ratio(self, ratio):
""" Set the starting conversion ratio for the next `read` call. """
from samplerate.lowlevel import src_set_ratio
if self._state is None:
self._create()
src_set_ratio(self._state, ratio)
self.ratio = ratio | def set_starting_ratio(self, ratio):
""" Set the starting conversion ratio for the next `read` call. """
from samplerate.lowlevel import src_set_ratio
if self._state is None:
self._create()
src_set_ratio(self._state, ratio)
self.ratio = ratio | [
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train | CallbackResampler.reset | Reset state. | samplerate/converters.py | def reset(self):
"""Reset state."""
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
src_reset(self._state) | def reset(self):
"""Reset state."""
from samplerate.lowlevel import src_reset
if self._state is None:
self._create()
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train | CallbackResampler.read | Read a number of frames from the resampler.
Parameters
----------
num_frames : int
Number of frames to read.
Returns
-------
output_data : ndarray
Resampled frames as a (`num_output_frames`, `num_channels`) or
(`num_output_frames`,) a... | samplerate/converters.py | 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
Resampled frames as a (`num_output_frames`, `num_channels`) ... | 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
Resampled frames as a (`num_output_frames`, `num_channels`) ... | [
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train | get_variance | Batch variance calculation. | dtree.py | def get_variance(seq):
"""
Batch variance calculation.
"""
m = get_mean(seq)
return sum((v-m)**2 for v in seq)/float(len(seq)) | def get_variance(seq):
"""
Batch variance calculation.
"""
m = get_mean(seq)
return sum((v-m)**2 for v in seq)/float(len(seq)) | [
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train | mean_absolute_error | Batch mean absolute error calculation. | dtree.py | 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)) | 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)) | [
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train | normalize | Scales each number in the sequence so that the sum of all numbers equals 1. | dtree.py | def normalize(seq):
"""
Scales each number in the sequence so that the sum of all numbers equals 1.
"""
s = float(sum(seq))
return [v/s for v in seq] | 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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train | normcdf | 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 | dtree.py | 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... | 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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train | normpdf | Describes the relative likelihood that a real-valued random variable X will
take on a given value.
http://en.wikipedia.org/wiki/Probability_density_function | dtree.py | def normpdf(x, mu, sigma):
"""
Describes the relative likelihood that a real-valued random variable X will
take on a given value.
http://en.wikipedia.org/wiki/Probability_density_function
"""
u = (x-mu)/abs(sigma)
y = (1/(math.sqrt(2*pi)*abs(sigma)))*math.exp(-u*u/2)
return y | def normpdf(x, mu, sigma):
"""
Describes the relative likelihood that a real-valued random variable X will
take on a given value.
http://en.wikipedia.org/wiki/Probability_density_function
"""
u = (x-mu)/abs(sigma)
y = (1/(math.sqrt(2*pi)*abs(sigma)))*math.exp(-u*u/2)
return y | [
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train | entropy | Calculates the entropy of the attribute attr in given data set data.
Parameters:
data<dict|list> :=
if dict, treated as value counts of the given attribute name
if list, treated as a raw list from which the value counts will be generated
attr<string> := the name of the class attribute | dtree.py | 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> :=
if dict, treated as value counts of the given attribute name
if list, treated as a raw list from which the valu... | 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> :=
if dict, treated as value counts of the given attribute name
if list, treated as a raw list from which the valu... | [
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train | entropy_variance | Calculates the variance fo a continuous class attribute, to be used as an
entropy metric. | dtree.py | 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... | 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,)
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train | get_gain | Calculates the information gain (reduction in entropy) that would
result by splitting the data on the chosen attribute (attr).
Parameters:
prefer_fewer_values := Weights the gain by the count of the attribute's
unique values. If multiple attributes have the same gain, but one has
s... | dtree.py | 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... | 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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train | majority_value | 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. | dtree.py | 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]):
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"""
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]):
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train | most_frequent | Returns the item that appears most frequently in the given list. | dtree.py | 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:
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highest_freq = lst.count(val)
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"""
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:
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train | unique | Returns a list made up of the unique values found in lst. i.e., it
removes the redundant values in lst. | dtree.py | def unique(lst):
"""
Returns a list made up of the unique values found in lst. i.e., it
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"""
lst = lst[:]
unique_lst = []
# Cycle through the list and add each value to the unique list only once.
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"""
Returns a list made up of the unique values found in lst. i.e., it
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train | choose_attribute | Cycles through all the attributes and returns the attribute with the
highest information gain (or lowest entropy). | dtree.py | 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
... | 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:
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train | create_decision_tree | Returns a new decision tree based on the examples given. | dtree.py | 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 =... | 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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train | DDist.add | Increments the count for the given element. | dtree.py | def add(self, k, count=1):
"""
Increments the count for the given element.
