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
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NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/translate.py | # -*- coding: utf-8 -*-
import logging
import torch
import os
from beaver.data import build_dataset
from beaver.infer import beam_search
from beaver.model import NMTModel
from beaver.utils import parseopt, get_device, calculate_bleu
logging.basicConfig(format="%(asctime)s - %(message)s", level=logging.INFO)
opt = pa... | 2,731 | 33.582278 | 122 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/train.py | # -*- coding: utf-8 -*-
import logging
import torch
import torch.cuda
from beaver.data import build_dataset
from beaver.infer import beam_search
from beaver.loss import WarmAdam, LabelSmoothingLoss
from beaver.model import NMTModel
from beaver.utils import Saver
from beaver.utils import calculate_bleu
from beaver.uti... | 5,295 | 40.700787 | 187 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/tools/model_average.py | # -*- coding: utf-8 -*-
import os
import torch
import sys
def main():
if len(sys.argv) != 3:
print("python model_average.py model_path n")
exit()
model_path = sys.argv[1]
n = int(sys.argv[2]) # last n model to be averaged
fs = [os.path.join(model_path, f) for f in os.listdir(model... | 941 | 29.387097 | 114 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/tools/build_vocab.py | # -*- coding: utf-8 -*-
import sys
import collections
log = sys.stderr.write
def main():
size = int(sys.argv[1])
counter = collections.Counter()
for line in sys.stdin:
counter.update(line.strip().split())
items = counter.most_common()
for word, _ in items[:size]:
print(word)
t... | 635 | 23.461538 | 66 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/loss/optimizers.py | # -*- coding: utf-8 -*-
import torch.nn as nn
import torch.optim as optim
class WarmAdam(object):
def __init__(self, params, lr, hidden_size, warm_up, n_step):
self.original_lr = lr
self.n_step = n_step
self.hidden_size = hidden_size
self.warm_up_step = warm_up
self.optimi... | 1,529 | 36.317073 | 87 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/loss/__init__.py | # -*- coding: utf-8 -*-
from beaver.loss.optimizers import WarmAdam, LabelSmoothingLoss
| 90 | 17.2 | 63 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/utils/saver.py | import json
import torch
import os
import datetime
class Saver(object):
def __init__(self, opt):
self.ckpt_names = []
self.model_path = opt.model_path + datetime.datetime.now().strftime("-%y%m%d-%H%M%S")
self.max_to_keep = opt.max_to_keep
os.mkdir(self.model_path)
with op... | 1,647 | 40.2 | 140 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/utils/parseopt.py | # -*- coding: utf-8 -*-
import argparse
import json
def common_opts(parser):
parser.add_argument("-vocab", type=str, nargs="*", help="Vocab file")
parser.add_argument("-batch_size", type=int, default=8192, help="Batch size")
parser.add_argument("-beam_size", type=int, default=4, help="Beam size")
pa... | 3,348 | 41.935897 | 114 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/utils/rouge.py | # -*- coding: utf-8 -*-
def get_ngrams(n, text):
ngram_set = set()
text_length = len(text)
max_index_ngram = text_length - n
for i in range(max_index_ngram + 1):
ngram_set.add(tuple(text[i:i+n]))
return ngram_set
def rouge_n(evaluated_sentences, reference_sentences, n=2): #默认rouge_2
... | 2,152 | 35.491525 | 86 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/utils/__init__.py | # -*- coding: utf-8 -*-
import torch.cuda
from beaver.utils.metric import calculate_bleu, file_bleu
from beaver.utils.saver import Saver
def get_device():
if torch.cuda.is_available():
return torch.device('cuda')
else:
return torch.device('cpu')
def printing_opt(opt):
return "\n".join(... | 405 | 21.555556 | 105 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/utils/metric.py | import os
import re
import subprocess
import tempfile
from beaver.utils.rouge import F_1
def calculate_bleu(hypotheses, references, lowercase=False):
hypothesis_file = tempfile.NamedTemporaryFile(mode="w", encoding="UTF-8", delete=False)
