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Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/old_code/model.py | # model.py
import torch
from torch import nn
from torch import Tensor
from torch.autograd import Variable
import numpy as np
from sklearn.metrics import accuracy_score
class fastText(nn.Module):
def __init__(self, config):
super(fastText, self).__init__()
self.config = config
# Hi... | 2,569 | 32.815789 | 82 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/old_code/config.py | # config.py
class Config(object):
embed_size = 300
hidden_size = 10
output_size = 4
max_epochs = 20
lr = 0.5
batch_size = 128 | 150 | 15.777778 | 21 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/old_code/train.py | # train.py
from utils import *
from config import Config
from sklearn.model_selection import train_test_split
import numpy as np
from tqdm import tqdm
import sys
import torch.optim as optim
from torch import nn, Tensor
from torch.autograd import Variable
import torch
from sklearn.metrics import accuracy_score
def get... | 3,314 | 36.247191 | 108 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_RCNN/utils.py | # utils.py
import torch
from torchtext import data
from torchtext.vocab import Vectors
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test... | 4,498 | 37.452991 | 110 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_RCNN/model.py | # model.py
import torch
from torch import nn
import numpy as np
from torch.nn import functional as F
from utils import *
class RCNN(nn.Module):
def __init__(self, config, vocab_size, word_embeddings):
super(RCNN, self).__init__()
self.config = config
# Embedding Layer
self... | 4,267 | 35.793103 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_RCNN/config.py | # config.py
class Config(object):
embed_size = 300
hidden_layers = 1
hidden_size = 64
output_size = 4
max_epochs = 15
hidden_size_linear = 64
lr = 0.5
batch_size = 128
seq_len = None # Sequence length for RNN
dropout_keep = 0.8
| 269 | 18.285714 | 44 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_RCNN/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,717 | 32.038462 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/utils.py | # utils.py
import torch
from torchtext import data
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test_iterator = None
self.val_it... | 4,255 | 36.663717 | 110 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/model.py | # Model.py
import torch
import torch.nn as nn
from copy import deepcopy
from train_utils import Embeddings,PositionalEncoding
from attention import MultiHeadedAttention
from encoder import EncoderLayer, Encoder
from feed_forward import PositionwiseFeedForward
import numpy as np
from utils import *
class Transformer(n... | 3,390 | 35.858696 | 124 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/encoder.py | # encoder.py
from torch import nn
from train_utils import clones
from sublayer import LayerNorm, SublayerOutput
class Encoder(nn.Module):
'''
Transformer Encoder
It is a stack of N layers.
'''
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.layers = clones(l... | 1,248 | 30.225 | 104 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/feed_forward.py | # feed_forward.py
from torch import nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
"Positionwise feed-forward network."
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self... | 515 | 31.25 | 58 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/sublayer.py | # sublayer.py
import torch
from torch import nn
class LayerNorm(nn.Module):
"Construct a layer normalization module."
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
... | 950 | 29.677419 | 71 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/train_utils.py | # train_utils.py
import torch
from torch import nn
from torch.autograd import Variable
import copy
import math
def clones(module, N):
"Produce N identical layers."
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Embeddings(nn.Module):
'''
Usual Embedding layer with weights multi... | 1,577 | 34.863636 | 129 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/config.py | # config.py
class Config(object):
N = 1 #6 in Transformer Paper
d_model = 256 #512 in Transformer Paper
d_ff = 512 #2048 in Transformer Paper
h = 8
dropout = 0.1
output_size = 4
lr = 0.0003
max_epochs = 35
batch_size = 128
max_sen_len = 60 | 280 | 20.615385 | 43 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/attention.py | # attention.py
import torch
from torch import nn
import math
import torch.nn.functional as F
from train_utils import clones
def attention(query, key, value, mask=None, dropout=None):
"Implementation of Scaled dot product attention"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) /... | 1,915 | 35.846154 | 76 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Transformer/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,640 | 31.82 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/data/query_wellformedness/reformat_data.py | import sys
import os
if __name__=='__main__':
if len(sys.argv) < 2:
print("Expected filename as an argument")
sys.exit()
filepath = sys.argv[1]
path, filename = os.path.split(filepath)
name, ext = os.path.splitext(os.path.basename(filename))
new_filepath = os.path.join(path, 'processed_'+name+'.txt')
with op... | 634 | 29.238095 | 60 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextRNN/utils.py | # utils.py
import torch
from torchtext import data
from torchtext.vocab import Vectors
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test... | 4,498 | 37.452991 | 110 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextRNN/model.py | # model.py
import torch
from torch import nn
import numpy as np
from utils import *
class TextRNN(nn.Module):
def __init__(self, config, vocab_size, word_embeddings):
super(TextRNN, self).__init__()
self.config = config
# Embedding Layer
self.embeddings = nn.Embedding(voca... | 3,586 | 37.569892 | 115 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextRNN/config.py | # config.py
class Config(object):
embed_size = 300
hidden_layers = 2
hidden_size = 32
bidirectional = True
output_size = 4
max_epochs = 10
lr = 0.25
batch_size = 64
max_sen_len = 20 # Sequence length for RNN
dropout_keep = 0.8 | 267 | 19.615385 | 46 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextRNN/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,720 | 32.096154 | 98 | py |
bfh_python | bfh_python-master/arcslide.py | """Arcslides and their type DD structures."""
