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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Augmentation-Adapted-Retriever | Augmentation-Adapted-Retriever-main/src/Retriever/retriever/__init__.py | from .dense_retriever import Retriever, SuccessiveRetriever
from .reranker import RRPredictDataset, Reranker | 108 | 53.5 | 59 | py |
Augmentation-Adapted-Retriever | Augmentation-Adapted-Retriever-main/src/Retriever/retriever/reranker.py | import logging
import os
from contextlib import nullcontext
from typing import Dict
import torch
from torch.cuda import amp
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
from tqdm import tqdm
from transformers import PreTrainedTokenizer
from transformers.trainer... | 4,921 | 35.731343 | 115 | py |
Augmentation-Adapted-Retriever | Augmentation-Adapted-Retriever-main/src/Retriever/retriever/dense_retriever.py | import gc
import glob
import logging
import os
import pickle
from contextlib import nullcontext
from typing import Dict, List
import faiss
import numpy as np
import torch
from torch.cuda import amp
from torch.utils.data import DataLoader, IterableDataset
from tqdm import tqdm
from ..arguments import InferenceArgument... | 10,514 | 38.382022 | 155 | py |
tifresi | tifresi-master/setup.py | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name='tifresi',
version='0.1.5',
description='Time Frequency Spectrogram Inversion',
url='https://github.com/andimarafioti/tifresi',
author='Andrés Marafioti, Nathanael Perraudin, Nicki Holligha... | 1,478 | 38.972973 | 99 | py |
tifresi | tifresi-master/tifresi/stft.py | from ltfatpy import dgtreal, idgtreal
from ltfatpy.gabor.gabdual import gabdual
import numpy as np
from tifresi.hparams import HParams as p
from tifresi.phase.modGabPhaseGrad import modgabphasegrad
from tifresi.phase.pghi import pghi
class GaussTF(object):
"""Time frequency transform object based on a Gauss wind... | 4,423 | 40.735849 | 101 | py |
tifresi | tifresi-master/tifresi/utils.py | import numpy as np
import librosa
from tifresi.hparams import HParams as p
# This function might need another name
def preprocess_signal(y, M=p.M):
"""Trim and cut signal.
The function ensures that the signal length is a multiple of M.
"""
# Trimming
y, _ = librosa.effects.trim(y)
# Pre... | 920 | 21.463415 | 67 | py |
tifresi | tifresi-master/tifresi/hparams.py | import librosa
import numpy as np
class HParams(object):
# Signal parameters
sr = 22050 # Sampling frequency of the signal
M = 1024 # Ensure that the signal will be a multiple of M
# STFT parameters
stft_channels = 1024 # Number of frequency channels
hop_size = 256 # Hop size
... | 841 | 30.185185 | 100 | py |
tifresi | tifresi-master/tifresi/metrics.py | import numpy as np
from tifresi.transforms import inv_log_spectrogram
__author__ = 'Andres'
def projection_loss(target_spectrogram, original_spectrogram):
magnitude_error = np.linalg.norm(np.abs(target_spectrogram) - np.abs(original_spectrogram), 'fro') / \
np.linalg.norm(np.abs(target_spectrogram), 'fro')
... | 1,127 | 36.6 | 114 | py |
tifresi | tifresi-master/tifresi/__init__.py | try:
import matplotlib.pyplot as pyplot
except:
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as pyplot
from tifresi import stft
from tifresi import hparams
from tifresi import metrics
from tifresi import utils
| 251 | 20 | 38 | py |
tifresi | tifresi-master/tifresi/transforms.py | import librosa
import numpy as np
from tifresi.hparams import HParams as p
__author__ = 'Andres'
def log_spectrogram(spectrogram, dynamic_range_dB=p.stft_dynamic_range_dB):
"""Compute the log spectrogram representation from a spectrogram."""
