text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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!> \brief \b DLASSQ updates a sum of squares represented in scaled form.
!
! =========== DOCUMENTATION ===========
!
! Online html documentation available at
! http://www.netlib.org/lapack/explore-html/
!
!> \htmlonly
!> Download DLASSQ + dependencies
!> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tg... | {"hexsha": "fddd1bf38f0958891627f2bf452fb6829401ab0a", "size": 7189, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "SRC/dlassq.f90", "max_stars_repo_name": "quellyn/lapack", "max_stars_repo_head_hexsha": "79aa0f2e0641cd48b27c7fc9a96922bf033193fa", "max_stars_repo_licenses": ["BSD-3-Clause-Open-MPI"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 21 10:31:38 2021
@author: crisprhhx
"""
import os
import pandas as pd
import numpy as np
import tensorflow as tf
import keras
from keras import backend as K
from skimage import io
from keras.applications.vgg16 import VGG16
import matplotlib.pyplot as plt
from keras import... | {"hexsha": "2af5933b831504190672cc6d90ccd940708447fd", "size": 6231, "ext": "py", "lang": "Python", "max_stars_repo_path": "xiangyaMedTask/Stage2/utils.py", "max_stars_repo_name": "satoshiSchubert/WorkSpace", "max_stars_repo_head_hexsha": "5558b3573e6b897b6684240ea5497cf08ae35145", "max_stars_repo_licenses": ["Apache-2... |
from lenstronomy.LensModel.Optimizer.optimizer import Optimizer
import unittest
import numpy as np
import pytest
class TestSinglePlaneOptimizer(object):
np.random.seed(0)
x_pos_simple,y_pos_simple = np.array([ 0.69190974, -0.58959536, 0.75765166, -0.70329933]),\
np.array([-0.... | {"hexsha": "e5b41e646da6e2054f6cd913956ee41163749987", "size": 3643, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_LensModel/test_Optimizer/test_single_plane.py", "max_stars_repo_name": "lucateo/lenstronomy", "max_stars_repo_head_hexsha": "3ab6cfd4adea2222f02d3f0f1a9cb5390c533aab", "max_stars_repo_li... |
%!TEX root = ../../main.tex
\subsection{Supervised deep anomaly detection}
\label{sec:supervisedDAD}
Supervised anomaly detection techniques are superior in performance compared to unsupervised anomaly detection techniques since these techniques use labeled samples.~\cite{gornitz2013toward}. Supervised anomaly dete... | {"hexsha": "2913b12f77f24693ce8115568cde64d0ec316243", "size": 2833, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ARXIV_DAD_Survey/sections/models/supervised.tex", "max_stars_repo_name": "raghavchalapathy/Deep-Learning-for-Anomaly-Detection-A-Survey", "max_stars_repo_head_hexsha": "aa775990a4b23306885979c4ef8e8... |
import group_theory.quotient_group
import group_theory.order_of_element
import .simple_group .quotient_group
namespace subgroup
variables {G : Type*} [group G] [fintype G]
@[to_additive]
lemma card_pos : fintype.card G > 0 := fintype.card_pos_iff.mpr ⟨1⟩
variables {H : subgroup G} [decidable_pred (λ h, h ∈ H)]
@[t... | {"author": "AdrianDoM", "repo": "IMOinLEAN", "sha": "672faa5bc8dd42a26fb1540ad8b9a325362be361", "save_path": "github-repos/lean/AdrianDoM-IMOinLEAN", "path": "github-repos/lean/AdrianDoM-IMOinLEAN/IMOinLEAN-672faa5bc8dd42a26fb1540ad8b9a325362be361/src/jordanholder/fingroup.lean"} |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CS624: Analysis of Algorithms
% Copyright 2015 Pejman Ghorbanzade <pejman@ghorbanzade.com>
% Creative Commons Attribution-ShareAlike 4.0 International License
% More info: https://github.com/ghorbanzade/beacon
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | {"hexsha": "ee38661d4ca6f6fd0ebaf53b02e355a814f64559", "size": 1309, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "umb-cs624-2015s/src/tex/hw03/hw03q03.tex", "max_stars_repo_name": "ghorbanzade/beacon", "max_stars_repo_head_hexsha": "c36e3d1909b9e1e47b1ad3cda81f7f33b713adc4", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/env python
# pylint: disable=wrong-import-position,too-many-statements
import os
import time
import traceback
from argparse import ArgumentParser
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from evaluation import Evaluation
from utils... | {"hexsha": "5c3cadc4153f80dac39dfd20006c1aef0a25ea56", "size": 6481, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/find_good_sample.py", "max_stars_repo_name": "furgerf/GAN-for-dermatologic-imaging", "max_stars_repo_head_hexsha": "e90b06c46c7693e984a4c5b067e18460113cd23b", "max_stars_repo_licenses": ["Apac... |
"""
Copyright (C) 2019 Patrick Schwab, ETH Zurich
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish... | {"hexsha": "a32872e7951b3d9eb12f87a35d1f4de81d91ce3b", "size": 4837, "ext": "py", "lang": "Python", "max_stars_repo_path": "dsmt_nets/model/model_evaluation.py", "max_stars_repo_name": "d909b/DSMTNets", "max_stars_repo_head_hexsha": "17518e7f3dd3150469081b07899b771312cb9e3b", "max_stars_repo_licenses": ["MIT"], "max_st... |
import pytest
import pandas as pd
import numpy as np
# --------------------------------------------------------------------------- #
# TEST DATA MOCKS
# --------------------------------------------------------------------------- #
@pytest.fixture(scope="module")
def reprice_data():
dates = pd.date_range(
"... | {"hexsha": "36d6316959d05dac84306bd39fa36703b445737c", "size": 641, "ext": "py", "lang": "Python", "max_stars_repo_path": "digging-into-python-testing/conftest.py", "max_stars_repo_name": "Tincre/technical-content", "max_stars_repo_head_hexsha": "7e10a65c1f46013b63a9d56391b4a248d92329db", "max_stars_repo_licenses": ["M... |
#ifndef WAVE_TYPES_HPP
#define WAVE_TYPES_HPP
#include <Eigen/Eigen>
namespace wave {
template<typename T>
using Vec = std::vector<T>;
template<typename T>
using VecE = std::vector<T, Eigen::aligned_allocator<T>>;
}
#endif //WAVE_TYPES_HPP
| {"hexsha": "cb6f51aa02855cc87d687ea7b7366c81a117e04e", "size": 246, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "wave_utils/include/wave/utils/types.hpp", "max_stars_repo_name": "Jebediah/libwave", "max_stars_repo_head_hexsha": "c04998c964f0dc7d414783c6e8cf989a2716ad54", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright(c) 2014, The LIMIX developers (Christoph Lippert, Paolo Francesco Casale, Oliver Stegle)
#
# 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/LICE... | {"hexsha": "d642730c50d6b0cab51ee6899fe6558ab1e15712", "size": 9550, "ext": "py", "lang": "Python", "max_stars_repo_path": "svca_limix/limix/io/output_writer.py", "max_stars_repo_name": "DenisSch/svca", "max_stars_repo_head_hexsha": "bd029c120ca8310f43311253e4d7ce19bc08350c", "max_stars_repo_licenses": ["Apache-2.0"], ... |
function score = Task1_Min_value(Population,~)
% <min> <single> <real/integer/label/binary/permutation> <large/none> <constrained/none> <expensive/none> <sparse/none> <multitask>
% The minimum objective value of the first task (for multitask optimization)
%------------------------------- Copyright --------------------... | {"author": "BIMK", "repo": "PlatEMO", "sha": "c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5", "save_path": "github-repos/MATLAB/BIMK-PlatEMO", "path": "github-repos/MATLAB/BIMK-PlatEMO/PlatEMO-c5b5b7c37a9bb42689a5ac2a0d638d9c4f5693d5/PlatEMO/Metrics/Task1_Min_value.m"} |
import pytest
import numpy as np
from discopy import Cup, Word
from discopy.quantum.circuit import Id
from lambeq import AtomicType, IQPAnsatz, SPSAOptimizer
N = AtomicType.NOUN
S = AtomicType.SENTENCE
ansatz = IQPAnsatz({N: 1, S: 1}, n_layers=1, n_single_qubit_params=1)
diagrams = [
ansatz((Word("Alice", N) ... | {"hexsha": "6bb55653f7d851fe9f8d08dd0ca1b3b8d6269c22", "size": 4427, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/training/test_spsa_optimizer.py", "max_stars_repo_name": "CQCL/lambeq", "max_stars_repo_head_hexsha": "04e4f736552c1ed51087dc9913f33464fad3783e", "max_stars_repo_licenses": ["Apache-2.0"], "... |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright 2020 by ShabaniPy Authors, see AUTHORS for more details.