hypothesis_file.write("\n".join(hypotheses) + "\n")
hypothesis_file... | 1,745 | 35.375 | 108 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/data/field.py | # -*- coding: utf-8 -*-
from typing import List
import torch
EOS_TOKEN = "<eos>"
BOS_TOKEN = "<bos>"
UNK_TOKEN = "<unk>"
PAD_TOKEN = "<pad>"
class Field(object):
def __init__(self, bos: bool, eos: bool, pad: bool, unk: bool):
self.bos_token = BOS_TOKEN if bos else None
self.eos_token = EOS_TOKEN... | 2,418 | 25.582418 | 115 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/data/dataset_wrapper.py | # -*- coding: utf-8 -*-
import itertools
from beaver.data.dataset import TranslationDataset
class Dataset(object):
def __init__(self, task1_dataset: TranslationDataset, task2_dataset: TranslationDataset):
self.task1_dataset = task1_dataset
self.task2_dataset = task2_dataset
self.fields =... | 748 | 28.96 | 93 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/data/utils.py | # -*- coding: utf-8 -*-
from beaver.data.dataset import TranslationDataset
from beaver.data.dataset_wrapper import Dataset
from beaver.data.field import Field
def build_dataset(opt, data_path, vocab_path, device, train=True):
task1_source_path = data_path[0]
task1_target_path = data_path[1]
task2_source_... | 1,896 | 40.23913 | 102 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/data/dataset.py | # -*- coding: utf-8 -*-
import random
from collections import namedtuple
from typing import Dict
import torch
from beaver.data.field import Field
Batch = namedtuple("Batch", ['src', 'tgt', 'batch_size'])
Example = namedtuple("Example", ['src', 'tgt'])
class TranslationDataset(object):
def __init__(self,
... | 2,164 | 29.069444 | 89 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/data/__init__.py | # -*- coding: utf-8 -*-
from beaver.data.utils import build_dataset
| 69 | 16.5 | 43 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/infer/beam.py | # -*- coding: utf-8 -*-
import torch
class Beam(object):
def __init__(self, beam_size, pad, bos, eos, device, lp):
self.size = beam_size
self.alpha = lp
self.scores = torch.full([beam_size], -1e20).float().to(device)
self.scores[0] = 0.
self.hypotheses = torch.full([1, ... | 1,652 | 32.06 | 118 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/infer/__init__.py | # -*- coding: utf-8 -*-
from beaver.infer.translator import beam_search
| 74 | 14 | 47 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/infer/translator.py | # -*- coding: utf-8 -*-
import torch
from beaver.infer.beam import Beam
def beam_search(opt, model, src, fields, flag):
batch_size = src.size(0)
beam_size = opt.beam_size
device = src.device
encoder = model.encoder
if flag:
decoder = model.task1_decoder
generator = model.task1_... | 2,156 | 32.184615 | 86 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/model/embeddings.py | # -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
def positional_encoding(dim, max_len=5000):
pe = torch.zeros(max_len, dim)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) * -(math.log(10000.0) / dim)))
pe[:, 0::... | 1,313 | 31.04878 | 110 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/model/transformer.py | # -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
class FeedForward(nn.Module):
def __init__(self, hidden_size, inner_size, dropout):
super(FeedForward, self).__init__()
self.linear_in = nn.Linear(hidden_size, inner_size, bias=False)
self.linear_out = nn.Linear(inner_... | 6,591 | 35.622222 | 120 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/model/__init__.py | # -*- coding: utf-8 -*-
from beaver.model.nmt_model import NMTModel
| 70 | 13.2 | 43 | py |
NCLS-Corpora | NCLS-Corpora-master/code/beaver-2task+/beaver/model/nmt_model.py | # -*- coding: utf-8 -*-
from typing import Dict
import torch
import torch.nn as nn
from beaver.model.embeddings import Embedding
from beaver.model.transformer import Decoder, Encoder
class Generator(nn.Module):
def __init__(self, hidden_size: int, tgt_vocab_size: int):
self.vocab_size = tgt_vocab_size
... | 4,315 | 38.962963 | 100 | py |
