sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e4822c17c70bdb5e74790f775b5219ddd3298835a6e4db7896a0fee1b26e4b8e | Python | 122 | 4 | # Copyright (c) OpenMMLab. All rights reserved.
from .gap import GlobalAveragePooling
__all__ = ['GlobalAveragePooling']
|
0bea5748fc7d6cbad784b2227dd2e22ca0c8263e9860ecfc94d40971e16e4d02 | Python | 123 | 5 | """ARAR forecasting."""
__all__ = ["ARARForecaster"]
from sktime.forecasting.arar._arar_forecaster import ARARForecaster
|
72331914e12ada2ab854975613902488f2a790ad801c7c257429ea7cceaacbfb | Python | 123 | 2 | # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Tests for detector base class functionality."""
|
adf99761188f83765eb12b4b9fa5a21b6ea31cd695172cd59580650579d610b9 | Python | 123 | 9 | # roifile/__main__.py
"""Roifile package command line script."""
import sys
from .roifile import main
sys.exit(main())
|
14da9459cac92a2537dc99d0085acc45931d567a5eaaf2fed36f997c66882ca1 | Python | 126 | 9 | # tifffile/__main__.py
"""Tifffile package command line script."""
import sys
from .tifffile import main
sys.exit(main())
|
2acbae87e02aaa81ef0f9c828c515bac43d50e59f87dd58bbc72bb706d6b1509 | Python | 126 | 7 | """Dummy classifier."""
__all__ = [
"DummyClassifier",
]
from sktime.classification.dummy._dummy import DummyClassifier
|
a455d0282268640f9bf989e9660df6a541ca265a5c68467050a4971920bc3013 | Python | 126 | 5 | """Benchmarking module."""
from sktime.benchmarking.benchmarks import BenchmarkingResults
__all__ = ["BenchmarkingResults"]
|
4dacf53b1f3b210604d18b39104a4f090e767b905ddcba58dbdb67d97e5372e9 | Python | 130 | 5 | # SAFE TEAM
#
#
# distributed under license: CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.txt) #
# |
eada39b5d6db86e44eaecaadf85eea09c50bcf6b27bc33f4898b1fa5a9c26724 | Python | 130 | 6 | # SPDX-License-Identifier: LGPL-3.0-or-later
from deepmd.dpmodel.loss.unimol import (
UniMolLoss,
)
__all__ = ["UniMolLoss"]
|
a973313f98b0a2de8affe04169db8a5eee30cc069321c5ac8285912741a0cc0e | Python | 132 | 5 | import torch
DEFAULT_DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DEFAULT_DTYPE = torch.float
EPS = 1e-16
|
e6edb3036ec6465e18284fea6588c7dbf0a1e71e338f29c726da51e565ac41f0 | Python | 132 | 6 | # SAFE TEAM
#
#
# distributed under license: CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.txt) #
#
|
f932208ffa3388a0ea33269dde77322947f6bc972347c0b646b1ee9ab7fb7841 | Python | 133 | 8 | # -*- coding: utf-8 -*-
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
import wordsegment
|
513791ee8cd1e1777366f2078a524ffe28deabd1bb5898dbc57385a0a8295718 | Python | 134 | 4 | """Checker function."""
# This alias function from NeuroDSP
from neurodsp.utils.checks import check_param_range, check_param_options
|
7c8c6285d48ed3372c836a57841b92a1dcce144f9f98661ead5681100b0c2b9d | Python | 135 | 5 | import pandas as pd
file = "/Users/joshua/Developer/CognitiveSubtypes/data/processed_2022-02-04.csv"
df = pd.read_csv(file)
print(df)
|
153d60ca145fa025a32bb2a8529f0bb80ce449c2194792ad0181329981ff6b3c | Python | 137 | 3 | from .opt import NeuraLVAR, NeuraLVARCV
from ._nlgc import NLGC, nlgc_map, assign_labels
from ._plot_utils import visualize_connectivity
|
2fce8640cda742666876a29d712dd3fbdef1eb92f837e4b2bbabb8691972ca6d | Python | 137 | 4 | """ByCycle: cycle-by-cycle analyses of neural oscillations."""
