repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
mmda | mmda-main/tests/test_types/test_span.py | import unittest
from mmda.types import box as mmda_box
from mmda.types import span as mmda_span
class TestSpan(unittest.TestCase):
def setUp(cls):
cls.span = mmda_span.Span(start=0, end=0)
cls.span_dict = {
"start": 0,
"end": 8,
"box": {
"left":... | 3,660 | 34.892157 | 87 | py |
mmda | mmda-main/tests/test_types/test_metadata.py | """
Tests for Metadata
@soldni
"""
from copy import deepcopy
import unittest
from mmda.types import Metadata
class TestSpanGroup(unittest.TestCase):
def test_add_keys(self):
metadata = Metadata()
metadata['foo'] = 1
self.assertEqual(metadata.foo, 1)
metadata.bar = 2
s... | 1,847 | 25.028169 | 77 | py |
mmda | mmda-main/tests/test_types/test_span_group.py | """
Tests for SpanGroup
@rauthur
"""
import json
import unittest
from mmda.types import SpanGroup, Document, Span
class TestSpanGroup(unittest.TestCase):
doc: Document
def setUp(self) -> None:
self.doc = Document("This is a test document!")
def test_annotation_attaches_document(self):
... | 534 | 18.107143 | 68 | py |
mmda | mmda-main/tests/test_types/test_json_conversion.py | '''
Description: Test whether all properties for an mmda doc are preserved when
converting to json and back.
Author: @soldni
'''
import json
from pathlib import Path
from mmda.types import BoxGroup, SpanGroup, Document, Metadata
from mmda.parsers import PDFPlumberParser
PDFFILEPATH = Path(__file_... | 2,013 | 31.483871 | 83 | py |
mmda | mmda-main/tests/test_types/test_annotation.py | from mmda.types.annotation import BoxGroup
from mmda.types.box import Box
import unittest
class TestBoxGroup(unittest.TestCase):
def setUp(cls) -> None:
cls.box_group_json = {'boxes': [{'left': 0.1,
'top': 0.6,
'width': 0.36... | 1,430 | 37.675676 | 80 | py |
mmda | mmda-main/tests/test_recipes/test_core_recipe.py | """
@kylel
"""
import os
import unittest
from mmda.recipes import CoreRecipe
from mmda.types import BoxGroup, Document, PILImage, SpanGroup
from tests.test_recipes.core_recipe_fixtures import (
BASE64_PAGE_IMAGE,
FIRST_3_BLOCKS_JSON,
FIRST_5_ROWS_JSON,
FIRST_10_TOKENS_JSON,
FIRST_10_VILA_JSONS,
... | 4,970 | 33.282759 | 97 | py |
mmda | mmda-main/tests/test_recipes/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/tests/test_recipes/core_recipe_fixtures.py | FIRST_1000_SYMBOLS = """Field\nTask\nDataset\nSOTA\nB ERT -Base\nS CI B ERT\nFrozen\nFinetune\nFrozen\nFinetune\nBio\nNER\nBC5CDR (Li et al., 2016)\n88.85 7\n85.08\n86.72\n88.73\n90.01\nJNLPBA (Collier and Kim, 2004)\n78.58\n74.05\n76.09\n75.77\n77.28\nNCBI-disease (Dogan et al., 2014)\n89.36\n84.06\n86.88\n86.39\n88.5... | 249,118 | 509.489754 | 234,906 | py |
mmda | mmda-main/tests/test_utils/test_stringify.py | """
@kylel
"""
import json
import pathlib
import unittest
from mmda.types.annotation import SpanGroup
from mmda.types.box import Box
from mmda.types.document import Document
from mmda.types.span import Span
from mmda.utils.stringify import stringify_span_group
class TestStringify(unittest.TestCase):
def test... | 15,427 | 38.660668 | 128 | py |
mmda | mmda-main/tests/test_utils/test_tools.py | """
@kylel
"""
import json
import pathlib
import unittest
from mmda.types.annotation import BoxGroup, SpanGroup
from mmda.types.span import Span
from mmda.types.box import Box
from mmda.types.document import Document
from mmda.utils.tools import MergeSpans
from mmda.utils.tools import box_groups_to_span_groups
f... | 25,116 | 55.064732 | 153 | py |
mmda | mmda-main/tests/test_utils/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/tests/test_utils/test_outline_metadata.py | """
Test extraction of outline metadata from a PDF.