labelImg2 | labelImg2-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
from libs.version import __version__
with open('README.rst') as readme_file:
readme = readme_file.read()
with open('HISTORY.rst') as history_file:
history = history_file.read()
requirements = [
# TODO: Different OS... | 1,974 | 29.384615 | 107 | py |
labelImg2 | labelImg2-master/labelImg.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import codecs
import distutils.spawn
import os
import platform
import re
import sys
import subprocess
from functools import partial
from collections import defaultdict
from libs.naturalsort import natsort
try:
from PyQt5.QtGui ... | 53,244 | 37.667393 | 144 | py |
labelImg2 | labelImg2-master/__init__.py | 0 | 0 | 0 | py | |
labelImg2 | labelImg2-master/libs/shape.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from libs.lib import distance
from libs.ustr import ustr
import sys
import math
DEFAULT_LINE_COLOR = QColor(0, 255, 0, 128)
DEF... | 9,215 | 32.512727 | 119 | py |
labelImg2 | labelImg2-master/libs/zoomWidget.py | try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
class ZoomWidget(QSpinBox):
def __init__(self, value=100):
super(ZoomWidget, self).__init__()
self.setButtonSymbols(... | 899 | 29 | 67 | py |
labelImg2 | labelImg2-master/libs/settings.py | #import json
import pickle
import os
import sys
class Settings(object):
def __init__(self):
# Be default, the home will be in the same folder as labelImg
self.data = {}
if sys.version_info < (3, 0, 0):
self.path = './labelImg2Settings2.pkl'
else:
self.path = ... | 1,298 | 26.638298 | 69 | py |
labelImg2 | labelImg2-master/libs/pascal_voc_io.py | #!/usr/bin/env python
# -*- coding: utf8 -*-
import sys
from xml.etree import ElementTree
from xml.etree.ElementTree import Element, SubElement
from lxml import etree
import codecs
import math
XML_EXT = '.xml'
ENCODE_METHOD = 'utf-8'
class PascalVocWriter:
def __init__(self, foldername, filename, imgSize,databas... | 10,867 | 37.676157 | 116 | py |
labelImg2 | labelImg2-master/libs/canvas.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
#from PyQt4.QtOpenGL import *
from .shape import Shape
from .lib import d... | 36,751 | 35.460317 | 153 | py |
labelImg2 | labelImg2-master/libs/ustr.py | import sys
def ustr(x):
'''py2/py3 unicode helper'''
if sys.version_info < (3, 0, 0):
from PyQt4.QtCore import QString
if type(x) == str:
return x.decode('utf-8')
if type(x) == QString:
return unicode(x)
return x
else:
return x # py3
| 313 | 19.933333 | 40 | py |
labelImg2 | labelImg2-master/libs/constants.py | SETTING_FILENAME = 'filename'
SETTING_RECENT_FILES = 'recentFiles'
SETTING_WIN_SIZE = 'window/size'
SETTING_WIN_POSE = 'window/position'
SETTING_WIN_GEOMETRY = 'window/geometry'
SETTING_LINE_COLOR = 'line/color'
SETTING_FILL_COLOR = 'fill/color'
SETTING_ADVANCE_MODE = 'advanced'
SETTING_WIN_STATE = 'window/state'
SETTI... | 566 | 32.352941 | 40 | py |
labelImg2 | labelImg2-master/libs/labelView.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import sys
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from .ustr import ustr
class HashableQStandardItem(QStandardIte... | 6,485 | 33.5 | 108 | py |
labelImg2 | labelImg2-master/libs/lib.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from math import sqrt
from .ustr import ustr
import hashlib
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
def newIcon(i... | 2,121 | 23.113636 | 69 | py |
labelImg2 | labelImg2-master/libs/labelFile.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
try:
from PyQt5.QtGui import QImage
from PyQt5.QtGui import QImageReader
except ImportError:
from PyQt4.QtGui import QImage
from PyQt4.QtGui import QImageReader
from base64 import b64encode, b64decode
from .pascal_voc_io import PascalVocW... | 5,130 | 32.318182 | 91 | py |
labelImg2 | labelImg2-master/libs/naturalsort.py | # Simple natural order sorting API for Python.
#
# Author: Peter Odding <peter@peterodding.com>
# Last Change: November 2, 2015
# URL: https://github.com/xolox/python-naturalsort
"""Simple natural order sorting API for Python."""