"""
self.counts[k] += count
self.total += count | def add(self, k, count=1):
"""
Increments the count for the given element.
"""
self.counts[k] += count
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train | DDist.best | Returns the element with the highest probability. | dtree.py | 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] | def best(self):
"""
Returns the element with the highest probability.
"""
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b = max(b, (c, k))
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train | DDist.probs | Returns a list of probabilities for all elements in the form
[(value1,prob1),(value2,prob2),...]. | dtree.py | 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))
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"""
Returns a list of probabilities for all elements in the form
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return [
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train | DDist.update | Adds the given distribution's counts to the current distribution. | dtree.py | 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 | 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
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train | CDist.probability_lt | Returns the probability of a random variable being less than the
given value. | dtree.py | 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) | def probability_lt(self, x):
"""
Returns the probability of a random variable being less than the
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"""
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train | CDist.probability_in | Returns the probability of a random variable falling between the given
values. | dtree.py | 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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"""
Returns the probability of a random variable falling between the given
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"""
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... | [
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train | CDist.probability_gt | Returns the probability of a random variable being greater than the
given value. | dtree.py | def probability_gt(self, x):
"""
Returns the probability of a random variable being greater than the
given value.
"""
if self.mean is None:
return
p = normdist(x=x, mu=self.mean, sigma=self.standard_deviation)
return 1-p | def probability_gt(self, x):
"""
Returns the probability of a random variable being greater than the
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"""
if self.mean is None:
return
p = normdist(x=x, mu=self.mean, sigma=self.standard_deviation)
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train | Data.validate_row | Ensure each element in the row matches the schema. | dtree.py | def validate_row(self, row):
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train | Node._get_attribute_value_for_node | Gets the closest value for the current node's attribute matching the
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train | Node.get_best_splitting_attr | Returns the name of the attribute with the highest gain. | dtree.py | def get_best_splitting_attr(self):
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train | Node.get_value_prob | Returns the value probability of the given attribute at this node. | dtree.py | def get_value_prob(self, attr_name, value):
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train | Node.predict | Returns the estimated value of the class attribute for the given
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Returns the estimated value of the class attribute for the given
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"""
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train | Node.ready_to_split | Returns true if this node is ready to branch off additional nodes.
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train | Node.set_leaf_dist | Sets the probability distribution at a leaf node. | dtree.py | def set_leaf_dist(self, attr_value, dist):
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train | Node.train | Incrementally update the statistics at this node. | dtree.py | 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... | 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]
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train | Tree.build | Constructs a classification or regression tree in a single batch by
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"""
Constructs a classification or regression tree in a single batch by
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"""
assert isinstance(data, Data)
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"""
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train | Tree.out_of_bag_mae | Returns the mean absolute error for predictions on the out-of-bag
samples. | dtree.py | 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 = ... | 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:
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self._out_of_bag_mae = self.test(self.out_of_bag_samples)
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train | Tree.out_of_bag_samples | Returns the out-of-bag samples list, inside a wrapper to keep track
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"""
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"""
#TODO:replace with more a generic pass-through wrapper?
class O(object):
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self.tree = tree
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train | Tree.set_missing_value_policy | 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. | dtree.py | 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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train | Tree.train | Incrementally updates the tree with the given sample record. | dtree.py | def train(self, record):
"""
Incrementally updates the tree with the given sample record.
"""
assert self.data.class_attribute_name in record, \
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record = record.copy()
self.sample_count += 1
self.tre... | def train(self, record):
"""
Incrementally updates the tree with the given sample record.
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assert self.data.class_attribute_name in record, \
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train | Forest._fell_trees | Removes trees from the forest according to the specified fell method. | dtree.py | 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) | def _fell_trees(self):
"""
Removes trees from the forest according to the specified fell method.