from .version import __version__
from .objs import Bycycle, BycycleGroup
|
cd8f52381339dce2c3d5fb1d714977197c6feb33d9eeeca9e64e462ddf3eb4a5 | Python | 137 | 3 | from .core.filters import low_variance_filter
from .core.filters import high_expression_filter
from .core.filters import quantile_filter
|
2ff4b639549998e9f728409be43b522111e1234001b4ade50cdcb56e7280e09e | Python | 139 | 7 | """Probability distribution objects."""
from sktime.base._proba._mixin import _PredictProbaMixin
__all__ = [
"_PredictProbaMixin",
]
|
e36cca4b02942bf575471c56128968cf0c3ff438b83ea7ecd7e8dce68f80fbea | Python | 139 | 3 | from .chamfer_loss.chamfer_loss import ChamferLoss
from .emd_loss import EMDLoss
from .hungarian_mse.hungarian_mse import HungarianMSELoss
|
c5f2ef3fcf942d65f08e4951ad08e1a60a013ba4efbe7b18e55ccc3604fdd23d | Python | 140 | 6 | from django.apps import AppConfig
class PollsConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'api'
|
decd2ebfee72514383c888931f01e8841c64b58c9c82f805e48526152dd3aee7 | Python | 140 | 7 | from PIL import Image
def pil_loader(path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
|
22c955498dab31bca635a2eb2a48bad41330f1e7a40786c53e05a9bb7ffb4562 | Python | 141 | 5 | from .model_base import ModelBase
from .model import Model
from .solver_flow_model import Solver
__all__ = ["ModelBase", "Model", "Solver"]
|
d1fe12d5164573a48f1b33e9ca9811ced4421e70764fefac6a0930bb55e75be4 | Python | 143 | 7 | import warnings
from .regression import * # noqa
msg = (
"The `linearmodels` module has been renamed `regression`."
)
warnings.warn(msg)
|
d862ce37f135b394256f5b65883d81499141d472aafe793c91f146b8c18416af | Python | 143 | 6 | # for vcnmodel_simulators
from . import all_gbc_AN
from . import all_gbc_iv
from . import all_gbcs_AN
from . import run_george_EPSCVariations
|
265d3ac1b195b35eb1ade636933ee3066be5941fdced9ce667e95e45925bd6d9 | Python | 145 | 5 | """Find & analyze cyclepoints."""
from .zerox import find_zerox
from .extrema import find_extrema
from .phase import extrema_interpolated_phase
|
7c86d9d33e07951423411c2ebbd398ad6b936aa985022b032c8a6b5907fbcfc9 | Python | 145 | 11 | from . import CLI
from . import version
from . import src
__version__ = version.VERSION
__all__ = [
"CLI",
"version",
"src"
]
|
9c5814526e8dfb5fd63e871585f5f83eed7b95ac9edc913df1a5a9eb69b0a3d5 | Python | 146 | 10 | """
@file: __init__.py
Created on 26.09.19
@project: CrazyAra
@author: queensgambit
Please describe what the content of this file is about
"""
|
ddf57921f098361c80554ada3149c5643c318756b4e65d5feb34470010e2799b | Python | 146 | 5 | from .model_base import ModelBase
from .model import Model
from .solver_zero_flow_model import Solver
__all__ = ["ModelBase", "Model", "Solver"]
|
4125761c09eeb388cbbf8720f7fe92119f5801c50934098dafae4d084f420e13 | Python | 147 | 8 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
class ReporterBase:
pass
|
7fb7312b7c93d3d27fe0e1740e414b5532feb2ebb3e2ada25ec41397f34f637b | Python | 147 | 5 | """Module for spatio-temporal clustering algorithms."""
from sktime.clustering.spatio_temporal._st_dbscan import STDBSCAN
__all__ = ["STDBSCAN"]
|
01a1fb324439e36cc6bc96f3e25c5fbb5db341f14224a9bab47d25d8d6b26e03 | Python | 148 | 5 | """Monash forecasting datasets."""
from sktime.datasets.forecasting.monash._forecasting_data import ForecastingData
__all__ = ["ForecastingData"]
|
95e314704f1ecd4c1ff17e0f66521bbeb4c330a0b91cd79d16a5653177cf71b8 | Python | 150 | 8 | """
@file: __init__.py
Created on 10.06.18
@project: DeepCrazyhouse
@author: queensgambit
Please describe what the content of this file is about
"""
|
fd7e103ad89cc4064dd64a23b60621d22c81a776391bafcaae5744d7b52a1200 | Python | 151 | 4 | from ._full_pipeline import full_pipeline
from ._custom_tree_pipeline import custom_tree_pipeline
__all__ = ['custom_tree_pipeline', 'full_pipeline']
|
b70d0ffa844ba4efa80470a75747dad12834548381571286e667cb8b09b413e7 | Python | 152 | 6 | # encoding: utf8
'Packaging configuration.'