spectrogram = np.abs(spectrogram) # for safety
minimum_relativ... | 2,168 | 47.2 | 129 | py |
tifresi | tifresi-master/tifresi/phase/pghi.py | import numpy as np
import heapq
import numba
from numba import njit
__author__ = 'Andres'
@njit
def pghi(spectrogram, tgrad, fgrad, a, M, L, tol=1e-7):
""""Implementation of "A noniterativemethod for reconstruction of phase from STFT magnitude". by Prusa, Z., Balazs, P., and Sondergaard, P. Published in IEEE/ACM... | 3,471 | 40.831325 | 245 | py |
tifresi | tifresi-master/tifresi/phase/modGabPhaseGrad.py | # -*- coding: utf-8 -*-
# ######### COPYRIGHT #########
# Credits
# #######
#
# Copyright(c) 2015-2018
# ----------------------
#
# * `LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_
# * `Laboratoire d'Informatique Fondamentale <http://www.lif.univ-mrs.fr/>`_
# (now `Laboratoire d'Informatique et Systèmes <ht... | 15,380 | 33.956818 | 81 | py |
tifresi | tifresi-master/tifresi/phase/pghi_masked.py | import numpy as np
import heapq
import numba
from numba import njit
__author__ = 'Andres'
@njit
def pghi(spectrogram, tgrad, fgrad, a, M, L, mask, tol=1e-7, phase=None):
""""Implementation of "A noniterativemethod for reconstruction of phase from STFT magnitude". by Prusa, Z., Balazs, P., and Sondergaard, P. Pub... | 4,078 | 40.622449 | 244 | py |
tifresi | tifresi-master/tifresi/phase/__init__.py | from tifresi.phase import modGabPhaseGrad
from tifresi.phase import pghi_masked
from tifresi.phase import pghi | 110 | 36 | 41 | py |
tifresi | tifresi-master/tifresi/tests/test_stft.py | import sys
# sys.path.append('../')
import numpy as np
from tifresi.stft import GaussTF, GaussTruncTF
def test_stft_different_length(a = 128, M = 1024, trunc=False):
L = 128 * 1024
if trunc:
tfsystem = GaussTruncTF(a, M)
else:
tfsystem = GaussTF(a, M)
x = np.random.rand(L) ... | 2,917 | 30.042553 | 65 | py |
tifresi | tifresi-master/tifresi/tests/__init__.py | 0 | 0 | 0 | py | |
tifresi | tifresi-master/tifresi/tests/test_transforms.py | import librosa
import numpy as np
import sys
# sys.path.append('../')
from tifresi.transforms import log_spectrogram, inv_log_spectrogram, log_mel_spectrogram, mel_spectrogram
__author__ = 'Andres'
def test_log_spectrogram():
x = np.random.rand(1024 * 1024).reshape([1024, 1024])
log_x = log_spectrogram(x, ... | 1,922 | 25.342466 | 105 | py |
tifresi | tifresi-master/tifresi/pipelines/LJspeech.py | import numpy as np
import librosa
from tifresi.stft import GaussTF, GaussTruncTF
from tifresi.pipelines.LJparams import LJParams as p
from tifresi.transforms import mel_spectrogram, log_spectrogram
from tifresi.utils import downsample_tf_time, preprocess_signal, load_signal
def compute_mag_mel_from_path(path):
y, ... | 1,611 | 32.583333 | 113 | py |
tifresi | tifresi-master/tifresi/pipelines/LJparams.py | from tifresi.hparams import HParams
from tifresi.utils import downsample_tf_time
import librosa
import numpy as np
class LJParams(HParams):
# Signal parameters
sr = 22050 # Sampling frequency of the signal
M = 2*1024 # Ensure that the signal will be a multiple of M
# STFT parameters
stft_... | 1,044 | 32.709677 | 100 | py |
tifresi | tifresi-master/tifresi/pipelines/__init__.py | from tifresi.pipelines import LJspeech | 38 | 38 | 38 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_iemocap_gemaps_ccc.py | # Dimensional speech emotion recognition
# To evaluate loss function (MSE vs CCC)
# Coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog
# 2020-02-13: Modified from gemaps-paa hfs
# 2020-02-14: Use 'tanh' activation to lock the output range in [-1, 1]
# with RMSprop optimizer
import numpy as np
impo... | 4,905 | 34.042857 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_iemocap_gemaps_mse.py | # Dimensional speech emotion recognition
# To evaluate loss function (MSE vs CCC)
# Coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog
# 2020-02-13: Modified from gemaps-paa hfs
import numpy as np
import pickle
import pandas as pd
import keras.backend as K