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train | Forest._get_best_prediction | Gets the prediction from the tree with the lowest mean absolute error. | dtree.py | 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))
... | def _get_best_prediction(self, record, train=True):
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Gets the prediction from the tree with the lowest mean absolute error.
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if not self.trees:
return
best = (+1e999999, None)
for tree in self.trees:
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train | Forest.best_oob_mae_weight | Returns weights so that the tree with smallest out-of-bag mean absolute error | dtree.py | def best_oob_mae_weight(trees):
"""
Returns weights so that the tree with smallest out-of-bag mean absolute error
"""
best = (+1e999999, None)
for tree in trees:
oob_mae = tree.out_of_bag_mae
if oob_mae is None or oob_mae.mean is None:
cont... | def best_oob_mae_weight(trees):
"""
Returns weights so that the tree with smallest out-of-bag mean absolute error
"""
best = (+1e999999, None)
for tree in trees:
oob_mae = tree.out_of_bag_mae
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train | Forest.mean_oob_mae_weight | Returns weights proportional to the out-of-bag mean absolute error for each tree. | dtree.py | def mean_oob_mae_weight(trees):
"""
Returns weights proportional to the out-of-bag mean absolute error for each tree.
"""
weights = []
active_trees = []
for tree in trees:
oob_mae = tree.out_of_bag_mae
if oob_mae is None or oob_mae.mean is None:
... | def mean_oob_mae_weight(trees):
"""
Returns weights proportional to the out-of-bag mean absolute error for each tree.
"""
weights = []
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for tree in trees:
oob_mae = tree.out_of_bag_mae
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train | Forest._grow_trees | Adds new trees to the forest according to the specified growth method. | dtree.py | def _grow_trees(self):
"""
Adds new trees to the forest according to the specified growth method.
"""
if self.grow_method == GROW_AUTO_INCREMENTAL:
self.tree_kwargs['auto_grow'] = True
while len(self.trees) < self.size:
self.trees.append(Tree(data... | def _grow_trees(self):
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Adds new trees to the forest according to the specified growth method.
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train | Forest.predict | Attempts to predict the value of the class attribute by aggregating
the predictions of each tree.
Parameters:
weighting_formula := a callable that takes a list of trees and
returns a list of weights. | dtree.py | def predict(self, record):
"""
Attempts to predict the value of the class attribute by aggregating
the predictions of each tree.
Parameters:
weighting_formula := a callable that takes a list of trees and
returns a list of weights.
"""
... | def predict(self, record):
"""
Attempts to predict the value of the class attribute by aggregating
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weighting_formula := a callable that takes a list of trees and
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train | Forest.train | Updates the trees with the given training record. | dtree.py | def train(self, record):
"""
Updates the trees with the given training record.
"""
self._fell_trees()
self._grow_trees()
for tree in self.trees:
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tree.train(record)
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tree.o... | def train(self, record):
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train | get_configfile_paths | Return a list of local configuration file paths.
Search paths for configuration files on the local system
are based on homebase_ and depend on operating system; for example, for Linux systems
these might include ``dwave.conf`` in the current working directory (CWD),
user-local ``.config/dwave/``, and s... | dwave/cloud/config.py | def get_configfile_paths(system=True, user=True, local=True, only_existing=True):
"""Return a list of local configuration file paths.
Search paths for configuration files on the local system
are based on homebase_ and depend on operating system; for example, for Linux systems
these might include ``dwav... | def get_configfile_paths(system=True, user=True, local=True, only_existing=True):
"""Return a list of local configuration file paths.
Search paths for configuration files on the local system
are based on homebase_ and depend on operating system; for example, for Linux systems
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train | get_default_configfile_path | Return the default configuration-file path.
Typically returns a user-local configuration file; e.g:
``~/.config/dwave/dwave.conf``.
Returns:
str:
Configuration file path.
Examples:
This example displays the default configuration file on an Ubuntu Unix system
runnin... | dwave/cloud/config.py | def get_default_configfile_path():
"""Return the default configuration-file path.
Typically returns a user-local configuration file; e.g:
``~/.config/dwave/dwave.conf``.
Returns:
str:
Configuration file path.
Examples:
This example displays the default configuration fi... | def get_default_configfile_path():
"""Return the default configuration-file path.
Typically returns a user-local configuration file; e.g:
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Returns:
str:
Configuration file path.
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train | load_config_from_files | Load D-Wave Cloud Client configuration from a list of files.
