code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from dataclasses import dataclass
from enum import Enum
@dataclass
class PaperInfo:
url: str
title: str
doi: str
publisher: str
authors: str
@dataclass
class PaperDetailDescription:
authors: str
title: str
publisher: str
doi: str
class SearchEngine(Enum):
google_scholar = 1... | /scihub-cn-0.1.1.tar.gz/scihub-cn-0.1.1/scihub_cn/models.py | 0.809012 | 0.165121 | models.py | pypi |
[](https://badge.fury.io/py/scikick)
[](https://pypistats.org/packages/scikick)
[](https://pypi.python.org/pypi/scikick/)
[](https://pypi.... | /scikick-0.1.2.tar.gz/scikick-0.1.2/README.md | 0.407333 | 0.981185 | README.md | pypi |
import numpy as np
from random import randint
import matplotlib.pyplot as plt
class Fluid:
def __init__(self, i_size, i_p):
"""
:param i_size: size of the lattice
:param i_p: percolation probability. Must be < 1
"""
self.size = i_size
self.percolation = np.zeros((se... | /randomSystems/percolation.py | 0.564579 | 0.49292 | percolation.py | pypi |
from randomSystems.walker import Walker
import matplotlib.pyplot as plt
class RWPopulation:
def __init__(self, i_walkers_list=None):
"""
:param i_walkers_list: Initial list of random walkers
"""
if i_walkers_list is not None:
if isinstance(i_walkers_list, list):
... | /randomSystems/SIM_RandomWalkers.py | 0.576423 | 0.342998 | SIM_RandomWalkers.py | pypi |
import random
import copy
class AttributeTracking:
def __init__(self,model):
self.percent = 0
self.probabilityList = []
self.attAccuracySums = [[0]*model.env.formatData.numAttributes for i in range(model.env.formatData.numTrainInstances)]
def updateAttTrack(self,model,pop):
dat... | /scikit-ExSTraCS-1.1.1.tar.gz/scikit-ExSTraCS-1.1.1/skExSTraCS/AttributeTracking.py | 0.428473 | 0.336658 | AttributeTracking.py | pypi |
import time
class Timer:
def __init__(self):
""" Initializes all Timer values for the algorithm """
# Global Time objects
self.globalStartRef = time.time()
self.globalTime = 0.0
self.addedTime = 0.0
# Match Time Variables
self.startRefMatching = 0.0
... | /scikit-ExSTraCS-1.1.1.tar.gz/scikit-ExSTraCS-1.1.1/skExSTraCS/Timer.py | 0.565899 | 0.184327 | Timer.py | pypi |
from skExSTraCS.Classifier import Classifier
import copy
import random
class ClassifierSet:
def __init__(self):
self.popSet = [] # List of classifiers/rules
self.matchSet = [] # List of references to rules in population that match
self.correctSet = [] # List of references to rules in pop... | /scikit-ExSTraCS-1.1.1.tar.gz/scikit-ExSTraCS-1.1.1/skExSTraCS/ClassifierSet.py | 0.549882 | 0.196248 | ClassifierSet.py | pypi |
from __future__ import print_function
from collections import defaultdict
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.ensemble import BaggingClassifier
from sklearn.metrics import accuracy_score
from .mdr import MDR
from ._version import __version__
class MDREnsemble(Ba... | /scikit_MDR-0.4.5-py3-none-any.whl/mdr/mdr_ensemble.py | 0.924022 | 0.352592 | mdr_ensemble.py | pypi |
from __future__ import print_function
from collections import defaultdict
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from scipy.stats import ttest_ind
class ContinuousMDR(BaseEstimator, TransformerMixin):
"""Continuous Multifactor Dimensionality Reduction (CMDR) for feature constr... | /scikit_MDR-0.4.5-py3-none-any.whl/mdr/continuous_mdr.py | 0.943958 | 0.526951 | continuous_mdr.py | pypi |
from __future__ import print_function
from collections import defaultdict
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin, ClassifierMixin
from sklearn.metrics import accuracy_score
class MDRBase(BaseEstimator):
"""Base Multifactor Dimensionality Reduction (MDR) functions.