from keras.models import Model
from keras.layers imp... | 4,769 | 34.333333 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_improv_gemaps_mse.py | # ser_improv_paa_ccc.py
# speech emotion recognition for MSP-IMPROV dataset with pyAudioAnalysis
# HFS features using CCC-based loss function
# coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog:
# 2020-02-13: Inital code, modified from deepMLP repo
import numpy as np
import pickle
import pandas as pd
impor... | 4,639 | 32.868613 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_iemocap_paa_ccc.py | # Dimensional speech emotion recognition from acoustic
# Changelog:
# 2019-09-01: initial version
# 2019-10-06: optimizer MTL parameters with linear search (in progress)
# 2020-12-25: modified fot ser_iemocap_loso_hfs.py
# feature is either std+mean or std+mean+silence (uncomment line 44)
# 2020-02-13: Modi... | 4,495 | 35.552846 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_improv_paa_ccc.py | # ser_improv_paa_ccc.py
# speech emotion recognition for MSP-IMPROV dataset with pyAudioAnalysis
# HFS features using CCC-based loss function
# coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog:
# 2020-02-13: Inital code, modified from deepMLP repo
import numpy as np
import pickle
import pandas as pd
impor... | 4,666 | 32.818841 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_improv_paa_mse.py | # ser_improv_paa_ccc.py
# speech emotion recognition for MSP-IMPROV dataset with pyAudioAnalysis
# HFS features using CCC-based loss function
# coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog:
# 2020-02-13: Inital code, modified from deepMLP repo
import numpy as np
import pickle
import pandas as pd
impor... | 4,663 | 32.797101 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_improv_gemaps_ccc.py | # ser_improv_paa_ccc.py
# speech emotion recognition for MSP-IMPROV dataset with pyAudioAnalysis
# HFS features using CCC-based loss function
# coded by Bagus Tris Atmaja (bagus@ep.its.ac.id)
# changelog:
# 2020-02-13: Inital code, modified from deepMLP repo
import numpy as np
import pickle
import pandas as pd
impor... | 4,545 | 32.426471 | 123 | py |
ccc_mse_ser | ccc_mse_ser-master/code/ser_iemocap_paa_mse.py | # CSL Paper: Dimensional speech emotion recognition from acoustic and text
# Changelog:
# 2019-09-01: initial version
# 2019-10-06: optimizer MTL parameters with linear search (in progress)
# 2012-12-25: modified fot ser_iemocap_loso_hfs.py
# feature is either std+mean or std+mean+silence (uncomment line 44... | 4,452 | 35.203252 | 123 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/audio_process/audio_cut.py | '''
The code is to get the audio in the
video and downsample it to 16Khz.
'''
import os
import subprocess
from tqdm import tqdm
# Setting audio Parameters
sr = 16000 # sample rate
start_time = 0.0 # cut start time
length_time = 2.0 # cut audio length
outpath = '../raw_audio'
os.makedirs(outpath, exist_ok=True)
train... | 2,692 | 38.028986 | 118 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/audio_process/check_file.py | file = open('mix_2_spk_tt.txt', 'r').readlines()
lines = []
for l in file:
lines.append(l.replace('\n', ''))
non_same = []
for line in lines:
line = line.split(' ')
if line[0] not in non_same:
non_same.append(line[0])
if line[2] not in non_same:
non_same.append(line[2])
print(len(no... | 328 | 18.352941 | 48 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/audio_process/audio_check.py | from tqdm import tqdm
file = open('mix_2_spk_tt.txt', 'r').readlines()
index = []
for line in file:
line = line.split(' ')
s1 = line[0].split('/')[-1]
s2 = line[2].replace('\n','').split('/')[-1]
if s1 not in index:
index.append(s1)
if s2 not in index:
index.append(s2)
print(len... | 328 | 18.352941 | 48 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/audio_process/audio_path.py | '''
This part of the code is mainly to
generate a txt file of mixed audio,
the file format is: spk1 SDR spk2 SDR.
