text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
I_trues =
[0.44129802 0.39444956 0.30091208 0.25465866 0.30253774 0.39726529 0.44454934 ;
0.39250948 0.35223311 0.27210130 0.23339588 0.27639321 0.35966691 0.40109329 ;
0.35082997 0.31575536 0.24622966 0.21348201 0.25258702 0.32676664 0.36354469 ;
0.29863696 0.26973242 0.21277028 0.18702656 0.22140580 0.28468958... | {"hexsha": "cd6b1f49a0affa440e8c679f14c99189a0de771f", "size": 3792, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/benchmarks/natraj_trues.jl", "max_stars_repo_name": "RemoteSensingTools/vSmartMOM.jl", "max_stars_repo_head_hexsha": "fe5b7d28ca99bef0d1702293749d217e8c839db6", "max_stars_repo_licenses": ["MI... |
"""End to end testing on feedforward models
"""
# pylint: disable=C0103
# pylint: disable=C0325
# pylint: disable=E1101
import numpy as np
from nltk.corpus import brown
from model_wrangler.model_wrangler import ModelWrangler
from model_wrangler.dataset_managers import DatasetManager
from model_wrangler.model.losse... | {"hexsha": "fbea7322ea44f98c3a214394488f77433a7f684d", "size": 2583, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_text.py", "max_stars_repo_name": "bmcmenamin/model_wrangler", "max_stars_repo_head_hexsha": "c5471cc106d475c50bf26791b913f2d556a1de0a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import pathlib
import warnings
import numpy as np
import pytest
import xarray as xr
from tests.fixtures import generate_dataset
from xcdat.dataset import (
_has_cf_compliant_time,
_keep_single_var,
_postprocess_dataset,
_preprocess_non_cf_dataset,
_split_time_units_attr,
decode_non_cf_time,
... | {"hexsha": "4ca343867c3d63321e254619ee276e43efce15fa", "size": 35933, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_dataset.py", "max_stars_repo_name": "jasonb5/xcdat", "max_stars_repo_head_hexsha": "4a35d6a6131fe3fec22593f54a9e48b640ceac4f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
[STATEMENT]
lemma differentiable_on_Pair:
"f differentiable_on S \<Longrightarrow> g differentiable_on S \<Longrightarrow> (\<lambda>x. (f x, g x)) differentiable_on S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>f differentiable_on S; g differentiable_on S\<rbrakk> \<Longrightarrow> (\<lambda>x. (f x,... | {"llama_tokens": 416, "file": "Smooth_Manifolds_Analysis_More", "length": 3} |
"""
Created on Okt 01 16:11 2019
@author: nishit
"""
from pyomo.environ import *
from pyomo.opt import SolverStatus, TerminationCondition
import pyutilib.subprocess.GlobalData
pyutilib.subprocess.GlobalData.DEFINE_SIGNAL_HANDLERS_DEFAULT = False
class OptUt:
def thread_solver(self, single_ev, data_dict, in... | {"hexsha": "9c52a139c17cfa76cc2296b2c1265a8f95528df7", "size": 3823, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimization/optut.py", "max_stars_repo_name": "storage4grid/PROFESS-PROFEV", "max_stars_repo_head_hexsha": "adf4e26488225206c249938c9eecc394a06f9677", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import numpy as np
import tensorflow as tf
from random import choice,shuffle
#g_ps = []
#s_ps = []
#for g in glob.glob('../data/training_set/ffp10_p/*.npy'):
# g_ps.append(np.load(g))
#g_ps = np.array(g_ps)
#print 'g_ps.shape:',g_ps.shape
#for s in glob.glob('../data/training_set/string_p/*.npy'):
# s_ps.appen... | {"hexsha": "fef652ebd8c37afc107a4fb2409e84884e7cbea8", "size": 8109, "ext": "py", "lang": "Python", "max_stars_repo_path": "cosmic_string/deep_measure/utils.py", "max_stars_repo_name": "vafaei-ar/DeePlanck", "max_stars_repo_head_hexsha": "9e9aab4dc069ed5810a6316cdcc55a1f4a58938e", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
r"""
Morphic words
This modules implements morphic words (letter-to-letter coding of fixed
point of a morphism).
AUTHORS:
- Jana Lepsova (January 2021): initial version
EXAMPLES:
Creation of the fixed point of a morphism::
sage: m = WordMorphism('a->abc,b->baba,c->ca')
sage: w = m.... | {"hexsha": "3fbbe48f26bf8c9adc8451e738b02e8fdd523a1c", "size": 11077, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/combinat/words/morphic.py", "max_stars_repo_name": "sheerluck/sage", "max_stars_repo_head_hexsha": "b5e572b7d231f70c139d9978d68add80c4ef353d", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
# system utilities
from __future__ import print_function
import os, datetime, argparse
# pytorch utilities
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# computing utilities
import numpy as np
import math
# custom utilities
from BayesNets import Baye... | {"hexsha": "edd83681ec5aa0b44d7e1c8c4a0f105734e24f56", "size": 27207, "ext": "py", "lang": "Python", "max_stars_repo_path": "BQN/class_bqn_train.py", "max_stars_repo_name": "umd-huang-lab/Bayesian-Quantized-Networks", "max_stars_repo_head_hexsha": "eb56fa1cb142cf235dde9cec7badea86009c3fcb", "max_stars_repo_licenses": [... |
import numpy as np
import hyperparameters as hp
class ActionMeta(type):
def __init__(cls, name, bases, d):
type.__init__(cls, name, bases, d)
cls.action_to_num = dict()
cls.num_to_action = dict()
options = [True, False]
counter = 0
for left in options:
fo... | {"hexsha": "d24d423fb79c7d42cdaa52822fdaca3af524b0d6", "size": 2066, "ext": "py", "lang": "Python", "max_stars_repo_path": "action.py", "max_stars_repo_name": "tigerneil/cs231n-project", "max_stars_repo_head_hexsha": "2c520fd79fabbba09ad995360c1e21f49e10a52a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24, ... |
import numpy as np
import pandas as pd
from corsempy.model import Model as md
from corsempy.optimizer import Optimizer as opt
from corsempy.identifier import Identifier as id
from corsempy.stats import Statistics as stat
df1 = pd.read_csv('data_poli.csv')
mod = """xi_1~=x1+x2+x3
eta_1 ~= y1+y2+y3+y4
eta_2 ~= y5+y6+y... | {"hexsha": "18539b9a4a0c27834824f9e740086a795de17914", "size": 421, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "iaousse/corsempy_project", "max_stars_repo_head_hexsha": "e369016e1edd9372556e13d0038088628dc7bb40", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def collect_trajectories(envs, action_dist, ep_length, policy, rollout_length=200):
"""
collect trajectories for a parallelized parallelEnv object
Returns : Shape
======
log_probs_old (tens... | {"hexsha": "7744abf530d38f8a8508ef475f71c02271b4836d", "size": 2890, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "rish-16/gym-navmaze", "max_stars_repo_head_hexsha": "cc21d730ec6ab1e96a4a1a8f602a5bbb951d2929", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
C ALGORITHM 680, COLLECTED ALGORITHMS FROM ACM.
C THIS WORK PUBLISHED IN TRANSACTIONS ON MATHEMATICAL SOFTWARE,
C VOL. 16, NO. 1, PP. 47.
