Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
null | ConjVI-main/MATLAB package with illustration/eval_func.m | function F = eval_func(f,X)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for input-affine dynamics,
% preprint arXiv... | 1,851 | 21.864198 | 102 | m |
null | ConjVI-main/MATLAB package with illustration/eval_func_constr.m | function F_h = eval_func_constr(f,X,h)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for input-affine dynamics,
% pre... | 2,222 | 24.551724 | 102 | m |
null | ConjVI-main/MATLAB package with illustration/ext_constr.m | function f_g_y = ext_constr(X,f,Y,g,h,interpol,extrapol)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for input-affine dyna... | 2,822 | 31.448276 | 126 | m |
null | ConjVI-main/MATLAB package with illustration/ext_constr_expect.m | function E_f_g_y = ext_constr_expect(X,f,Y,g,h,W,P,interpol,extrapol)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for inpu... | 3,761 | 32.891892 | 114 | m |
null | ConjVI-main/MATLAB package with illustration/feasibility_check.m | function [] = feasibility_check(ProblemData)
% Disclosure: This routine is modified versioin of the "feas_check_2"
% routineborrowed in the d-CDP MATLAB package avialable at
% "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynam... | 4,885 | 23.80203 | 111 | m |
null | ConjVI-main/MATLAB package with illustration/forward_iter.m | function [x,u,traj_cost] = forward_iter(ProblemData, VF)
% Disclosure: This routine is a modification of a similar routine borrowed
% from the d-CDP MATLAB package avialable at
% "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dy... | 4,050 | 31.408 | 92 | m |
null | ConjVI-main/MATLAB package with illustration/fusionma.m | function [fS,fH]=fusionma(C,S)
% Disclosure: This routine is borrowed from the LLT MATLAB package
% avialable at "netlib.org/numeralgo/na13"; see also
% YVES LUCET, FASTER THAN THE FAST LEGENDRE TRANSFORM, THE LINEAR-TIME
% LEGENDRE TRANSFORM, NUMERICAL ALGORITHMS, 16 (1997), PP. 171-185
%
% fusionma(C,S) ... | 1,288 | 30.439024 | 77 | m |
null | ConjVI-main/MATLAB package with illustration/slope_range.m | function Delta_s = slope_range(X,J)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for input-affine dynamics,
% prepri... | 3,251 | 30.882353 | 124 | m |
null | ConjVI-main/MATLAB package with illustration/unif_grid.m | function X = unif_grid(Delta_x,N_vec)
% Disclosure: This routine is borrowed from the d-CDP MATLAB package
% avialable at "https://github.com/AminKolarijani/d-CDP"; see also
% M.A.S. Kolarijani and P. Mohajerin Esfahani (2020),
% Fast approximate dynamic programming for input-affine dynamics,
% prep... | 1,180 | 31.805556 | 125 | m |
BAR | BAR-master/README.md | <p align="center"><img src="https://sophron.github.io/BAR/bar-logo-2.png" /></p>
BAR (Broadcast Anonymous Routing) is a practical and scalable system that can provide anonymous, real-time Internet communications against powerful adversaries. It is a hybrid system and makes use by employing both dc-net characteristics (... | 621 | 123.4 | 305 | md |
BAR | BAR-master/setup.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
import sqlite3 as lite
import sys
from setuptools import setup, find_packages
setup(
name = "bar",
author = "sophron",
author_email = "sophron@latthi.com",
description = ("A broadcast anonymity network"),
license = "BSD",
keywords = ['anonymity', 'bro... | 1,225 | 26.863636 | 133 | py |
BAR | BAR-master/bar/__init__.py | 0 | 0 | 0 | py | |
BAR | BAR-master/bar/pybar.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
import sys, os, argparse, subprocess, signal
import sqlite3 as lite
from Crypto import Random
from bar.network.bar_daemon import daemon_main
from bar.network.bar_server import server_main
def parse_cli():
'''
The top-level parser is called "operation". Each subparser ... | 9,784 | 29.96519 | 106 | py |
BAR | BAR-master/bar/common/__init__.py | 0 | 0 | 0 | py | |
BAR | BAR-master/bar/common/aes.py | from Crypto import Random
from Crypto.Cipher import AES
from binascii import unhexlify
def aes_encrypt(skey, m):
'''
Encrypt given message with shared key.