@rauthur
"""
import pathlib
import unittest
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.utils.outline_metadata import (
Outline,
PDFMinerOutlineExtractor,
PDFMinerOutlineExtractorError,
)
class TestPDFMinerOutlineExtractor(... | 2,325 | 30.432432 | 85 | py |
mmda | mmda-main/tests/test_parsers/test_override.py | import os
import pathlib
import unittest
from typing import List
from mmda.types.document import Document
from mmda.types.annotation import SpanGroup
from mmda.types.names import TokensField
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.base_predictors.base_predictor import BasePredi... | 1,360 | 26.22 | 72 | py |
mmda | mmda-main/tests/test_parsers/test_pdf_plumber_parser.py | """
@kylel
"""
import json
import os
import pathlib
import re
import unittest
import numpy as np
from mmda.parsers import PDFPlumberParser
from mmda.types import Box, BoxGroup, Document, Span, SpanGroup
class TestPDFPlumberParser(unittest.TestCase):
def setUp(cls) -> None:
cls.fixture_path = pathlib.P... | 9,608 | 35.536122 | 126 | py |
mmda | mmda-main/tests/test_parsers/test_grobid_header_parser.py | import os
import pathlib
import unittest
import unittest.mock as um
import pytest
from mmda.parsers.grobid_parser import GrobidHeaderParser
os.chdir(pathlib.Path(__file__).parent)
XML_OK = open("../fixtures/grobid-tei-maml-header.xml").read()
XML_NO_TITLE = open("../fixtures/grobid-tei-no-title.xml").read()
XML_NO_A... | 3,260 | 35.640449 | 84 | py |
mmda | mmda-main/tests/test_parsers/test_grobid_augment_existing_document_parser.py | import json
import logging
import os
import pathlib
import unittest
import unittest.mock as um
import pytest
from mmda.types.document import Document
from mmda.parsers.grobid_augment_existing_document_parser import (
GrobidAugmentExistingDocumentParser,
)
os.chdir(pathlib.Path(__file__).parent)
PDF_PATH = "../fi... | 3,677 | 34.028571 | 137 | py |
mmda | mmda-main/tests/test_internal_ai2/test_api.py | import unittest
from pydantic.error_wrappers import ValidationError
import ai2_internal.api as mmda_api
import mmda.types.annotation as mmda_ann
from mmda.types import Metadata
from mmda.types.box import Box as mmdaBox
from mmda.types.span import Span as mmdaSpan
class ClassificationAttributes(mmda_api.Attributes):... | 4,062 | 31.766129 | 112 | py |
mmda | mmda-main/tests/test_eval/test_metrics.py | import unittest
from mmda.eval.metrics import box_overlap, levenshtein
from mmda.types.box import Box
class TestLevenshteinDistance(unittest.TestCase):
def test_calculates(self):
assert levenshtein("hello", "kelm") == 3
assert levenshtein("kelm", "hello") == 3
assert levenshtein("", "hel... | 2,670 | 31.975309 | 72 | py |
mmda | mmda-main/release/pypi-aliases/scipdf/src/scipdf/__init__.py | from mmda import (
eval,
featurizers,
parsers,
predictors,
rasterizers,
types,
utils
)
__all__ = [
"eval",
"featurizers",
"parsers",
"predictors",
"rasterizers",
"types",
"utils"
]
| 238 | 10.95 | 18 | py |
mmda | mmda-main/release/pypi-aliases/papermage/src/papermage/__init__.py | from mmda import (
eval,
featurizers,
parsers,
predictors,
rasterizers,
types,
utils
)
__all__ = [
"eval",
"featurizers",
"parsers",
"predictors",
"rasterizers",
"types",
"utils"
]
| 238 | 10.95 | 18 | py |
PRISim | PRISim-master/setup.py | import glob
import os
import re
from subprocess import Popen, PIPE
from setuptools import setup, find_packages
githash = 'unknown'
if os.path.isdir(os.path.dirname(os.path.abspath(__file__)) + '/.git'):
try:
gitproc = Popen(['git', 'rev-parse', 'HEAD'], stdout=PIPE)
githash = gitproc.communicate()... | 3,158 | 38.987342 | 100 | py |
PRISim | PRISim-master/prisim/interferometry.py | from __future__ import division
import numpy as NP
import scipy.constants as FCNST
from scipy import interpolate, ndimage
import datetime as DT
import progressbar as PGB
import os, ast
import copy
import astropy
from astropy.io import fits, ascii
from astropy.coordinates import Galactic, SkyCoord, ICRS, FK5, AltAz, Ear... | 579,057 | 57.526177 | 587 | py |