SUBROUTINE WOFZ (XI, YI, U, V, FLAG)
C
C GIVEN A COMPLEX NUMBER Z = (XI,YI), THIS SUBROUTINE COMPUTES
C THE VALUE OF THE FADDEEVA-FUNCTION W(Z) = EXP(-Z**2)*ER... | {"hexsha": "e44ca90816029a4b60166b42f3dff3695e8d2dce", "size": 6385, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "fortran/algo-680-erf.f", "max_stars_repo_name": "lloda/slatec-bessel-c-", "max_stars_repo_head_hexsha": "1140750c3b5374573f92190753e7535ad066e0e7", "max_stars_repo_licenses": ["CC0-1.0"], "max_sta... |
import unittest
from mixture_net.utils import nnelu, register_custom_activation
from mixture_net.model import MDN
import numpy as np
from sklearn.model_selection import train_test_split
from mixture_net.losses import gnll_loss
class TestModel(unittest.TestCase):
def setUp(self):
samples = int(100)
... | {"hexsha": "4f7dfe74923fa3fbeddebac95f24afe542457c2e", "size": 1237, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_model.py", "max_stars_repo_name": "arrigonialberto86/mixture_nets", "max_stars_repo_head_hexsha": "9965ac9c7f378eb7d7e6277e609574344602152b", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma Bag_s_mul_ext:
"(Bag xs, Bag ys) \<in> s_mul_ext {(x, y). snd (f x y)} {(x, y). fst (f x y)} \<longleftrightarrow>
fst (mul_ext f (ass_list_to_single_list (DAList.impl_of xs)) (ass_list_to_single_list (DAList.impl_of ys)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((Bag xs, Bag ys) \<in... | {"llama_tokens": 254, "file": "Weighted_Path_Order_Multiset_Extension2_Impl", "length": 1} |
import numpy as np
import tensorflow as tf
DIV2K_RGB_MEAN = np.array([0.4488, 0.4371, 0.4040]) * 255
def normalize(x, rgb_mean=DIV2K_RGB_MEAN):
return (x - rgb_mean) / 127.5
def denormalize(x, rgb_mean=DIV2K_RGB_MEAN):
return x * 127.5 + rgb_mean
def pixel_shuffle(scale):
return lambda x: tf.nn.depth_to... | {"hexsha": "803989a8b549c188f5c951f5ad75eb9f384b8a1b", "size": 336, "ext": "py", "lang": "Python", "max_stars_repo_path": "Algorithms/EDSR/common.py", "max_stars_repo_name": "TheStarkor/SuperResolution", "max_stars_repo_head_hexsha": "823aa004b15d1477f685b31bef0c3d8e181741bf", "max_stars_repo_licenses": ["MIT"], "max_s... |
import logging
import sys
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from neural_punctuator.base.BaseTrainer import BaseTrainer
from neural_punctuator.data.dataloader import BertDataset, collate, get_data_loaders, get_datasets
from neural_punctuator.models.BertPunctuator import BertPunct... | {"hexsha": "599682f51a622c2d45b19cafecb18e38b547808a", "size": 6828, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/neural_punctuator/trainers/BertPunctuatorTrainer.py", "max_stars_repo_name": "juliandarley/neural-punctuator", "max_stars_repo_head_hexsha": "2b3ff7e052380ec463b90a74c6960e1e90515c05", "max_st... |
from notebooks.profiles import BaseProfile
import numpy as np
import pandas as pd
import math
from io import BytesIO
import pickle
class VotingProfile(BaseProfile):
def __init__(self, p=None, bytesIO=None):
if bytesIO is not None:
state_dict = pickle.load(bytesIO)
if state_dict["m... | {"hexsha": "a23c414a336d1bb631ec775bbf1476168bcf9064", "size": 3300, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/notebooks/profiles/_voting_profile.py", "max_stars_repo_name": "grchristensen/avpd", "max_stars_repo_head_hexsha": "f7617844ae454a93825aa231e04c125cb4e58a20", "max_stars_repo_licenses": ... |
%%=======================================================
%% Chapter 4: JOST SOLUTIONS \& THE DIRECT SCATTERING MAP
%%=======================================================
\documentclass[../dissertation.tex]{subfiles}
\begin{document}
\chapter{\chfourtitle}\label{cptr04:DM}
%%==========================
%% Section ... | {"hexsha": "e0d7e35c2b2d18af17921607114725f585d5ff10", "size": 956, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter4-Jost/Chapter4-Jost.tex", "max_stars_repo_name": "ADGC/ilw-dsm-dissertation", "max_stars_repo_head_hexsha": "de0f27b6389ee55c24d155ff482743acbe6a35a1", "max_stars_repo_licenses": ["MIT"], "ma... |
section \<open>
Equivalence of a Diamond-Shaped Forwarding Praos Network and a Cross-Shaped Broadcasting Praos
Network
\<close>
theory Ouroboros_Praos_Forwarding_Broadcasting_Equivalence
imports
"Chi_Calculus_Examples.Network_Equivalences-Forwarding_Broadcasting"
Ouroboros_Praos_Implementation
begin
the... | {"author": "input-output-hk", "repo": "ouroboros-high-assurance", "sha": "f1b63cb176b119183bcbe14786cd5a61e0c5bf97", "save_path": "github-repos/isabelle/input-output-hk-ouroboros-high-assurance", "path": "github-repos/isabelle/input-output-hk-ouroboros-high-assurance/ouroboros-high-assurance-f1b63cb176b119183bcbe14786c... |
using Gtk
using Test
using DrugInteractions
DrugInteractions._apps_should_persist[1] = false
drug_interactions_app()
@test DrugInteractions._apps[end] isa GtkWindow
| {"hexsha": "a82fd3997f5d79aa1fbfbd7c3f47dd77a959368a", "size": 173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "wherrera10/DrugInteractions.jl", "max_stars_repo_head_hexsha": "1a1ba27d64f48e0ca844b50d928c47d1d3c5044d", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
import numpy as np
from tensorflow.compat.v1 import set_random_seed
import os
from mp3_to_wav import MP3Processor
from wav_to_spectrogram import WavProcessor
from autoencoder_network import Autoencoder
from cluster_latent_features import HClust
import matplotlib.pyplot as plt
config = {
"mp3_file_dir": "dsilt-ml-... | {"hexsha": "c82f82ed20dce644bac4d5a8337134a20f7a0876", "size": 3497, "ext": "py", "lang": "Python", "max_stars_repo_path": "09 Generative Models/song_similarity/song_similarity_pipeline.py", "max_stars_repo_name": "nlinc1905/dsilt-ml-code", "max_stars_repo_head_hexsha": "d51fffd16e83f93ea7d49f65102e731abd3ba70c", "max_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : 河北雪域网络科技有限公司 A.Star
# @contact: astar@snowland.ltd
# @site: www.snowland.ltd
# @file: color.py
# @time: 2018/7/26 0:24
# @Software: PyCharm
import numpy as np
npa = np.array
def rgb2ycbcr(img):
origT = npa([[65.481, 128.553, 24.966], [-37.797, -74.203, ... | {"hexsha": "d160b7b2ca0f3b9d6fc2db03537338e3e370d4cc", "size": 1862, "ext": "py", "lang": "Python", "max_stars_repo_path": "snowland/image/color/color.py", "max_stars_repo_name": "astar-club/scikit-snowland", "max_stars_repo_head_hexsha": "fc2e058f61fe44b3f065bcb4dc8de47f95055bfc", "max_stars_repo_licenses": ["BSD-2-Cl... |
[STATEMENT]
lemma language_equivalence_classes_preserve_observability:
assumes "transitions M' = (\<lambda> t . ({q \<in> states M . LS M q = LS M (t_source t)} , t_input t, t_output t, {q \<in> states M . LS M q = LS M (t_target t)})) ` transitions M"
and "observable M"
shows "observable M'"
[PROOF STATE]
proo... | {"llama_tokens": 5572, "file": "FSM_Tests_Minimisation", "length": 34} |
import numpy as np
from dsmlt import stats
def test_trimean():
data = np.array(range(101))
true_trimean = (25 + 50 * 2 + 75) / 4
assert stats.trimean(data) == true_trimean
data = np.array(range(1, 101))
true_trimean = (25.75 + 50.5 * 2 + 75.25) / 4
assert stats.trimean(data) == true_trimean
... | {"hexsha": "4c924421134468a02aec570da1cd13f50e416e63", "size": 449, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/stats/test_stats.py", "max_stars_repo_name": "pawlyk/dsml-tools", "max_stars_repo_head_hexsha": "6717ff6b4e58c951140e2abfad20f8d306b01d97", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
def confidence_transform(R, param_alpha, param_epsilon):
C = R.copy()
C.data = param_alpha * np.log(1 + param_epsilon * C.data)
return C
| {"hexsha": "81235bc67a634555efdcb9d6d3f28404bcbcc597", "size": 169, "ext": "py", "lang": "Python", "max_stars_repo_path": "krotos/msd/latent/als.py", "max_stars_repo_name": "KelvinLu/krotos-convnet", "max_stars_repo_head_hexsha": "e37218aeaf10b73d77dfac911be46d8ab689e41d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# importing libraries
library(plyr)