#
# Distributed under the terms of the MIT license.
#
# The full license is in the file LICENCE, distributed with this software.
# ----------------... | {"hexsha": "bb6e76407e327dcda06383623444eb90d9e85622", "size": 4128, "ext": "py", "lang": "Python", "max_stars_repo_path": "shabanipy/jj/shapiro/utils.py", "max_stars_repo_name": "ShabaniLab/DataAnalysis", "max_stars_repo_head_hexsha": "e234b7d0e4ff8ecc11e58134e6309a095abcd2c0", "max_stars_repo_licenses": ["MIT"], "max... |
from pywrap.testing import cython_extension_from
import os
import numpy as np
from numpy.testing import assert_array_equal
from nose.plugins.skip import SkipTest
from pywrap.type_conversion import AbstractTypeConverter
from pywrap.defaultconfig import Config
from pywrap.utils import lines
def test_convert_vector():
... | {"hexsha": "18bce3f97ddbe73d1e615c93d6b9637098b51741", "size": 2922, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_custom_conversions.py", "max_stars_repo_name": "steffanschlein/cythonwrapper", "max_stars_repo_head_hexsha": "ef30a3bc1a24024b9845dad4aa8a42e05219bd91", "max_stars_repo_licenses": ["BSD-... |
module Dave.Structures.Definitions where
open import Dave.Equality public
op₁ : Set → Set
op₁ A = A → A
op₂ : Set → Set
op₂ A = A → A → A
associative : {A : Set} → op₂ A → Set
associative _·_ = ∀ m n p → (m · n) · p ≡ m · (n · p)
commutative : {A : Set} → op₂ A → Set
commutative ... | {"hexsha": "af9e804f95385034508198bd150ed74f0496bccc", "size": 547, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Dave/Structures/Definitions.agda", "max_stars_repo_name": "DavidStahl97/formal-proofs", "max_stars_repo_head_hexsha": "05213fb6ab1f51f770f9858b61526ba950e06232", "max_stars_repo_licenses": ["MIT"],... |
import tensorflow as tf
import cv2
import numpy as np
WIDTH = 100
HEIGHT = 100
INPUT_CHANNELS = 1
OUTPUT_CHANNELS = 3
def img_to_gray(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def rescale_img(img):
return cv2.resize(img,(WIDTH, HEIGHT), interpolation = cv2.INTER_CUBIC)
import subprocess
def sendme... | {"hexsha": "c499920388445a7d282ed0d08f9c242fa5f6ae6f", "size": 5756, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/other_model/colorize.py", "max_stars_repo_name": "MIT-6819-team/TF_colorization", "max_stars_repo_head_hexsha": "30bee77244e5595b855821a1e0ada9e69159b1c1", "max_stars_repo_licenses": [... |
\documentclass{rsaaReport}
% To be compiled with pdfLaTeX
\Project{GMTAO}
\DocVersion{0.1}
\DocNumber{ANU-AO-}
% This is the master file of this template, the one to be actually
% compiled with pdfLaTeX
% Absolutely necessary packages
\usepackage{graphicx}
\usepackage[pdftex,bookmarks,colorlinks]{hyperref}
\usepacka... | {"hexsha": "2212d256ccd13271129daddde1f52cf43cf52b49", "size": 2083, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/Sim.tex", "max_stars_repo_name": "rconan/OOMAO", "max_stars_repo_head_hexsha": "be6b64e55ddfd55d4925190d2f34f5e3e80a8008", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31, "max_stars_r... |
#! /usr/bin/env python3
import numpy as np
from scipy.stats import loguniform, truncnorm, multivariate_normal
import argparse
from sklearn.neighbors import KernelDensity
from sklearn.model_selection import GridSearchCV
parser = argparse.ArgumentParser(description="Fit a Gaussian Mixture Model to posterior samples and... | {"hexsha": "b151185a46a9effbbe3c74867bef5dc1f58cfc91", "size": 9334, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/generate_next_grid.py", "max_stars_repo_name": "liz-champion/lc_fit", "max_stars_repo_head_hexsha": "f86d28781252783240a33a4b8854e9ecefeab27c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
Function KTF2TC(J,M,K,L,R) ! Tit for Two Tats, Col rule
if(m .eq. 1) jold = 0
ktf2tc = 0
if ((jold .EQ. 1) .and. (j .eq. 1)) ktf2tc = 1
jold = j
Return
End ! TF2T Col Rule
| {"hexsha": "a6b0f4284079fd81f4ceb9a9e64c296b2dddf7ab", "size": 229, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/strategies/KTF2TC.f", "max_stars_repo_name": "Axelrod-Python/TourExec", "max_stars_repo_head_hexsha": "498b07394d215ce7d7df5bb7fd3aaa35eeda8317", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from sklearn.base import BaseEstimator, clone
from sklearn.metrics import r2_score
from .utils import my_fit
class EraBoostXgbRegressor(BaseEstimator):
def __init__(self, base_estimator=None, num_iterations=3, proportion=0.5, n_estimators=None):
self.base_estimator = base_estimator
... | {"hexsha": "0e4cfe48f6a435ed070e2422651d733c3ef665f9", "size": 1936, "ext": "py", "lang": "Python", "max_stars_repo_path": "era_boost_xgb_estimators.py", "max_stars_repo_name": "richmanbtc/bot_snippets", "max_stars_repo_head_hexsha": "a498cdb97f8568c1e05c117462a85b877d7dcf7d", "max_stars_repo_licenses": ["CC0-1.0"], "m... |
from copy import copy
import numpy as np
def get_evs(posteriors, thresholds):
return np.sum(thresholds * (posteriors / posteriors.sum(axis=1, keepdims=True)),
axis=1) / 100.