# Standard library modules.
import re
__version__ = '1.5.1'
"""Semi-standard module ver... | 6,441 | 35.191011 | 107 | py |
labelImg2 | labelImg2-master/libs/labelDialog.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import sys
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from .lib import newIcon, labelValidator
BB = QDialogButtonBox... | 4,640 | 30.147651 | 91 | py |
labelImg2 | labelImg2-master/libs/__init__.py | 1 | 0 | 0 | py | |
labelImg2 | labelImg2-master/libs/fileView.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import os
import sys
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from .pascal_voc_io import PascalVocReader, XML_EXT
c... | 3,115 | 31.8 | 141 | py |
rpn_bo | rpn_bo-main/Code and results/brusselator_pde_MLP.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
#
from jax import vmap, random, jit
from jax import numpy as np
import numpy as onp
from rpn_bo_utilities import uniform_prior
from rpn_bo_models import EnsembleRegression
from rpn_bo_dataloaders import BootstrapLoader
from rpn_bo_acquisitions import MCAcqu... | 6,056 | 32.65 | 150 | py |
rpn_bo | rpn_bo-main/Code and results/environmental_model_function_DON.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
import numpy as onp
from rpn_bo_utilities import uniform_prior
from rpn_bo_models import ParallelDeepOnet
from rpn_bo_dataloaders import DataGenerator_batch
from rpn_bo_acquisitions import MCAcq... | 5,739 | 32.764706 | 126 | py |
rpn_bo | rpn_bo-main/Code and results/brusselator_pde_DON.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
from pyDOE import lhs
import numpy as onp
from rpn_bo_utilities import uniform_prior, output_weights
from rpn_bo_models import ParallelDeepOnet
from rpn_bo_dataloaders import DataGenerator_batch... | 6,387 | 35.090395 | 199 | py |
rpn_bo | rpn_bo-main/Code and results/environmental_model_function_MLP.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
import numpy as onp
from rpn_bo_utilities import uniform_prior
from rpn_bo_models import EnsembleRegression
from rpn_bo_dataloaders import BootstrapLoader
from rpn_bo_acquisitions import MCAcqui... | 5,266 | 33.424837 | 126 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_architectures.py | from jax import numpy as np
from jax import random
def MLP(layers, activation=np.tanh):
def init(rng_key):
def init_layer(key, d_in, d_out):
k1, k2 = random.split(key)
glorot_stddev = 1. / np.sqrt((d_in + d_out) / 2.)
W = glorot_stddev*random.normal(k1, (d_in, d_out))
... | 783 | 33.086957 | 69 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_optimizers.py | from scipy.optimize import minimize
def minimize_lbfgs(objective, x0, bnds = None, verbose = False, maxfun = 15000):
if verbose:
def callback_fn(params):
print("Loss: {}".format(objective(params)[0]))
else:
callback_fn = None
result = minimize(objective, x0, jac=True,
... | 480 | 33.357143 | 80 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_MLP_step_0.py | from jax import numpy as np
from jax.scipy.special import logsumexp
from jax import vmap
N_y = 64 # each frame is an N_y by N_y image
xx, yy = np.meshgrid( np.arange(N_y) / N_y, np.arange(N_y) / N_y )
# prediction function mapping vectorial output to scalar obective value
def output(y):
y = y.reshape((16,N_y,N_y)... | 1,179 | 37.064516 | 144 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_acquisitions.py | from jax import numpy as np
from jax import jit, vjp, random
from jax.scipy.special import expit as sigmoid
import numpy as onp
from functools import partial
from pyDOE import lhs
from tqdm import trange
from rpn_bo_optimizers import minimize_lbfgs
class MCAcquisition:
def __init__(self, posterior, bounds, *args,... | 5,600 | 42.418605 | 116 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_models.py | from jax import numpy as np
from jax import grad, vmap, random, jit
from jax.example_libraries import optimizers
from jax.nn import relu, gelu
from functools import partial
from tqdm import trange
import itertools
from rpn_bo_architectures import MLP
class EnsembleRegression:
def __init__(self, layers, ensemble_s... | 8,651 | 41.411765 | 121 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_DON_step_0.py | from jax import numpy as np
from jax.scipy.special import logsumexp
output_dim = (64, 64, 16) # 16 frames of 64 by 64 images
P1 = output_dim[0]
P2 = output_dim[1]
xx, yy = np.meshgrid( np.arange(P1) / P1, np.arange(P2) / P2 )
# prediction function mapping vectorial output to scalar obective value
def output(new_y):
... | 1,269 | 34.277778 | 144 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_MLP_all_steps.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
from jax.scipy.special import logsumexp
from jax.nn import relu
from gym_interf import InterfEnv
import numpy as onp
from rpn_bo_utilities import uniform_prior