from ddstructure import DDStrFromChords
from grading import SimpleDbGradingSet, SimpleDbGradingSetElement, \
SmallGradingGroup
from hdiagram import getArcslideDiagram
from pmc import Idempotent, PMC, Strands
from pmc import connectSumPMC, splitPMC
from utility import m... | 18,536 | 40.101996 | 82 | py |
bfh_python | bfh_python-master/utilitytest.py | """Unit test for utility.py"""
from utility import *
import unittest
class ToListTest(unittest.TestCase):
def testToList(self):
"""Testing various cases for the tolist function."""
self.assertEqual(tolist(3), [3])
self.assertEqual(tolist("3"), ["3"])
self.assertEqual(tolist(()), [(... | 3,009 | 34.411765 | 74 | py |
bfh_python | bfh_python-master/involutivetest.py | """Unit test for involutive.py"""
from involutive import *
from dstructure import zeroTypeD, infTypeD
from arcslide import Arcslide
from arcslideda import ArcslideDA
from pmc import splitPMC
import unittest
class InvolutiveTest(unittest.TestCase):
def testInvolutiveRankS3(self):
"Check that the involutive... | 1,614 | 30.666667 | 106 | py |
bfh_python | bfh_python-master/localpmc.py | """This module offers minimal support for PMC with boundaries and unmatched
points. A normal PMC (defined in pmc.py) can be split into one or several
local PMC's like this.
"""
from algebra import DGAlgebra, Element, Generator
from algebra import E0
from pmc import Strands, StrandDiagram
from utility import memorize,... | 30,724 | 37.991117 | 83 | py |
bfh_python | bfh_python-master/involutive.py | """
Created on Wed May 31 11:22:54 2017
@author: lipshitz
"""
from utility import SummableDict, F2, fracToInt, ACTION_LEFT, ACTION_RIGHT
from algebra import Element, E0
from dstructure import MorDtoDGenerator, DGenerator, SimpleDStructure
from algebra import SimpleChainComplex, SimpleGenerator, SimpleChainMorphism, Ge... | 14,774 | 41.335244 | 141 | py |
bfh_python | bfh_python-master/arcslideda.py | """Producing type DA structures for arcslides, using local actions."""
from algebra import TensorGenerator
from dastructure import DAStructure, SimpleDAGenerator
from extendbyid import ExtendedDAStructure, LocalDAStructure
from hdiagram import getArcslideDiagram
from localpmc import LocalIdempotent, LocalStrandAlgebra... | 43,764 | 50.977435 | 96 | py |
bfh_python | bfh_python-master/dehntwistda.py | """Producing type DA structures for Dehn twists, using local actions."""
from algebra import CobarAlgebra, TensorDGAlgebra, TensorGenerator, \
TensorStarGenerator
from algebra import E0
from autocompleteda import autoCompleteDA, autoCompleteMorphism
from dastructure import DAStructure, MorDAtoDAGenerator, SimpleDA... | 15,939 | 38.068627 | 81 | py |
bfh_python | bfh_python-master/minusalg.py | """Strand algebra for the minus theory."""
from algebra import Generator, SimpleChainComplex
from algebra import E0
from pmc import PMC, Strands, StrandAlgebra, StrandDiagram
from pmc import splitPMC
from utility import memorize
from utility import F2
class MinusStrands(Strands):
"""The corresponding Strands clas... | 8,385 | 36.106195 | 80 | py |
bfh_python | bfh_python-master/utility.py | """Various utilities useful for the project."""
from math import gcd
from numbers import Number
def memorize(function):
"""Function decorator: memorize returned values of this function.
Based on: Daniel Lawrence, A simple example using a python cache decorator.
"""
memo = {}
class NoneSymbol(obje... | 11,442 | 27.046569 | 80 | py |
bfh_python | bfh_python-master/linalg.py | """Linear algebra, including integral linear algebra."""
from fractions import Fraction
from utility import fracToInt, memorize
class RowSystem(object):
"""Manage a list of row vectors of integers. Find both integer and rational
linear combinations of these vectors that sum to zero or another row
vector.