library(foreign)
library(RWeka)
library(dplyr)
library(caret)
library(xgboost)
# reading datasets
esl = read.arff("esl.arff")
era = read.arff("era.arff")
lev = read.arff("lev.arff")
swd = read.arff("swd.arff")
# function to create dataset train and test partitions
createPartitions ... | {"hexsha": "60319db05ca2e21a2e660d8f44251b88b29ca438", "size": 4668, "ext": "r", "lang": "R", "max_stars_repo_path": "monoxgboost.r", "max_stars_repo_name": "CarlosSequi/DataMining-OrdinalMonotonicClassification", "max_stars_repo_head_hexsha": "4a4c5055b37540f5394779b89746d21964b5e727", "max_stars_repo_licenses": ["Apa... |
import cv2
import sys
import imgaug.augmenters as iaa
sys.path.insert(1,"D:\\source\\repos\\rdt-reader\\object_detection_v2")
import core.model_new as model
from core.config import cfg
import numpy as np
from utils import data_loader
inpImg="../object_detection_mobile_v2/train_hor_ratioCropped/I4.jpg"
import ntpath
im... | {"hexsha": "90a40103dff9439f3cece114fc73b1fc09591374", "size": 11439, "ext": "py", "lang": "Python", "max_stars_repo_path": "heatmap_newModel.py", "max_stars_repo_name": "DigitalHealthIntegration/rdt-reader", "max_stars_repo_head_hexsha": "242a4d813a6b58b3668f4d4ce35cea8f55bd651f", "max_stars_repo_licenses": ["MIT"], "... |
import unittest
import umo
import math
class TestUmoApi(unittest.TestCase):
def test_creation(self):
m = umo.Model()
def test_constants(self):
m = umo.Model()
b1 = m.constant(False)
b2 = m.constant(True)
self.assertEqual(b1.value, False)
self.assertEqual(b2.va... | {"hexsha": "bc510e79c7cc7c14693f4b1bbe7f71090f79d168", "size": 19197, "ext": "py", "lang": "Python", "max_stars_repo_path": "apis/python/test.py", "max_stars_repo_name": "Coloquinte/umo", "max_stars_repo_head_hexsha": "1f39c316d6584bbed22913aabaa4bfb5ee02d72b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
#include <cradle/gui/app/instance.hpp>
#include <wx/glcanvas.h>
#include <wx/msgdlg.h>
#include <cradle/external/clean.hpp>
#include <json\json.h>
#include <boost/program_options.hpp>
#include <alia/ui/utilities/styling.hpp>
#include <cradle/gui/app/internals.hpp>
#include <cradle/gui/app/top_level_ui.hpp>
#include... | {"hexsha": "f176b0a7ac68540e95ea775d07f8a4c283d65648", "size": 7445, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cradle/src/cradle/gui/app/instance.cpp", "max_stars_repo_name": "dotdecimal/open-cradle", "max_stars_repo_head_hexsha": "f8b06f8d40b0f17ac8d2bf845a32fcd57bf5ce1d", "max_stars_repo_licenses": ["MIT"]... |
import logging
import numpy as np
from astropy.wcs import Sip
__author__ = 'drharbeck@gmail.com'
log = logging.getLogger(__name__)
akwcslookup = {
'ak01': {'SIPA_1_1': 2.8875384573560257e-06, 'SIPA_0_2': -1.2776642259520679e-05, 'SIPA_2_0': 6.873210426347869e-06,
'SIPB_1_1': 1.8322056773537455e-05,... | {"hexsha": "a7b5598a1127ebe269c692d1133b1bb1f7fa968b", "size": 3412, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/LCOWCSLookupProvider.py", "max_stars_repo_name": "LCOGT/lcowcstools", "max_stars_repo_head_hexsha": "e5db536647dbb143a1f75293ec4f2a4acb68182b", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
"""test_poisson.py [options]
Solves the Heterogeneous Poisson equation on a unit cube. A full
script for testing generation and tools provided by proteus.
"""
import numpy as np
import sys
from proteus import Comm, Profiling, NumericalSolution, TransportCoefficients, default_so, default_s
from proteus.FemTools impor... | {"hexsha": "12224a77d7874221fed2cfb2da931124d5d10bba", "size": 9849, "ext": "py", "lang": "Python", "max_stars_repo_path": "ignition/utils/proteus/test/test_poisson.py", "max_stars_repo_name": "IgnitionProject/ignition", "max_stars_repo_head_hexsha": "0eeb3a7878d828bc3c06d2cb2dd781e17776a8a6", "max_stars_repo_licenses"... |
function cd2EditedFile()
% cd2EditedFile: goes to the the file opened in editor
%
% references at:
% <<http://blogs.mathworks.com/community/2011/05/16/matlab-editor-api-examples/>>
% Copyright 2012, Clemens Ager
%%
a = fileparts(matlab.desktop.editor.getActiveFilename);
cd(a); | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/38104-cd-to-edited-file/cd2EditedFile.m"} |
module Languages
using Compat
export Language, EnglishLanguage, SpanishLanguage, GermanLanguage
export isocode, name
export articles, definite_articles, indefinite_articles
export prepositions
export pronouns
export stopwords
cache = Dict()
include("types.jl")
include("utils.jl")
include("word_lists.jl")
en... | {"hexsha": "96bc3d9575d1c95cc510a34278b3aa23615427e9", "size": 322, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Languages.jl", "max_stars_repo_name": "JuliaPackageMirrors/Languages.jl", "max_stars_repo_head_hexsha": "1a333b0ccf8850f7a7fffe51ef8f4ca56f9d531b", "max_stars_repo_licenses": ["MIT"], "max_stars... |
```python
%matplotlib inline
from sympy import *
init_printing(use_unicode=True)
```
```python
r, u, v, c, r_c, u_c, v_c, E, p, r_p, u_p, v_p, e, a, b, q, b_0, b_1, b_2, b_3, q_0, q_1, q_2, q_3, q_4, q_5, t, g, c_0, c_1, c_2, c_3, c_4, c_5 = symbols('r u v c r_c u_c v_c E p r_p u_p v_p e a b q b_0 b_1 b_2 b_3 q_0 q_1... | {"hexsha": "9cb5eecf6a868ad7bcd9949cd51870f880d47e91", "size": 194750, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Smectic/VPEqualZero.ipynb", "max_stars_repo_name": "brettavedisian/Liquid-Crystals", "max_stars_repo_head_hexsha": "c7c6eaec594e0de8966408264ca7ee06c2fdb5d3", "max_stars_repo_licens... |
'''
This script computes the acuity scores corresponding to the Sepsis patient cohort extracted with
the procedure provided at: https://github.com/microsoft/mimic_sepsis using the raw features.
============================================================================================================================... | {"hexsha": "a05c812377b58514a9cf71839e76f551c62cfecd", "size": 7752, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/compute_acuity_scores.py", "max_stars_repo_name": "lysuk96/rl_representations", "max_stars_repo_head_hexsha": "19de69305e40c9b3a1d746a7af26d232c9fb3f6f", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma suicide_aux_r:
"\<lbrakk> (\<forall>w\<in>Y. 0\<le>length w); (\<forall>w\<in>X\<^bsup>Suc n\<^esup>. n \<le> length w) \<rbrakk> \<Longrightarrow> (\<forall>w\<in>Y \<cdot> X\<^bsup>Suc n\<^esup>. n \<le> length w)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<forall>w\<in>Y. 0 \<l... | {"llama_tokens": 245, "file": "Regular_Algebras_Regular_Algebra_Models", "length": 1} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Implementation in Chainer of https://github.com/tensorflow/models/tree/master/video_prediction
# ==============================================================================================
import types
import random
import math
from math import floor, log
import nump... | {"hexsha": "f93f8f5f54c8b0335e4d92045f0fd57308c74a55", "size": 46527, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/train_model.py", "max_stars_repo_name": "kristofbc/physical-interaction-video-prediction", "max_stars_repo_head_hexsha": "845286629065c6a62580064943800b2b4ef0e04c", "max_stars_repo_lic... |
import os
import numpy as np
from rfvision.datasets import DATASETS
from torch.utils.data import Dataset
import os
import numpy as np
import rflib
from rfvision.datasets import DATASETS
from rfvision.datasets.custom3d import Custom3DDataset
SNAP_PARENT = [
0, # 0's parent
0, # 1's parent
1,
2,
3,... | {"hexsha": "07d7d990228e1363282741543661628b06ce23a4", "size": 6048, "ext": "py", "lang": "Python", "max_stars_repo_path": "rfvision/datasets/ik_dataset.py", "max_stars_repo_name": "tycoer/rfvision-1", "max_stars_repo_head_hexsha": "db6e28746d8251d1f394544c32b9e0af388d9964", "max_stars_repo_licenses": ["Apache-2.0"], "... |
/* vim: set tabstop=4 expandtab shiftwidth=4 softtabstop=4: */
/**
* \file src/fgt_coalform.cpp
*
* \brief Form stable coalition among a set of fog nodes.