from rpn_bo_models import Ensemble... | 5,958 | 35.335366 | 174 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_DON_step_1.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
from jax.scipy.special import logsumexp
from rpn_bo_models import ParallelDeepOnet
from rpn_bo_dataloaders import DataGenerator_batch
from rpn_bo_acquisitions import MCAcquisition
from rpn_bo_ut... | 4,309 | 33.206349 | 185 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_MLP_step_2.py | from jax import numpy as np
from jax.scipy.special import logsumexp
from gym_interf import InterfEnv
# function mapping the vectorial input x to the vectorial output consisting of the 16 images
def f(x):
gym = InterfEnv()
gym.reset(actions=(1e-4, 1e-4, 1e-4, 1e-4))
action = x[:4]
state = gym.step(actio... | 2,088 | 51.225 | 211 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_DON_step_2.py | from jax import numpy as np
from jax.scipy.special import logsumexp
from gym_interf import InterfEnv
output_dim = (64, 64, 16) # 16 frames of 64 by 64 images
soln_dim = output_dim[2]
P1 = output_dim[0]
P2 = output_dim[1]
# function mapping the vectorial input x to the vectorial output consisting of the 16 images
def ... | 1,953 | 45.52381 | 136 | py |
rpn_bo | rpn_bo-main/Code and results/optical_interferometer_MLP_step_1.py | import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
from jax import vmap, random, jit
from jax import numpy as np
from jax.scipy.special import logsumexp
from jax.nn import relu
import numpy as onp
from rpn_bo_models import EnsembleRegression
from rpn_bo_dataloaders import BootstrapLoader
from rpn_bo_acquis... | 3,973 | 31.842975 | 106 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_utilities.py | from jax import numpy as np
from jax import jit, vmap, random
from jax.scipy.stats import multivariate_normal, uniform
import numpy as onp
from scipy.stats import gaussian_kde
from sklearn import mixture
from pyDOE import lhs
from KDEpy import FFTKDE
def fit_kde(predict_fn, prior_pdf, bounds, num_samples=10000, bw=Non... | 4,097 | 34.327586 | 97 | py |
rpn_bo | rpn_bo-main/Code and results/create_BO_cv_plots.py | problem = 'comp_blades_shape' # choose from 'environment' 'brusselator' 'optical_interferometer' 'comp_blades_shape'
from matplotlib import pyplot as plt
plt.close('all')
plt.rcParams.update(plt.rcParamsDefault)
plt.rcParams.update({'font.weight': 'bold',
'font.size': 28,
'lin... | 20,736 | 52.862338 | 128 | py |
rpn_bo | rpn_bo-main/Code and results/rpn_bo_dataloaders.py | from jax import vmap, random, jit
from jax import numpy as np
from functools import partial
from torch.utils import data
class BootstrapLoader(data.Dataset):
def __init__(self, X, y, batch_size=128, ensemble_size=32, fraction=0.5, is_Gauss=1, LF_pred=None, rng_key=random.PRNGKey(1234)):
'Initialization'
... | 6,289 | 42.680556 | 163 | py |
mcfit | mcfit-master/setup.py | from setuptools import setup
def find_version(path):
with open(path, 'r') as fp:
file = fp.read()
import re
match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
file, re.M)
if match:
return match.group(1)
raise RuntimeError("Version not found")
setup... | 762 | 29.52 | 76 | py |
mcfit | mcfit-master/mcfit/kernels.py | from numpy import arange, exp, log, ndim, pi, sqrt
from scipy.special import gamma
try:
from scipy.special import loggamma
except ImportError:
def loggamma(x):
return log(gamma(x))
def _deriv(MK, deriv):
"""Real deriv is wrt :math:`t`, complex deriv is wrt :math:`\ln t`"""
if deriv == 0:
... | 4,440 | 35.401639 | 90 | py |
mcfit | mcfit-master/mcfit/cosmology.py | """Cosmology applications"""
from mcfit.mcfit import mcfit
from mcfit import kernels
from numpy import pi
__all__ = ['P2xi', 'xi2P', 'TophatVar', 'GaussVar']
class P2xi(mcfit):
"""Power spectrum to correlation function.
Parameters
----------
k : see `x` in :class:`mcfit.mcfit`
l : int
... | 2,644 | 25.717172 | 79 | py |
mcfit | mcfit-master/mcfit/__init__.py | """Multiplicatively Convolutional Fast Integral Transform"""
from mcfit.mcfit import mcfit
from mcfit.transforms import *
from mcfit.cosmology import *
__version__ = "0.0.18"
| 177 | 21.25 | 60 | py |
mcfit | mcfit-master/mcfit/transforms.py | """Common Integral transforms and applications"""
from mcfit.mcfit import mcfit
from mcfit import kernels
from numpy import pi
from scipy.special import gamma
__all__ = ['Hankel', 'SphericalBessel', 'DoubleBessel', 'DoubleSphericalBessel',
'FourierSine', 'FourierCosine', 'TophatSmooth', 'GaussSmooth']
c... | 4,441 | 24.676301 | 80 | py |
mcfit | mcfit-master/mcfit/mcfit.py | import math
import cmath
import warnings
import numpy
try:
import jax
jax.config.update("jax_enable_x64", True)
except ModuleNotFoundError as e:
JAXNotFoundError = e
class mcfit(object):
r"""Compute integral transforms as a multiplicative convolution.