PRISim | PRISim-master/prisim/baseline_delay_horizon.py | import numpy as NP
import scipy.constants as FCNST
from astroutils import geometry as GEOM
#################################################################################
def delay_envelope(bl, dircos, units='mks'):
"""
---------------------------------------------------------------------------
Estimat... | 9,054 | 36.110656 | 122 | py |
PRISim | PRISim-master/prisim/bispectrum_phase.py | from __future__ import division
import glob
import numpy as NP
from functools import reduce
import numpy.ma as MA
import progressbar as PGB
import h5py
import healpy as HP
import warnings
import copy
import astropy.cosmology as CP
from astropy.time import Time, TimeDelta
from astropy.io import fits
from astropy import ... | 309,270 | 62.310338 | 533 | py |
PRISim | PRISim-master/prisim/primary_beams.py | import numpy as NP
import scipy.constants as FCNST
import scipy.special as SPS
import h5py
from astroutils import geometry as GEOM
#################################################################################
def primary_beam_generator(skypos, frequency, telescope, freq_scale='GHz',
sk... | 152,224 | 52.808766 | 256 | py |
PRISim | PRISim-master/prisim/__init__.py | import os as _os
__version__='2.2.1'
__description__='Precision Radio Interferometry Simulator'
__author__='Nithyanandan Thyagarajan'
__authoremail__='[email protected]'
__maintainer__='Nithyanandan Thyagarajan'
__maintaineremail__='[email protected]'
__url__='http://github.com/nithyanandan/prisim'
with... | 453 | 33.923077 | 93 | py |
PRISim | PRISim-master/prisim/delay_spectrum.py | from __future__ import division
import numpy as NP
import multiprocessing as MP
import itertools as IT
import progressbar as PGB
# import aipy as AP
import astropy
from astropy.io import fits
import astropy.cosmology as CP
import scipy.constants as FCNST
import healpy as HP
from distutils.version import LooseVersion
im... | 265,227 | 57.368838 | 341 | py |
PRISim | PRISim-master/prisim/examples/codes/BispectrumPhase/combine_pol_multiday_closure_PS_analysis.py | import copy
import numpy as NP
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import matplotlib.ticker as PLTick
import yaml, argparse, warnings
import progressbar as PGB
from prisim import bispectrum_phase as BSP
import ipdb as PDB
PLT.switch_backend("TkAgg")
if __name__ == '__main__':
## ... | 32,485 | 74.37355 | 306 | py |
PRISim | PRISim-master/prisim/examples/codes/BispectrumPhase/multiday_closure_PS_analysis.py | import copy, glob
import progressbar as PGB
import numpy as NP
import numpy.ma as MA
from scipy import interpolate
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import matplotlib.ticker as PLTick
import yaml, argparse, warnings
from astropy.io import ascii
import astropy.units as U
import astropy.con... | 159,173 | 78.547226 | 481 | py |
PRISim | PRISim-master/prisim/scriptUtils/replicatesim_util.py | import pprint, yaml
import numpy as NP
from pyuvdata import UVData
from astroutils import geometry as GEOM
import prisim
from prisim import interferometry as RI
prisim_path = prisim.__path__[0]+'/'
def replicate(parms=None):
if (parms is None) or (not isinstance(parms, dict)):
example_yaml_filepath = pris... | 6,296 | 49.782258 | 255 | py |
PRISim | PRISim-master/prisim/scriptUtils/write_PRISim_bispectrum_phase_to_npz_util.py | import pprint, yaml
import numpy as NP
from prisim import bispectrum_phase as BSP
import prisim
prisim_path = prisim.__path__[0]+'/'
def write(parms=None):
if (parms is None) or (not isinstance(parms, dict)):
example_yaml_filepath = prisim_path+'examples/ioparms/model_bispectrum_phase_to_npz_parms.yaml'
... | 2,073 | 44.086957 | 205 | py |
PRISim | PRISim-master/prisim/scriptUtils/__init__.py | import os as _os
__version__='0.1.0'
__description__='Precision Radio Interferometry Simulator'
__author__='Nithyanandan Thyagarajan'
__authoremail__='[email protected]'
__maintainer__='Nithyanandan Thyagarajan'
__maintaineremail__='[email protected]'
__url__='http://github.com/nithyanandan/prisim'
with... | 456 | 34.153846 | 96 | py |