#def uncertaintify(reward, ev):
# uncertainty_const = 0.5
# assert 0. <= uncertainty_const <= 1.
# return np.av... | {"hexsha": "a91087a6b967d516ffc41d2dfcc8136e1b8e949b", "size": 5368, "ext": "py", "lang": "Python", "max_stars_repo_path": "handcrafted_agents/discrete_bayesian_greedy.py", "max_stars_repo_name": "IsaiahPressman/Kaggle_Santa_2020", "max_stars_repo_head_hexsha": "ff5c6aa78dbe234cef338f4c721cc30d7dbc3df8", "max_stars_rep... |
import numpy as np
from skimage import transform
import gym
from gym.spaces import Box
from gym.wrappers import FrameStack, GrayScaleObservation, TransformObservation
from nes_py.wrappers import JoypadSpace
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
class ResizeObservation(gym.ObservationWrapper):
de... | {"hexsha": "290c85e37f72f42fa4809e56533fe108f405c3af", "size": 3950, "ext": "py", "lang": "Python", "max_stars_repo_path": "a2c/wrappers.py", "max_stars_repo_name": "plusoneee/rl-a2c-supermario", "max_stars_repo_head_hexsha": "c2d4ab6c1f0a162b2c66f66835300f1f91de9f8b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
from pathlib import Path
import pandas as pd
from grid_simulations import MacroGen
if __name__ == "__main__":
np.random.seed(65432)
macgen = MacroGen()
macgen._base_geometry_cmd = "/control/execute setup_normal_run.mac"
macgen.run_macs = [#"run696keV.mac",
# "... | {"hexsha": "b9563140e7e3b0b436a65de8de9501d81be4f5e0", "size": 1918, "ext": "py", "lang": "Python", "max_stars_repo_path": "HPCscripts/grid_inbeam.py", "max_stars_repo_name": "vetlewi/AFRODITE", "max_stars_repo_head_hexsha": "4aa42184c0f94613e7e2b219bc8aca371094143e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import dash_resumable_upload
import dash
import dash_html_components as html
from dash.dependencies import Input, Output
import base64
from os import listdir,system,path,remove
import dash_table_experiments as dt
import dash_core_components as dcc
from os.path import isfile, join
import shutil
import time
... | {"hexsha": "3fa60b77eda35ff0038e41a24311506fa34c5593", "size": 8580, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/app.py", "max_stars_repo_name": "sabiharustam/TBD5", "max_stars_repo_head_hexsha": "2dafad06e866dabc7f16c51d8961e905991a1287", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_st... |
[STATEMENT]
lemma lift_pref_profile_permute_agents:
assumes "\<pi> permutes agents" "agents \<subseteq> agents'"
shows "lift_pref_profile agents alts agents' alts' (R \<circ> \<pi>) =
lift_pref_profile agents alts agents' alts' R \<circ> \<pi>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lift_... | {"llama_tokens": 308, "file": "Fishburn_Impossibility_Social_Choice_Functions", "length": 2} |
import wave
import numpy as np
import pygame
##### Parameters #####
SAMPLERATE = 48000 # Hz
AMPLITUDE = 10000
NCHANNELS = 1 # mono: sound played identically in both channels
SOUNDLEN = .4
SOUNDFREQ = 800
##### Constructing tone #####
# calculate the total amount of cycles in the SOUNDLEN
ncycles = SO... | {"hexsha": "1793f2e6fb6bd0e44f4811ff88d5ba116e476db3", "size": 1550, "ext": "py", "lang": "Python", "max_stars_repo_path": "plain_tone.py", "max_stars_repo_name": "Stiltstiltstilts/Music-Language-Tapping", "max_stars_repo_head_hexsha": "13cf607affdb1025295b0153085c7c4d12e84a3b", "max_stars_repo_licenses": ["MIT"], "max... |
#ifndef DART_CPP14_SHIM_H
#define DART_CPP14_SHIM_H
// Figure out what compiler we have.
#if defined(__clang__)
#define DART_USING_CLANG 1
#elif defined(__GNUC__) || defined(__GNUG__)
#define DART_USING_GCC 1
#elif defined(_MSC_VER)
#define DART_USING_MSVC 1
#endif
#ifdef DART_USING_MSVC
#define _CRT_SECURE_NO_WARNIN... | {"hexsha": "74236f85a8436bd02b1ec963bf21586dfe81907b", "size": 7298, "ext": "h", "lang": "C", "max_stars_repo_path": "include/dart/shim.h", "max_stars_repo_name": "Cfretz244/libdart", "max_stars_repo_head_hexsha": "987b01aa1f11455ac6aaf89f8e60825e92e6ec25", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import torch
import numpy as np
from queue import Queue
from utils import load_obj, export
import copy
from pathlib import Path
import pickle
from pytorch3d.ops.knn import knn_gather, knn_points
class Mesh:
def __init__(self, file, hold_history=False, vs=None, faces=None, device='cpu', gfmm=True):
if file... | {"hexsha": "367adafceccdf978a003bfccc85bf09e79f57d17", "size": 20729, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/layers/mesh.py", "max_stars_repo_name": "Sanjay-Ganeshan/point2mesh", "max_stars_repo_head_hexsha": "0b5f8eade103d4408529d94ec5ca55cf64a9a2c4", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from __future__ import print_function
import unittest
import numpy as np
from scipy.sparse.linalg import eigsh
from discretize import TensorMesh
from SimPEG import simulation, data_misfit
from SimPEG.maps import IdentityMap
from SimPEG.regularization import Tikhonov
from SimPEG.utils.mat_utils import eigenvalue_by_pow... | {"hexsha": "0bc6c7fa7bd002b1bee5aa9f61611a956e7a1ffc", "size": 3977, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/utils/test_mat_utils.py", "max_stars_repo_name": "JKutt/simpeg", "max_stars_repo_head_hexsha": "a0d9cf88e4551bfbfda3792521f4c85724686103", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/* Copyright (C) 2014 InfiniDB, Inc.
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; version 2 of
the License.