'''
import os
import random
import decimal
# step1: get all audio path
train_audio = []
val_audio = []
test_audio = []
path = '/data2/likai/AV-Model-lrs3/AV_data/raw_audio'
for root, dirs, files in os.w... | 5,128 | 30.466258 | 134 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/audio_process/.ipynb_checkpoints/audio_mix-checkpoint.py | import os
import librosa
import numpy as np
from tqdm import tqdm
data_type = ['tr', 'cv', 'tt']
dataroot = '../raw_audio'
output_dir16k = '../audio_mouth/2speakers/wav16k'
output_dir8k = '../audio_mouth/2speakers/wav8k'
# create data path
for i_type in data_type:
# 16k
os.makedirs(os.path.join(output_dir16k, ... | 2,795 | 41.363636 | 111 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/video_process/video_process.py | import cv2
import os
import matplotlib.pyplot as plt
import dlib
import numpy as np
from tqdm import tqdm
import subprocess
import face_recognition
def get_frames(pathlist, fps=25):
# pathlist type list
for path in tqdm(pathlist):
index = path.split('/')[-2]+'_'+path.split('/')[-1].split('.')[0]
... | 6,490 | 39.56875 | 111 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/video_process/video_to_np.py | '''
reading image file to npz file
'''
import numpy as np
import os
import cv2
from tqdm import tqdm
root = '../mouth'
save_path = '../npz'
#save_path = './'
os.makedirs(save_path, exist_ok=True)
mouth = open('valid_mouth.txt', 'r').readlines()
if_use_mouth = True # if use mouth image(True: use mouth, False: use ... | 2,814 | 28.631579 | 83 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/video_process/.ipynb_checkpoints/video_to_np-checkpoint.py | '''
reading image file to npz file
'''
import numpy as np
import os
import cv2
from tqdm import tqdm
root = '../mouth'
#save_path = '../video_mouth'
save_path = '../video/'
os.makedirs(save_path, exist_ok=True)
mouth = open('valid_mouth.txt', 'r').readlines()
if_use_mouth = True # if use mouth image(True: use mou... | 3,037 | 27.933333 | 82 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/video_process/.ipynb_checkpoints/video_process-checkpoint.py | import cv2
import os
import matplotlib.pyplot as plt
import dlib
import numpy as np
from tqdm import tqdm
import subprocess
import face_recognition
def get_frames(file, fps=25):
# file: path of video txt
pathlist = []
with open(file, 'r') as f:
lines = f.readlines()
for line in lines:
... | 6,427 | 39.683544 | 111 | py |
LRS3-For-Speech-Separation | LRS3-For-Speech-Separation-master/video_process/.ipynb_checkpoints/check_mouth-checkpoint.py | '''
Check if all mouths contain 50 frames.
'''
import os
from tqdm import tqdm
file = open('valid_mouth.txt', 'w')
mouth = '../mouth'
folder = os.listdir(mouth)
for f in tqdm(folder):
flag = True
for i in range(1, 51):
fi = os.path.join(mouth, f, '{:02d}.png').format(i)
if not os.path.exists(fi... | 434 | 18.772727 | 59 | py |
LDU | LDU-main/monocular_depth_estimation/utils/eval_with_pngs.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 8,447 | 35.89083 | 153 | py |
LDU | LDU-main/monocular_depth_estimation/utils/extract_official_train_test_set_from_mat.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#######################################################################################
# The MIT License
# Copyright (c) 2014 Hannes Schulz, University of Bonn <schulz@ais.uni-bonn.de>
# Copyright (c) 2013 Benedikt Waldvogel, University of Bonn <mail@bwaldvog... | 3,664 | 37.989362 | 106 | py |
LDU | LDU-main/monocular_depth_estimation/utils/download_from_gdrive.py | # Source: https://stackoverflow.com/a/39225039
import requests
def download_file_from_google_drive(id, destination):
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
def s... | 1,353 | 28.434783 | 82 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/distributed_sampler_no_evenly_divisible.py | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class DistributedSamplerNoEvenlyDivisible(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParall... | 2,659 | 35.438356 | 82 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_live_3d.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 17,345 | 34.4 | 148 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_main.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 28,993 | 47.976351 | 165 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_ldu.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 19,379 | 47.693467 | 180 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_test_kitti_ldu.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 11,451 | 40.492754 | 235 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/sparsification.py | import numpy as np
import torch
"""Calculate the sparsification error.
Calcualte the sparsification error for a given array according to a reference array.