PRISim | PRISim-master/scripts/write_PRISim_bispectrum_phase_to_npz.py | #!python
import yaml, argparse
import prisim
from prisim.scriptUtils import write_PRISim_bispectrum_phase_to_npz_util
import ipdb as PDB
prisim_path = prisim.__path__[0]+'/'
if __name__ == '__main__':
## Parse input arguments
parser = argparse.ArgumentParser(description='Program to extract bispectrum p... | 875 | 32.692308 | 209 | py |
PRISim | PRISim-master/scripts/make_redundant_visibilities.py | #!python
import yaml
import argparse
import numpy as NP
from prisim import interferometry as RI
import write_PRISim_visibilities as PRISimWriter
import ipdb as PDB
if __name__ == '__main__':
## Parse input arguments
parser = argparse.ArgumentParser(description='Program to duplicate redundant baseline me... | 5,594 | 49.863636 | 199 | py |
PRISim | PRISim-master/scripts/FEKO_beam_to_healpix.py | #!python
import ast
import numpy as NP
import healpy as HP
import yaml, h5py
from astropy.io import fits
import argparse
from scipy import interpolate
import progressbar as PGB
from astroutils import mathops as OPS
import ipdb as PDB
def read_FEKO(infile):
freqs = []
theta_list = []
phi_list = []
gain... | 13,656 | 47.088028 | 250 | py |
PRISim | PRISim-master/scripts/replicate_sim.py | #!python
import yaml, argparse, copy, warnings
import prisim
from prisim.scriptUtils import replicatesim_util
import ipdb as PDB
prisim_path = prisim.__path__[0]+'/'
if __name__ == '__main__':
## Parse input arguments
parser = argparse.ArgumentParser(description='Program to replicate simulated interfer... | 1,250 | 28.785714 | 218 | py |
PRISim | PRISim-master/scripts/altsim_interface.py | #!python
import yaml, argparse, ast, warnings
import numpy as NP
from astropy.io import ascii
from astropy.time import Time
import prisim
prisim_path = prisim.__path__[0]+'/'
def simparms_from_pyuvsim_to_prisim(pyuvsim_parms, prisim_parms):
if not isinstance(pyuvsim_parms, dict):
raise TypeError('Input p... | 8,667 | 49.988235 | 376 | py |
PRISim | PRISim-master/scripts/prisim_resource_monitor.py | #!python
import numpy as NP
import os
import subprocess
import psutil
import time
import argparse
from astroutils import writer_module as WM
def monitor_memory(pids, tint=2.0):
if not isinstance(pids , list):
raise TypeError('Input PIDs must be specified as a list')
try:
pids = map(int, pids... | 2,118 | 36.175439 | 130 | py |
PRISim | PRISim-master/scripts/prisim_to_uvfits.py | #!python
import yaml
import argparse
import numpy as NP
import prisim
from prisim import interferometry as RI
prisim_path = prisim.__path__[0]+'/'
def write(parms, verbose=True):
if 'infile' not in parms:
raise KeyError('PRISim input file not specified. See example in {0}examples/ioparms/uvfitsparms.yam... | 2,451 | 42.017544 | 162 | py |
PRISim | PRISim-master/scripts/setup_prisim_data.py | #!python
import os
import argparse
import yaml
import gdown
import tarfile
import prisim
prisim_path = prisim.__path__[0]+'/'
tarfilename = 'prisim_data.tar.gz'
def download(url=None, outfile=None, verbose=True):
if url is not None:
if not isinstance(url, str):
raise TypeError('Input url must... | 3,358 | 34.734043 | 209 | py |
PRISim | PRISim-master/scripts/write_PRISim_visibilities.py | #!python
import yaml
import argparse
import numpy as NP
from prisim import interferometry as RI
import ipdb as PDB
def save(simobj, outfile, outformats, parmsfile=None):
parms = None
for outfmt in outformats:
if outfmt.lower() == 'hdf5':
simobj.save(outfile, fmt=outfmt, verbose=True, tabty... | 5,632 | 46.737288 | 199 | py |
PRISim | PRISim-master/scripts/test_mpi4py_for_prisim.py | #!python
from mpi4py import MPI
## Set MPI parameters
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
name = MPI.Get_processor_name()
if rank == 0:
print('\n{0} processes initiated...'.format(nproc))
print('\tProcess #{0} completed'.format(rank))
if rank == 0:
print('MPI test successfu... | 326 | 17.166667 | 55 | py |
PRISim | PRISim-master/scripts/prisim_grep.py | #!python
import os, glob, sys
import yaml
import argparse
import numpy as NP
import astroutils.nonmathops as NMO
import prisim
prisim_path = prisim.__path__[0]+'/'
def findType(refval):
valtype = ''
if isinstance(refval, bool):
valtype = 'bool'
elif isinstance(refval, str):
valtype = 'str... | 5,512 | 37.552448 | 205 | py |
PRISim | PRISim-master/scripts/run_prisim.py | #!python
import os, shutil, subprocess, pwd, errno, warnings
from mpi4py import MPI
import yaml
import h5py
import argparse
import copy
import numpy as NP
from astropy.io import fits, ascii
from astropy.coordinates import Galactic, FK5, ICRS, SkyCoord, AltAz, EarthLocation
from astropy import units as U
from astropy.t... | 122,758 | 51.461111 | 537 | py |
PRISim | PRISim-master/scripts/update_PRISim_noise.py | #!python
import yaml, argparse
import numpy as NP
import prisim
from prisim import interferometry as RI
import write_PRISim_visibilities as PRISimWriter
import ipdb as PDB
prisim_path = prisim.__path__[0]+'/'
if __name__ == '__main__':
## Parse input arguments
parser = argparse.ArgumentParser(descripti... | 5,922 | 43.871212 | 253 | py |
PRISim | PRISim-master/scripts/prisim_ls.py | #!python
import glob
import itertools
import yaml
import argparse
import numpy as NP
import prisim
prisim_path = prisim.__path__[0]+'/'
def lsPRISim(args):
project_dir = args['project']
simid = args['simid']
folder_separator = ''
if not project_dir.endswith('/'):
folder_separator = '/'
si... | 4,293 | 38.394495 | 171 | py |
dstqa | dstqa-master/multiwoz_format.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import sys
import os
import json
import pdb
import copy
import random
assert(len(sys.argv) == 4)
ontology_... | 15,023 | 35.914005 | 171 | py |
dstqa | dstqa-master/multiwoz_2.1_format.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import sys
import os
import json
import pdb
import copy
import random
assert(len(sys.argv) == 4)
ontology_... | 18,246 | 35.567134 | 171 | py |
dstqa | dstqa-master/calc_elmo_embeddings.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
# pre-calculate elmo embeddings of each sentence in each dialog
import sys
import pdb
import json
import pi... | 4,043 | 42.956522 | 294 | py |
dstqa | dstqa-master/formulate_pred_belief_state.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
# formulate pred generated by predictor
import sys
import json
import pdb
from copy import deepcopy
def re... | 3,511 | 27.552846 | 75 | py |
dstqa | dstqa-master/dstqa/dstqa.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import pdb
import math
import logging
import os.path
import pickle
import random
from typing import Any, Di... | 26,073 | 48.103578 | 193 | py |
dstqa | dstqa-master/dstqa/dstqa_reader.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import os
import pdb
import json
import logging
import numpy as np
from overrides import overrides
from typ... | 12,637 | 45.29304 | 165 | py |
dstqa | dstqa-master/dstqa/accuracy.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
from allennlp.training.metrics import Average, BooleanAccuracy, CategoricalAccuracy
from . import dstqa_uti... | 2,200 | 43.02 | 104 | py |
dstqa | dstqa-master/dstqa/__init__.py | from .dstqa import DSTQA
from .dstqa_reader import DSTQAReader
#from .dstqa_predictor import DSTQAPredictor
| 109 | 21 | 44 | py |
dstqa | dstqa-master/dstqa/dstqa_predictor.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import json
import os
import numpy as np
from overrides import overrides
from allennlp.common.util import ... | 7,830 | 41.559783 | 152 | py |
dstqa | dstqa-master/dstqa/dstqa_util.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
def find_span(utt, ans):
def is_match(utt, ans, i):
match = True
for j in range(len(ans)):
... | 1,386 | 25.673077 | 74 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/generate.py | from turtle import color
import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