... | 13,142 | 39.070122 | 79 | py |
bfh_python | bfh_python-master/dstructuretest.py | """Unit test for dstructure.py"""
from dstructure import *
import unittest
class DStructureTest(unittest.TestCase):
def testDStructure(self):
pmc = splitPMC(1)
dstr = SimpleDStructure(F2, pmc.getAlgebra())
genx = SimpleDGenerator(dstr, pmc.idem([0]), "x")
dstr.addGenerator(genx)
... | 2,592 | 32.24359 | 60 | py |
bfh_python | bfh_python-master/ddstructuretest.py | """Unit test for ddstructure.py"""
from ddstructure import *
from dstructure import infTypeD, zeroTypeD
from pmc import PMC
from pmc import linearPMC, splitPMC
from utility import DEFAULT_GRADING, SMALL_GRADING
import unittest
class DDStructureTest(unittest.TestCase):
def testCommonIdentityDD(self):
id2 =... | 5,899 | 37.562092 | 80 | py |
bfh_python | bfh_python-master/localpmctest.py | """Unit test for localpmc.py"""
from localpmc import *
from pmc import splitPMC
import unittest
class LocalPMCTest(unittest.TestCase):
def testLocalPMC(self):
# One piece: (0-1-2-3*), with 0 paired with 2. Appears in short
# underslide at bottom of full PMC.
pmc1 = LocalPMC(4, [(0, 2),(1,)... | 10,748 | 39.258427 | 80 | py |
bfh_python | bfh_python-master/arcslidedatest.py | """Unit test for arcslideda.py"""
from arcslideda import *
from arcslide import Arcslide
from autocompleteda import autoCompleteDA
from dstructure import zeroTypeD
from ddstructure import identityDD
from latex import beginDoc, endDoc, showArrow
from pmc import PMC
from pmc import antipodalPMC, linearPMC, splitPMC
impo... | 11,848 | 40 | 79 | py |
bfh_python | bfh_python-master/braidtest.py | """Unit test for braid.py"""
from math import gcd
from braid import *
import unittest
import time # benchmarking
import cProfile
import pstats
class BraidTest(unittest.TestCase):
def testGetArcslide(self):
br2 = Braid(6)
pos_size = [1,2,2,1,4]
for i in range(1, 6):
self.assert... | 9,516 | 39.156118 | 80 | py |
bfh_python | bfh_python-master/cobordism.py | """Type DD structures for cobordisms between linear pointed matched circles."""
from ddstructure import DDStrFromChords
from pmc import linearPMC
from pmc import Idempotent, Strands
from utility import memorize
# Two sides for the larger PMC:
LEFT, RIGHT = 0, 1
class Cobordism(object):
"""Represents a cobordism.... | 6,619 | 36.613636 | 80 | py |
bfh_python | bfh_python-master/dstructure.py | """Defines type D structures."""
from fractions import Fraction
from algebra import DGAlgebra, FreeModule, Generator, SimpleChainComplex, \
Tensor, TensorGenerator
from algebra import simplifyComplex
from algebra import E0
from grading import GeneralGradingSet, GeneralGradingSetElement
from hdiagram import getZero... | 23,399 | 38.065109 | 86 | py |
bfh_python | bfh_python-master/digraphtest.py | """Unit test for digraph.py"""
from digraph import *
from arcslide import Arcslide
from dstructure import infTypeD, platTypeD, zeroTypeD, zeroTypeDAdm
from ddstructure import identityDD
from pmc import PMC
from pmc import splitPMC
from utility import DEFAULT_GRADING, SMALL_GRADING
import unittest
class TypeDGraphTest... | 4,570 | 34.710938 | 74 | py |
bfh_python | bfh_python-master/minusalgtest.py | """Unit test for minusalg.py"""
from minusalg import *
from ddstructure import SimpleDDGenerator, SimpleDDStructure
from pmc import Idempotent
import unittest
class MinusAlgTest(unittest.TestCase):
def testGenerators(self):
gens = MinusStrandAlgebra(F2, splitPMC(1)).getGenerators()
self.assertEqua... | 2,240 | 36.35 | 76 | py |
bfh_python | bfh_python-master/digraph.py | """Handles things related to directed graphs."""