from setuptools import setup # type: ignore
setup() # everything configured via pyproject.toml
|
feffa06b2bbfc882857ea3d37fd71e1eb7967ec53a79c5f6e869ec847912c438 | Python | 152 | 3 | #!/usr/bin/env python3 -u
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Implements base classes for plotting in sktime."""
|
519fb56f46d1eb4ef0d70dfe5a7728d241694197c2fa6148690812e902f39e85 | Python | 153 | 9 | #!/usr/bin/python
#
# Channel decorator class for cnmodel
#
# Paul B. Manis, Ph.D. January 2016
#
from neuron import h
from .decorator import Decorator
|
ca16a697dfab7d53d23ce36dc2225d595374f76116fccce17f71e26edc80b41a | Python | 153 | 4 | __all__ = ['combinations', 'comparisons', 'symmetry', 'random_surf',
'identify', 'analysis', 'md', 'benson']
from pyRDTP.operations import *
|
e4617cbc82eb926bf5f5757be5541829bd59560e63b89a897c91cb9a804dd318 | Python | 153 | 6 | from torchvision.models.alexnet import AlexNet
class ImageClassifier(AlexNet):
def __init__(self):
super(ImageClassifier, self).__init__()
|
40549b063b0b0070976136a6591c49d44138f203f45375da6c65b0933b161d34 | Python | 154 | 6 | # SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
__all__ = ["load_op"]
from onnx.reference.ops.aionnxml._op_list import load_op
|
528586843cbedd1bb4109d062abe13e36ed61f89ee9e9e38ee8726af6dc994a2 | Python | 154 | 2 | # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Base classes for specific panel estimators, e.g., classifier and regressor."""
|
d223b78a5efc1440ea9dc64b86d5712e3c7ddaf312fe2816ebad7173a8db8886 | Python | 154 | 8 | """
@file: __init__.py
Created on 14.10.18
@project: crazy_ara_refactor
@author: queensgambit
Please describe what the content of this file is about
"""
|
0543937dea4e92a7cdd3a5a74cd58d31a756fad988bfd74301007615e7cfaeb5 | Python | 155 | 3 | from .MOTClient import MOTClient
from .SingleThreadedClientSim import SingleThreadedClientSim
from .move import apply_mvs, apply_queue_of_mvs, extract_mvs
|
16e86262edb0737938c6624d3effbf2193c85f26dca99005db6302e22fb8c7a5 | Python | 156 | 6 | """Tests for the forecaster base module."""
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["mloning"]
__all__ = []
|
7be03112c15f24e9140569a9d5e26ccf9da6563de07b210a5b1c54f23681a9c3 | Python | 156 | 7 | #%%
from giskard.match import GraphMatchSolver
import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.zeros((3, 3))
solver = GraphMatchSolver([A, B], A)
|
6191419e4d283e44c1e73461c25bcf73e183f8a3e8d12b10712c3b430bb689cf | Python | 157 | 4 | """Detect bursts: dual threshold approach."""
# This approach is currently aliased in from NeuroDSP
from neurodsp.burst import detect_bursts_dual_threshold
|
3eb7e10958dba061320501458909486029a6ff91de7f40a5e37809f33d61f276 | Python | 162 | 5 | """Base class for datasets."""