This program is distributed in the hope that it will be useful,
but W... | {"hexsha": "5772e7135cc4a5a22a9752fb48eec01d55fad7a6", "size": 7198, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/vendor/mariadb-10.6.7/storage/columnstore/columnstore/versioning/BRM/tablelockserver.cpp", "max_stars_repo_name": "zettadb/zettalib", "max_stars_repo_head_hexsha": "3d5f96dc9e3e4aa255f4e61054897... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from pyqtgraph.Qt import QtGui, QtCore
import pyqtgraph as pg
# https://www.institutoptique.fr/content/download/3234/22015/file/Optique%20Statistique%20cours%20ecrit.pdf
class Laser:
def __init__(self, fs, n, D_phi):
# Sampling... | {"hexsha": "7e96632de172009ba358887b98c3579c23dbd212", "size": 6388, "ext": "py", "lang": "Python", "max_stars_repo_path": "laser.py", "max_stars_repo_name": "Koheron/phase-noise", "max_stars_repo_head_hexsha": "e87ad9bdd3ff594fc3b62c2436745c7db4655675", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_st... |
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
import tensorflow as tf
tf.enable_eager_execution()
import tensorflow_probability as tfp
from tensorflow_proba... | {"hexsha": "33cd7255bbba76e2a841f4c023d7958020291128", "size": 2730, "ext": "py", "lang": "Python", "max_stars_repo_path": "ex2_graph/tut2_infer_sigma.py", "max_stars_repo_name": "trungnt13/uef_bay1_2018", "max_stars_repo_head_hexsha": "48a0f684eb4d18777d9f03998233774baa0524a8", "max_stars_repo_licenses": ["MIT"], "max... |
PROGRAM FILTERFIX
C-------------------------
C Fix up HST filter discriptions
C Read in wavelength and throughput
C Interpolate between the bins to give even bin sizes
C--------------------------
C
IMPLICIT NONE
C
INTEGER NFILT,IFILT
PARAMETER (NFILT=8)
c
CHARACTER*20 FNAME(NFILT),FNAMEI(N... | {"hexsha": "bd46bf99553ed2db7af680797455647b74703e4f", "size": 2009, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/piscola/filters/HST_GOODS/filterfix.f", "max_stars_repo_name": "temuller/PISCoLA", "max_stars_repo_head_hexsha": "e380603155991c267c26c4c93dfd650b9777b6b9", "max_stars_repo_licenses": ["MIT"],... |
# Copyright 2020 The SQLFlow Authors. 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 applicable law o... | {"hexsha": "d58739d637ebcdcb6b12f52617ec3456b060a674", "size": 3506, "ext": "py", "lang": "Python", "max_stars_repo_path": "sqlflow_models/one_class_svm.py", "max_stars_repo_name": "hebafer/models", "max_stars_repo_head_hexsha": "5dc6421f562ea447e501fa355a48a6ee89856a1d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma trms\<^sub>s\<^sub>s\<^sub>t_append[simp]: "trms\<^sub>s\<^sub>s\<^sub>t (A@B) = trms\<^sub>s\<^sub>s\<^sub>t A \<union> trms\<^sub>s\<^sub>s\<^sub>t B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. trms\<^sub>s\<^sub>s\<^sub>t (A @ B) = trms\<^sub>s\<^sub>s\<^sub>t A \<union> trms\<^sub>s\<^sub>s... | {"llama_tokens": 305, "file": "Stateful_Protocol_Composition_and_Typing_Stateful_Strands", "length": 2} |
module Mod_plcd_Elmope
use Mod_plcd_BaseElmope
use Mod_plcd_HangingNodes
use Mod_plcd_UPFormulation
use Mod_plcd_LargeStrainsOperations
use Mod_plcd_TransientProblem
use Mod_plcd_RotatingFrame
contains
subroutine SetPointers
call ResetProcedureComposition
call SetPointersAndHooksToNU... | {"hexsha": "c6483e684f1c1291985de70d9f9864fe856ef5c2", "size": 4261, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/modules/plcd/Elmopes/plcd_Elmope.f90", "max_stars_repo_name": "ciaid-colombia/InsFEM", "max_stars_repo_head_hexsha": "be7eb35baa75c31e3b175e95286549ccd84f8d40", "max_stars_repo_licenses"... |
import os
import sys
import urllib.request
import zipfile
import tensorflow as tf
from download_datasets import ensure_dataset_exists
import numpy as np
# Loads a morphological dataset in a vertical format.
# - The data consists of three Datasets
# - train
# - dev
# - test
# - Each dataset is composed of factor... | {"hexsha": "ef16c2d94b46dac1b81fe7db5f02d4c0e82eb5b4", "size": 7133, "ext": "py", "lang": "Python", "max_stars_repo_path": "morpho_dataset.py", "max_stars_repo_name": "jkulhanek/lemmatag-tf2", "max_stars_repo_head_hexsha": "816c376d8e6f894e34af67bc9076aed68f540bf8", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pytest
from smt_solver.sat_solver.tests.test_sat_solver import TestSATSolver
from smt_solver.uf_solver.tests.test_uf_solver import TestUFSolver
from smt_solver.tq_solver.tests.test_tq_solver import TestTQSolver
from smt_solver.smt_solver import SMTSolver
from random import randint
import numpy as np
class Test... | {"hexsha": "5e253fec5a58255e360583091f82fb52835a68f9", "size": 1674, "ext": "py", "lang": "Python", "max_stars_repo_path": "smt_solver/tests/test_smt_solver.py", "max_stars_repo_name": "AvivYaish/SMTsolver", "max_stars_repo_head_hexsha": "773041311ed8195ab48f669310df26ead3061912", "max_stars_repo_licenses": ["MIT"], "m... |
module Torch
# package code goes here
end # module
| {"hexsha": "5ba90093ec44c8ca4a0a72c178a030254396446d", "size": 54, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Torch.jl", "max_stars_repo_name": "Faldict/Torch.jl", "max_stars_repo_head_hexsha": "5f7b90647ef1dd1a9b5a8c87df8e1d50853bc1e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
from torchvision.datasets import MNIST, CIFAR10
import albumentations as A
from albumentations.pytorch import ToTensorV2
import torch
import cv2
from torch.utils.data import DataLoader
import torchvision
import numpy as np
DATA_MEAN = (0.4914, 0.4822, 0.4465)
DATA_STD = (0.247, 0.2435, 0.2616)
class Transforms:
... | {"hexsha": "fa4a2f7717df15d55888a44b2f0d1272ad0eab90", "size": 2083, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment_7/src/dataLoader.py", "max_stars_repo_name": "amitbcp/tsai-vision", "max_stars_repo_head_hexsha": "14a66d4c3295714fdcc97db13804ffba9d6f06cc", "max_stars_repo_licenses": ["Apache-2.0"], ... |
function constructnetwork!(m::JuMP.AbstractModel, branch_models::Array{NamedTuple{(:device, :formulation), Tuple{DataType,DataType}}}, netinjection::BalanceNamedTuple, system_formulation::Type{S}, sys::PowerSystems.PowerSystem; args...) where {S <: CopperPlatePowerModel}
copperplatebalance(m, netinjection, sys.tim... | {"hexsha": "86b0d26dc73cff5046f888459c7bcbff9255486f", "size": 3173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/component_constructors/network_constructor.jl", "max_stars_repo_name": "gitter-badger/PowerSimulations.jl", "max_stars_repo_head_hexsha": "608671297c4b813505aef4073932eae3d8875af6", "max_stars_... |
[STATEMENT]
lemma primfun_dominates:
"f < g \<Longrightarrow> dominates at_top (eval_primfun' f) (eval_primfun' g)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. f < g \<Longrightarrow> dominates at_top (eval_primfun' f) (eval_primfun' g)
[PROOF STEP]
by (elim less_primfun.elims; hypsubst) (simp_all add: ln_chain... | {"llama_tokens": 133, "file": "Landau_Symbols_Landau_Real_Products", "length": 1} |
import os
import sys
import schemasim.schemas.l0_schema_templates as st
import schemasim.schemas.l1_geometric_primitives as gp
import schemasim.schemas.l2_geometric_primitive_relations as gpr
import schemasim.schemas.l3_primitive_movement as pm
import schemasim.schemas.l3_location as location
import numpy as np
clas... | {"hexsha": "89bdcc4eb4860d59d8455ae6bc4756a614ebba27", "size": 3953, "ext": "py", "lang": "Python", "max_stars_repo_path": "schemasim/schemas/l4_path.py", "max_stars_repo_name": "mpomarlan/schemasim", "max_stars_repo_head_hexsha": "daf4a8273f743b4f5ac24549aeb1e60ea7402d2c", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""Check if Stieltjes method, both analytical and discretized works as expected."""