MDR c... | /scikit_MDR-0.4.5-py3-none-any.whl/mdr/mdr.py | 0.942599 | 0.499756 | mdr.py | pypi |
from __future__ import print_function
import itertools
from collections import Counter
import scipy
import numpy as np
import copy
import matplotlib.pyplot as plt
from ..mdr import MDR
def entropy(X, base=2):
"""Calculates the entropy, H(X), in the given base
Parameters
----------
X: array-like (# sam... | /scikit_MDR-0.4.5-py3-none-any.whl/mdr/utils/utils.py | 0.945514 | 0.773024 | utils.py | pypi |
import time
# --------------------------------------
class Timer:
def __init__(self):
# Global Time objects
self.globalStartRef = time.time()
self.globalTime = 0.0
self.globalAdd = 0
# Match Time Variables
self.startRefMatching = 0.0
self.globalMatching = ... | /scikit-XCS-1.0.8.tar.gz/scikit-XCS-1.0.8/skXCS/Timer.py | 0.533397 | 0.171408 | Timer.py | pypi |
import csv
import numpy as np
class IterationRecord():
'''
IterationRecord Tracks 1 dictionary:
1) Tracking Dict: Cursory Iteration Evaluation. Frequency determined by trackingFrequency param in eLCS. For each iteration evaluated, it saves:
KEY-iteration number
0-accuracy (approximate from ... | /scikit-XCS-1.0.8.tar.gz/scikit-XCS-1.0.8/skXCS/IterationRecord.py | 0.594904 | 0.479138 | IterationRecord.py | pypi |
import random
import copy
class Classifier:
def __init__(self,xcs):
self.specifiedAttList = []
self.condition = []
self.action = None
self.prediction = xcs.init_prediction
self.fitness = xcs.init_fitness
self.predictionError = xcs.init_e
self.numerosity = 1... | /scikit-XCS-1.0.8.tar.gz/scikit-XCS-1.0.8/skXCS/Classifier.py | 0.542136 | 0.327359 | Classifier.py | pypi |
import warnings
from abc import ABC, abstractmethod
from copy import deepcopy
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.metrics import accuracy_score
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import (
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/base.py | 0.908798 | 0.393909 | base.py | pypi |
from functools import partial
import numpy as np
from sklearn import clone
from sklearn.utils import check_array
from sklearn.metrics import mean_squared_error
from skactiveml.base import (
ProbabilisticRegressor,
SingleAnnotatorPoolQueryStrategy,
)
from skactiveml.pool.utils import _update_reg, _conditional_... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_expected_model_output_change.py | 0.957068 | 0.647756 | _expected_model_output_change.py | pypi |
import warnings
import numpy as np
from scipy.interpolate import griddata
from scipy.optimize import minimize_scalar, minimize, LinearConstraint
from sklearn import clone
from sklearn.linear_model import LogisticRegression
from sklearn.utils.extmath import safe_sparse_dot, log_logistic
from ..base import SingleAnnota... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_epistemic_uncertainty_sampling.py | 0.892217 | 0.511839 | _epistemic_uncertainty_sampling.py | pypi |
import numpy as np
from sklearn.base import clone
from ..base import SingleAnnotatorPoolQueryStrategy
from ..classifier import MixtureModelClassifier
from ..utils import (
rand_argmax,
is_labeled,
check_type,
MISSING_LABEL,
check_equal_missing_label,
check_scalar,
)
class FourDs(SingleAnnot... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_four_ds.py | 0.914823 | 0.534005 | _four_ds.py | pypi |
import numpy as np
from sklearn import clone
from sklearn.metrics import pairwise_distances, pairwise
from skactiveml.base import (
SingleAnnotatorPoolQueryStrategy,
SkactivemlRegressor,
)
from skactiveml.utils import (
rand_argmax,
labeled_indices,
MISSING_LABEL,
is_labeled,
check_type,
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_greedy_sampling.py | 0.932645 | 0.633566 | _greedy_sampling.py | pypi |
import numpy as np
from sklearn.utils import check_array
from ..base import SkactivemlClassifier
from ..pool._query_by_committee import _check_ensemble, QueryByCommittee
from ..utils import (
rand_argmax,
MISSING_LABEL,
check_type,
check_scalar,
check_random_state,
)
class BatchBALD(QueryByCommit... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_batch_bald.py | 0.893699 | 0.677741 | _batch_bald.py | pypi |
import math
import numpy as np
from sklearn import clone
from skactiveml.base import (
SkactivemlRegressor,
SingleAnnotatorPoolQueryStrategy,
SkactivemlClassifier,
)
from skactiveml.utils import (
check_type,
simple_batch,
check_scalar,
MISSING_LABEL,
check_X_y,
check_random_state,... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_expected_model_change_maximization.py | 0.922591 | 0.682537 | _expected_model_change_maximization.py | pypi |
import numpy as np
from sklearn import clone
from sklearn.utils.validation import check_array
from ..base import SingleAnnotatorPoolQueryStrategy, SkactivemlClassifier
from ..utils import (
MISSING_LABEL,
check_cost_matrix,
simple_batch,
check_classes,
check_type,
check_equal_missing_label,
)
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_uncertainty_sampling.py | 0.918366 | 0.697165 | _uncertainty_sampling.py | pypi |
import warnings
import numpy as np
from joblib import Parallel, delayed
from sklearn import clone
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.isotonic import IsotonicRegression
from sklearn.metrics import euclidean_distances
from sklearn.neighbors import NearestNeighbors
from sklearn.svm import... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_cost_embedding_al.py | 0.908412 | 0.534612 | _cost_embedding_al.py | pypi |
import numpy as np
from sklearn import clone
from sklearn.utils import check_array
from skactiveml.base import (
ProbabilisticRegressor,
SingleAnnotatorPoolQueryStrategy,
)
from skactiveml.utils import check_type, simple_batch, MISSING_LABEL
from skactiveml.pool.utils import _update_reg, _conditional_expect
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_expected_model_variance.py | 0.939345 | 0.689364 | _expected_model_variance.py | pypi |
import warnings
from copy import deepcopy
import numpy as np
import scipy
from scipy import integrate
from scipy.special import roots_hermitenorm
from sklearn import clone
from sklearn.exceptions import NotFittedError
from sklearn.metrics import pairwise_kernels
from sklearn.utils import column_or_1d
from sklearn.util... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/utils.py | 0.781956 | 0.437523 | utils.py | pypi |
import numpy as np
from sklearn import clone
from sklearn.utils import check_array
from skactiveml.base import (
SingleAnnotatorPoolQueryStrategy,
ProbabilisticRegressor,
)
from skactiveml.pool.utils import (
_update_reg,
_conditional_expect,
_cross_entropy,
)
from skactiveml.utils import (
ch... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_information_gain_maximization.py | 0.934447 | 0.677541 | _information_gain_maximization.py | pypi |
import numpy as np
from ..base import SingleAnnotatorPoolQueryStrategy
from ..utils import MISSING_LABEL, simple_batch
class RandomSampling(SingleAnnotatorPoolQueryStrategy):
"""Random Sampling.