__all__ = ["BaseDataset", "_DatasetFromLoaderMixin"]
from sktime.datasets.base._base import BaseDataset, _DatasetFromLoaderMixin
|
284400ecc02cdbdcc220a6750a90068be3bb576d9a18335b74abfc984eae1a8f | Python | 163 | 7 | import unittest
loader = unittest.TestLoader()
suite = loader.discover(start_dir='./', pattern='*_test.py')
runner = unittest.TextTestRunner()
runner.run(suite)
|
d27f435976b34f5777200ca735d847781ac85b96e234f77d7afc1fffd6e9cdc2 | Python | 163 | 7 | import sys
from setuptools import setup
# For backward compatibility, use setup.py as a proxy to pyproject.toml
# All configuration is in pyproject.toml
setup()
|
92560b9d1b01cfb9095ae2596bc55dba478ef515b53c0e74081be71d267e3bae | Python | 164 | 6 | from .async_sampler import AsyncSampler
from .learning_monitor import LearningMonitor
from .sample_spec import SampleSpec
from . import utils
from .utils import *
|
5a98db1dd98e319a6501f888394679d8d0c69dd01439ed4a633008c73fe21e0c | Python | 165 | 5 | # Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseClassifier
from .image import ImageClassifier
__all__ = ['BaseClassifier', 'ImageClassifier']
|
b1580cccf4c49bffe9942c1383fddbe7a255ce0d3d7c0b8639540f3bf0e02b07 | Python | 165 | 3 | LOCAL_MIX = ("local", "local_mix", "node", "node_mix")
GLOBAL_MIX = ("global", "global_mix", "graph", "graph_mix")
MIXING_CHOICES = tuple([*LOCAL_MIX, *GLOBAL_MIX])
|
77724a20cb2ff3315a9ddc4056ff68af1d2a611753b8550be323130356c39282 | Python | 166 | 3 | from .mtrf import lag_gen, mtrf_train, mtrf_predict, \
mtrf_crossval, mtrf_multicrossval, mtrf_transform
from .helper import lag_builder, quadratic_regularization |
9a5245ae1f76e3bb1badd2706522ac2e2355ba32307d9c869dcae774bb1185d3 | Python | 166 | 5 | """Time series forecasting with LLM and agents."""
__all__ = ["AutoResearchForecaster"]
from sktime.forecasting.agentic._autoresearch import AutoResearchForecaster
|
3bbefea6f44dfd61fde8148513151fbf2da0dda12cd1f8c80766eb393585f135 | Python | 167 | 6 | from torchvision.models.squeezenet import SqueezeNet
class ImageClassifier(SqueezeNet):
def __init__(self):
super(ImageClassifier, self).__init__('1_1')
|
5d8ea9a77897b2f7ab993662dddca73f2cb777228e8c945f5f6879a08737be97 | Python | 167 | 7 | import pandas as pd
MODEL_FILE = './data/embeddings.csv'
MODEL_DF = pd.read_csv(MODEL_FILE, index_col=0)
def get_embs(smiles):
return MODEL_DF.loc[smiles].values |
61834588a416ffef176900d420cd286310c640c9fa4b4b9b7c964453415aa682 | Python | 167 | 7 | SEED = 42
UNKNOWN_DIAGNOSES_FILE = "unknown_diagnoses.json"
PATIENT_DF_FILE = "patient_df.csv"
SAMPLES_DF_FILE = "samples_df.csv"
AUDIO_DATA_FILE = "audio_data.npy"
|
97a90cbdc1a45cf4bb46cdcc6cf3f29423490c4ccff4d3762660da9b1d32a690 | Python | 167 | 4 | # Copyright (c) OpenMMLab. All rights reserved.
from .test import ONNXRuntimeClassifier, TensorRTClassifier
__all__ = ['ONNXRuntimeClassifier', 'TensorRTClassifier']
|
08bc8711e091f121e710cb79bb0ffa1d161dd7d24f1af593fee74283384ebe35 | Python | 169 | 4 | from .language_model_trainer import LanguageModelTrainer, TextCorpus
from .trainer import ModelTrainer
__all__ = ["LanguageModelTrainer", "ModelTrainer", "TextCorpus"]
|
69b7d915d41a32de238df86953d30c236e485341131141804a4748aa3d7f20cd | Python | 170 | 7 | """Unit test package for bycycle.
NOTES
-----
Current tests are not strong tests for accuracy.
They serve rather as 'smoke tests', for if anything fails completely.