Args:
unc_tensor: Flatten estimated uncertainty tensor.
pred_tensor: Flatten depth prediction tensor.
gt_tensor: Flatten ground... | 3,034 | 42.357143 | 192 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_dataloader.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 11,674 | 38.982877 | 122 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_eval.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 12,104 | 38.819079 | 143 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts_test.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 9,732 | 43.040724 | 116 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/run_bts_eval_schedule.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 1,900 | 39.446809 | 109 | py |
LDU | LDU-main/monocular_depth_estimation/pytorch/bts.py | # Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Thi... | 17,122 | 50.575301 | 180 | py |
ros-sharp | ros-sharp-master/ROS/unity_simulation_scene/scripts/mouse_to_joy.py | #!/usr/bin/env python
# Siemens AG, 2018
# Author: Berkay Alp Cakal (berkay_alp.cakal.ct@siemens.com)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# <http://www.apache.org/licenses/LICENSE-... | 2,113 | 28.774648 | 74 | py |
ros-sharp | ros-sharp-master/ROS/gazebo_simulation_scene/scripts/joy_to_twist.py | #!/usr/bin/env python
# Siemens AG, 2018
# Author: Berkay Alp Cakal (berkay_alp.cakal.ct@siemens.com)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# <http://www.apache.org/licenses/LICENSE-... | 1,921 | 26.855072 | 75 | py |
PSE | PSE-master/PSEv1/variant.py | ## \package PSEv1.variant
# classes representing the variant class to facilitate box_resize
from hoomd.PSEv1 import _PSEv1
from hoomd.PSEv1 import shear_function
from hoomd import variant
from hoomd import _hoomd
import hoomd
import sys
## Variant class holding a functional form of shear field
# Used as an argument... | 1,327 | 39.242424 | 130 | py |
PSE | PSE-master/PSEv1/integrate.py | # First, we need to import the C++ module. It has the same name as this module (plugin_template) but with an underscore
# in front
from hoomd.PSEv1 import _PSEv1
from hoomd.PSEv1 import shear_function
# Next, since we are extending an integrator, we need to bring in the base class integrator and some other parts from
... | 5,578 | 43.277778 | 154 | py |
PSE | PSE-master/PSEv1/__init__.py | # this file exists to mark this directory as a python module
# need to import all submodules defined in this directory
from hoomd.PSEv1 import integrate
from hoomd.PSEv1 import shear_function
from hoomd.PSEv1 import variant
| 224 | 36.5 | 60 | py |
PSE | PSE-master/PSEv1/shear_function.py | ## \package PSEv1.shear_function
# classes representing shear functions, which can be input of an integrator and variant
# to shear the box of a simulation
from hoomd.PSEv1 import _PSEv1
import hoomd
## shear function interface representing shear flow field described by a function
class _shear_function:
## Const... | 5,763 | 49.121739 | 144 | py |
PSE | PSE-master/examples/run.py | import hoomd;
from hoomd import _hoomd
from hoomd.md import _md
import hoomd.PSEv1
import os;
import math
hoomd.context.initialize('');
# Time stepping information
dt = 1e-3 # time step
tf = 1e0 # the final time of the simulation (in units of bare particle diffusion time)
nrun = tf / dt # number of steps
#... | 2,068 | 33.483333 | 136 | py |
online-active-model-selection | online-active-model-selection-master/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
online-active-model-selection | online-active-model-selection-master/src/__init__.py | 0 | 0 | 0 | py | |
online-active-model-selection | online-active-model-selection-master/src/methods/model_picker.py | import numpy as np
"""This code runs stream based model picker (proposed algorithm)."""
def model_picker(data, idx_budget, streaming_data_indices, tuning_par, mode):
"""
:param data:
:param streaming_data_indices:
:param tuning_par:
:param mode: modes include {predictive}
:return:
"""
#... | 4,004 | 32.375 | 129 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/query_by_committee.py | import numpy as np
import scipy.stats as stats
def query_by_committee(data, idx_budget, streaming_data_indices, tuning_par):
# Set vals, params
if idx_budget == 'tuning mode':
budget = data._num_instances
else:
budget = data._budgets[idx_budget]
# Edit the input data accordingly with ... | 1,601 | 31.693878 | 91 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/efficient_active_learning.py | import numpy as np
import sys
import mpmath
sys.modules['sympy.mpmath'] = mpmath
from sympy.solvers.solvers import *
def efficient_active_learning(data, idx_budget, streaming_data_indices, c0, constant_efal):
# Set vals, params
c1 = 1
c2 = c1
if idx_budget == 'tuning mode':
budget = data._nu... | 4,531 | 24.60452 | 97 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/importance_weighted_active_learning.py | import numpy as np
import numpy.matlib
def importance_weighted_active_learning(data, idx_budget, streaming_data_indices, tuning_par, constant_iwal):
# Set vals, params
if idx_budget == 'tuning mode':
budget = data._num_instances
else:
budget = data._budgets[idx_budget]
# Edit the inp... | 4,912 | 31.322368 | 126 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/random_sampling_disagreement.py | import numpy as np
"""Random sampling code for stream based model selection."""