import numpy
import numpoly
import chaospy
def test_analytical_stieltjes(analytical_distribution):
"""Assert that Analytical Stieltjes produces orthogonality."""
coeffs, [orth], norms = chaospy.analytical_stieltjes(
or... | {"hexsha": "fe9a66f94a5a10c082f4cb71d0eb40114534088c", "size": 1240, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/recurrence/test_stieltjes_method.py", "max_stars_repo_name": "utsekaj42/chaospy", "max_stars_repo_head_hexsha": "0fb23cbb58eb987c3ca912e2a20b83ebab0514d0", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from starchive import identifiers
def linear(x, m, b):
model = m*x + b
return model
root_dir = '../data/'
a = np.genfromtxt(root_dir+'final_abundances_w_ncapture.csv', delimi... | {"hexsha": "b4442afa4a2cd41dadd42b38457779561ab8a9cf", "size": 3056, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/mkplot_nissen.py", "max_stars_repo_name": "megbedell/solartwin-abundances", "max_stars_repo_head_hexsha": "200f3da3863edb39ee6a7a40271c294b8f36b16e", "max_stars_repo_licenses": ["MIT"], "m... |
/-
Copyright (c) 2022 Michael Stoll. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Michael Stoll
! This file was ported from Lean 3 source module number_theory.legendre_symbol.gauss_sum
! leanprover-community/mathlib commit d11893b411025250c8e61ff2f12ccbd7ee35ab15
! ... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/NumberTheory/Legend... |
#-*-coding:utf-8-*-
'''
Created on Nov14 31,2018
@author: pengzhiliang
'''
import time
import numpy as np
import os
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from torch.utils.data import Dataset,DataLoader
from torch.o... | {"hexsha": "ff6b3fc1e47aa6ed402abc44bbe9d8841a9a016a", "size": 5864, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_unet.py", "max_stars_repo_name": "pengzhiliang/MRBrainS_seg", "max_stars_repo_head_hexsha": "52c392edb0b3d3988cdf526002f2e6df5c8401fe", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using Surrogates
using ForwardDiff
using LinearAlgebra
using Flux
using Flux: @epochs
using Flux.Tracker
using Zygote
#using Zygote: @nograd
#=
#FORWARD
###### 1D ######
lb = 0.0
ub = 10.0
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> x^2
y = f.(x)
#Radials
my_rad = RadialBasis(x,y,lb,ub,x->norm(x),2)
g = x -> For... | {"hexsha": "4fc941928ae49424cf73b709e623531bc9cd73b0", "size": 5660, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/AD_compatibility.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Surrogates.jl-6fc51010-71bc-11e9-0e15-a3fcc6593c49", "max_stars_repo_head_hexsha": "9680039453db69ccc9bad8721287e340381912f1... |
[STATEMENT]
lemma splits_iff: "(l, a, r) \<in> set (splits ll) = (ll = l @ a # r)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((l, a, r) \<in> set (splits ll)) = (ll = l @ a # r)
[PROOF STEP]
by(induction ll arbitrary: l a r)(auto simp add: Cons_eq_append_conv) | {"llama_tokens": 119, "file": "ADS_Functor_Inclusion_Proof_Construction", "length": 1} |
macro inline_widget(ex)
gname, name = ex.args
quote
function $(esc(name))(args...; props...)
widget = $(esc(gname))(args...)
length(props) > 0 && set!(widget; props...)
return widget
end
end
end
macro container_widget(ex)
gname, name = ex.args
quo... | {"hexsha": "a19325011eed9bc8d394f220830b03717f37beb9", "size": 2797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/widgets.jl", "max_stars_repo_name": "jorge-brito/Alexya.jl", "max_stars_repo_head_hexsha": "731f9357bedaefd1a015302623194f9108674003", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
""" Unit test for the Problem class. """
import unittest
import numpy as np
from six import text_type, PY3
from six.moves import cStringIO
import warnings
from openmdao.components.linear_system import LinearSystem
from openmdao.core.component import Component
from openmdao.core.problem import Problem
from openmdao.co... | {"hexsha": "726d0053669b7be144b422a5b74f47fc052d58f1", "size": 33755, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmdao/core/test/test_problem.py", "max_stars_repo_name": "jcchin/project_clippy", "max_stars_repo_head_hexsha": "ed38e11a96848a81c024c5a0e5821bc5db04fdc7", "max_stars_repo_licenses": ["Apache-... |
# Copyright 2016 Intel Corporation
#
# 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 applicable law or agreed to... | {"hexsha": "dc80e4f76f7cc015b13db24494952074260908bc", "size": 14358, "ext": "py", "lang": "Python", "max_stars_repo_path": "brainiak/fcma/preprocessing.py", "max_stars_repo_name": "osaaso3/brainiak", "max_stars_repo_head_hexsha": "153552c9b65e8354fa45985454f96978e0a92579", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy as np
from pyrr import Quaternion, matrix44, Matrix44, Vector3
from ..base.utils import *
class Transform:
"""
This class will manage the basic transformation that can be
performed to a geometry.