.. note:: This method is not standardly used to set up D-Wave Cloud Client configuration.
It is recommended you use :meth:`.Client.from_config` or
:meth:`.config.load_config` instead.
Configuration files comply with standard Windows ... | dwave/cloud/config.py | def load_config_from_files(filenames=None):
"""Load D-Wave Cloud Client configuration from a list of files.
.. note:: This method is not standardly used to set up D-Wave Cloud Client configuration.
It is recommended you use :meth:`.Client.from_config` or
:meth:`.config.load_config` instead.
... | def load_config_from_files(filenames=None):
"""Load D-Wave Cloud Client configuration from a list of files.
.. note:: This method is not standardly used to set up D-Wave Cloud Client configuration.
It is recommended you use :meth:`.Client.from_config` or
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train | load_config | Load D-Wave Cloud Client configuration based on a configuration file.
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train | legacy_load_config | Load configured URLs and token for the SAPI server.
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train | _check_data | Check whether `data` is a valid input/output for libsamplerate.
Returns
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Number of frames in `data`.
channels
Number of channels in `data`.
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Number of frames in `data`.
channels
Number of channels in `data`.
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Number of frames in `data`.
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Number of channels in `data`.
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train | src_new | Initialise a new sample rate converter.
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Converter to be used.
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Number of channels.
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An anonymous pointer to the internal state of the converter.
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Converter to be used.
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Number of channels.
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Number of channels.
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train | src_process | Standard processing function.
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train | Client.from_config | Client factory method to instantiate a client instance from configuration.
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.. note:: Ensure your code does not submit new work while the connection is closing.
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Waits for all the currently scheduled work to finish, kills the workers,
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.. note:: Ensure your code does not submit new work while the connection is closing.
Where possible, it is recommended you use... | def close(self):
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Force refresh of cached list of solvers/properties.
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Force refresh of cached list of solvers/properties.
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train | Client.solvers | Deprecated in favor of :meth:`.get_solvers`. | dwave/cloud/client.py | def solvers(self, refresh=False, **filters):
"""Deprecated in favor of :meth:`.get_solvers`."""
warnings.warn("'solvers' is deprecated in favor of 'get_solvers'.", DeprecationWarning)
return self.get_solvers(refresh=refresh, **filters) | def solvers(self, refresh=False, **filters):
"""Deprecated in favor of :meth:`.get_solvers`."""
warnings.warn("'solvers' is deprecated in favor of 'get_solvers'.", DeprecationWarning)
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train | Client.get_solver | Load the configuration for a single solver.
Makes a blocking web call to `{endpoint}/solvers/remote/{solver_name}/`, where `{endpoint}`
is a URL configured for the client, and returns a :class:`.Solver` instance
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train | Client._submit | Enqueue a problem for submission to the server.
This method is thread safe. | dwave/cloud/client.py | def _submit(self, body, future):
"""Enqueue a problem for submission to the server.
This method is thread safe.
"""
self._submission_queue.put(self._submit.Message(body, future)) | def _submit(self, body, future):
"""Enqueue a problem for submission to the server.
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train | Client._do_submit_problems | Pull problems from the submission queue and submit them.
Note:
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"""Pull problems from the submission queue and submit them.
Note:
This method is always run inside of a daemon thread.
"""
try:
while True:
# Pull as many problems as we can, block on the first one,
#... | def _do_submit_problems(self):
"""Pull problems from the submission queue and submit them.
Note:
This method is always run inside of a daemon thread.
"""
try:
while True:
# Pull as many problems as we can, block on the first one,
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train | Client._handle_problem_status | Handle the results of a problem submission or results request.
This method checks the status of the problem and puts it in the correct queue.
Args:
message (dict): Update message from the SAPI server wrt. this problem.
future `Future`: future corresponding to the problem
... | dwave/cloud/client.py | def _handle_problem_status(self, message, future):
"""Handle the results of a problem submission or results request.
This method checks the status of the problem and puts it in the correct queue.
Args:
message (dict): Update message from the SAPI server wrt. this problem.
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Args:
message (dict): Update message from the SAPI server wrt. this problem.
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train | Client._do_cancel_problems | Pull ids from the cancel queue and submit them.
Note:
This method is always run inside of a daemon thread. | dwave/cloud/client.py | def _do_cancel_problems(self):
"""Pull ids from the cancel queue and submit them.