"""
|
95a81fff697a8e820f06c84cdcb428916647af124db3be2eafc9f1ed4f945c92 | Python | 170 | 5 | __all__ = ["convert_from_multiindex_to_listdataset"]
from sktime.datatypes._adapter.pd_multiindex_to_list_dataset import (
convert_from_multiindex_to_listdataset,
)
|
c50c01a259cbeb1c48774b8a18924e20988ce0d9493117da3e0de52851765dad | Python | 170 | 8 | from abc import ABC
from .base_handler import BaseHandler
class VisionHandler(BaseHandler, ABC):
def __init__(self):
super(VisionHandler, self).__init__()
|
491c3bd14525420995f74e1afbdf71fd14b020bd3f298e6b784e0261ced0df36 | Python | 171 | 5 | from __future__ import annotations
from .jetnet import JetNet # noqa: F401
from .qgjets import QuarkGluon # noqa: F401
from .toptagging import TopTagging # noqa: F401
|
d30154e465d8ec9e0737b96c17df4f8f79a2179a861ad3bd260ce8de1370a578 | Python | 171 | 11 | """
Scatterplot Matrix
==================
_thumb: .5, .43
"""
import seaborn as sns
sns.set(style="ticks")
df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")
|
ef3025e6f2cb642abc1a24c952b10d24afa600340fa893d5e5bc940aa129b4a0 | Python | 171 | 7 | """TSC datasets from UCR UEA archive."""
from sktime.datasets.classification.ucr_uea_archive._ucr_uea_datasets import (
UCRUEADataset,
)
__all__ = ["UCRUEADataset"]
|
3bb20b954877b33faad6adaa04e3c0446f8ca401bff38b50ff5f1dbd3b907efc | Python | 173 | 8 | # SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class OpRunAiOnnxMl(OpRun):
op_domain = "ai.onnx.ml"
|
7606954e0925c8264b1dba5d283b1098405de8bf7707976ac259e6a40b466a8e | Python | 174 | 5 | """Docstring for sktime.catalogues.classification.bakeoff."""
from sktime.catalogues.classification.bakeoff._bakeoff import BakeOffCatalogue
__all__ = ["BakeOffCatalogue"]
|
68d35718b4d8c5806e2f35e5ba8a8994cd3a663019586a2dfd0111342bc2bfcf | Python | 175 | 3 | from utils.losses.chamfer_loss.chamfer_loss import ChamferLoss
from utils.losses.emd_loss import EMDLoss
from utils.losses.hungarian_mse.hungarian_mse import HungarianMSELoss
|
7011ec22e8c47d5a91191455d11e095c767448b4c1b15878f5a502faa9083182 | Python | 176 | 1 | from .datasets import LMDBDataset, PDBDataset, PTGDataset, SilentDataset, load_dataset, make_lmdb_dataset, get_file_list, extract_coordinates_as_numpy_arrays, download_dataset
|
a59d84fa8c8ab72b78f5e019316f4a23868f26a6c93bac1287c6dd4e71dd058e | Python | 176 | 5 | """Base module for datatypes."""
from sktime.datatypes._base._base import BaseConverter, BaseDatatype, BaseExample
__all__ = ["BaseConverter", "BaseDatatype", "BaseExample"]
|
593f8915c9e336d4cf483ed9751c1b01ed414e621d53cf4db0045391d3334310 | Python | 177 | 8 | import dnnbrain
import os
import pytest
# Define dnnbrain_path as a global variable in pytest
@pytest.fixture
def dnnbrain_path():
return os.path.dirname(dnnbrain.__file__) |
d5fee9edeaa92ea7abbf1ce37c9db4daab1f2d134987b37ad73f196dd92f749c | Python | 177 | 4 | """Functions relevant to optimal default templateflow behaviour."""
from .surface_detection import _get_surface_level_for_template
__all__ = ['_get_surface_level_for_template'] |
216ddf357686180b789b7de51a7c7fb11e48f8a1d38db8843b5998c6ab4748be | Python | 178 | 5 | deco = "_CN_centrality"
cellline = ['HCT116']
#cellline = ["HELA", "HCT116", "HL60", "K562","A549","NB4"]
# CTCF, RRBS, H3K4me3
epi_feature = ["CTCF","RRBS", "H3K4me3","DNase"]
|
0caf7bee51b72604f97a88e6d79e1d2adb499ceb3fbef39a9a064cee2b00af6d | Python | 180 | 9 | # SPDX-License-Identifier: LGPL-3.0-or-later
import torch
from deepmd.dpmodel.long_range import (
make_base_long_range,
)
BaseLongRange = make_base_long_range(torch.Tensor)
|
40bdcb9e51eae3d850e9605cfe3a7ff369c616573a8a2e30b8e9888b3ad1eecf | Python | 180 | 4 | from .__version__ import __version__ as __version__
from .pyxdf import load_xdf, match_streaminfos, resolve_streams
__all__ = ["load_xdf", "match_streaminfos", "resolve_streams"]
|
6dfabd089132ac9cb47f391553bd9cb3f9f9d5e482875db26142bdf92172294b | Python | 180 | 11 | """
Scatterplot Matrix
==================
_thumb: .3, .2
"""
import seaborn as sns
sns.set_theme(style="ticks")
df = sns.load_dataset("penguins")
sns.pairplot(df, hue="species")
|
35f96fcef830e445ed5cfad5ef0da26c44df726369c840d029ed3c0ce1e4a343 | Python | 181 | 10 | """Lists of datasets provided in this directory."""