This class uses pyrr module that it's a packadge with many
operations tha... | {"hexsha": "aeed010058c390f860ed8dc810d39c946486ad5a", "size": 3934, "ext": "py", "lang": "Python", "max_stars_repo_path": "zero/core/geometry/transform.py", "max_stars_repo_name": "jsa4000/OpenGL-Python", "max_stars_repo_head_hexsha": "62055ba0c16f54507b7ba709d6691b2e9c7bc152", "max_stars_repo_licenses": ["Apache-2.0"... |
import numpy as np
import cv2
def getNormalMask(coco, imageObj, filterClasses):
"""
iscrowd is set to None, therefore it only works for single
mask : (height, width)
Parameters
------------------------------------
"""
# Load categorical ids for filterclasses
catIds = coco.getCatIds(cat... | {"hexsha": "95bb1e245cfd0e2c7901f309512df73fc5c07678", "size": 845, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/cocoFunctions.py", "max_stars_repo_name": "qualiphal/parallel-phal", "max_stars_repo_head_hexsha": "a6bbfdb104d13c4c45914e02d53f32e1b134ca3c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
//-------------------------------------------------------------------
// MetaInfo Framework (MIF)
// https://github.com/tdv/mif
// Created: 03.2017
// Copyright (C) 2016-2017 tdv
//-------------------------------------------------------------------
// STD
#include <cstdint>
#include <sstream>
#include <stdexce... | {"hexsha": "b0f8636c70a08b63163d96f1e5ca9b5720dd17eb", "size": 9536, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/db_client/src/main.cpp", "max_stars_repo_name": "paceholder/mif", "max_stars_repo_head_hexsha": "ff3c18f577048c94887220bb92477ce102f01599", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
Despite all her names Pennys owners arent really big fans of Dio, Warren Zevon or Prince.
Penny has many hobbies and interests, some of which include the following:
Chasing / wrestling with cats
Sleeping
Making sure nobody is sleeping
Users/TaylorStreet Coprophagia
Humping inanimate objects
Chewing (she ... | {"hexsha": "3ed9e676b4cd39000866ae91ee64847c66d58247", "size": 949, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Penny_the_Dog.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from scipy.spatial.distance import pdist
from sklearn.metrics import pairwise_kernels
def kernel_matrix(x):
n_samples, _ = x.shape
h = np.identity(n_samples) - np.full((n_samples, n_samples), 1 / n_samples)
kx = pairwise_kernels(x, metric='rbf', gamma=np.median(pdist(x)))
return h @... | {"hexsha": "c298e06da1b7f8c76d3e27b067f1cefd465f108e", "size": 328, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/causality/pc/independence/utils.py", "max_stars_repo_name": "AnverK/VK_Graduation", "max_stars_repo_head_hexsha": "a8d457d1fcb677d417a5ea82011393160762c0b1", "max_stars_repo_licenses": ["MIT"],... |
import click
import platform
import cv2
import numpy as np
try:
import urllib.request as urllib
except:
import urllib
from ggb import GGB, ColorSpace
import ggb
def print_version(ctx: click.Context, param: click.Parameter, value: bool) -> None:
if not value or ctx.resilient_parsing:
return
cl... | {"hexsha": "2024e784ba2466b2ddbfdf6500b1669c3c3194d6", "size": 1377, "ext": "py", "lang": "Python", "max_stars_repo_path": "ggb/__main__.py", "max_stars_repo_name": "reshalfahsi/GGB", "max_stars_repo_head_hexsha": "f56994ffcd6a83762d67705116e690c7a64c9093", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
# -*- coding: utf-8 -*-
"""
Es 2
"""
import allMethods as fz
import math
import sympy as sym
import sympy.utilities.lambdify
import numpy as np
import matplotlib.pyplot as plt
x = sym.symbols("x")
fx = sym.tan(x) - x
dfx = sym.diff(fx, x, 1)
f = sym.lambdify(x, fx, np)
df = sym.lambdify(x, dfx, np)
... | {"hexsha": "45248cb473652829da86a12c6473f359b5e79624", "size": 748, "ext": "py", "lang": "Python", "max_stars_repo_path": "zeri_di_funzione/esercizi/es2.py", "max_stars_repo_name": "luigi-borriello00/Metodi_SIUMerici", "max_stars_repo_head_hexsha": "cf1407c0ad432a49a96dcd08303213e48723c57a", "max_stars_repo_licenses": ... |
%!TEX root = ../thesis.tex
%*******************************************************************************
%*********************************** Seventh Chapter *****************************
%*******************************************************************************
\chapter{Centre Vortex Visualisations}\label{ch... | {"hexsha": "9069de8d438a718350206f84e5bc61cde672a384", "size": 28705, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter7/chapter7.tex", "max_stars_repo_name": "jamesbiddle/Masters_Thesis", "max_stars_repo_head_hexsha": "275177c3167b490d678575f0078cc6c87614b7bb", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Implements evaluating the tight-binding model by checking the distance
between its orbitals and the atomic positions.
"""
import tbmodels
import numpy as np
from aiida import orm
from aiida.engine import calcfunction, run_get_node
from aiida.engine.processes import ExitCode
from ._base im... | {"hexsha": "8459c9856b050ded7ad92849b089d04dad932187", "size": 1866, "ext": "py", "lang": "Python", "max_stars_repo_path": "aiida_tbextraction/model_evaluation/_pos_distance.py", "max_stars_repo_name": "greschd/aiida-tbextraction", "max_stars_repo_head_hexsha": "6b51cd6fce8feaea6c7a9235a49073a2500eead3", "max_stars_rep... |