Note:
This method is always run inside of a daemon thread.
"""
try:
while True:
# Pull as many problems as we can, block when none are available.
# ... | def _do_cancel_problems(self):
"""Pull ids from the cancel queue and submit them.
Note:
This method is always run inside of a daemon thread.
"""
try:
while True:
# Pull as many problems as we can, block when none are available.
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train | Client._poll | Enqueue a problem to poll the server for status. | dwave/cloud/client.py | def _poll(self, future):
"""Enqueue a problem to poll the server for status."""
if future._poll_backoff is None:
# on first poll, start with minimal back-off
future._poll_backoff = self._POLL_BACKOFF_MIN
# if we have ETA of results, schedule the first poll for then
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"""Enqueue a problem to poll the server for status."""
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future._poll_backoff = self._POLL_BACKOFF_MIN
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train | Client._do_poll_problems | Poll the server for the status of a set of problems.
Note:
This method is always run inside of a daemon thread. | dwave/cloud/client.py | def _do_poll_problems(self):
"""Poll the server for the status of a set of problems.
Note:
This method is always run inside of a daemon thread.
"""
try:
# grouped futures (all scheduled within _POLL_GROUP_TIMEFRAME)
frame_futures = {}
def... | def _do_poll_problems(self):
"""Poll the server for the status of a set of problems.
Note:
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train | Client._do_load_results | Submit a query asking for the results for a particular problem.
To request the results of a problem: ``GET /problems/{problem_id}/``
Note:
This method is always run inside of a daemon thread. | dwave/cloud/client.py | def _do_load_results(self):
"""Submit a query asking for the results for a particular problem.
To request the results of a problem: ``GET /problems/{problem_id}/``
Note:
This method is always run inside of a daemon thread.
"""
try:
while True:
... | def _do_load_results(self):
"""Submit a query asking for the results for a particular problem.
To request the results of a problem: ``GET /problems/{problem_id}/``
Note:
This method is always run inside of a daemon thread.
"""
try:
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train | encode_bqm_as_qp | Encode the binary quadratic problem for submission to a given solver,
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Args:
solver (:class:`dwave.cloud.solver.Solver`):
The solver used.
linear (dict[variable, bias]/list[variable, bias]):
Linear terms of the model.
quadratic (d... | dwave/cloud/coders.py | def encode_bqm_as_qp(solver, linear, quadratic):
"""Encode the binary quadratic problem for submission to a given solver,
using the `qp` format for data.
Args:
solver (:class:`dwave.cloud.solver.Solver`):
The solver used.
linear (dict[variable, bias]/list[variable, bias]):
... | def encode_bqm_as_qp(solver, linear, quadratic):
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Args:
solver (:class:`dwave.cloud.solver.Solver`):
The solver used.
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train | decode_qp | Decode SAPI response that uses `qp` format, without numpy.
The 'qp' format is the current encoding used for problems and samples.
In this encoding the reply is generally json, but the samples, energy,
and histogram data (the occurrence count of each solution), are all
base64 encoded arrays. | dwave/cloud/coders.py | def decode_qp(msg):
"""Decode SAPI response that uses `qp` format, without numpy.
The 'qp' format is the current encoding used for problems and samples.
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base64 ... | def decode_qp(msg):
"""Decode SAPI response that uses `qp` format, without numpy.
The 'qp' format is the current encoding used for problems and samples.
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train | _decode_byte | Helper for decode_qp, turns a single byte into a list of bits.
Args:
byte: byte to be decoded
Returns:
list of bits corresponding to byte | dwave/cloud/coders.py | def _decode_byte(byte):
"""Helper for decode_qp, turns a single byte into a list of bits.
Args:
byte: byte to be decoded
Returns:
list of bits corresponding to byte
"""
bits = []
for _ in range(8):
bits.append(byte & 1)
byte >>= 1
return bits | def _decode_byte(byte):
"""Helper for decode_qp, turns a single byte into a list of bits.
Args:
byte: byte to be decoded
Returns:
list of bits corresponding to byte
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bits = []
for _ in range(8):
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train | _decode_ints | Helper for decode_qp, decodes an int array.
The int array is stored as little endian 32 bit integers.