*
* \author Marco Guazzone (marco.guazzone@gmail.com)
*
* <hr/>
*
* Copyright 2017 Marco Guazzone (marco.guazzone@gmail.com)
*
* Licensed under the Apach... | {"hexsha": "f897c82d8fd0b2adb994e08f3e245efbbc425ce1", "size": 13335, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/fog_coalform.cpp", "max_stars_repo_name": "sguazt/fog-gt", "max_stars_repo_head_hexsha": "92a01de4f3d71bf89741c7e4af1bebb965c64d28", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
from __future__ import division, print_function, absolute_import
from math import sqrt, exp, cos, sin
import numpy as np
# Import testing parameters
try:
from scipy.optimize._tstutils import methods, mstrings, functions, fstrings
except ImportError:
pass
from scipy.optimize import newton # newton predates be... | {"hexsha": "37eb95890965155c5d77a89789201381794a0065", "size": 3843, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/benchmarks/optimize_zeros.py", "max_stars_repo_name": "magnusja/scipy", "max_stars_repo_head_hexsha": "c4a5a1f984e28840010f20a7e41caa21b8f41979", "max_stars_repo_licenses": ["FSFAP"], "... |
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <float.h>
#define COMPEARTH_PRIVATE_DET3X3 1
#define COMPEARTH_PRIVATE_CROSS3 1
#define COMPEARTH_PRIVATE_NORM3 1
#define COMPEARTH_PRIVATE_GEM3 1
#define COMPEARTH_PRIVATE_GEMT3 1
#include "compearth.h"
#ifdef COMPEARTH_USE_MKL
#ifde... | {"hexsha": "184e22a232b6a8a23ab9768b171b66a56cf9acf0", "size": 5879, "ext": "c", "lang": "C", "max_stars_repo_path": "c_src/Uorth.c", "max_stars_repo_name": "OUCyf/mtbeach", "max_stars_repo_head_hexsha": "188058083602cebf1471ea88939b07999c90b655", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9.0, "max_stars_r... |
import asyncio
import sys
import numpy as np
import pytest
import ucp
def _skip_if_not_supported(message_type):
if message_type == "am" and not ucp._libs.ucx_api.is_am_supported():
pytest.skip("AM only supported in UCX >= 1.11")
async def _shutdown_send(ep, message_type):
msg = np.arange(10 ** 6)
... | {"hexsha": "acffff045cd59ca3aa1ac5136002a4eca0990cc3", "size": 6351, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_shutdown.py", "max_stars_repo_name": "pentschev/ucx-py", "max_stars_repo_head_hexsha": "d701a3facd85ef2deece619a4f707fdebee36e3c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
module allel_module
use atom_module, only: zn_atom
use ggrid_module, only: GG
use ps_local_variables, only: vqlg
use io_tools_module
implicit none
private
public :: init_allel
public :: init_ae_local_allel
logical,public :: flag_allel=.false.
real(8) :: Vcell
contains
subroutine init_allel(... | {"hexsha": "fd60f2a5133c8895d607c28eccba6d591fc4092f", "size": 1090, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/allel_module.f90", "max_stars_repo_name": "j-iwata/RSDFT_DEVELOP", "max_stars_repo_head_hexsha": "14e79a4d78a19e5e5c6fd7b3d2f2f0986f2ff6df", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
\documentclass{article}
\usepackage{tabularx}
\usepackage{booktabs}
\title{Reflection Report on [Title of Project]}
\author{author name}
\date{}
\input{../Comments}
\begin{document}
\begin{table}[hp]
\caption{Revision History} \label{TblRevisionHistory}
\begin{tabularx}{\textwidth}{llX}
\toprule
\textbf{Date} & ... | {"hexsha": "85d2ea582b8ef4394069bbee22b4b178dd9f0339", "size": 1033, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/Reflection/Reflection.tex", "max_stars_repo_name": "Ao99/MISEG", "max_stars_repo_head_hexsha": "7e07da67e34c460de33fce555e93acb8e795d80b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib
from matplotlib import animation, rc
colors = dict()
colors[0] = [0.2, 0.2, 0.2]
colors[1] = [0, 0, 1]
colors[2] = [0, 1, 0]
colors[3] = [1, 1, 0]
colors[4] = [0, 1, 1]
colors[5] = [1, 0, 1]
colors[6] = [0, 0.5, 0... | {"hexsha": "36d0a3c1456022fb1883057534debde61986a243", "size": 8444, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/articulated_multi_object/visualize.py", "max_stars_repo_name": "Fzaero/Object-and-Relation-Centric-Representations-for-Push-Effect-Prediction", "max_stars_repo_head_hexsha": "8f5e120983fe2e268... |
import torch
import numpy as np
import time
from sklearn.feature_extraction import image
from tqdm import tqdm
from glob import glob
from sklearn.cluster import MeanShift
from matplotlib import pyplot as plt
from IPython.display import clear_output
def normalize(img, mean, std):
"""Normalize an array of images wit... | {"hexsha": "9642a4a85a22c8fd69bb43b5e0911e87cb8671a9", "size": 11549, "ext": "py", "lang": "Python", "max_stars_repo_path": "divnoising/utils.py", "max_stars_repo_name": "mangalp/DivNoising", "max_stars_repo_head_hexsha": "19336a1dd0878526de119100dd33b26ec5dab2c4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
"""Module using IndRNNCell to solve the addition problem
The addition problem is stated in https://arxiv.org/abs/1803.04831. The
hyper-parameters are taken from that paper as well. The network should converge
to a MSE around zero after 1500-20000 steps, depending on the number of time
steps.