classification_data = [
"ACSF1",
"ArrowHead",
"BasicMotions",
"ItalyPowerDemand",
"OSULeaf",
"PLAID",
]
|
0871ab9f0e06322bd0cb335ffbfe6fee88fc7750ac054fdf3081fd75db2bed0c | Python | 182 | 9 |
"""
This module does the following:
Exports the model folder to generate a Model Archive file out of it in .mar format
"""
from . import version
__version__ = version.__version__
|
0aea18a14e84033b28409db9d76241bebcc38e01fb14aefc4ea3a2993023602c | Python | 182 | 6 | from qiime2.plugin import SemanticType
from q2_types.sample_data import SampleData
Matching = SemanticType('Matching',
variant_of=SampleData.field['type'])
|
068effb05cac96e232d5edae67a32fc9ec03c196b6832ffe19c6d0a2de03d140 | Python | 184 | 12 |
"""
Dummy custom service which is function based
"""
from ts.context import Context
# noinspection PyUnusedLocal
def infer(data, context):
return isinstance(context, Context)
|
eb3f7aadf2fc8335accb29c1439ac2759d670a1a245d44570c3ab2049ec24dac | Python | 184 | 7 | """Naive detectors for simple baselines and use in composites or pipelines."""
from sktime.detection.naive._threshold import ThresholdDetector
__all__ = [
"ThresholdDetector",
]
|
e071bf2307cfc70e1895ba1fea07954271f70c644662e696a894c04195e85686 | Python | 185 | 9 | import numpy as np
import pandas as pd
import atom3d.datasets as da
import atom3d.protein.scop as sc
def test_get_scop_index():
# TODO: write test for sc.get_scop_index()
pass |
4c3656d762bcc6ab1b8f6fcac5d578d048d25cf447834f3fe1b2a6b331389473 | Python | 186 | 9 | """Dummy forecasters catalogue."""
from sktime.catalogues.forecasting.dummy._dummy_forecasting import (
DummyForecastingCatalogue,
)
__all__ = [
"DummyForecastingCatalogue",
]
|
987f20724951d463695aa7d5a8e53d3df28513d6068a055a20f83200fb968cda | Python | 186 | 4 | from .transformer_decoder import TransformerDecoder
from .single_layer_decoder import SingleLayerDecoder
from .bilinear_decoder import BilinearDecoder
from .ntn_decoder import NTNDecoder |
00fabf69c0b4aa4b276127553dcefce3b5a1a59f0698d565f95321d2d2b5f7ee | Python | 187 | 7 | from torchvision.models.resnet import ResNet, BasicBlock
class ImageClassifier(ResNet):
def __init__(self):
super(ImageClassifier, self).__init__(BasicBlock, [2, 2, 2, 2])
|
51ae5fb6926e79317cd6830840d4a230fa41334c21cd6c29e3e3963219302a2b | Python | 187 | 6 | """Burst detection."""
from .amp import detect_bursts_amp
from .cycle import detect_bursts_cycles
from .dualthresh import detect_bursts_dual_threshold
from .utils import recompute_edges
|
f6166844af3e57a553cea31ad482a5e3a98a66ae3c3a01a4f50b33870adb29eb | Python | 187 | 8 | # Copyright (c) 2019. Martin Haesemeyer. All rights reserved.