The array has then been base64 encoded. Since we are decoding we do these
steps in reverse. | dwave/cloud/coders.py | def _decode_ints(message):
"""Helper for decode_qp, decodes an int array.
The int array is stored as little endian 32 bit integers.
The array has then been base64 encoded. Since we are decoding we do these
steps in reverse.
"""
binary = base64.b64decode(message)
return struct.unpack('<' + (... | def _decode_ints(message):
"""Helper for decode_qp, decodes an int array.
The int array is stored as little endian 32 bit integers.
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"""
binary = base64.b64decode(message)
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train | _decode_doubles | Helper for decode_qp, decodes a double array.
The double array is stored as little endian 64 bit doubles.
The array has then been base64 encoded. Since we are decoding we do these
steps in reverse.
Args:
message: the double array
Returns:
decoded double array | dwave/cloud/coders.py | def _decode_doubles(message):
"""Helper for decode_qp, decodes a double array.
The double array is stored as little endian 64 bit doubles.
The array has then been base64 encoded. Since we are decoding we do these
steps in reverse.
Args:
message: the double array
Returns:
decod... | def _decode_doubles(message):
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message: the double array
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train | decode_qp_numpy | Decode SAPI response, results in a `qp` format, explicitly using numpy.
If numpy is not installed, the method will fail.
To use numpy for decoding, but return the results a lists (instead of
numpy matrices), set `return_matrix=False`. | dwave/cloud/coders.py | def decode_qp_numpy(msg, return_matrix=True):
"""Decode SAPI response, results in a `qp` format, explicitly using numpy.
If numpy is not installed, the method will fail.
To use numpy for decoding, but return the results a lists (instead of
numpy matrices), set `return_matrix=False`.
"""
import ... | def decode_qp_numpy(msg, return_matrix=True):
"""Decode SAPI response, results in a `qp` format, explicitly using numpy.
If numpy is not installed, the method will fail.
To use numpy for decoding, but return the results a lists (instead of
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"""
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train | evaluate_ising | Calculate the energy of a state given the Hamiltonian.
Args:
linear: Linear Hamiltonian terms.
quad: Quadratic Hamiltonian terms.
state: Vector of spins describing the system state.
Returns:
Energy of the state evaluated by the given energy function. | dwave/cloud/utils.py | def evaluate_ising(linear, quad, state):
"""Calculate the energy of a state given the Hamiltonian.
Args:
linear: Linear Hamiltonian terms.
quad: Quadratic Hamiltonian terms.
state: Vector of spins describing the system state.
Returns:
Energy of the state evaluated by the gi... | def evaluate_ising(linear, quad, state):
"""Calculate the energy of a state given the Hamiltonian.
Args:
linear: Linear Hamiltonian terms.
quad: Quadratic Hamiltonian terms.
state: Vector of spins describing the system state.
Returns:
Energy of the state evaluated by the gi... | [
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train | active_qubits | Calculate a set of all active qubits. Qubit is "active" if it has
bias or coupling attached.
Args:
linear (dict[variable, bias]/list[variable, bias]):
Linear terms of the model.
quadratic (dict[(variable, variable), bias]):
Quadratic terms of the model.
Returns:
... | dwave/cloud/utils.py | def active_qubits(linear, quadratic):
"""Calculate a set of all active qubits. Qubit is "active" if it has
bias or coupling attached.
Args:
linear (dict[variable, bias]/list[variable, bias]):
Linear terms of the model.
quadratic (dict[(variable, variable), bias]):
Q... | def active_qubits(linear, quadratic):
"""Calculate a set of all active qubits. Qubit is "active" if it has
bias or coupling attached.
Args:
linear (dict[variable, bias]/list[variable, bias]):
Linear terms of the model.
quadratic (dict[(variable, variable), bias]):
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train | generate_random_ising_problem | Generates an Ising problem formulation valid for a particular solver,
using all qubits and all couplings and linear/quadratic biases sampled
uniformly from `h_range`/`j_range`. | dwave/cloud/utils.py | def generate_random_ising_problem(solver, h_range=None, j_range=None):
"""Generates an Ising problem formulation valid for a particular solver,
using all qubits and all couplings and linear/quadratic biases sampled
uniformly from `h_range`/`j_range`.