"""
import tensorflow as t... | {"hexsha": "762c3c3ce0e0880c42263b8f7e57b0120d511e45", "size": 4635, "ext": "py", "lang": "Python", "max_stars_repo_path": "ptb/ptb_rnn.py", "max_stars_repo_name": "narutowang/indrnn", "max_stars_repo_head_hexsha": "434e1200b5e742a0eac92bed661c69e97b8b8711", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import csv
from typing import Dict, List
import numpy as np
from .features import Features
CACHED_DATA: Dict[str, List[int]] = {}
class TextualFeatures(Features):
def __init__(self):
super().__init__()
self.emotional_words_count = np.zeros(2)
self.emoticon_count = np.zeros(3)
se... | {"hexsha": "64a151875a8576d9e53d4c7660ffdfef01192cc1", "size": 1559, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/features/textual_features.py", "max_stars_repo_name": "icycookies/dd_benchmark", "max_stars_repo_head_hexsha": "5551c0654d3dc30d72b817096d0877a02f28f116", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# 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-2.0
#
# Unless required by ... | {"hexsha": "e6ca3e340b8f371de16ee64e61f736833db724dc", "size": 20704, "ext": "py", "lang": "Python", "max_stars_repo_path": "nfnets/dataset.py", "max_stars_repo_name": "bruinxiong/deepmind-research", "max_stars_repo_head_hexsha": "4899440e3eb2dee9335c469c7f01aadcbf21cc72", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Christian Heider Nielsen"
__doc__ = r"""
Created on 29/07/2020
"""
__all__ = ["plot_grad_flow"]
import numpy
import torch
from matplotlib import pyplot
from matplotlib.lines import Line2D
from draugr.torch_utilities.optimisation.par... | {"hexsha": "536bf4ff9933c7b56e3db22b9b2a5e2602140972", "size": 3286, "ext": "py", "lang": "Python", "max_stars_repo_path": "draugr/torch_utilities/optimisation/debugging/gradients/flow.py", "max_stars_repo_name": "cnHeider/draugr", "max_stars_repo_head_hexsha": "b95e0bb1fa5efa581bfb28ff604f296ed2e6b7d6", "max_stars_rep... |
import numpy as np
a ='/home/peter/workspace/projects/tradance/traditional-dance-recognition/logs/kordance600_13-rgb-i3d-resnet-18-ts-f32/val_3crops_3clips_224_details.npy'
npy = np.load(a)
print(npy.shape)
print(npy[0])
| {"hexsha": "44cd904a8916c456dec0d18dc26ead6847ab8ca6", "size": 228, "ext": "py", "lang": "Python", "max_stars_repo_path": "debug.py", "max_stars_repo_name": "peter-yys-yoon/traditional-dance-recognition", "max_stars_repo_head_hexsha": "be4939d53b838624a04dba0826532c65421d1325", "max_stars_repo_licenses": ["Apache-2.0"]... |
from spacy.en import English
from numpy import dot
from numpy.linalg import norm
# from subject_object_extraction import findSVOs
import json
"""
tokenization, sentence recognition, part of speech tagging, lemmatization,
dependency parsing, and named entity recognition
"""
def get_message_info(parsedData):
... | {"hexsha": "786324c45720315d0763b356daf12d68b4ab6676", "size": 6742, "ext": "py", "lang": "Python", "max_stars_repo_path": "chatbot/chatAPI/helper.py", "max_stars_repo_name": "iSuperMostafa/nlp-chatbot-poc", "max_stars_repo_head_hexsha": "519ce184686fd40fcc4ea86bcc200e7bfa01e37f", "max_stars_repo_licenses": ["MIT"], "m... |
#include "options.h"
#include <boost/program_options.hpp>
#include <iostream>
namespace po = boost::program_options;
bool is_help(const po::variables_map& vm) {
return vm.count("help") > 0;
}
po::variables_map process_cmd_line(const int argc, char** argv) {
po::options_description desc("Options");
desc.add_op... | {"hexsha": "952e088618f6b3cc6b58f547fdc655b5d61e8e04", "size": 2162, "ext": "cc", "lang": "C++", "max_stars_repo_path": "examples/test_cpp/options.cc", "max_stars_repo_name": "orenmichaely/reinforcement_learning", "max_stars_repo_head_hexsha": "1a1570641255fdcd03a33996986aa58f3c0c58e2", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma mfinalD:
fixes ln
assumes "mfinal s" "thr s t = \<lfloor>(x, ln)\<rfloor>"
shows "final x" "ln = no_wait_locks" "wset s t = None"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. final x &&& ln = no_wait_locks &&& wset s t = None
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
... | {"llama_tokens": 347, "file": "JinjaThreads_Framework_FWSemantics", "length": 3} |
\chapter{\label{resources}Resources}
Examples of output from the thesis besides research:
\begin{itemize}
\item mystatsfunctions plus other packages (eg. FaIR)
\item Code accompanying papers
\item CEDA archive data
\item MARS datasets
\end{itemize} | {"hexsha": "2fd16309e231108ea838360128d334f883e12aef", "size": 265, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Appendices/A1.tex", "max_stars_repo_name": "njleach/Thesis", "max_stars_repo_head_hexsha": "a7594eb080d439c01312d44c20b922869c69f8ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
[STATEMENT]
lemma map_pred_comp: "map_pred f \<circ> map_pred g = map_pred (g \<circ> f)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. map_pred f \<circ> map_pred g = map_pred (g \<circ> f)
[PROOF STEP]
using map_fun_comp[where g=id and g'=id]
[PROOF STATE]
proof (prove)
using this:
?f ---> id \<circ> (?f' ---> id... | {"llama_tokens": 211, "file": "BNF_CC_Concrete_Examples", "length": 2} |
[STATEMENT]
lemma sturm_id_PR_prio0:
"{x::real. P x} = {x::real. (PR_TAG P) x}"
"(\<forall>x::real. f x < g x) = (\<forall>x::real. PR_TAG (\<lambda>x. f x < g x) x)"
"(\<forall>x::real. P x) = (\<forall>x::real. \<not>(PR_TAG (\<lambda>x. \<not>P x)) x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {x. P x}... | {"llama_tokens": 255, "file": "Sturm_Sequences_Sturm_Method", "length": 1} |
[STATEMENT]
lemma cfs_times_X:
assumes "g \<in> carrier P"
shows "(X \<otimes>\<^bsub>P\<^esub> g) (Suc n) = g n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (X_poly R \<otimes>\<^bsub>P\<^esub> g) (Suc n) = g n
[PROOF STEP]
apply(rule poly_induct3[of g])
[PROOF STATE]
proof (prove)
goal (3 subgoals):
1. g \... | {"llama_tokens": 783, "file": "Padic_Ints_Cring_Poly", "length": 4} |
#!/usr/bin/env python
"""
ONS Address Index - Test the Performance of the Probabilistic Parser
====================================================================
A simple script to test the performance of a trained probabilistic parser
using holdout data. Computes the number of tokens that were correctly identified.... | {"hexsha": "e3b752c4f39faaf623367894b5edb77d5f7570a1", "size": 12336, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataScience/ProbabilisticParser/tests/test_performance.py", "max_stars_repo_name": "Yasir326/address-index-data", "max_stars_repo_head_hexsha": "f95da1f5ecda911d5d5a83ce396b33837b629bdd", "max_st... |
"""Experiment Runner. It's great!"""
from functools import partial
import json
import os
import inspect
import random
import time
import traceback
from multiprocessing import Pool
import copy
import signal
import numpy as np
from tqdm import tqdm
try:
import ray
@ray.remote(max_calls=1)
def ray_eval_fit(pre... | {"hexsha": "5b9345594662b4c873c43351ab4076909dfadb62", "size": 12254, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment_runner/experiment_runner.py", "max_stars_repo_name": "philippjh/experiment_runner", "max_stars_repo_head_hexsha": "e2b1424dfeb9612f92a96ccd96693a6b5556ade5", "max_stars_repo_licenses":... |
------------------------------------------------------------------------
-- Pointwise equalities can be lifted
------------------------------------------------------------------------
module Stream.Pointwise where
open import Codata.Musical.Notation hiding (∞)
open import Stream
open import Stream.Equality
import Str... | {"hexsha": "a967b51db6b7f8f8af079c1b2de4aa3612734dcf", "size": 6430, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Stream/Pointwise.agda", "max_stars_repo_name": "nad/codata", "max_stars_repo_head_hexsha": "1b90445566df0d3b4ba6e31bd0bac417b4c0eb0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
function [ A, T, error, alignedShape ] = AlignShapesWithScale( alignFrom, alignTo )
%ALIGNSHAPESWITHSCALE Summary of this function goes here
% Detailed explanation goes here
numPoints = size(alignFrom,1);
meanFrom = mean(alignFrom);
meanTo = mean(alignTo);
alignFromMeanNormed = bsxfun(@minu... | {"author": "TadasBaltrusaitis", "repo": "OpenFace", "sha": "3d4b5cf8d96138be42bed229447f36cbb09a5a29", "save_path": "github-repos/MATLAB/TadasBaltrusaitis-OpenFace", "path": "github-repos/MATLAB/TadasBaltrusaitis-OpenFace/OpenFace-3d4b5cf8d96138be42bed229447f36cbb09a5a29/matlab_version/PDM_helpers/AlignShapesWithScale.... |
[STATEMENT]
lemma finite_fold_rbt_fold_eq:
assumes "comp_fun_commute f"
shows "Finite_Set.fold f A (set (RBT.entries t)) = RBT.fold (curry f) t A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Finite_Set.fold f A (set (RBT.entries t)) = RBT.fold (curry f) t A
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
go... | {"llama_tokens": 844, "file": null, "length": 10} |
module par-swap.confluent where
open import par-swap
open import par-swap.properties
open import Data.Nat using (_+_ ; _≤′_ ; _<′_ ; suc ; zero ; ≤′-refl)
open import Esterel.Lang.CanFunction
open import utility
open import Esterel.Lang
open import Esterel.Context
open import Data.Product
open import Data.Sum
open im... | {"hexsha": "32c6ca63d810cc016f5eae4322931bd8eb0bea2c", "size": 9893, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "agda/par-swap/confluent.agda", "max_stars_repo_name": "florence/esterel-calculus", "max_stars_repo_head_hexsha": "4340bef3f8df42ab8167735d35a4cf56243a45cd", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import os
import pickle
import gzip
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from pybasicbayes.util.text import progprint_xrange
from pyhawkes.models import \
DiscreteTimeNetworkHawkesModelGammaMixture, \
DiscreteTimeStandardHawkesModel
if __name__ == "_... | {"hexsha": "f1103638ea31a1190bfcd9ada8eb876eec8d886c", "size": 4725, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/inference/gibbs_demo.py", "max_stars_repo_name": "thonic/pyhawkes", "max_stars_repo_head_hexsha": "99804deb9ea22ba3e1a99584420722abdf8eb56b", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright (c) 2021. Nicolai Oswald
# Copyright (c) 2021. University of Edinburgh
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above c... | {"hexsha": "4eb63f5cb4135c3626e9fa713e9c97ee64a04a79", "size": 10587, "ext": "py", "lang": "Python", "max_stars_repo_path": "Parser/NetworkxParser/TransTreeGeneration/ClassProtoTransObjectTree.py", "max_stars_repo_name": "Errare-humanum-est/HeteroGen", "max_stars_repo_head_hexsha": "600a7bde441cc1365a465746e15564bd8de8... |
#reading data.dat
import numpy as np
import numpy.random as rd
import random as random
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
file1 = open("data2.dat",'r')
out2 = open("out2.txt", 'w')
data_string = []
#print(len(file1.readlines()))
#looping through the lines
for line in file1:
if l... | {"hexsha": "06404fc4657a0eb4435cb60a4d8ee3668f4adbbd", "size": 657, "ext": "py", "lang": "Python", "max_stars_repo_path": "Mathematics/PBC_simulations/readingDat2.py", "max_stars_repo_name": "grohalex/Final-Project", "max_stars_repo_head_hexsha": "41ac4e56e1a688a5f03f81d40d99eb2f839f9a26", "max_stars_repo_licenses": ["... |
\setlength{\footskip}{8mm}
\chapter{Extracting the Object from the Shadows: Maximum Likelihood
Object/Shadow Discrimination}
\label{ch:shadow}
\textit{In this chapter, we propose and experimentally evaluate a new
method for detecting shadows using a simple maximum likelihood
formulation based on color information. We... | {"hexsha": "5333fc15b6af359b1c45613f011f901c7dc396a7", "size": 41853, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "first-revision/shadow.tex", "max_stars_repo_name": "zkan/dissertation", "max_stars_repo_head_hexsha": "458c5fce241973008bdcc3958bdf962b9197e593", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""Provides an easy way of generating several geometric objects.
CONTAINS
--------
vtkArrowSource
vtkCylinderSource
vtkSphereSource
vtkPlaneSource
vtkLineSource
vtkCubeSource
vtkConeSource
vtkDiskSource
vtkRegularPolygonSource
vtkPyramid
vtkPlatonicSolidSource
vtkSuperquadricSource
as well as some pure-python helpers... | {"hexsha": "d9f6103fa8f9bd7245bcf4a74d66e9b71bc04dcd", "size": 42631, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyvista/utilities/geometric_objects.py", "max_stars_repo_name": "basnijholt/pyvista", "max_stars_repo_head_hexsha": "b1786b99217137e2c67566f5c09374c7a810f597", "max_stars_repo_licenses": ["MIT"],... |
__author__ = 'sibirrer'
import numpy as np
import numpy.testing as npt
from lenstronomy.Util import util
from lenstronomy.ImSim.Numerics.grid import AdaptiveGrid
from lenstronomy.ImSim.Numerics.grid import RegularGrid
from lenstronomy.LightModel.light_model import LightModel
import pytest
class TestAdaptiveGrid(obj... | {"hexsha": "87d0dff27f6614ab760339861fe03601ccdc02ce", "size": 4457, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_ImSim/test_Numerics/test_grid.py", "max_stars_repo_name": "heather999/lenstronomy", "max_stars_repo_head_hexsha": "8102fe026c1f3ba6e81d8a1f59cceb90e68430b4", "max_stars_repo_licenses": [... |
import pandas as pd
import numpy as np
ITEM_COL = 'item_id'
USER_COL = 'user_id'
FAKE_ITEM_ID = 999999
# Предфильтрация
def prefilter_items(data, prevalence_range = (0.05, 0.95), price_range = (1.0, 100.0)):
# Уберем самые популярные товары и самые непопулярные товары
pop_thr, unpop_thr = prevalence_range
... | {"hexsha": "1e264b5ee81dcbe1834c807a83632f83a6e4cefb", "size": 1729, "ext": "py", "lang": "Python", "max_stars_repo_path": "final/student_utils.py", "max_stars_repo_name": "avidbrain/recsys", "max_stars_repo_head_hexsha": "9f552602b95413ba6389735c51f2253f04d56537", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""Engine that performs decisions about whether to employ a surrogate"""
from proxima.inference import BaseInferenceEngine, ScikitLearnInferenceEngine
from proxima.data import BaseDataSource
import numpy as np
from sklearn.neighbors import NearestNeighbors
# TODO (wardlt): Provide some mechanism for checking if UQ t... | {"hexsha": "85d6dd70b63d2ed233b072c475cb3c928c4fc0b2", "size": 2601, "ext": "py", "lang": "Python", "max_stars_repo_path": "proxima/uq.py", "max_stars_repo_name": "YulianaGomez/proxima-1", "max_stars_repo_head_hexsha": "cfb4d9a530bed1b222b27b9c74b210e61ed7919a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# needs `AbstractObjective` (which in turn needs the Surrogate Interface)
Broadcast.broadcastable( mop :: AbstractMOP ) = Ref( mop );
# MANDATORY methods
"Return full vector of lower variable vectors for original problem."
full_lower_bounds( :: AbstractMOP ) :: Vec = nothing
"Return full vector of upper variable vec... | {"hexsha": "ad0c38a7cd451e2eaf9e553c496eceaf750a91bc", "size": 12770, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AbstractMOPInterface.jl", "max_stars_repo_name": "manuelbb-upb/Morbit.jl", "max_stars_repo_head_hexsha": "bfc6b1a7982d2c0003042ec9af75e64ad7ef5cf1", "max_stars_repo_licenses": ["MIT"], "max_st... |
import torch.nn.functional as F
import torch
from torch.autograd import Variable
import numpy as np
from src.data_ops.wrapping import wrap
from src.admin.utils import see_tensors_in_memory
def loss(y_pred, y, y_mask, bm):
l = nll
return l(y_pred, y, y_mask, bm)
def kl(y_pred, y, y_mask):
n = y_pred.shape... | {"hexsha": "04febf0e2bb6dd4ed7033686c91bb2890a33a4b9", "size": 3024, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/proteins/loss.py", "max_stars_repo_name": "isaachenrion/jets", "max_stars_repo_head_hexsha": "59aeba81788d0741af448192d9dfb764fb97cf8d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
from srtmath import *
from srtshapes import *
import numpy as np
import scipy.misc
import time
WIDTH = 1280
HEIGHT = 720
SPHERE_COLOR = [0,255,0]
if __name__ == "__main__":
objects = []
objects.append(Sphere(Point(), 500))
# objects.append(Plane(Point(0, 0, 750), Vector(0,0,-1)))
light = Point(0, 700, 1... | {"hexsha": "4306d1a94390867eb6394d07d38537112bdd44b6", "size": 1451, "ext": "py", "lang": "Python", "max_stars_repo_path": "srt.py", "max_stars_repo_name": "Seek/SimpleRT", "max_stars_repo_head_hexsha": "fa9ba1fa1c843b417b1e19a5315a63da718eaa43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_r... |
# Simple mnist convutional network
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import math
from glob import glob
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
# the data, split between train and test sets
(x_train, y_tra... | {"hexsha": "a18fb45f3b3195311ea2a1d416c715eebe341b38", "size": 2242, "ext": "py", "lang": "Python", "max_stars_repo_path": "recognizer.py", "max_stars_repo_name": "ColdBacon/Digit-recognizer", "max_stars_repo_head_hexsha": "af039cf16eff02595cd9806cbc4e0ee314970be6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#xortraintest-5.jl
#trains an xornet using noisy and unreliable data. Ten data inputs, top two xor'ed to get the
#correct values, 5% of the time.