This class implements random sampling
Parameters
----------
missing_label : scalar or string or np.n... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_random_sampling.py | 0.93206 | 0.557484 | _random_sampling.py | pypi |
import copy
import numpy as np
from sklearn import clone
from sklearn.utils.validation import check_array, check_is_fitted
from ..base import (
SingleAnnotatorPoolQueryStrategy,
SkactivemlClassifier,
SkactivemlRegressor,
)
from ..utils import (
simple_batch,
check_type,
compute_vote_vectors,
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_query_by_committee.py | 0.870982 | 0.557604 | _query_by_committee.py | pypi |
import itertools
import numpy as np
from scipy.special import factorial, gammaln
from sklearn import clone
from sklearn.utils.validation import check_array
from ..base import SkactivemlClassifier
from ..base import SingleAnnotatorPoolQueryStrategy
from ..classifier import ParzenWindowClassifier
from ..utils import (
... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/_probabilistic_al.py | 0.890735 | 0.426859 | _probabilistic_al.py | pypi |
import numpy as np
from scipy.stats import t, rankdata
from sklearn.base import BaseEstimator, clone
from sklearn.utils.validation import check_array, check_is_fitted
from ...base import (
MultiAnnotatorPoolQueryStrategy,
SkactivemlClassifier,
AnnotatorModelMixin,
)
from ...pool._uncertainty_sampling impor... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/multiannotator/_interval_estimation_threshold.py | 0.941027 | 0.551815 | _interval_estimation_threshold.py | pypi |
from inspect import signature, Parameter
import numpy as np
from scipy.stats import rankdata
from sklearn.utils.validation import check_array, _is_arraylike
from ...base import (
MultiAnnotatorPoolQueryStrategy,
SingleAnnotatorPoolQueryStrategy,
)
from ...utils import (
rand_argmax,
check_type,
MI... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/pool/multiannotator/_wrapper.py | 0.943925 | 0.632162 | _wrapper.py | pypi |
import numpy as np
from ..base import SingleAnnotatorStreamQueryStrategy
from ..utils import check_scalar
class StreamRandomSampling(SingleAnnotatorStreamQueryStrategy):
"""Random Sampling for Datastreams.
The RandomSampling samples instances completely randomly. The
probability to sample an instance is... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/_stream_baselines.py | 0.887887 | 0.620305 | _stream_baselines.py | pypi |
import numpy as np
from sklearn import clone
from sklearn.utils import check_array, check_consistent_length
from ..classifier import ParzenWindowClassifier
from .budgetmanager import BalancedIncrementalQuantileFilter
from ..base import (
SingleAnnotatorStreamQueryStrategy,
SkactivemlClassifier,
BudgetManag... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/_stream_probabilistic_al.py | 0.912858 | 0.513912 | _stream_probabilistic_al.py | pypi |
import numpy as np
from sklearn.base import clone
from sklearn.utils import check_array, check_consistent_length
from .budgetmanager import (
FixedUncertaintyBudgetManager,
VariableUncertaintyBudgetManager,
SplitBudgetManager,
RandomVariableUncertaintyBudgetManager,
)
from ..base import (
BudgetMan... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/_uncertainty_zliobaite.py | 0.898817 | 0.412471 | _uncertainty_zliobaite.py | pypi |
from collections import deque
from copy import copy
import warnings
import numpy as np
from sklearn.utils import check_array, check_consistent_length, check_scalar
from sklearn.base import clone
from sklearn.metrics.pairwise import pairwise_distances
from skactiveml.base import (
BudgetManager,
SingleAnnotato... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/_density_uncertainty.py | 0.875041 | 0.461745 | _density_uncertainty.py | pypi |
import numpy as np
from copy import deepcopy
from skactiveml.base import (
BudgetManager,
)
from skactiveml.utils import check_scalar, check_random_state
class DensityBasedSplitBudgetManager(BudgetManager):
"""Budget manager which checks, whether the specified budget has been
exhausted already. If not, ... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/budgetmanager/_threshold_budget.py | 0.819605 | 0.588594 | _threshold_budget.py | pypi |
from collections import deque
from copy import copy
import numpy as np
from ...base import BudgetManager
from ...utils import check_scalar
class BalancedIncrementalQuantileFilter(BudgetManager):
"""
The Balanced Incremental Quantile Filter has been proposed together with
Probabilistic Active Learning fo... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/stream/budgetmanager/_balanced_incremental_quantile_filter.py | 0.94795 | 0.701209 | _balanced_incremental_quantile_filter.py | pypi |
import copy
import warnings
from collections.abc import Iterable
from inspect import Parameter, signature
import numpy as np