"""
if h_range is None:
h_range = solver... | def generate_random_ising_problem(solver, h_range=None, j_range=None):
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train | uniform_iterator | Uniform (key, value) iteration on a `dict`,
or (idx, value) on a `list`. | dwave/cloud/utils.py | def uniform_iterator(sequence):
"""Uniform (key, value) iteration on a `dict`,
or (idx, value) on a `list`."""
if isinstance(sequence, abc.Mapping):
return six.iteritems(sequence)
else:
return enumerate(sequence) | def uniform_iterator(sequence):
"""Uniform (key, value) iteration on a `dict`,
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if isinstance(sequence, abc.Mapping):
return six.iteritems(sequence)
else:
return enumerate(sequence) | [
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train | uniform_get | Uniform `dict`/`list` item getter, where `index` is interpreted as a key
for maps and as numeric index for lists. | dwave/cloud/utils.py | def uniform_get(sequence, index, default=None):
"""Uniform `dict`/`list` item getter, where `index` is interpreted as a key
for maps and as numeric index for lists."""
if isinstance(sequence, abc.Mapping):
return sequence.get(index, default)
else:
return sequence[index] if index < len(s... | def uniform_get(sequence, index, default=None):
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if isinstance(sequence, abc.Mapping):
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train | strip_head | Strips elements of `values` from the beginning of `sequence`. | dwave/cloud/utils.py | def strip_head(sequence, values):
"""Strips elements of `values` from the beginning of `sequence`."""
values = set(values)
return list(itertools.dropwhile(lambda x: x in values, sequence)) | def strip_head(sequence, values):
"""Strips elements of `values` from the beginning of `sequence`."""
values = set(values)
return list(itertools.dropwhile(lambda x: x in values, sequence)) | [
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train | strip_tail | Strip `values` from the end of `sequence`. | dwave/cloud/utils.py | def strip_tail(sequence, values):
"""Strip `values` from the end of `sequence`."""
return list(reversed(list(strip_head(reversed(sequence), values)))) | def strip_tail(sequence, values):
"""Strip `values` from the end of `sequence`."""
return list(reversed(list(strip_head(reversed(sequence), values)))) | [
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train | click_info_switch | Decorator to create eager Click info switch option, as described in:
http://click.pocoo.org/6/options/#callbacks-and-eager-options.
Takes a no-argument function and abstracts the boilerplate required by
Click (value checking, exit on done).
Example:
@click.option('--my-option', is_flag=True, ... | dwave/cloud/utils.py | def click_info_switch(f):
"""Decorator to create eager Click info switch option, as described in:
http://click.pocoo.org/6/options/#callbacks-and-eager-options.
Takes a no-argument function and abstracts the boilerplate required by
Click (value checking, exit on done).
Example:
@click.opt... | def click_info_switch(f):
"""Decorator to create eager Click info switch option, as described in:
http://click.pocoo.org/6/options/#callbacks-and-eager-options.
Takes a no-argument function and abstracts the boilerplate required by
Click (value checking, exit on done).
Example:
@click.opt... | [
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train | datetime_to_timestamp | Convert timezone-aware `datetime` to POSIX timestamp and
return seconds since UNIX epoch.
Note: similar to `datetime.timestamp()` in Python 3.3+. | dwave/cloud/utils.py | def datetime_to_timestamp(dt):
"""Convert timezone-aware `datetime` to POSIX timestamp and
return seconds since UNIX epoch.
Note: similar to `datetime.timestamp()` in Python 3.3+.
"""
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=UTC)
return (dt - epoch).total_seconds() | def datetime_to_timestamp(dt):
"""Convert timezone-aware `datetime` to POSIX timestamp and
return seconds since UNIX epoch.
Note: similar to `datetime.timestamp()` in Python 3.3+.
"""
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=UTC)
return (dt - epoch).total_seconds() | [
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train | user_agent | Return User-Agent ~ "name/version language/version interpreter/version os/version". | dwave/cloud/utils.py | def user_agent(name, version):
"""Return User-Agent ~ "name/version language/version interpreter/version os/version"."""
def _interpreter():
name = platform.python_implementation()
version = platform.python_version()
bitness = platform.architecture()[0]
if name == 'PyPy':
... | def user_agent(name, version):
"""Return User-Agent ~ "name/version language/version interpreter/version os/version"."""
def _interpreter():
name = platform.python_implementation()
version = platform.python_version()
bitness = platform.architecture()[0]
if name == 'PyPy':
... | [
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