colors = [
[233/256, 110/256, 236/256], # #e96e... | 12,969 | 35.432584 | 103 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/ES_ICNN.py | import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
import torch
import random
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
# torch.cuda.... | 4,181 | 31.169231 | 169 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/ES_Quadratic.py | import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
import torch
import random
import math
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
# ... | 5,582 | 30.016667 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/Control_Nonlinear_Icnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ICNN(nn.Module):
def __init__(self, input_shape, layer_sizes, activation_fn):
super(ICNN, self).__init__()
self._input_shape = input_shape
self._layer_sizes = layer_sizes
self._activation_fn = activation_fn
... | 3,750 | 34.386792 | 122 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/AS.py | import torch
import torch.nn.functional as F
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_h... | 1,720 | 21.064103 | 70 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/functions.py | import numpy as np
import math
import torch
import timeit
from scipy import integrate
start = timeit.default_timer()
np.random.seed(1)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.L... | 3,792 | 26.092857 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/plot.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
matplotlib.rcParams['text.usetex'] = True
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdot', lw=1.5)
# minor grid lin... | 3,641 | 34.359223 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Table2/table2.py | import numpy as np
import sys
sys.path.append('./data')
# generate the data of table2
def L2_norm(a,case):
# the case for P_1
if case == 0:
Y = np.load('./data/{}_data_P1_Q2_20.npy'.format(a))
ind = np.delete(np.arange(20),np.array([1,3,11,15]))
Y = Y[ind,:].astype('float64')
... | 1,237 | 31.578947 | 88 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 1,843 | 22.341772 | 82 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/generate.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
import sys
sys.path.append('./neural_sde/stuart')
from AS import *
from functions import *
start = timeit.default_timer()
stuart_model = Net(D_in,H1,D_out)
# stuar... | 1,915 | 24.210526 | 96 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/functions.py | import torch
import numpy as np
import timeit
import matplotlib.pyplot as plt
'''
x = rho_1,rho_2,rho_n, w1,w2,wn-1
'''
#Transform \Tilde{\theta} to \theta
def theta(W):
W = torch.cat([W,torch.tensor([1.0])],0)
T = torch.eye(len(W))
for i in range(len(T)):
for k in range(len(T)):
... | 3,921 | 30.376 | 181 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/plot.py | from functions import *
import numpy as np
import torch
import matplotlib.pyplot as plt
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 35
def plot_grid():
plt.grid(b... | 4,294 | 34.204918 | 184 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Figure_in_Prop_3.2/plot.py | import numpy as np
import matplotlib.pyplot as plt
import math
# import pylustrator
# pylustrator.start()
np.random.seed(10)
def nonlinear(N,dt,x0):
X = []
X.append(x0)
z = np.random.normal(0,1,N)
for i in range(N):
x = X[i][0]
new_x = x + x*math.log(abs(x))*dt + 2*x*x*math.sqrt(dt)*z... | 5,741 | 44.212598 | 347 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/invert_pendulum_control_1227.py | import numpy as np
import math
import torch
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
from functions import *
from base_function import colors
alpha = 1.0
fontsize=35
fontsize_legend = 20
MarkerSize = 60
linewidth = 5
color_w = 0.15 #0.5
framealpha = 0.7
N_seg = ... | 6,416 | 33.315508 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/algo2.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = ... | 2,276 | 24.021978 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/functions.py | import numpy as np