from sklearn.utils.validation import (
check_array,
column_or_1d,
assert_all_finite,
check_consistent_length,
check_random_state as check_random_state_sklearn,
)
from ._labe... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_validation.py | 0.897243 | 0.548734 | _validation.py | pypi |
import operator
import warnings
from functools import reduce
import numpy as np
from scipy.stats import rankdata
from sklearn.utils import check_array
from ._validation import check_random_state, check_scalar, check_type
def rand_argmin(a, random_state=None, **argmin_kwargs):
"""Returns index of minimum value. ... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_selection.py | 0.902655 | 0.599397 | _selection.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from ._label import MISSING_LABEL, is_labeled, check_missing_label
from ._validation import check_classifier_params
class... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_label_encoder.py | 0.937861 | 0.638751 | _label_encoder.py | pypi |
import inspect
from functools import update_wrapper
from operator import attrgetter
def call_func(
f_callable, only_mandatory=False, ignore_var_keyword=False, **kwargs
):
"""Calls a function with the given parameters given in kwargs if they
exist as parameters in f_callable.
Parameters
----------... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_functions.py | 0.826852 | 0.171685 | _functions.py | pypi |
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.utils.validation import (
check_consistent_length,
column_or_1d,
check_array,
)
from ._label import MISSING_LABEL, is_labeled, is_unlabeled
from ._label_encoder import ExtLabelEncoder
def ext_confusion_matrix(
y_true, y_pred... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_multi_annot.py | 0.912054 | 0.665686 | _multi_annot.py | pypi |
import numpy as np
from iteration_utilities import deepflatten
# Define constant for missing label used throughout the package.
MISSING_LABEL = np.nan
def is_unlabeled(y, missing_label=MISSING_LABEL):
"""Creates a boolean mask indicating missing labels.
Parameters
----------
y : array-like, shape (... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_label.py | 0.902791 | 0.686423 | _label.py | pypi |
import numpy as np
from sklearn.utils import check_array, check_consistent_length
from ._label import is_labeled, is_unlabeled
from ._label_encoder import ExtLabelEncoder
from ._selection import rand_argmax
def compute_vote_vectors(y, w=None, classes=None, missing_label=np.nan):
"""Counts number of votes per cla... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/utils/_aggregation.py | 0.933043 | 0.599573 | _aggregation.py | pypi |
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import Colormap
from ..utils import check_scalar, check_type, check_bound
def mesh(bound, res):
"""
Function to get instances of a mesh grid as well as x-mesh and y-mesh
with given resolution in the specified bounds.
Parameter... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/visualization/_misc.py | 0.866698 | 0.725393 | _misc.py | pypi |
import warnings
import numpy as np
from matplotlib import lines, pyplot as plt
from matplotlib.axes import Axes
from sklearn.base import ClassifierMixin
from sklearn.neighbors import KNeighborsRegressor
from sklearn.utils.validation import (
check_array,
check_consistent_length,
column_or_1d,
)
from ._mis... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/visualization/_feature_space.py | 0.856512 | 0.632233 | _feature_space.py | pypi |
import numpy as np
import warnings
from sklearn.metrics.pairwise import pairwise_kernels, KERNEL_PARAMS
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted, check_scalar
from ..base import ClassFrequencyEstimator
from ..utils import MISSING_LABEL, compute_vote_vectors, is_labele... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/classifier/_parzen_window_classifier.py | 0.94822 | 0.724261 | _parzen_window_classifier.py | pypi |
from copy import deepcopy
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.mixture import GaussianMixture, BayesianGaussianMixture
from sklearn.utils.validation import (
check_array,
check_is_fitted,
NotFittedError,
)
from ..base import ClassFrequencyEstimator
from ..utils import ... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/classifier/_mixture_model_classifier.py | 0.95903 | 0.693398 | _mixture_model_classifier.py | pypi |
from copy import deepcopy
import numpy as np
from sklearn.ensemble._base import _BaseHeterogeneousEnsemble
from sklearn.utils.validation import check_array, check_is_fitted
from ...base import SkactivemlClassifier
from ...utils import MISSING_LABEL, is_labeled, compute_vote_vectors
class AnnotatorEnsembleClassifie... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/classifier/multiannotator/_annotator_ensemble_classifier.py | 0.938906 | 0.681806 | _annotator_ensemble_classifier.py | pypi |