#
# Licensced under the MIT license. See LICENSE
"""
Module for interfacing with CMTK for imaging stack registration
"""
|
89c093816a58d354593ec943951a22fdcf5ae05297c86d4d02ea107184ab7ba9 | Python | 188 | 5 | """Predict IsoPrism proportions from a short-read H5AD and a checkpoint."""
from short2long.competitive_predict import align_genes, infer_h5ad, main
if __name__ == "__main__":
main()
|
f67c25f6036b16cbd94650fc4dc41f35d9ce8e29e91748cf9836c48d48b37641 | Python | 188 | 5 | """Loads NetworkUnit model classes"""
from .nest_simulation import nest_simulation
from .loaded_spiketrains import loaded_spiketrains
from .stochastic_activity import stochastic_activity
|
b31ae79aa8a8cca471dab034079693eabdc778a571d3e8a329eb2abaa5758b5c | Python | 189 | 8 | from .wrapper import model_ihc, model_synapse, seed_rng
try:
from .wrapper import get_matlab
MATLAB_FOUND = True
except:
MATLAB_FOUND = False
from .cache import get_spiketrain
|
d3eb18d42c18a9a821eaca7e6740bca1e1d387e9f5b03e6d5581a2d5ddadc9da | Python | 189 | 8 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
__all__ = ["load_op"]
from onnx.reference.ops._op_list import load_op
|
caa5708cb84ea8900704e5595dcf5328ea417ff04ccb0eeccac729c354008f3e | Python | 190 | 5 | """Module exports: Panel type checkers, converters and mtype inference."""
from sktime.datatypes._panel._convert import convert_dict as convert_dict_Panel
__all__ = ["convert_dict_Panel"]
|
056032a2121e0010e5fa4b589f63a23a817a538281b56c9db846dbc8273c4ee7 | Python | 191 | 7 | import redis
SETTINGS = {
# 'DB_URL': os.path.join(get_out_dir(pciSeq_cfg._BASE['output_path'], ''), 'pciSeq.db'),
'POOL': redis.ConnectionPool(host='localhost', port=6379, db=0)
}
|
b38eb8f52675b698108d450fe90653ad9c5365903a7d457917b44e87ec983cb2 | Python | 191 | 5 | """Module exports: Series type checkers, converters and mtype inference."""
from sktime.datatypes._table._convert import convert_dict as convert_dict_Table
__all__ = ["convert_dict_Table"]
|
daa9f694a39b645afce74c28f2fdb6c43e42cf6da64a0c55bc829c8b03df9a30 | Python | 191 | 8 | from __future__ import print_function
import sys
filename = sys.argv[1]
extension = sys.argv[2]
suffix = '.'.join(filename.split('.')[1:])
print(filename.split('_')[0]+extension+'.'+suffix) |
1a9b8502fe308426fb596ec7776bfac989720769a9e23259e77311e802100e1e | Python | 193 | 8 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
for line in fi:
seq=line.rstrip().split('\t')
fo.write(seq[0].replace('-','\t')+'\t'+seq[1].replace('-','\t')+'\n')
fo.close()
|
866574165b037b26b3be4eba6c6dce9250a2ec14f76c55f657bf853347109039 | Python | 193 | 6 | """Compute features."""
from .features import compute_features
from .burst import compute_burst_features
from .cyclepoints import compute_cyclepoints
from .shape import compute_shape_features
|
5522b33ed49dd62361714d8947103625bb4a4455a6c57c8a93de3523f9a2eba3 | Python | 194 | 5 | """Module exports: Series type checkers, converters and mtype inference."""
from sktime.datatypes._series._convert import convert_dict as convert_dict_Series
__all__ = ["convert_dict_Series"]
|
6990938cf35326a6b45e68015a297190274cae96ce8ca9b7c60b8a8060db333e | Python | 194 | 5 | """Module defining models."""
from onmt.models.model_saver import build_model_saver, ModelSaver
from onmt.models.model import NMTModel
__all__ = ["build_model_saver", "ModelSaver", "NMTModel"]
|
d2f4710270ec1e569812eb164e0e2ffe761e605eea1c790fa411880f3fa7237b | Python | 195 | 5 | """Metric for clustering."""
__all__ = ["medoids", "dba", "mean_average"]
from sktime.clustering.metrics.averaging import dba, mean_average
from sktime.clustering.metrics.medoids import medoids
|
e54e85028651d7aef972ce3e1c64444c6ca05a1f125ee216a6c94f7a051a5282 | Python | 195 | 5 | # Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
from .repeat_aug import RepeatAugSampler
__all__ = ('DistributedSampler', 'RepeatAugSampler')
|
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