module TestPkg
using FilePathsBase
import Base: ==
__init__() = FilePathsBase.register(TestPath)
# Warning: We only expect this test to work on posix systems.
struct TestPath <: AbstractPath
segments::Tuple{Vararg{String}}
root::String
drive::String
separator::String
end
TestPath() = TestPath(tupl... | {"hexsha": "c10463ebf3a1ad26c6d3ab831843ead09cfb89e5", "size": 2872, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testpkg.jl", "max_stars_repo_name": "UnofficialJuliaMirror/FilePathsBase.jl-48062228-2e41-5def-b9a4-89aafe57970f", "max_stars_repo_head_hexsha": "9ea8d7cb5e638a386cf41b042875569620302d32", "ma... |
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
N = 2 # Size of minibatch
H = 3 # Number of dimension of hidden vec
T = 20 # Length of time data
dh = np.ones((N, H))
np.random.seed(3) # Set seed of random number due to reproducibility
# Wh = np.random.randn... | {"hexsha": "1c8b83ebeabd5fbd4651eefd165761b54641f711", "size": 649, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch06/rnn_gradient_graph.py", "max_stars_repo_name": "YaGiNA/DLfS2", "max_stars_repo_head_hexsha": "3dbaba7a62c198b50849de2e3b74d92897a4cae7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from typing import Optional
import math
import numpy as np
from banditpylib.arms import PseudoArm
from banditpylib.data_pb2 import Context, Actions, Feedback
from .utils import MABLearner
class Softmax(MABLearner):
r"""Softmax policy
At time :math:`t`, sample arm :math:`i` to play with sampling weight
.. m... | {"hexsha": "08abf3ff4ed3e871bb926c63f79397fcd80bb599", "size": 2055, "ext": "py", "lang": "Python", "max_stars_repo_path": "banditpylib/learners/mab_learner/softmax.py", "max_stars_repo_name": "Alanthink/banditpylib", "max_stars_repo_head_hexsha": "ba6dc84d87ae9e9aec48cd622ec9988dccdd18c6", "max_stars_repo_licenses": [... |
import numpy as np
import cv2
from pyzbar.pyzbar import decode
def checkQR(img):
qrList = []
for qrcode in decode(img):
data = qrcode.data.decode('utf-8')
qrList.append(data)
if len(qrList)>0:
return True,qrList
else:
return False,qrList
im... | {"hexsha": "c7bd3591b247e9573f0dff2d784619fba82d2c97", "size": 581, "ext": "py", "lang": "Python", "max_stars_repo_path": "QR_code/save/QR _function.py", "max_stars_repo_name": "RobEn-AAST/AI-UAVC", "max_stars_repo_head_hexsha": "732683fd5821d492b772cc5f966e86aed164a68c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
## characters defined by actions/homomorphisms
function _action_class_fun(
conjugacy_cls::AbstractVector{CCl},
) where {CCl <: AbstractOrbit{<:PermutationGroups.AbstractPerm}}
vals = Int[PermutationGroups.nfixedpoints(first(cc)) for cc in conjugacy_cls]
# in general:
# vals = [tr(matrix_representative(... | {"hexsha": "545648b6387cba31de45a9596657802a7fc440c0", "size": 3054, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/action_characters.jl", "max_stars_repo_name": "thinh-le/SymbolicWedderburn.jl", "max_stars_repo_head_hexsha": "fe363d2e269602dd487d9f33665141e0cbdfc87b", "max_stars_repo_licenses": ["MIT"], "ma... |
import tensorflow as tf
import numpy as np
import scipy.misc
def normalization(data):
"""
normalized the input data
:param data: input
:return: normalized data
"""
_range = np.max(data) - np.min(data)
return (data - np.min(data)) / _range
def img255_normalization(img):
"""
Standa... | {"hexsha": "d626e7dba9a9cda24f0d9b285b45db4de5dea5e2", "size": 7069, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/utils.py", "max_stars_repo_name": "qxdnfsy/PEN-Net-Keras-Img_Inpainting", "max_stars_repo_head_hexsha": "bc81f696689cb264104be94951af8405fe118450", "max_stars_repo_licenses": ["MIT"], "max_st... |
/* Copyright (c) 2010-2018, Delft University of Technology
* All rigths reserved
*
* This file is part of the Tudat. Redistribution and use in source and
* binary forms, with or without modification, are permitted exclusively
* under the terms of the Modified BSD license. You should have received
*... | {"hexsha": "d549a2d3574ceb9dd9627d424edfccb462836f4e", "size": 9502, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/Propagators/variationalEquations.cpp", "max_stars_repo_name": "J-Westin/tudat", "max_stars_repo_head_hexsha": "82ebe9e6e2dd51d0688b77960e62e980e6b8bcb8", "max_stars_repo_licenses... |
# taion.py
# A simple python script to extract body temperature
# from lcd on a thermometer.
# This code is based on code in https://github.com/yan9yu/sdr
#
# Copyright (c) 2020, Masami Yamakawa (MONOxIT)
# This software is released under the MIT License.
# http://opensource.org/licenses/mit-license.php
# 使用するライブラリ... | {"hexsha": "749434429096eba1aaaa47d8577e2fe37aad38d9", "size": 6746, "ext": "py", "lang": "Python", "max_stars_repo_path": "taion.py", "max_stars_repo_name": "monoxit/Thermometer-OCR", "max_stars_repo_head_hexsha": "b92e1b590c86bd66003447646fc03cff95eba6bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import os
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
from skimage.morphology import skeletonize
from skimage import morphology
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
from skimage import draw
import matplotlib as mpl
from matplotlib.colors... | {"hexsha": "3181f9411d811fdf565fbc3991deba99dd87bd89", "size": 5107, "ext": "py", "lang": "Python", "max_stars_repo_path": "archived_NOT_working/main_2 copy.py", "max_stars_repo_name": "bendevlin18/sholl-analysis-python", "max_stars_repo_head_hexsha": "edc69a649b9fb160fd081553f109146cd6da5bca", "max_stars_repo_licenses... |
program test_qhashtbl
use qhashtbl_m
use iso_c_binding, only: c_ptr, c_loc, c_f_pointer
implicit none
type value_t
integer :: nv
integer, allocatable :: val(:)
end type
type(qhashtbl_t) :: qh
type(qhashtbl_obj_t) :: hobj
type(value_t), pointer :: pval, pback
in... | {"hexsha": "e1e0d9604fbe144e1e425d412bf25c5811a053c4", "size": 1759, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docs/tutorial_qhashtbl.f90", "max_stars_repo_name": "darmar-lt/qcontainers", "max_stars_repo_head_hexsha": "bb1423dded02588898530c3ac7aa709e3f4eb5c3", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
# -*-coding:utf-8-*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
from six.moves import xrange
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.python.framework import dtypes
from tensorflow.python.framework impo... | {"hexsha": "1d45112d70de6bca47d4f8f2f28c4d895f1dcbb0", "size": 5572, "ext": "py", "lang": "Python", "max_stars_repo_path": "TFDataset_context.py", "max_stars_repo_name": "XURIGHT/Advisor-Advisee_SAE", "max_stars_repo_head_hexsha": "2bb0a221bea05af0ddc4ebd87e5ec86a8d14d12f", "max_stars_repo_licenses": ["MIT"], "max_star... |
# gfa_parser.py assembly_graph_with_scaffolds.gfa graph_pack.grseq outdir
import sys, os, subprocess
import pandas as pd
import networkx as nx
from Bio.Seq import reverse_complement
import graphs
def get_one_type_gfa(gfa, type, outdir):
one_type_gfa = os.path.join(outdir, '{}.gfa'.format(type))
os.system... | {"hexsha": "ef80d67144e5565829f051ed1cb09ca2d3735644", "size": 3912, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/gfa_parser.py", "max_stars_repo_name": "letovesnoi/clusterassembly", "max_stars_repo_head_hexsha": "9edcab8afe5601195a40e497d06200a38daf0325", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
open import Agda.Primitive using (_⊔_)
import Categories.Category as Category
import Categories.Category.Cartesian as Cartesian
open import MultiSorted.AlgebraicTheory
-- Finite products indexed by contexts
module MultiSorted.Product
{o ℓ e}
(𝒞 : Category.Category o ℓ e)
{𝓈 ℴ}
{Σ : Sign... | {"hexsha": "9bf57beabd160cb002d600973d7adf04a165cda7", "size": 3803, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/MultiSorted/Product.agda", "max_stars_repo_name": "cilinder/formaltt", "max_stars_repo_head_hexsha": "0a9d25e6e3965913d9b49a47c88cdfb94b55ffeb", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Function to test inference of the smoothing parameter of a hidden Potts-MRF.