import warnings
import numpy as np
from scipy.optimize import minimize
from scipy.special import softmax
from scipy.stats import dirichlet
from scipy.stats import multivariate_normal as multi_normal
from sklearn.utils.validation import check_array, check_is_fitted, column_or_1d
from ...base import SkactivemlClassifi... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/classifier/multiannotator/_annotator_logistic_regression.py | 0.922692 | 0.685903 | _annotator_logistic_regression.py | pypi |
import numpy as np
from scipy.stats import t
from sklearn.metrics.pairwise import pairwise_kernels, KERNEL_PARAMS
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from skactiveml.base import ProbabilisticRegressor
from skactiveml.utils import (
is_labeled,
MISSING_LABE... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/regressor/_nic_kernel_regressor.py | 0.935051 | 0.671834 | _nic_kernel_regressor.py | pypi |
import inspect
import warnings
from copy import deepcopy
from operator import attrgetter
import numpy as np
from scipy.stats import norm
from sklearn.base import MetaEstimatorMixin, is_regressor
from sklearn.exceptions import NotFittedError
from sklearn.utils import metaestimators
from sklearn.utils.validation import ... | /scikit_activeml-0.4.1-py3-none-any.whl/skactiveml/regressor/_wrapper.py | 0.89772 | 0.584093 | _wrapper.py | pypi |
from __future__ import division
import numpy as np
import scipy as sp
import scipy.optimize
def mach_from_area_ratio(fl, A_ratio):
"""Computes the Mach number given an area ratio asuming isentropic flow.
Uses the relation between Mach number and area ratio for isentropic flow,
and returns both the subso... | /scikit-aero-v0.1.0.tar.gz/Pybonacci-scikit-aero-cc233f6/skaero/gasdynamics/isentropic.py | 0.956074 | 0.684658 | isentropic.py | pypi |
from contextlib import contextmanager
from functools import update_wrapper
import atexit
import os
import warnings
import numpy as np
@contextmanager
def ignore_invalid():
err = np.seterr(invalid='ignore')
try:
yield
finally:
np.seterr(**err)
def check_array_like(a, *ndims, **kwargs):
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/util.py | 0.760917 | 0.47859 | util.py | pypi |
import numpy as np
class ArrayWrapper(object):
"""Abstract base class that delegates to a wrapped array-like object."""
def __init__(self, data):
if isinstance(data, ArrayWrapper):
# don't wrap a wrapper
data = data.values
if not hasattr(data, 'shape') or not hasattr(d... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/abc.py | 0.798894 | 0.383815 | abc.py | pypi |
from .model.ndarray import *
from .model.chunked import *
from .model.util import *
try:
import dask
except ImportError:
pass
else:
from .model.dask import *
from .stats.window import moving_statistic, windowed_count, \
windowed_statistic, per_base, equally_accessible_windows, moving_mean, \
movin... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/__init__.py | 0.521715 | 0.174868 | __init__.py | pypi |
import operator
from functools import reduce
import numpy as np
storage_registry = dict()
def get_storage(storage=None):
if storage is None:
try:
return storage_registry['default']
except KeyError:
raise RuntimeError('no default storage available; is either h5py '
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/chunked/util.py | 0.523177 | 0.407157 | util.py | pypi |
import operator
from functools import reduce
import zarr
import zarr.util
import numcodecs
from allel.chunked import util as _util
def default_chunks(data, expectedlen):
# here we will only ever chunk first dimension
rowsize = data.dtype.itemsize
if data.ndim > 1:
# pretend array is 1D
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/chunked/storage_zarr.py | 0.553505 | 0.500183 | storage_zarr.py | pypi |
import tempfile
import atexit
import operator
import os
from types import MethodType
from functools import reduce
import h5py
from allel.chunked import util as _util
def h5fmem(**kwargs):
"""Create an in-memory HDF5 file."""
# need a file name even tho nothing is ever written
fn = tempfile.mktemp()
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/chunked/storage_hdf5.py | 0.545286 | 0.289709 | storage_hdf5.py | pypi |
import numpy as np
from allel.util import asarray_ndim
def array_to_hdf5(a, parent, name, **kwargs):
"""Write a Numpy array to an HDF5 dataset.
Parameters
----------
a : ndarray
Data to write.
parent : string or h5py group
Parent HDF5 file or group. If a string, will be treated ... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/io/util.py | 0.85741 | 0.709824 | util.py | pypi |
import numpy as np
from allel.model.ndarray import GenotypeArray
from allel.util import ignore_invalid, asarray_ndim
def heterozygosity_observed(g, fill=np.nan):
"""Calculate the rate of observed heterozygosity for each variant.