import math
import torch
import timeit
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
from scipy.integrate import odeint
import numpy as np
np.random.seed(10)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
... | 4,192 | 29.830882 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/base_function.py | import numpy as np
import matplotlib.pyplot as plt
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6, 0.2], # olive
# [0.5333333333333333, 0.13333333333333333, 0.3333333333333333], # wine
[255/255, 165/255, 0],
# [0.8666666666666667, 0.8, 0.4666666666666667], # sand
# [223/256, 73... | 2,192 | 31.731343 | 103 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/Inver_pendulum_1227.py | # import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
from invert_pendulum_no_control_1227 import plot_fig1 as plot_fig1_no_control
from invert_pendulum_no_control_1227 import plot_fig2 as plot_fig2_no_control
from invert_pendulum_control_1... | 1,133 | 32.352941 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/invert_pendulum_no_control_1227.py | import numpy as np
import math
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import matplotlib.gridspec as gridspec
from functions import *
from base_function import colors
# colors = [
# [233/256, 110/256, 236/256], # #e96eec
# [223/256, 73/256, 54/256], # #d... | 7,041 | 36.259259 | 135 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot_trajectory.py | from statistics import mean
import sys
sys.path.append('./neural_sde')
import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
# import pylustrator
# pylustrator.start()
start = timeit.default_timer()
A = torch.load('.... | 816 | 26.233333 | 105 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot_loss.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import pylustrator
pylustrator.start()
import seaborn as sns
sns.set_theme(style="white")
def plot_a(a):
L = np.load('./neural_sde/hyper_a/a_{}.npy'.format(a))
r_L = np.zeros(1000-len(L))
L = np.concatenate((L,r_L),axis=0)
# np.concaten... | 1,949 | 30.967213 | 110 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 2,236 | 25.011628 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/test.py | import sys
sys.path.append('./neural_sde')
import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
A = torch.ones(2,100)
# B = torch.diagonal(A)
print(A[:,0:100:10].shape) | 273 | 20.076923 | 39 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/generate.py | import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_in... | 2,698 | 28.021505 | 92 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/u_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_i... | 1,330 | 26.729167 | 80 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/functions.py | from os import stat
import numpy as np
import math
import torch
import timeit
import random
import matplotlib.pyplot as plt
from matplotlib import cm
from scipy.integrate import odeint
import numpy as np
np.random.seed(10)
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
... | 4,265 | 30.6 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/calculate.py | import matplotlib.pyplot as plt
import torch
import numpy as np
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.5, linestyle='dashdot', lw=1.5)
# minor grid lines
plt.minorticks_on()
plt.grid(b=True, which='minor', color='beige', alpha=0.5, ls='-', lw=1)
'''
Calculate and plot t... | 1,901 | 40.347826 | 207 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot.py | import numpy as np
import matplotlib.pyplot as plt
from u_plot import *
from plot_trajectory import *
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 15
'''
Pick trajectories data for corresponding $\alpha$
'''
A = torch.... | 2,666 | 27.98913 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 2,120 | 24.554217 | 143 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/generate.py | import numpy as np