from algebra import Generator, SimpleChainComplex
from algebra import E0
from dstructure import DGenerator, SimpleDStructure
from ddstructure import DDGenerator, SimpleDDStructure
from dastructure import DATensorDGenerator, DATensorDDGenerator, \
SimpleDAGenerator, ... | 29,351 | 38.826323 | 82 | py |
bfh_python | bfh_python-master/extendbyidtest.py | """Unit test for extendbyid.py"""
from extendbyid import *
from dastructure import SimpleDAGenerator
from ddstructure import identityDD
from localpmc import LocalIdempotent
from pmc import Idempotent
from pmc import linearPMC, splitPMC
import unittest
class ExtendedDAStructureTest(unittest.TestCase):
def setUp(se... | 7,759 | 42.351955 | 80 | py |
bfh_python | bfh_python-master/signs.py | """Sign conventions."""
from fractions import Fraction
from algebra import findRankOverF2
from algebra import DGAlgebra, Element, Generator
from algebra import E0
from grading import standardRefinement, standardRefinementForIdem
from grading import DEFAULT_REFINEMENT
from linalg import F2RowSystem
from pmc import Str... | 21,494 | 39.480226 | 80 | py |
bfh_python | bfh_python-master/hdiagram.py | """Code for handling Heegaard diagrams."""
from fractions import Fraction
from grading import BigGradingGroup, SimpleDbGradingSet, \
SimpleDbGradingSetElement, SimpleGradingSet, SimpleGradingSetElement, \
SmallGradingGroup
from grading import DEFAULT_REFINEMENT
from linalg import RowSystem
from pmc import Idem... | 51,765 | 40.445957 | 81 | py |
bfh_python | bfh_python-master/regression.py | """Regression testing framework
This module will search for scripts in the same directory named
XYZtest.py. Each such script should be a test suite that tests a
module through PyUnit. (As of Python 2.1, PyUnit is included in
the standard library as 'unittest'.) This script will aggregate all
found test suites into ... | 1,313 | 36.542857 | 68 | py |
bfh_python | bfh_python-master/cobordismdatest.py | """Unit test for cobordismda.py"""
from cobordismda import *
from ddstructure import identityDD
from pmc import splitPMC
import unittest
class CobordismDATest(unittest.TestCase):
def testLeftCobordismDA(self):
for genus, c_pair in [(2, 0), (2, 1), (2, 2), (2, 3),
(3, 0), (3, ... | 4,142 | 36.663636 | 78 | py |
bfh_python | bfh_python-master/ddstructure.py | """Defines type DD structures."""
from algebra import ChainComplex, DGAlgebra, Element, FreeModule, Generator, \
SimpleChainComplex, Tensor, TensorDGAlgebra, TensorIdempotent, \
TensorGenerator
from algebra import expandTensor, simplifyComplex
from algebra import E0
from dstructure import DGenerator, SimpleDGe... | 29,142 | 40.632857 | 82 | py |
bfh_python | bfh_python-master/linalgtest.py | """Unit test for linalg.py."""
from linalg import *
from utility import F2
import unittest
class RowSystemTest(unittest.TestCase):
def testRowSystem(self):
rows1 = [[2,0],[3,1]]
sys1 = RowSystem(rows1)
self.assertEqual(sys1.getComb([1, 1]), [-1, 1])
self.assertEqual(sys1.getComb([0... | 1,493 | 36.35 | 76 | py |
bfh_python | bfh_python-master/latextest.py | """Unit test for latex.py"""
from latex import *
from arcslide import Arcslide
from arcslideda import ArcslideDA
from localpmc import LocalPMC, LocalStrandDiagram
from pmc import PMC
from pmc import antipodalPMC, splitPMC
import unittest
class LatexTest(unittest.TestCase):
def testPrintLocalDAStructure(self):
... | 1,004 | 33.655172 | 80 | py |
bfh_python | bfh_python-master/dastructuretest.py | """Unit test for dastructure.py"""
from dastructure import *
from arcslide import Arcslide
from arcslideda import ArcslideDA
from dstructure import infTypeD, zeroTypeD
from pmc import splitPMC
from utility import DEFAULT_GRADING, SMALL_GRADING
import unittest
class DAStructureTest(unittest.TestCase):
def testIden... | 1,180 | 30.918919 | 76 | py |
bfh_python | bfh_python-master/dehntwist.py | """Dehn twists starting at linear PMC and their type DD structures."""
from algebra import TensorDGAlgebra, TensorGenerator
from algebra import E0
from ddstructure import MorDDtoDDComplex, MorDDtoDDGenerator, \
SimpleDDGenerator, SimpleDDStructure
from ddstructure import DDStrFromChords, identityDD
from pmc import... | 29,086 | 41.094067 | 80 | py |
bfh_python | bfh_python-master/autocompleteda.py | """Auto-completion of arrows in a type DA structure, by solving certain
equations in linear algebra.