Parameters
----------
g : array_like, int, shape (n_variants, n_sampl... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/hw.py | 0.921473 | 0.818338 | hw.py | pypi |
import numpy as np
from allel.util import asarray_ndim
def get_scaler(scaler, copy, ploidy):
# normalise strings to lower case
if isinstance(scaler, str):
scaler = scaler.lower()
if scaler == 'patterson':
return PattersonScaler(copy=copy, ploidy=ploidy)
elif scaler == 'standard':
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/preprocessing.py | 0.672224 | 0.335378 | preprocessing.py | pypi |
import numpy as np
from allel.stats.preprocessing import get_scaler
def pca(gn, n_components=10, copy=True, scaler='patterson', ploidy=2):
"""Perform principal components analysis of genotype data, via singular
value decomposition.
Parameters
----------
gn : array_like, float, shape (n_variant... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/decomposition.py | 0.8758 | 0.654136 | decomposition.py | pypi |
import logging
import itertools
import numpy as np
from allel.util import asarray_ndim, check_dim0_aligned, ensure_dim1_aligned
from allel.model.ndarray import GenotypeArray
from allel.stats.window import windowed_statistic, moving_statistic
from allel.stats.diversity import mean_pairwise_difference, \
mean_pai... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/fst.py | 0.795301 | 0.455138 | fst.py | pypi |
import numpy as np
from allel.compat import memoryview_safe
from allel.model.ndarray import GenotypeArray, HaplotypeArray
from allel.util import check_ploidy, check_min_samples, check_type, check_dtype
from allel.opt.stats import phase_progeny_by_transmission as _opt_phase_progeny_by_transmission, \
phase_parents... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/mendel.py | 0.684053 | 0.681097 | mendel.py | pypi |
from collections import OrderedDict
import numpy as np
from allel.compat import memoryview_safe
from allel.model.ndarray import SortedIndex
from allel.util import asarray_ndim, check_dim0_aligned, check_integer_dtype
from allel.opt.stats import state_transitions
def jackknife(values, statistic):
"""Estimate s... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/misc.py | 0.919665 | 0.77343 | misc.py | pypi |
import itertools
import numpy as np
from allel.model.ndarray import SortedIndex
from allel.util import asarray_ndim, ensure_square
from allel.stats.diversity import sequence_divergence
from allel.chunked import get_blen_array
def pairwise_distance(x, metric, chunked=False, blen=None):
"""Compute pairwise dist... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/distance.py | 0.915543 | 0.780328 | distance.py | pypi |
from allel.model.ndarray import AlleleCountsArray
from allel.util import asarray_ndim, check_dim0_aligned
from allel.stats.window import moving_statistic
from allel.stats.misc import jackknife
import numpy as np
def h_hat(ac):
"""Unbiased estimator for h, where 2*h is the heterozygosity
of the population.
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/admixture.py | 0.91802 | 0.800107 | admixture.py | pypi |
import numpy as np
from allel.model.ndarray import GenotypeVector
from allel.util import asarray_ndim, check_dim0_aligned
from allel.stats.misc import tabulate_state_blocks
from allel.stats.window import equally_accessible_windows, windowed_statistic, position_windows
def roh_mhmm(gv, pos, phet_roh=0.001, phet_nonro... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/roh.py | 0.890651 | 0.761627 | roh.py | pypi |
import numpy as np
from allel.stats.window import windowed_statistic
from allel.util import asarray_ndim, ensure_square
from allel.chunked import get_blen_array
from allel.compat import memoryview_safe
from allel.opt.stats import gn_pairwise_corrcoef_int8, gn_pairwise2_corrcoef_int8, \
gn_locate_unlinked_int8
d... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/ld.py | 0.894242 | 0.676697 | ld.py | pypi |
import numpy as np
from allel.util import asarray_ndim, check_integer_dtype
def _check_dac_n(dac, n):
dac = asarray_ndim(dac, 1)
check_integer_dtype(dac)
mx = np.max(dac)
if n is None:
n = mx
elif n < mx:
raise ValueError('number of chromosomes too small; expected {}, found {}'
... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/sf.py | 0.873714 | 0.639342 | sf.py | pypi |
import numpy as np
from allel.model.ndarray import SortedIndex
from allel.util import asarray_ndim, ignore_invalid, check_equal_length
def moving_statistic(values, statistic, size, start=0, stop=None, step=None, **kwargs):
"""Calculate a statistic in a moving window over `values`.
Parameters
----------... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/stats/window.py | 0.916893 | 0.738245 | window.py | pypi |
import numpy as np
# internal imports
from allel.util import asarray_ndim, check_dim0_aligned, ensure_dim1_aligned
__all__ = ['create_allele_mapping', 'locate_private_alleles', 'locate_fixed_differences',
'sample_to_haplotype_selection']
def create_allele_mapping(ref, alt, alleles, dtype='i1'):
"""... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/model/util.py | 0.842053 | 0.557243 | util.py | pypi |
import numpy as np
# internal imports
from allel.util import contains_newaxis, check_ndim
def index_genotype_vector(g, item, cls):
# apply indexing operation on underlying values
out = g.values[item]
# decide whether to wrap the result
wrap = (
hasattr(out, 'ndim') and out.ndim == 2 and #... | /scikit_allel-1.3.6-cp311-cp311-macosx_10_9_x86_64.whl/allel/model/generic.py | 0.682362 | 0.627552 | generic.py | pypi |
from matplotlib import pyplot as plt
import numpy as np
from sklearn import preprocessing
from .animator import Animator
class SGDClassifierAnimator(Animator):
def __animation_init(self):
self.ax.set_title("Degree "+str(self.deg)+" Polynomial Classification")
self.score = 0.0
def __anim... | /scikit_animation-0.1.0-py3-none-any.whl/scikit_animation/sgdclassifier_animator.py | 0.624637 | 0.447762 | sgdclassifier_animator.py | pypi |
"""Validate if an input is one of the allowed named object formats."""