def random_sampling_disagreement(data, idx_budget, streaming_data_indices, tuning_par_rs):
"""
:param data:
:param streaming_data_indices:
:return:
"""
# Set params
num_instances = data._num_instances
budget... | 1,975 | 32.491525 | 100 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/random_sampling.py | import numpy as np
"""Random sampling code for stream based model selection (unused)."""
def random_sampling(data, idx_budget, streaming_data_indices):
"""
:param data:
:param streaming_data_indices:
:return:
"""
# Set params
num_instances = data._num_instances
budget = data._budgets[id... | 612 | 25.652174 | 69 | py |
online-active-model-selection | online-active-model-selection-master/src/methods/structural_query_by_committee.py | import numpy as np
import scipy.stats as stats
def structural_query_by_committee(data, idx_budget, streaming_data_indices, tuning_par, constant_sqbc):
# Set vals, params
if idx_budget == 'tuning mode':
budget = data._num_instances
else:
budget = data._budgets[idx_budget]
# Edit the i... | 2,392 | 32.704225 | 103 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/evaluate_base.py | """Base class for the evaluations."""
from src.evaluation.evaluation_pipeline.evaluate_main import *
class Evals:
def __init__(self, data, client=None):
"""Evaluate methods"""
eval_results = evaluate_main(data, client=client)
"""Assigns evaluations to the self"""
self._prob_succ ... | 842 | 31.423077 | 71 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/evaluation_pipeline/evaluate_realizations.py | from src.evaluation.aux.compute_precision_measures import *
import tqdm
import zlib, cloudpickle
def evaluate_realizations(log_slice, predictions, oracle, freq_window_size, method):
"""
This function evaluates the method in interest for given realization of the pool/streaming instances
Parameters:
:pa... | 7,747 | 39.778947 | 168 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/evaluation_pipeline/evaluate_main.py | # from src.evaluation.evaluation_pipeline.evaluate_method import *
from src.evaluation.evaluation_pipeline.evaluate_realizations import *
from src.evaluation.aux.load_results import *
from dask.distributed import Client, as_completed
from tqdm.auto import tqdm, trange
import cloudpickle, zlib
def evaluate_main(data,... | 8,318 | 41.015152 | 206 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/evaluation_pipeline/__init__.py | 0 | 0 | 0 | py | |
online-active-model-selection | online-active-model-selection-master/src/evaluation/aux/load_results.py | import numpy as np
def load_results(data, idx_budget):
"""
This function loads the experiment results in the results folder for a given budget
"""
# Load data
experiment_results = np.load(str(data._resultsdir) + '/experiment_results_'+ 'budget'+str(data._budgets[idx_budget]) + '.npz')
# Extrac... | 749 | 38.473684 | 130 | py |
online-active-model-selection | online-active-model-selection-master/src/evaluation/aux/compute_precision_measures.py | import numpy as np
import numpy.matlib
def compute_precisions(pred, orac, num_models):
"""
This function computes the agreements
"""
# Replicate oracle realization
orac_rep = np.matlib.repmat(orac.reshape(np.size(orac), 1), 1, num_models)
# Compute errors
true_pos = (pred == orac_rep) * 1
... | 3,628 | 26.08209 | 98 | py |
online-active-model-selection | online-active-model-selection-master/src/publish_evals/publish_evals.py | import matplotlib.pyplot as plt
plt.rc('text', usetex=True)
plt.style.use('classic')
plt.style.use('default')
import seaborn as sns
import numpy as np
sns.set()
"""This function plots the evaluation results for the streaming setting."""