import math
import matplotlib.pyplot as plt
import torch
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = t... | 3,077 | 27.766355 | 80 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/functions.py | import numpy as np
import torch
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
#向量场
def f(y,t) :
#parameters
x1,x2 = y
dydt = [-25.0*x1-x2+x1*(x1**2+x2**2),x1-25*x2+x2*(x1**2+x2**2)]
return dydt
#绘制向量场
def Plotflow(Xd, Yd, t):
# Plot phase ... | 4,576 | 33.674242 | 92 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/plot.py | import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from functions import *
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdo... | 1,598 | 30.352941 | 97 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=5000)
parser.add_argument('--lr', type=float, default=0.03)
args = parser.parse_args()
class Net(torch.nn.Module):
def __i... | 2,257 | 24.954023 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/generate.py | import numpy as np
import torch
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_hidden,n_hidden)
self.... | 2,073 | 26.653333 | 77 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/generate_matrix_A.py | import numpy as np
from numpy import linalg as LA
import matplotlib.pyplot as plt
import networkx as nx
from networkx.generators.classic import empty_graph, path_graph, complete_graph
from networkx.generators.random_graphs import barabasi_albert_graph, erdos_renyi_graph
def initial_W(shape, low_bound, up_bound):
... | 1,623 | 36.767442 | 126 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/energy_plot.py | import numpy as np
import matplotlib.pyplot as plt
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size=35
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.3, linestyle='dashdot', lw=1.5)
# minor gr... | 2,526 | 36.716418 | 139 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/plot.py | import numpy as np
import matplotlib.pyplot as plt
#Use latex font
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 15
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdot', lw=1.5... | 2,380 | 27.686747 | 105 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/plot_trajectory.py | import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
start = timeit.default_timer()
def plot_trajec(L,b):
mean_data = torch.mean(L,0).detach().numpy()
std_data =torch.std(L,0).detach().numpy()
plt.fi... | 639 | 28.090909 | 105 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/test.py | import numpy as np
import matplotlib.pyplot as plt
import time
start_time = time.time()
# Example data
t = np.arange(0.0, 1.0 + 0.01, 0.01)
s = np.cos(4 * np.pi * t) + 2
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.plot(t, s)
plt.xlabel(r'\textbf{time} (s)')
plt.ylabel(r'\textit{voltage} (mV)',font... | 625 | 24.04 | 68 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/V_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6,... | 2,205 | 28.413333 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/generate.py | import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.... | 2,768 | 30.827586 | 96 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/u_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_in... | 1,389 | 27.367347 | 81 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/calculate.py | import matplotlib.pyplot as plt
import torch
import numpy as np
# import pylustrator
# pylustrator.start()
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.5, linestyle='dashdot', lw=1.5)
# minor grid lines
plt.minorticks_on()
plt.grid(b=True, which='minor', color='beige', alpha=0... | 1,872 | 43.595238 | 243 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/ES_Quadratic.py | import sys
sys.path.append('./neural_sde')
import torch
import torch.nn.functional as F
import numpy as np
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidd... | 4,311 | 28.737931 | 142 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.