The generic form is
.. math:: G(y) = \in... | 15,495 | 34.541284 | 100 | py |
mcfit | mcfit-master/mcfit/tests/__init__.py | 0 | 0 | 0 | py | |
mcfit | mcfit-master/mcfit/tests/test_transforms.py | import numpy as np
from numpy.testing import assert_allclose
from mcfit.transforms import *
def test_Hankel():
def F_fun(x): return 1 / (1 + x*x)**1.5
def G_fun(y): return np.exp(-y)
x = np.logspace(-3, 3, num=60, endpoint=False)
F = F_fun(x)
H = Hankel(x, nu=0, q=1, N=128, lowring=True)
y, G ... | 672 | 31.047619 | 63 | py |
mcfit | mcfit-master/mcfit/tests/test_mcfit.py | import numpy as np
from numpy.testing import assert_allclose
from mcfit.transforms import *
def test_matrix():
N = 81
x = np.logspace(-3, 3, num=N, endpoint=False)
F = 1 / (1 + x*x)**1.5
H1 = Hankel(x, nu=0, q=1, N=N)
y, G = H1(F)
a1, b1, C1 = H1.matrix(full=False)
M1 = H1.matrix(full=Tr... | 1,606 | 26.706897 | 72 | py |
FCtL | FCtL-main/train_deep_globe.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
from tqdm import tqdm
from dataset.deep_globe import DeepGlobe, classToRGB, is_image_file
from utils.loss import Foc... | 8,219 | 46.514451 | 202 | py |
FCtL | FCtL-main/helper.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import absolute_import, division, print_function
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from utils.metrics import ConfusionMatrix
from ... | 16,004 | 42.140162 | 246 | py |
FCtL | FCtL-main/option.py | import os
import argparse
import torch
class Options():
def __init__(self):
parser = argparse.ArgumentParser(description='PyTorch Segmentation')
# model and dataset
parser.add_argument('--n_class', type=int, default=7, help='segmentation classes')
parser.add_argument('--data_path',... | 2,367 | 61.315789 | 136 | py |
FCtL | FCtL-main/dataset/deep_globe.py | import os
import torch.utils.data as data
import numpy as np
from PIL import Image, ImageFile
import random
from torchvision.transforms import ToTensor
from torchvision import transforms
import cv2
ImageFile.LOAD_TRUNCATED_IMAGES = True
def is_image_file(filename):
return any(filename.endswith(extension) for ext... | 3,527 | 32.6 | 120 | py |
FCtL | FCtL-main/dataset/__init__.py | 0 | 0 | 0 | py | |
FCtL | FCtL-main/models/base_model.py | import logging
import torch.nn as nn
import numpy as np
class BaseModel(nn.Module):
def __init__(self):
super(BaseModel, self).__init__()
self.logger = logging.getLogger(self.__class__.__name__)
def forward(self):
raise NotImplementedError
def summary(self):
model_paramete... | 734 | 32.409091 | 79 | py |
FCtL | FCtL-main/models/fcn.py | from .base_model import BaseModel
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from .helpers import get_upsampling_weight
import torch
from itertools import chain
from .FCtL import FCtL
class MiniFCN8(BaseModel):
def __init__(self, num_classes, pretrained=True):
... | 6,910 | 43.301282 | 122 | py |
FCtL | FCtL-main/models/FCtL.py | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import init
import math
class _FCtL(nn.Module):
def __init__(self, inplanes, planes, lr_mult, weight_init_scale):
conv_nd = nn.Conv2d
bn_nd = nn.BatchNorm2d
super(_FCtL, self).__init__()
self.co... | 5,866 | 39.462069 | 122 | py |
FCtL | FCtL-main/models/model_store.py | """Model store which provides pretrained models."""