'''
iv = '\x00' * 16
stream = AES.new(skey, AES.MODE_CFB, iv)
return stream.encrypt(m)
def aes_decrypt(skey, c):
'''
Decrypt given... | 447 | 21.4 | 44 | py |
BAR | BAR-master/bar/common/db.py | #!/usr/bin/env python
import sqlite3
import logging
# TODO: Grab this from constants file.
DBFILE = "bar/db/bar.db"
class Bardb():
""" Interface for db storage. Serves as segregation of the persistence layer
and the application logic
"""
def __init__(self):
self.con = False
#self._l... | 5,737 | 36.75 | 107 | py |
BAR | BAR-master/bar/common/label.py | from Crypto import Random
def gen_lbl():
'''
Generate a new Label.
'''
rpool = Random.new()
Random.atfork()
return rpool.read(16).encode("hex")
| 172 | 14.727273 | 39 | py |
BAR | BAR-master/bar/common/message.py | import bar.common.aes as aes
class Message:
def __init__(self, message):
self.message = message
self.label, self.encrypted_payload = message.split("|||")
def decrypt(self, sharedkey):
self.cleartext_payload = aes.aes_decrypt(sharedkey, self.encrypted_payload)
self.val_label,... | 481 | 29.125 | 96 | py |
BAR | BAR-master/bar/network/__init__.py | 0 | 0 | 0 | py | |
BAR | BAR-master/bar/network/bar_daemon.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
import argparse
import os
import time
import zlib
import sys
import re
import socket
import sqlite3 as lite
from twisted.internet.protocol import ServerFactory, ClientFactory, Protocol
from twisted.protocols.basic import LineReceiver, NetstringReceiver
from twisted.protocols ... | 6,644 | 33.076923 | 147 | py |
BAR | BAR-master/bar/network/bar_server.py | import os
import time
import socket
import sys
import urllib2
import sqlite3 as lite
from twisted.internet import defer, reactor
from twisted.internet.protocol import ServerFactory, ClientFactory, Protocol
from twisted.protocols.basic import LineReceiver, NetstringReceiver
from twisted.protocols import basic
clients =... | 1,774 | 29.603448 | 111 | py |
null | reco-main/README.md | # ReCo - Regional Contrast
This repository contains the source code of ReCo and baselines from the paper, [Bootstrapping Semantic Segmentation with Regional Contrast](https://arxiv.org/abs/2104.04465), introduced by [Shikun Liu](https://shikun.io/), [Shuaifeng Zhi](https://shuaifengzhi.com/), [Edward Johns](https://ww... | 9,765 | 90.271028 | 589 | md |
null | reco-main/build_data.py | from torch.utils.data.dataset import Dataset
from PIL import Image
from PIL import ImageFilter
import pandas as pd
import numpy as np
import torch
import os
import random
import glob
import torch.utils.data.sampler as sampler
import torchvision.transforms as transforms
import torchvision.transforms.functional as trans... | 23,510 | 40.983929 | 159 | py |
null | reco-main/module_list.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
from torch.optim.lr_scheduler import _LRScheduler
import torchvision.transforms.functional as transforms_f
# --------------------------------------------------------------------------------
# Define EMA: Mean Teacher Framework
# --... | 11,756 | 41.444043 | 120 | py |
null | reco-main/train_semisup.py | import torch
import torchvision.models as models
import torch.optim as optim
import argparse
from network.deeplabv3.deeplabv3 import *
from network.deeplabv2 import *
from build_data import *
from module_list import *
parser = argparse.ArgumentParser(description='Semi-supervised Segmentation with Perfect Labels')
pa... | 9,116 | 48.281081 | 168 | py |
null | reco-main/train_semisup_partial.py | import torch
import torchvision.models as models
import torch.optim as optim
import argparse
import matplotlib.pylab as plt
from network.deeplabv3.deeplabv3 import *
from network.deeplabv2 import *
from build_data import *
from module_list import *
parser = argparse.ArgumentParser(description='Semi-supervised Segmen... | 9,508 | 48.26943 | 168 | py |
null | reco-main/train_sup.py | import torch
import torchvision.models as models
import torch.utils.data.sampler as sampler
import torch.optim as optim
import argparse
import matplotlib.pylab as plt
from network.deeplabv3.deeplabv3 import *