def publish_evals(resultsdir):
"""
:param resultsdir:
:return:
"... | 2,742 | 34.166667 | 135 | py |
online-active-model-selection | online-active-model-selection-master/dev/cluster-up.py | #!/usr/bin/env python3
import os
import paramiko
import sys
print(os.environ.keys())
SCHEDULER_HOST = os.environ.get("SCHEDULER_HOST", None)
if not SCHEDULER_HOST:
raise ValueError("The variable SCHEDULER_HOST not defined.")
print("SCHEDULER_HOST=%s" % SCHEDULER_HOST)
WORKER_HOSTS = os.environ.get("WORKER_HOSTS... | 1,951 | 32.084746 | 118 | py |
online-active-model-selection | online-active-model-selection-master/dev/cluster-down.py | #!/usr/bin/env python3
import os
import paramiko
import sys
print(os.environ.keys())
SCHEDULER_HOST = os.environ.get("SCHEDULER_HOST", None)
if not SCHEDULER_HOST:
raise ValueError("The variable SCHEDULER_HOST not defined.")
print("SCHEDULER_HOST=%s" % SCHEDULER_HOST)
WORKER_HOSTS = os.environ.get("WORKER_HOSTS... | 1,949 | 32.050847 | 117 | py |
online-active-model-selection | online-active-model-selection-master/experiments/run_experiment.py | from experiments.base.tune_hyperpar_base import *
from experiments.base.experiments_base import *
from src.evaluation.evaluate_base import *
from experiments.base.set_data import *
from src.publish_evals.publish_evals import *
from datetime import datetime
import time
import os
import shelve
import sys
from dask.distri... | 6,687 | 39.533333 | 227 | py |
online-active-model-selection | online-active-model-selection-master/experiments/reproduce_experiment.py | from experiments.run_experiment import *
from dask.distributed import LocalCluster
def main(dataset_name, cluster=None):
experiment = dataset_name
load_hyperparameters = 'true'
if experiment == 'EmoContext':
# Emotion Detection
DatasetName = 'emotion_detection'
#
StreamSiz... | 4,600 | 27.937107 | 228 | py |
online-active-model-selection | online-active-model-selection-master/experiments/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
online-active-model-selection | online-active-model-selection-master/experiments/base/tune_hyperpar_base.py | import numpy as np
from src.methods.model_picker import *
from src.methods.random_sampling import *
from src.methods.query_by_committee import *
from src.methods.efficient_active_learning import *
from src.evaluation.aux.compute_precision_measures import *
from src.methods.structural_query_by_committee import *
from pa... | 12,220 | 43.60219 | 192 | py |
online-active-model-selection | online-active-model-selection-master/experiments/base/set_data.py | """Preprocess the model predictions"""
from src.evaluation.aux.compute_precision_measures import *
from pathlib import Path
import numpy as np
class SetData():
def __init__(self, data_set_name, pool_size, pool_setting, budgets, num_reals, eval_window_size, resultsdir, num_reals_tuning, grid_size, load_hyperparamet... | 8,799 | 43 | 588 | py |
online-active-model-selection | online-active-model-selection-master/experiments/base/experiments_base.py | from src.methods.model_picker import *
from src.methods.random_sampling import *
from src.methods.query_by_committee import *
from src.methods.efficient_active_learning import *
from src.methods.random_sampling_disagreement import *
from src.methods.importance_weighted_active_learning import *
from src.methods.structur... | 9,226 | 43.574879 | 167 | py |
online-active-model-selection | online-active-model-selection-master/experiments/base/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
online-active-model-selection | online-active-model-selection-master/resources/__init__.py | from . import *
| 16 | 7.5 | 15 | py |
Turkish-Word2Vec | Turkish-Word2Vec-master/trainCorpus.py | from __future__ import print_function
import logging
import sys
import multiprocessing
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
if __name__ == '__main__':
if len(sys.argv) < 3:
print("Please provide two arguments, first one is path to the revised corpus, second one is pa... | 770 | 29.84 | 130 | py |
Turkish-Word2Vec | Turkish-Word2Vec-master/preprocess.py | from __future__ import print_function
import os.path
import sys
from gensim.corpora import WikiCorpus
import xml.etree.ElementTree as etree
import warnings
import logging
import string
from gensim import utils
def tokenize_tr(content,token_min_len=2,token_max_len=50,lower=True):