This module is used to produce arrows in the local type DA structure for
arcslides, in arcslidedatest.py.
"""
from algebra import solveOverF2
from algebra import E0
from dastructure import DAStructure, MorDAtoDAGener... | 19,362 | 44.56 | 80 | py |
bfh_python | bfh_python-master/extendbyid.py | """Extension by identity of type DA structures."""
from algebra import TensorGenerator
from algebra import E0
from dastructure import DAGenerator, DAStructure, DATensorDGenerator, \
MorDAtoDAComplex, SimpleDAGenerator, SimpleDAStructure
from dstructure import SimpleDStructure
from grading import GeneralGradingSet,... | 28,828 | 44.257457 | 85 | py |
bfh_python | bfh_python-master/latex.py | """A collection of latex printing code."""
from utility import sumColumns
from functools import cmp_to_key
def beginDoc():
return "\\documentclass{article}\n" + \
"\\usepackage{tikz}\n" + \
"\\begin{document}\n"
def endDoc():
return "\\end{document}\n"
def beginTikz(scale):
return "\\beg... | 5,890 | 33.052023 | 87 | py |
bfh_python | bfh_python-master/identityaatest.py | """Unit test for identityaa.py"""
from identityaa import *
from identityaa import _getIntervalOrdering
from pmc import antipodalPMC, linearPMC, splitPMC
import unittest
class HomotopyAATest(unittest.TestCase):
def testHomotopyAA(self):
HomotopyAA(splitPMC(1)).testHomotopy()
HomotopyAA(splitPMC(2))... | 713 | 30.043478 | 62 | py |
bfh_python | bfh_python-master/signstest.py | """Unit test for signs.py."""
from signs import *
from grading import DEFAULT_REFINEMENT, lowerRefinement
from pmc import PMC
from pmc import antipodalPMC, linearPMC, splitPMC
from utility import ZZ
import unittest
class AbsZ2GradingTest(unittest.TestCase):
def testAbsGrading(self):
def testOneAlgebra(alg... | 5,832 | 46.040323 | 82 | py |
bfh_python | bfh_python-master/braid.py | """Handles braids and their type DD structures."""
import sys
from arcslide import Arcslide
from arcslideda import ArcslideDA
from cobordism import Cobordism
from cobordism import LEFT, RIGHT
from cobordismda import CobordismDALeft, CobordismDARight
from dehntwistda import DehnSurgeryDA
from digraph import computeATen... | 15,097 | 37.417303 | 80 | py |
bfh_python | bfh_python-master/experimental.py | """Try different things here by adding test cases. Tests added here are not
included in testmod.
"""
from braid import *
from dehntwist import *
from digraph import computeDATensorDD
from dstructure import SimpleDStructure, SimpleDGenerator
from dstructure import zeroTypeD
from ddstructure import SimpleDDGenerator, S... | 16,918 | 39.670673 | 80 | py |
bfh_python | bfh_python-master/cobordismtest.py | """Unit test for cobordismtest.py"""
from cobordism import *
from dstructure import platTypeD
import unittest
class CobordismTest(unittest.TestCase):
def testCobordism(self):
for genus, c_pair, side in [
(2, 1, RIGHT), (2, 2, RIGHT), (3, 1, RIGHT),
(3, 2, RIGHT), (3, 3, RIG... | 2,444 | 37.809524 | 80 | py |
bfh_python | bfh_python-master/grading.py | """Handles grading groups and grading sets."""
from fractions import Fraction
from math import gcd
from numbers import Number
from linalg import RowSystem
from utility import flatten, grTypeStr, memorize, oppSide, sideStr, tolist
from utility import ACTION_LEFT, ACTION_RIGHT, BIG_GRADING, SMALL_GRADING
class Group(ob... | 53,366 | 37.283357 | 80 | py |
bfh_python | bfh_python-master/algebratest.py | """Unit test for algebra.py"""
from algebra import *
from pmc import splitPMC
from utility import ZZ
import unittest
class ChainComplexTest(unittest.TestCase):
def testChainComplex(self):
cx = SimpleChainComplex(F2)
gens = [SimpleGenerator(cx, "gen%d"%i) for i in range(3)]
for gen in gens:... | 3,191 | 34.466667 | 78 | py |
bfh_python | bfh_python-master/gradingtest.py | """Unit test for grading.py"""
from grading import *
from pmc import *
import unittest
class BigGradingTest(unittest.TestCase):
def testMultiply(self):
pmc = splitPMC(1)
bgrp = BigGradingGroup(pmc)
elt1 = BigGradingElement(bgrp, 0, [1,0,0])
elt2 = BigGradingElement(bgrp, 0, [0,1,0]... | 6,731 | 44.181208 | 79 | py |
bfh_python | bfh_python-master/dastructure.py | """Defines type DA structures."""