function xortrain_4()
srand(10)
println("working on unreliable xor data set with backpropagation")
input_matrix = rand(Bool, 10, 500)
training_results = Array{B... | {"hexsha": "d9711dfb4c0a65e82860aab4fa1602dc0efc6e04", "size": 1173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/xortraintest-4.jl", "max_stars_repo_name": "interplanetary-robot/GenML", "max_stars_repo_head_hexsha": "f99015ab404250861334e75445b3a701293349e2", "max_stars_repo_licenses": ["MIT"], "max_star... |
'''
(*)~---------------------------------------------------------------------------
Pupil - eye tracking platform
Copyright (C) 2012-2018 Pupil Labs
Distributed under the terms of the GNU
Lesser General Public License (LGPL v3.0).
See COPYING and COPYING.LESSER for license details.
------------------------------------... | {"hexsha": "615d0f755db6e920713567a40463e412315bd0b3", "size": 10531, "ext": "py", "lang": "Python", "max_stars_repo_path": "pupil_src/shared_modules/calibration_routines/manual_marker_calibration.py", "max_stars_repo_name": "paulmathai01/Pupil-Interfaced", "max_stars_repo_head_hexsha": "4ec40c90876af3bdf75b1def47d21e4... |
import pip
def install():
if hasattr(pip, 'main'):
pip.main(['install', 'keras', 'tensorflow','opencv-python','numpy'])
else:
pip._internal.main(['install', 'keras', 'tensorflow','opencv-python','numpy'])
try:
import random,keras,cv2
import os
from keras.preprocessing import image
... | {"hexsha": "f3ad2875a683a54650123f86a586c19122952455", "size": 1625, "ext": "py", "lang": "Python", "max_stars_repo_path": "detect.py", "max_stars_repo_name": "newage-virtual-world/tb-and-pneumonia-detection", "max_stars_repo_head_hexsha": "7ac7171f776b90254c86a1cfbc72577470b673b9", "max_stars_repo_licenses": ["Apache-... |
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
class AggregateScalar(object):
"""
Computes and stores the average and std of stream.
Mostly used to average losses and accuracies.
"""
def __init__(self):
self.reset()
def reset... | {"hexsha": "adbe8a2320f62460528889c7a53e6d80a3e927b9", "size": 2392, "ext": "py", "lang": "Python", "max_stars_repo_path": "Authors' code/Zero_shot_learning/utils/helpers.py", "max_stars_repo_name": "onicolini/zero-shot_knowledge_transfer", "max_stars_repo_head_hexsha": "9dd6d08eadb8243881f0fb8e9ac2d5653dd25229", "max_... |
import autogp
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import sklearn.metrics.pairwise as sk
import time
import scipy
import seaborn as sns
import random
from kerpy.Kernel import Kernel
from kerpy.MaternKernel import MaternKernel
from kerpy.GaussianKernel import GaussianKernel
# T... | {"hexsha": "44da5af7b25874e6816b71b2b6b88574296afafc", "size": 7652, "ext": "py", "lang": "Python", "max_stars_repo_path": "LGCP_2sparse.py", "max_stars_repo_name": "VirgiAgl/updated_AutoGP", "max_stars_repo_head_hexsha": "2ec5671a4c1554555ab70c351944b3e8649e4237", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import numpy as np
import autoarray as aa
from autogalaxy.plane.plane import Plane
class SimulatorInterferometer(aa.SimulatorInterferometer):
def __init__(
self,
uv_wavelengths,
exposure_time: float,
transformer_class=aa.TransformerDFT,
noise_sigma=0.1,
... | {"hexsha": "3d79ee8efb9e7c816ee8b3dfe5bf350a21682602", "size": 3459, "ext": "py", "lang": "Python", "max_stars_repo_path": "autogalaxy/interferometer/interferometer.py", "max_stars_repo_name": "caoxiaoyue/PyAutoGalaxy", "max_stars_repo_head_hexsha": "ad2b4b27404f5bf0f65ba9a0cd7c3ee6570e2d05", "max_stars_repo_licenses":... |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.
// Use, modification and distribution is subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#ifnd... | {"hexsha": "eeabbc891dfa27dc6414777eed21c614fd231d01", "size": 4739, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Siv3D/src/ThirdParty/boost/geometry/extensions/gis/io/shapelib/shape_creator.hpp", "max_stars_repo_name": "yumetodo/OpenSiv3D", "max_stars_repo_head_hexsha": "ea191438ecbc64185f5df3d9f79dffc6757e419... |
const opts = Base.JLOptions()
const inline_flag = opts.can_inline == 1 ? `` : `--inline=no`
const cov_flag = (opts.code_coverage == 1) ? `--code-coverage=user` :
(opts.code_coverage == 2) ? `--code-coverage=all` :
``
function run_test(script)
srvrscript = joinpath(dirname(@__FILE_... | {"hexsha": "ffa5baed3fc0323f1fa61ddb7b2888ed2ab5ff39", "size": 1734, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tanmaykm/DagScheduler.jl", "max_stars_repo_head_hexsha": "00859c8f12589166443a04f71a57cacc5b4ccaf1", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
! try movement
SUBROUTINE try_conf(imol,itype)
use movetype
use ints
use coupling_pres
IMPLICIT NONE
integer :: imol, itype
logical :: success
! write(*,*) "try_move:"
movetype_i_try(itype) = movetype_i_try(itype) + 1
call conf_tran(imol,success)
if(success) then
movetype_i_success(it... | {"hexsha": "7da7175e85b00af92c3ab67f3b834d69c28d8df4", "size": 6085, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "MonteCarlo/mcrun_v2/try.f90", "max_stars_repo_name": "jht0664/Utility_python_gromacs", "max_stars_repo_head_hexsha": "4457b62e2f0252bcb38021d5deda0cfb932e3ed9", "max_stars_repo_licenses": ["MIT"... |
using Polyhedra
include("simplex.jl")
include("permutahedron.jl")
include("board.jl")
myeq(x::Real, y::Real) = myeq(promote(x, y)...)
myeq{T<:Real}(x::T, y::T) = x == y
myeq{T<:AbstractFloat}(x::T, y::T) = y < x+1024*eps(T) && x < y+1024*eps(T)
myeq{S<:Real,T<:Real}(x::Vector{S}, y::Vector{T}) = myeq(promote(x, y)...... | {"hexsha": "7ae7ba711d2ceb53a085cbfca2efd852ec8a4fff", "size": 2656, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/alltests.jl", "max_stars_repo_name": "JuliaPackageMirrors/Polyhedra.jl", "max_stars_repo_head_hexsha": "a4489180581383b750b1af4e043650f66fa61e76", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
Make a histogram of the masses.