if lower:
lowerMap = {ord(u'A'): u'... | 1,844 | 37.4375 | 494 | py |
DelayResolvedRL | DelayResolvedRL-main/Gym(Stochastic)/env_stochasticdelay.py | import gym
# import gym_minigrid
import numpy as np
import random
from collections import deque
import copy
class Environment:
def __init__(self, seed, game_name, gamma, use_stochastic_delay, delay, min_delay):
"""Initialize Environment"""
self.game_name = game_name
self.env = gym.make(sel... | 4,618 | 37.491667 | 108 | py |
DelayResolvedRL | DelayResolvedRL-main/Gym(Stochastic)/agent.py | import tensorflow as tf
import numpy as np
import random
import copy
from statistics import mean
from collections import deque
GPUs = tf.config.experimental.list_physical_devices('GPU')
if GPUs:
try:
for gpu in GPUs:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError a... | 12,554 | 41.849829 | 112 | py |
DelayResolvedRL | DelayResolvedRL-main/Gym(Stochastic)/plot.py | import numpy as np
import matplotlib.pyplot as plt
# import matplotlib.ticker as mtick
import os
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams.update({'font.size': 13})
# ver = '6.0'
def running_mean(x, n):
cumulative_sum = np.cumsum(np.in... | 9,961 | 40.508333 | 125 | py |
DelayResolvedRL | DelayResolvedRL-main/Gym(Stochastic)/train.py | import datetime
import os
import argparse
import time
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3" # Suppress Tensorflow Messages
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Set CPU/GPU
import numpy as np
from agent import *
from env_stochasticdelay import Environment
parser = argparse.ArgumentParser()
parser.add_argum... | 4,643 | 35.28125 | 121 | py |
DelayResolvedRL | DelayResolvedRL-main/Gym(Stochastic)/env.py | import gym
# import gym_minigrid
import numpy as np
from collections import deque
class Environment:
def __init__(self, game_name, delay, seed):
"""Initialize Environment"""
self.game_name = game_name
self.env = gym.make(self.game_name)
self.env.seed(seed)
np.random.seed(se... | 2,014 | 31.5 | 103 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/DQN/env_stochasticdelay.py | import numpy as np
from collections import deque
import copy
import random
class Environment:
"""Initialize Environment"""
def __init__(self, seed, gamma, use_stochastic_delay, delay, min_delay):
np.random.seed(seed)
random.seed(seed)
self.call = 0
self.breadth = 7
self... | 6,966 | 43.094937 | 115 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/DQN/agent.py | import tensorflow as tf
import numpy as np
import random
import copy
from statistics import mean
from collections import deque
GPUs = tf.config.experimental.list_physical_devices('GPU')
if GPUs:
try:
for gpu in GPUs:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError a... | 11,830 | 42.818519 | 134 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/DQN/plot.py | import numpy as np
import matplotlib.pyplot as plt
# import matplotlib.ticker as mtick
import os
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams.update({'font.size': 13})
def running_mean(x, n):
cumulative_sum = np.cumsum(np.insert(x, 0, 0)... | 9,639 | 39.504202 | 120 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/DQN/train.py | import datetime
import os
import argparse
import time
'''Hyperparameters'''
# W-Maze
# Number of Runs:10 \\
# Number of Frames: 1 Million \\
# Batch Size: 32 \\
# $\gamma$: 0.99 \\
# Learning Rate: 1e-3 \\
# $\epsilon$-Start: 1.0 \\
# $\epsilon$-Stop: 1e-4 \\
# $\epsilon$-Decay: 1e-5 \\
# Hidden Units: [200] \\
# Repl... | 5,018 | 33.854167 | 128 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/DQN/env.py | import numpy as np
from collections import deque
import random
class Environment:
"""Initialize Environment"""
def __init__(self, seed, delay):
np.random.seed(seed)
random.seed(seed)
self.call = 0
self.breadth = 7
self.length = 11
self.state_space = np.empty([se... | 4,592 | 41.925234 | 115 | py |
DelayResolvedRL | DelayResolvedRL-main/W-Maze/Tabular-Q/dr_agent.py | import numpy as np
from collections import deque
'''Q-learning agent for the augmented agent'''
class Agent:
def __init__(self, state_space, num_actions, delay):
self.epsilon = 1.0
self.num_actions = num_actions
self.delay = delay
self.actions_in_buffer = deque(maxlen=self.delay)
... | 1,859 | 38.574468 | 105 | py |
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