import collections.abc
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
Union,
overload,
)
from skbase.base import BaseObject
__all__: List[str] = [
"check_sequence_named_objec... | /scikit_base-0.5.1-py3-none-any.whl/skbase/validate/_named_objects.py | 0.954009 | 0.417093 | _named_objects.py | pypi |
"""Functionality for working with nested sequences."""
import collections
from typing import List
__author__: List[str] = ["RNKuhns", "fkiraly"]
__all__: List[str] = [
"flatten",
"is_flat",
"_remove_single",
"unflat_len",
"unflatten",
]
def _remove_single(x):
"""Remove tuple wrapping from sin... | /scikit_base-0.5.1-py3-none-any.whl/skbase/utils/_nested_iter.py | 0.951605 | 0.668048 | _nested_iter.py | pypi |
"""Functionality for working with sequences."""
from typing import Any, Iterable, List, MutableMapping, Optional, Union
__author__: List[str] = ["RNKuhns"]
__all__: List[str] = ["subset_dict_keys"]
def subset_dict_keys(
input_dict: MutableMapping[Any, Any],
keys: Union[Iterable, int, float, bool, str, type],... | /scikit_base-0.5.1-py3-none-any.whl/skbase/utils/_utils.py | 0.948692 | 0.646321 | _utils.py | pypi |
.. _vision:
Vision for a Common Python Package for x-ray, electron and neutrons
===================================================================
The following document summarizes a vision for a common Python package
used across the whole community, and how we can best all work together
to achieve this. In the fo... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/docs/source/development/vision.rst | 0.830353 | 0.878314 | vision.rst | pypi |
:orphan:
.. include:: links.inc
.. _scikit-beam-fix-example:
====================================================
Contributing code to Scikit-beam, a worked example
====================================================
.. warning::
This still needs to be adapted for skbeam
This example is based on fixing `Issue... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/docs/source/development/workflow/git_edit_workflow_examples.rst | 0.82485 | 0.68166 | git_edit_workflow_examples.rst | pypi |
.. _logging:
Logging
=======
Getting feed back from running programs is invaluable for assessing
the health and performance of the code. However copious ``print``
statements are not practical on projects larger than short scripts.
This is particularly true for libraries which are imported into user
code; it is rude ... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/docs/source/resource/dev_guide/logging.rst | 0.880438 | 0.863392 | logging.rst | pypi |
import numpy as np
from scipy.signal import fftconvolve
def sgolay2d(image, window_size, order, derivative=None):
"""
Savitzky-Golay filter for 2D image arrays.
See: http://scipy-cookbook.readthedocs.io/items/SavitzkyGolay.html
Parameters
----------
image : ndarray, shape (N,M)
image... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/smoothing.py | 0.896574 | 0.764012 | smoothing.py | pypi |
from __future__ import absolute_import, division, print_function
from numpy import add, divide, logical_and, logical_not, logical_or, logical_xor, multiply, subtract
__all__ = [
"add",
"subtract",
"multiply",
"divide",
"logical_and",
"logical_or",
"logical_nor",
"logical_xor",
"log... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/arithmetic.py | 0.927569 | 0.703397 | arithmetic.py | pypi |
from __future__ import absolute_import, division, print_function
from collections import deque
from string import Template
import numpy as np
import scipy.signal
from .constants import calibration_standards
from .feature import filter_peak_height, peak_refinement, refine_log_quadratic
from .utils import angle_grid, ... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/calibration.py | 0.918877 | 0.648439 | calibration.py | pypi |
from __future__ import absolute_import, division, print_function
import time
from collections import namedtuple
import numpy as np
from .utils import verbosedict
try:
from pyFAI import geometry as geo
except ImportError:
geo = None
import logging
logger = logging.getLogger(__name__)
def process_to_q(
... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/recip.py | 0.950926 | 0.685581 | recip.py | pypi |
from __future__ import absolute_import, division, print_function
import collections
import logging
import numpy as np
from scipy import ndimage
from skimage import color, draw, feature, img_as_float
from skimage.draw import line
from skimage.measure import CircleModel, ransac
from . import utils
logger = logging.ge... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/roi.py | 0.952153 | 0.512693 | roi.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import warnings
from collections import namedtuple
import numpy as np
from scipy.optimize import minimize
logger = logging.getLogger(__name__)
def image_reduction(im, roi=None, bad_pixels=None):
"""
Sum the image data over rows... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/dpc.py | 0.950428 | 0.633821 | dpc.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import time
import numpy as np
from scipy.ndimage import gaussian_filter
logger = logging.getLogger(__name__)
def _dist(dims):
"""
Create array with pixel value equals to the distance from array center.