"""
import matplotlib.pyplot as plt
plt.rc("text", usetex=True)
import numpy as np
h=0.7
def get_mass_array():
zs = np.loadtxt("data/z.txt")
lMs = []
for i in range(len(zs)):
lMs.append(np.loadtxt("results/bestfits/bf_cluster%d.txt"%i)[0])
return np.array(lMs... | {"hexsha": "1e941fd1611f3d04c9603abcdb041deab1a98987", "size": 670, "ext": "py", "lang": "Python", "max_stars_repo_path": "histM.py", "max_stars_repo_name": "tmcclintock/PLANCK_DES_Clusters", "max_stars_repo_head_hexsha": "c9864577d1baec753199327a5a8312576a2c33d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import json
import numpy as np
import pandas as pd
from keras.models import model_from_json
import matplotlib.pyplot as plt
from ADFA_DDQN import huber_loss
from network_classification import NetworkClassificationEnv
import itertools
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score, pre... | {"hexsha": "a28c7f6ee5b1c868efb4b956ef476ca6c3de4a33", "size": 5482, "ext": "py", "lang": "Python", "max_stars_repo_path": "estimators/universal_env/ADFA_DDQN_test.py", "max_stars_repo_name": "boyuruan/Anomaly-ReactionRL", "max_stars_repo_head_hexsha": "a82da87e2da28ad333a7e19af5a0608390c3312c", "max_stars_repo_license... |
#include <boost/test/unit_test.hpp>
#include "golden/include/gold.hpp"
using namespace golden;
BOOST_AUTO_TEST_CASE(my_test) {
// seven ways to detect and report the same error:
BOOST_CHECK(add(2, 2) == 4); // #1 continues on error
BOOST_REQUIRE(add(2, 2) == 4); // #2 throws on error
if (add(2, 2) != 4)
... | {"hexsha": "7ab15ac3be27256a834f1ce161bad435d8a4c348", "size": 691, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/unit/test_golden.cpp", "max_stars_repo_name": "nokx5/golden-cpp", "max_stars_repo_head_hexsha": "1eb5e05d35b315aeeeabf49b0795c9859707ae0a", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Created on Dec,27,2020
@author: junyun,Pan
Aim：反向拟合物性参数。
"""
######################################################################
#input
import sys
import os
mupif_dir=os.path.abspath(os.path.join(os.getcwd(), "../"))
sys.path.append(mupif_dir)
import mupif
import numpy as np
from scipy import stats
from bayes... | {"hexsha": "0dbd9be98a753425e48e7eb1b925cee0390e34e0", "size": 3512, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "PjyCMS1Beike/USTB_MGE_ICME_group414", "max_stars_repo_head_hexsha": "e16819eb71bfda5580e4e0147447017a1c22ae19", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma addO_assoc [simp]:
"addO n (addO m p) = addO (addO n m) p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. addO n (addO m p) = addO (addO n m) p
[PROOF STEP]
by (induct p) auto | {"llama_tokens": 95, "file": "Goodstein_Lambda_Goodstein_Lambda", "length": 1} |
from scipy.special import gammainc
from math import log
L = 0
R = 10**9
n = 10 ** 7
T = 0.75
for i in range(1000):
M = (L + R) * 0.5
v = gammainc(n, M)
if v < T:
L = M
else:
R = M
print(L / log(10.0)) | {"hexsha": "3a11dcf895624760371c2c41c5e4ad1bee962528", "size": 253, "ext": "py", "lang": "Python", "max_stars_repo_path": "600-700/697.py", "max_stars_repo_name": "Thomaw/Project-Euler", "max_stars_repo_head_hexsha": "bcad5d8a1fd3ebaa06fa52d92d286607e9372a8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from __future__ import division, print_function
from bm_tools import OnlineLogsumexp, sigmoid, log1pexp, logsumexp
import numpy
from scipy import linalg
TRAIN = "/home/mark/Projects/succotash/succotash/datasets/train_examples.npy"
TEST = "/home/mark/Projects/succotash/succotash/datasets/test_examples.npy"
X = numpy.l... | {"hexsha": "536b5d93b0662a5f3cdaf88d61f3c7970e94454c", "size": 3634, "ext": "py", "lang": "Python", "max_stars_repo_path": "structured_gaussian_mixtures/online_full_GMM_no_mean.py", "max_stars_repo_name": "markstoehr/structured_gaussian_mixtures", "max_stars_repo_head_hexsha": "f0c30770c8a851da7a7218b0b040b4f386f2bc5b"... |
#=
polylagrange:
- Julia version:
- Author: ymocquar
- Date: 2019-11-25
=#
include("polyexp.jl")
function getpolylagrange(k::Int64, j::Int64, N::DataType)
@assert k <= j "_getpolylagrange(k=$k,j=$j) k must be less or equal to j"
@assert N <: Signed "the type $N must be an Integer"
result = Polynomial([one(... | {"hexsha": "b3966e5473d6d73ac49f6874b54286ad1b18d961", "size": 4333, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/coefexp_ab.jl", "max_stars_repo_name": "vissarion/HOODESolver.jl", "max_stars_repo_head_hexsha": "cabc5b036c94f23a05a338c6dfc86c45982a8e24", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module AES
using StaticArrays, Random
abstract type AbstractSymmetricKey end
abstract type AbstractCipher end
abstract type AbstractCipherCache end
abstract type AbstractAESKey <: AbstractSymmetricKey end
abstract type AbstractAESCache <: AbstractCipherCache end
include("constants.jl")
include("types.jl")
... | {"hexsha": "1395eff31c2460f02ef2ffc23ee7d96c9e1b8fa5", "size": 619, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AES.jl", "max_stars_repo_name": "Seelengrab/AES.jl", "max_stars_repo_head_hexsha": "7af7764bb7918b91d2c495d238003649e0cc7ca3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_star... |
# Utils.jl
# [[file:~/Documents/Julia/scrap.org::*Utils.jl][Utils.jl:1]]
walk(x, inner, outer) = outer(x)
walk(x::T, inner, outer) where {T<:AbstractSymExpr} = outer(T(inner(x.op), map(inner, x.args)))
walk(x::Expr, inner, outer) = outer(Expr(x.head, map(inner, x.args)...))
postwalk(f, x) = walk(x, x -> postwalk(f, x)... | {"hexsha": "e2a4740961efa2d12ba40b9da9d0417988837244", "size": 1402, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utils.jl", "max_stars_repo_name": "jagot/Symbolics.jl", "max_stars_repo_head_hexsha": "b8994e3d79803daa3a57012fe9ccbe0b01c346d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 102, "ma... |
from __future__ import print_function
import os
import numpy as np
from tractor.ellipses import EllipseESoft
from tractor.utils import _GaussianPriors
def log_info(logger, args):
msg = ' '.join(map(str, args))
logger.info(msg)
def log_debug(logger, args):
import logging
if logger.isEnabledFor(logging... | {"hexsha": "68d0693491cdbe8fed3a219b6a539eb8fcb623d4", "size": 15999, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/legacypipe/utils.py", "max_stars_repo_name": "michaelJwilson/legacypipe", "max_stars_repo_head_hexsha": "47d005356cbd0c9fb864c960ee7bbf800e543cad", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import sys
import qprompt
import random
from enum import Enum
from copy import deepcopy
from itertools import groupby
import numpy as np
from scipy.ndimage import rotate
from rich.progress import (
BarColumn,
TimeRemainingColumn,
Progress,
)
class Player(Enum):
X = 1
O = 2
def initialize_board():
re... | {"hexsha": "2e049f26d8682cf332b16886bde11db4b2c16175", "size": 7522, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "spacekitcat/minimax-tic-tac-toe-python", "max_stars_repo_head_hexsha": "d50872137273cddc467485f1432ea28c86461c57", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import librosa
import math
import numpy as np
def wav_to_mfcc(wav_path, n_mfcc=13, n_fft=2048, hop_length=512):
SAMPLE_RATE = 48000
DA_FACTOR = 10 # data augmentation factor
X = []
signal, sr = librosa.load(wav_path, sr=None)
samples_per_track = len(signal)
num_samples_per_segment = 3 * sr
... | {"hexsha": "b827b74f7bc745ab5dd12ba4eae0263c3034c70e", "size": 1509, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/src/main/python/WAV_TO_MFCC.py", "max_stars_repo_name": "peisong0109/Detection", "max_stars_repo_head_hexsha": "16b50ca19a0f26818db897eb0f1c295f4bf42fe9", "max_stars_repo_licenses": ["Apache-2... |
import numpy as np
import sys, tempfile, subprocess
from functools import reduce
from calculate_axis import get_axis
from amino import get_atom_type_array
from Bio import AlignIO
from prody import parsePDB, LOGGER
LOGGER.verbosity = 'none'
def align_fasta(input_pdb_path, target_fasta_path):
pdb = parsePDB(input_... | {"hexsha": "55e28ec58869f1927f75cb1b36254849a7288063", "size": 3704, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/make_voxel.py", "max_stars_repo_name": "ishidalab-titech/3DCNN_MQA", "max_stars_repo_head_hexsha": "8f68a3719065338f03eca44da9a6eb0262da0ce9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.