from network.deeplabv2 import *
from build_data import *
from module_list import *
parser = argparse.Argumen... | 5,840 | 44.632813 | 140 | py |
null | reco-main/train_sup_partial.py | import torch
import torchvision.models as models
import torch.optim as optim
import argparse
import matplotlib.pylab as plt
from network.deeplabv3.deeplabv3 import *
from network.deeplabv2 import *
from build_data import *
from module_list import *
parser = argparse.ArgumentParser(description='Supervised Segmentatio... | 4,812 | 42.36036 | 140 | py |
null | reco-main/visual.py | import torch
import torchvision.models as models
import matplotlib.pylab as plt
from PIL import Image
from network.deeplabv3.deeplabv3 import *
from build_data import *
from module_list import *
# ++++++++++++++++++++ Cityscapes Visualisation +++++++++++++++++++++++++
data_path = 'dataset/cityscapes'
im_size = [512,... | 5,704 | 40.043165 | 126 | py |
null | reco-main/dataset/cityscapes_preprocess.py | import glob
import os
from collections import namedtuple
import random
import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from module_list import *
root = 'dataset'
im_root = 'dataset/cityscapes/images'
label_root = 'dataset/cityscapes/labels'
os.makedirs(im_root + '/train')
os.makedi... | 4,667 | 36.645161 | 89 | py |
null | reco-main/dataset/pascal_preprocess.py | import glob
import os
from collections import namedtuple
import random
import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from module_list import *
im_root = 'dataset/pascal/JPEGImages'
label_root = 'dataset/pascal/SegmentationClassAug'
for seed in range(3):
np.random.seed(seed)... | 2,841 | 37.931507 | 87 | py |
null | reco-main/dataset/sun_preprocess.py | import os
import glob
import random
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
from PIL import Image
from module_list import *
import re
def natural_key(string_):
"""See https://blog.codinghorror.com/sorting-for-humans-natural-sort-order/"""
return [int(s) if s.isdigit(... | 1,861 | 25.985507 | 82 | py |
null | reco-main/network/deeplabv2.py | import torch.nn as nn
from functools import partial
class Classifier_Module(nn.Module):
def __init__(self, dilation_series, padding_series, num_classes):
super(Classifier_Module, self).__init__()
self.conv2d_list = nn.ModuleList()
for dilation, padding in zip(dilation_series, padding_serie... | 2,847 | 36.473684 | 113 | py |
null | reco-main/network/deeplabv3/aspp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepLabHead(nn.Sequential):
def __init__(self, in_channels, num_classes):
super(DeepLabHead, self).__init__(
ASPP(in_channels, [12, 24, 36]),
nn.Conv2d(256, 256, 3, padding=1, bias=False),
nn.Batch... | 2,309 | 31.535211 | 101 | py |
null | reco-main/network/deeplabv3/deeplabv3.py | from .aspp import *
from functools import partial
class DeepLabv3Plus(nn.Module):
def __init__(self, orig_resnet, dilate_scale=16, num_classes=2, output_dim=256):
super(DeepLabv3Plus, self).__init__()
if dilate_scale == 8:
orig_resnet.layer3.apply(partial(self._nostride_dilate, dilate=... | 2,965 | 34.309524 | 106 | py |
null | grin-main/README.md | # Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks (ICLR 2022 - [open review](https://openreview.net/forum?id=kOu3-S3wJ7) - [pdf](https://openreview.net/pdf?id=kOu3-S3wJ7))
[](https://openreview.net/forum?id=kOu3-... | 4,627 | 39.955752 | 435 | md |
null | grin-main/conda_env.yml | name: grin
channels:
- defaults
- pytorch
- conda-forge
dependencies:
- pip
- pytables
- python=3.8
- pytorch=1.8
- torchvision
- torchaudio
- wheel
- pip:
- einops
- fancyimpute==0.6
- h5py
- openpyxl
- pandas
- pytorch-lightning==1.4
- pyyaml
- sci... | 364 | 13.6 | 30 | yml |
null | grin-main/config/bimpgru/air.yaml | dataset_name: 'air'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_size: ... | 364 | 14.208333 | 48 | yaml |
null | grin-main/config/bimpgru/air36.yaml | dataset_name: 'air36'
window: 36
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_size... | 366 | 14.291667 | 48 | yaml |