from __future__ import print_function
__all__ = ['get_model_file', 'purge']
import os
import zipfile
from .utils import download, check_sha1
_model_sha1 = {name: checksum for checksum, name in [
('ebb6acbbd1d1c90b7f446ae59d30bf70c74febc1', 'resnet50'),
('2a5... | 3,714 | 36.525253 | 97 | py |
FCtL | FCtL-main/models/__init__.py | 0 | 0 | 0 | py | |
FCtL | FCtL-main/models/helpers.py | import os
import torch
import torch.nn as nn
import numpy as np
import math
import PIL
def dir_exists(path):
if not os.path.exists(path):
os.makedirs(path)
def initialize_weights(*models):
for model in models:
for m in model.modules():
if isinstance(m, nn.Conv2d):
... | 1,837 | 31.245614 | 94 | py |
FCtL | FCtL-main/utils/lr_scheduler.py | import math
class LR_Scheduler(object):
"""Learning Rate Scheduler
Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}``
Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))``
Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9``
Args:
args: :attr:`args.lr_scheduler` lr s... | 2,393 | 37 | 92 | py |
FCtL | FCtL-main/utils/loss.py | import torch.nn as nn
import torch.nn.functional as F
import torch
def one_hot(index, classes):
# index is flatten (during ignore) ##################
size = index.size()[:1] + (classes,)
view = index.size()[:1] + (1,)
#####################################################
mask = torch.Tensor(size).fill... | 1,585 | 27.321429 | 90 | py |
FCtL | FCtL-main/utils/metrics.py | import numpy as np
import math
class ConfusionMatrix(object):
def __init__(self, n_classes):
self.n_classes = n_classes
# axis = 0: target
# axis = 1: prediction
self.confusion_matrix = np.zeros((n_classes, n_classes))
def _fast_hist(self, label_true, label_pred, n_cla... | 1,230 | 31.394737 | 133 | py |
FCtL | FCtL-main/utils/__init__.py | 0 | 0 | 0 | py | |
andi_datasets | andi_datasets-master/setup.py | from pkg_resources import parse_version
from configparser import ConfigParser
import setuptools
assert parse_version(setuptools.__version__)>=parse_version('36.2')
# note: all settings are in settings.ini; edit there, not here
config = ConfigParser(delimiters=['='])
config.read('settings.ini')
cfg = config['DEFAULT']
... | 2,539 | 44.357143 | 150 | py |
andi_datasets | andi_datasets-master/andi_datasets/datasets_theory.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/datasets_theory.ipynb.
# %% auto 0
__all__ = ['datasets_theory']
# %% ../source_nbs/lib_nbs/datasets_theory.ipynb 3
import numpy as np
import os
import inspect
import h5py
from tqdm.auto import trange
import csv
# %% ../source_nbs/lib_nbs/datasets_the... | 27,969 | 47.559028 | 158 | py |
andi_datasets | andi_datasets-master/andi_datasets/models_phenom.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/models_phenom.ipynb.
# %% auto 0
__all__ = ['models_phenom']
# %% ../source_nbs/lib_nbs/models_phenom.ipynb 2
import numpy as np
from stochastic.processes.noise import FractionalGaussianNoise as FGN
from .utils_trajectories import gaussian
import warni... | 57,628 | 40.10485 | 171 | py |
andi_datasets | andi_datasets-master/andi_datasets/models_theory.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/models_theory.ipynb.
# %% auto 0
__all__ = ['models_theory']
# %% ../source_nbs/lib_nbs/models_theory.ipynb 3
import numpy as np
from stochastic.processes.continuous import FractionalBrownianMotion as fbm
from math import pi as pi
from scipy.special im... | 17,039 | 44.198939 | 128 | py |
andi_datasets | andi_datasets-master/andi_datasets/_modidx.py | # Autogenerated by nbdev
d = { 'settings': { 'branch': 'master',
'doc_baseurl': '/andi_datasets',
'doc_host': 'https://andichallenge.github.io',
'git_url': 'https://github.com/andichallenge/andi_datasets',
'lib_path': 'andi_datasets'},
'syms': { 'andi_d... | 37,462 | 144.770428 | 213 | py |
andi_datasets | andi_datasets-master/andi_datasets/datasets_phenom.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/datasets_phenom.ipynb.
# %% auto 0
__all__ = ['datasets_phenom']
# %% ../source_nbs/lib_nbs/datasets_phenom.ipynb 2
from .models_phenom import models_phenom
import inspect
import numpy as np
import pandas as pd
import csv
from tqdm.auto import tqdm
im... | 13,682 | 41.626168 | 176 | py |
andi_datasets | andi_datasets-master/andi_datasets/analysis.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/analysis.ipynb.