Parameters
----... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/cdi.py | 0.931673 | 0.669326 | cdi.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import numpy as np
import scipy.stats as sts
logger = logging.getLogger(__name__)
def bad_to_nan_gen(images, bad):
"""
Convert the images marked as "bad" in `bad` by their index in
images into a np.nan array
Parameters... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/mask.py | 0.943958 | 0.734857 | mask.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
from collections import deque
import numpy as np
from six.moves import zip
from .fitting import fit_quad_to_peak
logger = logging.getLogger(__name__)
class PeakRejection(Exception):
"""Custom exception class to indicate that the r... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/feature.py | 0.924539 | 0.674885 | feature.py | pypi |
from __future__ import absolute_import, division, print_function
import numpy as np
import scipy.signal
_defaults = {"con_val_no_bin": 3, "con_val_bin": 5, "iter_num_no_bin": 3, "iter_num_bin": 5}
def snip_method(
spectrum,
e_off,
e_lin,
e_quad,
xmin=0,
xmax=4096,
epsilon=2.96,
width... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/fitting/background.py | 0.911731 | 0.589835 | background.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import numpy as np
import scipy.special
from scipy import stats
from scipy.special import gamma, gammaln
logger = logging.getLogger(__name__)
log2 = np.log(2)
s2pi = np.sqrt(2 * np.pi)
spi = np.sqrt(np.pi)
s2 = np.sqrt(2.0)
def gaus... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/fitting/lineshapes.py | 0.973582 | 0.637271 | lineshapes.py | pypi |
from __future__ import absolute_import, division, print_function
import inspect
import logging
from lmfit import Model
from .base.parameter_data import get_para
from .lineshapes import compton, elastic, lorentzian2
logger = logging.getLogger(__name__)
def set_default(model_name, func_name):
"""
Set values... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/fitting/models.py | 0.821975 | 0.173183 | models.py | pypi |
from __future__ import absolute_import, division, print_function
"""
Parameter dictionary are included for xrf fitting.
Element data not included.
Some parameters are defined as
bound_type :
fixed: value is fixed
lohi: with both low and high boundary
lo: with low boundary
hi: with high boundary
none: no fitting boun... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/fitting/base/parameter_data.py | 0.684897 | 0.513607 | parameter_data.py | pypi |
from __future__ import absolute_import, division, print_function
import warnings
import numpy as np
from ..utils import angle_grid, bin_edges_to_centers, radial_grid
class BinnedStatisticDD(object):
std_ = ("mean", "median", "count", "sum", "std")
def __init__(self, sample, statistic="mean", bins=10, rang... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/core/accumulators/binned_statistic.py | 0.915832 | 0.682164 | binned_statistic.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import os
import numpy as np
logger = logging.getLogger(__name__)
def save_output(tth, intensity, output_name, q_or_2theta, ext=".chi", err=None, dir_path=None):
"""
Save output diffraction intensities into .chi, .dat or .xye f... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/io/save_powder_output.py | 0.793426 | 0.499451 | save_powder_output.py | pypi |
from __future__ import absolute_import, division, print_function
import os
def load_netCDF(file_name):
"""
This function loads the specified netCDF file format data set (e.g.*.volume
APS-Sector 13 GSECARS extension) file into a numpy array for further
analysis.
Required Dependencies:
netcdf... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/io/net_cdf_io.py | 0.732305 | 0.324155 | net_cdf_io.py | pypi |
from __future__ import absolute_import, division, print_function
import logging
import os
import numpy as np
def _read_amira(src_file):
"""
Reads all information contained within standard AmiraMesh data sets.
Separate the header information from the image/volume, data.
Parameters
----------
... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/io/avizo_io.py | 0.894562 | 0.55254 | avizo_io.py | pypi |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
def gsas_reader(file):
"""
Parameters
----------
file: str
GSAS powder data file
Returns
-------
tth : ndarray
twotheta values (degrees) shape (N, ) array
intensity : ndar... | /scikit-beam-0.0.26.tar.gz/scikit-beam-0.0.26/skbeam/io/gsas_file_reader.py | 0.822973 | 0.488649 | gsas_file_reader.py | pypi |
from skbio.sequence import DNA, RNA, Protein
from skbio.alignment._tabular_msa import TabularMSA
import parasail
class SubstitutionMatrix(object):
""" Wrapper around a built-in Parasail substitution matrix.
"""
def __init__(self, parasail_matrix):
self._matrix = parasail_matrix
@classmethod... | /scikit-bio-parasail-0.0.4.tar.gz/scikit-bio-parasail-0.0.4/skbio_parasail/__init__.py | 0.661267 | 0.422862 | __init__.py | pypi |
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