null | grin-main/config/bimpgru/bay_block.yaml | dataset_name: 'bay_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_... | 370 | 14.458333 | 48 | yaml |
null | grin-main/config/bimpgru/bay_point.yaml | dataset_name: 'bay_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_... | 370 | 14.458333 | 48 | yaml |
null | grin-main/config/bimpgru/irish_block.yaml | dataset_name: 'irish_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kerne... | 372 | 14.541667 | 48 | yaml |
null | grin-main/config/bimpgru/irish_point.yaml | dataset_name: 'irish_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kerne... | 372 | 14.541667 | 48 | yaml |
null | grin-main/config/bimpgru/la_block.yaml | dataset_name: 'la_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_s... | 369 | 14.416667 | 48 | yaml |
null | grin-main/config/bimpgru/la_point.yaml | dataset_name: 'la_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'bimpgru'
d_hidden: 64
d_emb: 8
d_ff: 64
dropout: 0
kernel_s... | 369 | 14.416667 | 48 | yaml |
null | grin-main/config/brits/air.yaml | dataset_name: 'air'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 128 | 232 | 14.533333 | 42 | yaml |
null | grin-main/config/brits/air36.yaml | dataset_name: 'air36'
window: 36
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 64 | 233 | 14.6 | 42 | yaml |
null | grin-main/config/brits/bay_block.yaml | dataset_name: 'bay_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 256 | 238 | 14.933333 | 42 | yaml |
null | grin-main/config/brits/bay_point.yaml | dataset_name: 'bay_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 256 | 238 | 14.933333 | 42 | yaml |
null | grin-main/config/brits/irish_block.yaml | dataset_name: 'irish_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 256 | 240 | 15.066667 | 42 | yaml |
null | grin-main/config/brits/irish_point.yaml | dataset_name: 'irish_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 256 | 240 | 15.066667 | 42 | yaml |
null | grin-main/config/brits/la_block.yaml | dataset_name: 'la_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 128 | 237 | 14.866667 | 42 | yaml |
null | grin-main/config/brits/la_point.yaml | dataset_name: 'la_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'brits'
d_hidden: 128 | 237 | 14.866667 | 42 | yaml |
null | grin-main/config/brits/synthetic.yaml | window: 36
p_block: 0.025
p_point: 0.025
min_seq: 4
max_seq: 9
use_exogenous: False
epochs: 200
batch_size: 32
model_name: 'brits'
d_hidden: 32 | 145 | 11.166667 | 20 | yaml |
null | grin-main/config/grin/air.yaml | dataset_name: 'air'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
ff_dro... | 403 | 13.428571 | 48 | yaml |
null | grin-main/config/grin/air36.yaml | dataset_name: 'air36'
window: 36
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
ff_d... | 405 | 13.5 | 48 | yaml |
null | grin-main/config/grin/bay_block.yaml | dataset_name: 'bay_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
... | 409 | 13.642857 | 48 | yaml |
null | grin-main/config/grin/bay_point.yaml | dataset_name: 'bay_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
... | 409 | 13.642857 | 48 | yaml |
null | grin-main/config/grin/irish_block.yaml | dataset_name: 'irish_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 6... | 411 | 13.714286 | 48 | yaml |
null | grin-main/config/grin/irish_point.yaml | dataset_name: 'irish_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 6... | 411 | 13.714286 | 48 | yaml |
null | grin-main/config/grin/la_block.yaml | dataset_name: 'la_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
f... | 408 | 13.607143 | 48 | yaml |
null | grin-main/config/grin/la_point.yaml | dataset_name: 'la_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'grin'
pred_loss_weight: 1
d_hidden: 64
d_emb: 8
d_ff: 64
f... | 408 | 13.607143 | 48 | yaml |
null | grin-main/config/grin/synthetic.yaml | window: 36
p_block: 0.025
p_point: 0.025
min_seq: 4
max_seq: 9
use_exogenous: False
epochs: 200
batch_size: 32
model_name: 'grin'
d_hidden: 16
d_emb: 0
d_ff: 16
ff_dropout: 0
kernel_size: 1
decoder_order: 1
n_layers: 1
layer_norm: false
merge: 'mlp'