from algebra import CobarAlgebra, DGAlgebra, FreeModule, Generator, Tensor, \
TensorGenerator, TensorStarGenerator
from algebra import ChainComplex, E0, TensorDGAlgebra
from dstructure import DGenerator, SimpleDStructure
from ddstructure import DDGenerator, SimpleDDGenerator, Simp... | 34,595 | 42.299124 | 179 | py |
bfh_python | bfh_python-master/cobordismda.py | """Producing type DA structures for cobordisms, using local actions."""
from arcslide import Arcslide
from arcslideda import ArcslideDA
from autocompleteda import autoCompleteDA
from cobordism import Cobordism
from cobordism import LEFT, RIGHT
from dastructure import ComposedDAStructure, DAStructure, SimpleDAGenerator... | 19,009 | 39.190275 | 80 | py |
bfh_python | bfh_python-master/arcslidetest.py | """Unit test for arcslide.py"""
from arcslide import *
from utility import DEFAULT_GRADING, SMALL_GRADING
import unittest
class ArcslideTest(unittest.TestCase):
def testArcslide(self):
slide1 = Arcslide(splitPMC(1), 0, 1)
self.assertEqual(slide1.c2, 3)
self.assertEqual(slide1.slide_type, O... | 2,374 | 37.306452 | 72 | py |
bfh_python | bfh_python-master/identityaa.py | """Description of type AA structure for identity cobordism."""
from algebra import Generator, SimpleChainComplex, SimpleChainMorphism
from algebra import E0
from pmc import Strands, StrandDiagram
from utility import find
from utility import F2
# Convenient values for specifying sides. Use only in the context of large... | 14,565 | 38.367568 | 80 | py |
bfh_python | bfh_python-master/hdiagramtest.py | """Unit test for hdiagram.py"""
from hdiagram import *
from hdiagram import _Point, _Segment, _OrientedSegment, _Path, _Cell, \
_Domain, _OneChain
from arcslide import Arcslide
from cobordism import Cobordism
from cobordism import LEFT
from pmc import antipodalPMC, linearPMC, splitPMC
import unittest
class Orient... | 9,333 | 44.091787 | 79 | py |
bfh_python | bfh_python-master/algebra.py | """Definitions of core algebraic objects.
Some design decisions:
Each generator can belong to only one chain complex or module. This allows for
the possibility of ``a.diff()`` where ``a`` is a Generator.
"""
import heapq
from numbers import Number
from utility import NamedObject, SummableDict
from utility import fr... | 41,139 | 36.298277 | 126 | py |
bfh_python | bfh_python-master/pmc.py | """Pointed matched circle and its algebras."""
import itertools
from algebra import E0
from fractions import Fraction
from algebra import DGAlgebra, Element, Generator, Tensor, TensorGenerator
from grading import BigGradingElement, BigGradingGroup, SmallGradingElement, \
SmallGradingGroup
from grading import DEFAU... | 24,613 | 34.518038 | 80 | py |
bfh_python | bfh_python-master/dehntwisttest.py | """Unit test for dehntwist.py"""
from dehntwist import *
import unittest
class DehnTwistTest(unittest.TestCase):
def testDehnTwist(self):
twist = DehnTwist(3, 1, POS)
twist_dd = twist.getDDStructure()
class AntiBraidTest(unittest.TestCase):
def testAntiBraid(self):
for genus, c_pair i... | 2,609 | 37.955224 | 80 | py |
bfh_python | bfh_python-master/dehntwistdatest.py | """Unit test for dehntwistda.py"""
from ddstructure import identityDD
from dehntwist import AntiBraid, DehnSurgery
from dehntwistda import *
import unittest
class AntiBraidDATest(unittest.TestCase):
def testLocalDA(self):
da = AntiBraidDA(2, 1)
self.assertTrue(da.local_da.testDelta())
def tes... | 4,316 | 40.509615 | 75 | py |
bfh_python | bfh_python-master/pmctest.py | """Unit test for pmc.py"""
from grading import averageRefinement
from pmc import *
import unittest
class PMCTest(unittest.TestCase):
def testPMC(self):
pmc1 = PMC([(0,2),(1,3)])
for p, q in [(0,2),(2,0),(1,3),(3,1)]:
self.assertEqual(pmc1.otherp[p], q)
for p, i in [(0,0),(2,0),... | 11,205 | 40.350554 | 80 | py |
CppDNN-develop | CppDNN-develop/example/keras_simple/simple.py | from keras import models
from keras import layers
from numpy import array
mnistfile = open('mnist_example', 'r')
mnistdata = mnistfile.read()
mnistdata = mnistdata.splitlines()[0].split(' ')
mnistdataf = []
for m in mnistdata:
mnistdataf.append(float(m))
mnistdata = array(mnistdataf)
mnistdata = mnistdata.reshape(... | 788 | 27.178571 | 50 | py |
CppDNN-develop | CppDNN-develop/script/DecodeKerasModel.py | import sys
from keras import models
if len(sys.argv) < 3:
print('usage: python DecodeKerasModel.py input output')
exit(1)
input = sys.argv[1]
output = sys.argv[2]
print(input)
outputFile = open(output, 'w')
model = models.load_model(input)
weights_list = model.get_weights()
print("############################... | 1,171 | 30.675676 | 79 | py |