# %% auto 0
__all__ = ['get_angle', 'dataset_angles', 'msd_analysis', 'vacf', 'CH_changepoints']
# %% ../source_nbs/lib_nbs/analysis.ipynb 2
import numpy as np
import math
# %% ../source_nbs/lib_nbs/analysis.ipynb 5
def get_angle(a:tup... | 6,405 | 29.94686 | 124 | py |
andi_datasets | andi_datasets-master/andi_datasets/datasets_challenge.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/datasets_challenge.ipynb.
# %% auto 0
__all__ = ['challenge_theory_dataset', 'challenge_phenom_dataset']
# %% ../source_nbs/lib_nbs/datasets_challenge.ipynb 2
import numpy as np
from tqdm.auto import tqdm
import pandas as pd
import os
import csv
from ... | 36,775 | 47.969374 | 157 | py |
andi_datasets | andi_datasets-master/andi_datasets/utils_videos.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/utils_videos.ipynb.
# %% auto 0
__all__ = ['play_video', 'convert_uint8', 'psf_width', 'func_poisson_noise', 'mask', 'transform_to_video']
# %% ../source_nbs/lib_nbs/utils_videos.ipynb 2
import matplotlib.animation as animation
import matplotlib.pyplot... | 12,246 | 30.564433 | 215 | py |
andi_datasets | andi_datasets-master/andi_datasets/__init__.py | __version__ = "2.0.0"
| 22 | 10.5 | 21 | py |
andi_datasets | andi_datasets-master/andi_datasets/utils_challenge.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/utils_challenge.ipynb.
# %% auto 0
__all__ = ['majority_filter', 'label_filter', 'label_continuous_to_list', 'label_list_to_continuous', 'array_to_df',
'df_to_array', 'get_VIP', 'changepoint_assignment', 'changepoint_alpha_beta', 'jaccard_ind... | 58,236 | 37.288626 | 169 | py |
andi_datasets | andi_datasets-master/andi_datasets/utils_trajectories.py | # AUTOGENERATED! DO NOT EDIT! File to edit: ../source_nbs/lib_nbs/utils_trajectories.ipynb.
# %% auto 0
__all__ = ['pert', 'gaussian', 'bm1D', 'regularize', 'sample_sphere', 'normalize', 'normalize_fGN', 'trigo', 'find_nan_segments',
'segs_inside_fov', 'inside_fov_dataset', 'plot_trajs']
# %% ../source_nbs... | 17,485 | 39.01373 | 174 | py |
inFairness | inFairness-main/setup.py | from setuptools import setup, find_packages
with open("README.md", "r") as f:
long_description = f.read()
setup(
name="inFairness",
packages=[
"inFairness",
*["inFairness." + p for p in find_packages(where="./inFairness")],
],
package_dir={"": ".",},
install_requires=[
... | 1,031 | 27.666667 | 113 | py |
inFairness | inFairness-main/examples/postprocess-sentiment-analysis/data.py | import torch
import re
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
sns.set_context(rc={'figure.figsize': (9, 9)}, font_scale=2.)
TOKEN_RE = re.compi... | 11,968 | 44.858238 | 1,229 | py |
inFairness | inFairness-main/examples/fair-ranking-synthetic-data/trainer.py |
class Trainer(object):
"""Main trainer class that orchestrates the entire learning routine
Use this class to start training a model using individual fairness routines
Args:
dataloader (torch.util.data.DataLoader): training data loader
model (inFairness.fairalgo): Individual fairness algori... | 1,760 | 31.018182 | 94 | py |
inFairness | inFairness-main/examples/adult-income-prediction/data.py | import os
import requests
import pandas as pd
import numpy as np
import torch
from sklearn.preprocessing import StandardScaler
from sklearn.utils.random import sample_without_replacement
def _download_data_(rootdir=None):
URLS = {
'train': 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult... | 6,759 | 33.666667 | 118 | py |
inFairness | inFairness-main/examples/adult-income-prediction/metrics.py | import torch
import numpy as np
from sklearn.metrics import confusion_matrix
def accuracy(model, test_dl, device):
model.eval()
corr, total = 0, 0
for x, y in test_dl:
x, y = x.to(device), y.to(device)
y_pred = model(x)
_, y_pred = torch.max(y_pred, dim=1)
total += y.sha... | 1,950 | 24.012821 | 64 | py |
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