| 252 | 11.047619 | 20 | yaml |
null | grin-main/config/mpgru/air.yaml | dataset_name: 'air'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: 0
kern... | 360 | 14.695652 | 48 | yaml |
null | grin-main/config/mpgru/air36.yaml | dataset_name: 'air36'
window: 36
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: 0
ke... | 362 | 14.782609 | 48 | yaml |
null | grin-main/config/mpgru/bay_block.yaml | dataset_name: 'bay_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: ... | 366 | 14.956522 | 48 | yaml |
null | grin-main/config/mpgru/bay_point.yaml | dataset_name: 'bay_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: ... | 366 | 14.956522 | 48 | yaml |
null | grin-main/config/mpgru/irish_block.yaml | dataset_name: 'irish_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout... | 368 | 15.043478 | 48 | yaml |
null | grin-main/config/mpgru/irish_point.yaml | dataset_name: 'irish_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout... | 368 | 15.043478 | 48 | yaml |
null | grin-main/config/mpgru/la_block.yaml | dataset_name: 'la_block'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: 0... | 365 | 14.913043 | 48 | yaml |
null | grin-main/config/mpgru/la_point.yaml | dataset_name: 'la_point'
window: 24
adj_threshold: 0.1
detrend: False
scale: True
scaling_axis: 'global' # ['channels', 'global']
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
aggregate_by: ['mean']
model_name: 'mpgru'
pred_loss_weight: 1
d_hidden: 64
d_ff: 64
dropout: 0... | 365 | 14.913043 | 48 | yaml |
null | grin-main/config/rgain/air.yaml | dataset_name: 'air'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gain'
d_m... | 410 | 14.807692 | 42 | yaml |
null | grin-main/config/rgain/air36.yaml | dataset_name: 'air36'
window: 36
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gain'
d... | 411 | 14.846154 | 42 | yaml |
null | grin-main/config/rgain/bay_block.yaml | dataset_name: 'bay_block'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gai... | 416 | 15.038462 | 42 | yaml |
null | grin-main/config/rgain/bay_point.yaml | dataset_name: 'bay_point'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gai... | 416 | 15.038462 | 42 | yaml |
null | grin-main/config/rgain/irish_block.yaml | dataset_name: 'irish_block'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'g... | 418 | 15.115385 | 42 | yaml |
null | grin-main/config/rgain/irish_point.yaml | dataset_name: 'irish_point'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'g... | 418 | 15.115385 | 42 | yaml |
null | grin-main/config/rgain/la_block.yaml | dataset_name: 'la_block'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gain... | 415 | 15 | 42 | yaml |
null | grin-main/config/rgain/la_point.yaml | dataset_name: 'la_point'
window: 24
whiten_prob: 0.2
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
batch_size: 32
loss_fn: mse_loss
consistency_loss: False
use_lr_schedule: True
grad_clip_val: -1
aggregate_by: ['mean']
model_name: 'gain... | 415 | 15 | 42 | yaml |
null | grin-main/config/var/air.yaml | dataset_name: 'air'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 252 | 13.882353 | 42 | yaml |
null | grin-main/config/var/air36.yaml | dataset_name: 'air36'
window: 36
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 254 | 14 | 42 | yaml |
null | grin-main/config/var/bay_block.yaml | dataset_name: 'bay_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 258 | 14.235294 | 42 | yaml |
null | grin-main/config/var/bay_point.yaml | dataset_name: 'bay_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 258 | 14.235294 | 42 | yaml |
null | grin-main/config/var/irish_block.yaml | dataset_name: 'irish_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 260 | 14.352941 | 42 | yaml |
null | grin-main/config/var/irish_point.yaml | dataset_name: 'irish_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 260 | 14.352941 | 42 | yaml |
null | grin-main/config/var/la_block.yaml | dataset_name: 'la_block'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 257 | 14.176471 | 42 | yaml |
null | grin-main/config/var/la_point.yaml | dataset_name: 'la_point'