CppDNN-develop | CppDNN-develop/script/NeuralNetworkSaver.py | def NeuralNetworkSaver(ns, layers: list, save_in: str):
file = open(save_in, "w")
file.write("# Layer Numbers: " + str(len(layers)))
file.write('\n')
for i, layer in enumerate(layers):
file.write("# Layer Number: " + str(i) + "\n")
file.write(layer[0] + "\n")
file.write(str(ns... | 674 | 24.961538 | 58 | py |
CppDNN-develop | CppDNN-develop/script/DecodeTensorFlowModel.py | import tensorflow as tf
import NeuralNetworkSaver as nns
nx = 94
n1 = 256
n2 = 64
# n3 = 32
n4 = 11
with tf.variable_scope("Layer1"):
w1 = tf.Variable(tf.random_normal([nx, n1]), name="weight_1")
b1 = tf.Variable(tf.random_normal([1, n1]), name="bias_1")
# o1 = tf.nn.relu(tf.add(tf.matmul(x, w1), b1, name... | 1,131 | 32.294118 | 116 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/main_arxiv_node_classification.py |
"""
IMPORTING LIBS
"""
import dgl
import numpy as np
import os
import socket
import time
import random
import glob
import argparse, json
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch_geometric.d... | 19,431 | 41.060606 | 202 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/test.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch
import pickle
import torch.utils.data
import time
import os
import numpy as np
import csv
import dgl
from scipy import sparse as sp
import numpy as np
# # coding=gbk
# from tqdm import trange
# from random import... | 5,463 | 34.712418 | 145 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/main_Planetoid_node_classification.py |
"""
IMPORTING LIBS
"""
import dgl
import numpy as np
import os
import socket
import time
import random
import glob
import argparse, json
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWri... | 21,251 | 43 | 188 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/main_ogb_node_classification.py |
"""
IMPORTING LIBS
"""
import dgl
import numpy as np
import os
import socket
import time
import random
import glob
import argparse, json
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch_geometric.d... | 20,051 | 41.303797 | 202 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/main_SBMs_node_classification.py |
"""
IMPORTING LIBS
"""
import dgl
import numpy as np
import os
import socket
import time
import random
import glob
import argparse, json
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch_geometric.d... | 19,999 | 41.643923 | 226 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/gat_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from torch_geometric.typing import OptPairTensor
"""
GAT: Graph Attention Network
Graph Attention Networks (Veličković et al., ICLR 2018)
https://arxiv.org/abs/1710.10903
"""
from layers.gat_layer import GATLayer
from layers.mlp... | 3,492 | 38.247191 | 120 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/load_net.py | """
Utility file to select GraphNN model as
selected by the user
"""
from nets.ogb_node_classification.gated_gcn_net import GatedGCNNet, GatedGCNNet_pyg, ResGatedGCNNet_pyg
from nets.ogb_node_classification.gcn_net import GCNNet_pyg
from nets.ogb_node_classification.gat_net import GATNet_pyg
from nets.ogb_node... | 2,369 | 27.554217 | 103 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/graphsage_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
"""
GraphSAGE:
William L. Hamilton, Rex Ying, Jure Leskovec, Inductive Representation Learning on Large Graphs (NeurIPS 2017)
https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf
"""
from layers.graphsage_layer import... | 5,368 | 35.52381 | 122 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/gin_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling
"""
GIN: Graph Isomorphism Networks
HOW POWERFUL ARE GRAPH NEURAL NETWORKS? (Keyulu Xu, Weihua Hu, Jure Leskovec and Stefanie Jegelka, ICLR 2019)
https://arxiv.o... | 5,771 | 35.531646 | 113 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/gcn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
import dgl
import numpy as np
"""
GCN: Graph Convolutional Networks
Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017)
http://arxiv.org/abs/1609.0... | 3,608 | 33.701923 | 110 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/gated_gcn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import numpy as np
"""
ResGatedGCN: Residual Gated Graph ConvNets
An Experimental Study of Neural Networks for Variable Graphs (Xavier Bresson and Thomas Laurent, ICLR 2018)
https://arxiv.org/pdf/1711.07553v2.pdf
"""
from layers... | 8,653 | 37.807175 | 122 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/mlp_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from layers.mlp_readout_layer import MLPReadout