window: 24
detrend: False
scale: True
scaling_axis: 'channels'
scaled_target: True
epochs: 300
samples_per_epoch: 5120 # 160 batch of 32
lr: 0.0005
batch_size: 64
aggregate_by: ['mean']
model_name: 'var'
order: 5
padding: 'mean' | 257 | 14.176471 | 42 | yaml |
null | grin-main/lib/__init__.py | import os
base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
config = {
'logs': 'logs/'
}
datasets_path = {
'air': 'datasets/air_quality',
'la': 'datasets/metr_la',
'bay': 'datasets/pems_bay',
'synthetic': 'datasets/synthetic'
}
epsilon = 1e-8
for k, v in config.items():
... | 442 | 21.15 | 71 | py |
null | grin-main/lib/data/__init__.py | from .temporal_dataset import TemporalDataset
from .spatiotemporal_dataset import SpatioTemporalDataset
| 104 | 34 | 57 | py |
null | grin-main/lib/data/imputation_dataset.py | import numpy as np
import torch
from . import TemporalDataset, SpatioTemporalDataset
class ImputationDataset(TemporalDataset):
def __init__(self, data,
index=None,
mask=None,
eval_mask=None,
freq=None,
trend=None,
... | 1,615 | 34.911111 | 81 | py |
null | grin-main/lib/data/spatiotemporal_dataset.py | import numpy as np
import pandas as pd
from einops import rearrange
from .temporal_dataset import TemporalDataset
class SpatioTemporalDataset(TemporalDataset):
def __init__(self, data,
index=None,
trend=None,
scaler=None,
freq=None,
... | 3,006 | 38.051948 | 102 | py |
null | grin-main/lib/data/temporal_dataset.py | import numpy as np
import pandas as pd
import torch
from einops import rearrange
from pandas import DatetimeIndex
from torch.utils.data import Dataset
from .preprocessing import AbstractScaler
class TemporalDataset(Dataset):
def __init__(self, data,
index=None,
freq=None,
... | 12,077 | 36.981132 | 110 | py |
null | grin-main/lib/data/datamodule/__init__.py | from .spatiotemporal import SpatioTemporalDataModule
| 53 | 26 | 52 | py |
null | grin-main/lib/data/datamodule/spatiotemporal.py | import pytorch_lightning as pl
from torch.utils.data import DataLoader, Subset, RandomSampler
from .. import TemporalDataset, SpatioTemporalDataset
from ..preprocessing import StandardScaler, MinMaxScaler
from ...utils import ensure_list
from ...utils.parser_utils import str_to_bool
class SpatioTemporalDataModule(pl... | 5,840 | 39.006849 | 116 | py |
null | grin-main/lib/data/preprocessing/__init__.py | from .scalers import *
| 23 | 11 | 22 | py |
null | grin-main/lib/data/preprocessing/scalers.py | from abc import ABC, abstractmethod
import numpy as np
class AbstractScaler(ABC):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def __repr__(self):
params = ", ".join([f"{k}={str(v)}" for k, v in self.params().items()])
return "{}({})".form... | 2,851 | 27.52 | 101 | py |
null | grin-main/lib/datasets/__init__.py | from .air_quality import AirQuality
from .metr_la import MissingValuesMetrLA
from .pems_bay import MissingValuesPemsBay
from .synthetic import ChargedParticles
| 160 | 31.2 | 42 | py |
null | grin-main/lib/datasets/air_quality.py | import os
import numpy as np
import pandas as pd
from lib import datasets_path
from .pd_dataset import PandasDataset
from ..utils.utils import disjoint_months, infer_mask, compute_mean, geographical_distance, thresholded_gaussian_kernel
class AirQuality(PandasDataset):
SEED = 3210
def __init__(self, impute... | 4,732 | 44.07619 | 119 | py |
null | grin-main/lib/datasets/metr_la.py | import os
import numpy as np
import pandas as pd
from lib import datasets_path
from .pd_dataset import PandasDataset
from ..utils import sample_mask
class MetrLA(PandasDataset):
def __init__(self, impute_zeros=False, freq='5T'):
df, dist, mask = self.load(impute_zeros=impute_zeros)
self.dist = ... | 3,757 | 36.58 | 95 | py |
null | grin-main/lib/datasets/pd_dataset.py | import numpy as np
import pandas as pd
import torch
class PandasDataset:
def __init__(self, dataframe: pd.DataFrame, u: pd.DataFrame = None, name='pd-dataset', mask=None, freq=None,
aggr='sum', **kwargs):
"""
Initialize a tsl dataset from a pandas dataframe.
:param dataf... | 3,435 | 27.163934 | 112 | py |