class MLPNet(nn.Module):
def __init__(self, net_params):
super().__init__()
in_dim_node = net_params['in_dim'] # node_dim (feat is an integer)
hidden_dim = net_... | 4,176 | 29.268116 | 93 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/ogb_node_classification/mo_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_add
import dgl
import numpy as np
"""
GMM: Gaussian Mixture Model Convolution layer
Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs (Federico Monti et al., CVPR 2017)
https://arxiv... | 6,647 | 39.785276 | 121 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/gat_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from torch_geometric.typing import OptPairTensor
"""
GAT: Graph Attention Network
Graph Attention Networks (Veličković et al., ICLR 2018)
https://arxiv.org/abs/1710.10903
"""
from layers.gat_layer import GATLayer
from layers.mlp... | 5,639 | 35.862745 | 120 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/ring_gnn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import time
"""
Ring-GNN
On the equivalence between graph isomorphism testing and function approximation with GNNs (Chen et al, 2019)
https://arxiv.org/pdf/1905.12560v1.pdf
"""
from layers.ring_gnn_equiv_layer import RingGNNEqui... | 3,202 | 38.060976 | 141 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/load_net.py | """
Utility file to select GraphNN model as
selected by the user
"""
from nets.SBMs_node_classification.gated_gcn_net import GatedGCNNet, GatedGCNNet_pyg, ResGatedGCNNet_pyg
from nets.SBMs_node_classification.gcn_net import GCNNet, GCNNet_pyg
from nets.SBMs_node_classification.gat_net import GATNet, GATNet_pyg... | 2,797 | 28.452632 | 104 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/three_wl_gnn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import time
"""
3WLGNN / ThreeWLGNN
Provably Powerful Graph Networks (Maron et al., 2019)
https://papers.nips.cc/paper/8488-provably-powerful-graph-networks.pdf
CODE adapted from https://github.com/hadarser/ProvablyPowe... | 4,050 | 36.859813 | 118 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/graphsage_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
"""
GraphSAGE:
William L. Hamilton, Rex Ying, Jure Leskovec, Inductive Representation Learning on Large Graphs (NeurIPS 2017)
https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf
"""
from layers.graphsage_layer import... | 6,149 | 36.048193 | 122 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/gin_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling
"""
GIN: Graph Isomorphism Networks
HOW POWERFUL ARE GRAPH NEURAL NETWORKS? (Keyulu Xu, Weihua Hu, Jure Leskovec and Stefanie Jegelka, ICLR 2019)
https://arxiv.o... | 6,582 | 36.19209 | 113 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/gcn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
import dgl
import numpy as np
"""
GCN: Graph Convolutional Networks
Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017)
http://arxiv.org/abs/1609.0... | 5,901 | 34.769697 | 110 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/gated_gcn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import numpy as np
"""
ResGatedGCN: Residual Gated Graph ConvNets
An Experimental Study of Neural Networks for Variable Graphs (Xavier Bresson and Thomas Laurent, ICLR 2018)
https://arxiv.org/pdf/1711.07553v2.pdf
"""
from layers... | 9,626 | 37.818548 | 122 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/mlp_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from layers.mlp_readout_layer import MLPReadout
class MLPNet(nn.Module):
def __init__(self, net_params):
super().__init__()
in_dim_node = net_params['in_dim'] # node_dim (feat is an integer)
hidden_dim = net_... | 4,619 | 29.8 | 91 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/SBMs_node_classification/mo_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# from torch_scatter import scatter_add
# from num_nodes import maybe_num_nodes
import dgl
from torch_geometric.nn.conv import MessagePassing
import numpy as np
import torch.nn as nn
from torch import Tensor
# from torch_geometric.utils import degree
fr... | 7,669 | 40.236559 | 121 | py |
benchmarking-gnns-pyg | benchmarking-gnns-pyg-master/nets/Planetoid_node_classification/gat_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from torch_geometric.typing import OptPairTensor
"""
GAT: Graph Attention Network
Graph Attention Networks (Veličković et al., ICLR 2018)
https://arxiv.org/abs/1710.10903
"""
from layers.gat_layer import GATLayer
from layers.mlp... | 4,653 | 34.257576 | 120 | py |
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