Author: W.M.Kouw
Date: 18-09-2018
"""
import numpy as np
import numpy.random as rnd
import scipy.optimize as opt
import matplotlib.pyplot as plt
from tomopy.misc import phantom as ph
from hP... | {"hexsha": "81b2f5a33d5491ea31e77d6489556becfe6dd0ee", "size": 1372, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/tests/test_neighbourhoods.py", "max_stars_repo_name": "wmkouw/cc-infopriors", "max_stars_repo_head_hexsha": "653079f201c8bce570dacb3479f4270ebe0de953", "max_stars_repo_licenses": ["MIT... |
import numpy as np
from tensorflow.examples.tutorials import mnist
import os
import numpy as np
class Dataset(object):
def __init__(self, images, labels=None):
self._images = images.reshape(images.shape[0], -1)
self._labels = labels
self._epochs_completed = -1
self._num_examples =... | {"hexsha": "67ba10d171212fb4f549e1bb24aaaca559fd2049", "size": 4412, "ext": "py", "lang": "Python", "max_stars_repo_path": "vanilla_InfoGAN/infogan/misc/datasets.py", "max_stars_repo_name": "lbechberger/LearningConceputalDimensions", "max_stars_repo_head_hexsha": "332d0f520faad2e5788d658cb4f4b9cc9cfbb15d", "max_stars_r... |
#include <boost/fusion/include/filter_if.hpp>
| {"hexsha": "6d10d7403d31dadab721b5e5cfc0cdedbbcf1a50", "size": 46, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_fusion_include_filter_if.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-... |
program main
use plantfem
implicit none
type(FEMDomain_) :: domain
call domain%create(meshtype="Cube",x_num=10,y_num=10,z_num=10)
call domain%resize(x=1.0d0, y=3.0d0, z=10.0d0)
call domain%json(name="domain.json")
call domain%msh(name="domain.msh")
end program main | {"hexsha": "53d0635a8745e3daf914aa9f25714dc8f190e754", "size": 296, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Tutorial/playon_fem/ex0014_CreateMeshEx6.f90", "max_stars_repo_name": "kazulagi/plantfem_min", "max_stars_repo_head_hexsha": "ba7398c031636644aef8acb5a0dad7f9b99fcb92", "max_stars_repo_licenses":... |
# invert the ITMIX 2 with IV for ESA
include("itmix-setup.jl") # run this first on single process to make sure all precompilation this through
# before the parallel run starts
if nprocs()<2
if Sys.CPU_CORES>=17
addprocs(17) # /2 to get to physical cores
else
addprocs(... | {"hexsha": "1437a4e838ed6aeaf6bd33edffa3c0f07345433d", "size": 1628, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/itmix2-parallel-ESA.jl", "max_stars_repo_name": "mauro3/BITEmodel.jl", "max_stars_repo_head_hexsha": "897eca85fc3c3b736ef49e23850b8f4bd6f2806a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import cProfile
import datetime as dt
import numpy as np
from tests.datasynthesis import unit_function_pattern
from qalatgir import fill_missing
original_data = unit_function_pattern(dt.timedelta(minutes=5))
missed_period = slice(11 * 12, 13 * 12)
deleted = original_data.iloc[missed_period]['value'].copy()
original... | {"hexsha": "e76728142df7aee9c7216f3e8ba4286bbd345781", "size": 679, "ext": "py", "lang": "Python", "max_stars_repo_path": "bottleneck_analysis.py", "max_stars_repo_name": "boof-tech/qalatgir", "max_stars_repo_head_hexsha": "4f5adfc1bb4f82c1c5478fb228b4121d7f9784ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import random
from math import exp
from tqdm import trange
import numpy as np
import sys
from graphical_model_learning.scores import MemoizedDecomposableScore
from graphical_model_learning.algorithms import permutation2dag
from graphical_model_learning.samplers.proposals import adjacent_transposition_proposer
sys.path.... | {"hexsha": "8fadb49ae8fb0502e0933bce25a6132b0dfb9199", "size": 4789, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphical_model_learning/samplers/minimal_imap_mcmc.py", "max_stars_repo_name": "uhlerlab/graphical_model_learning", "max_stars_repo_head_hexsha": "19a1885af073b35d1f9b16585482af30d4db7264", "max_... |
import numpy as np
from openmdao.api import ExplicitComponent
class Shaft(ExplicitComponent):
"""Calculates power balance for shaft"""
def initialize(self):
self.options.declare('num_ports', default=2,
desc="number shaft connections to make")
def setup(self):
... | {"hexsha": "676f0569aa7d452dd82e00dfe25022084cb3f0a5", "size": 5008, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycycle/elements/shaft.py", "max_stars_repo_name": "naylor-b/pyCycle", "max_stars_repo_head_hexsha": "787743b39b17443631debb145a976b0ccdee43ab", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
[STATEMENT]
lemma increasing_Bseq_subseq_iff:
assumes "\<And>x y. x \<le> y \<Longrightarrow> norm (f x :: 'a::real_normed_vector) \<le> norm (f y)" "strict_mono g"
shows "Bseq (\<lambda>x. f (g x)) \<longleftrightarrow> Bseq f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Bseq (\<lambda>x. f (g x)) = Bseq f
[... | {"llama_tokens": 2071, "file": null, "length": 26} |
# Testing reference values and precisions
# Each test block of varr and parr should be followed by an append to refVals, refPrecs arrays.
# e.g.
# refVals=[]
# refPrecs=[]
#
# varr = ..........
# par = ..........
#
# append!(refVals ,[ varr ] )
# append!(refPrecs,[ parr ] )
#
# varr = ..........
# par ... | {"hexsha": "d0ab567ce460722c90a3819637f6d60efda7f2d1", "size": 16700, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Ocean/HydrostaticBoussinesq/test_ocean_gyre_refvals.jl", "max_stars_repo_name": "leios/CLIMA", "max_stars_repo_head_hexsha": "44c45eb762b8dc4c5af091079f2d65c024cb8d27", "max_stars_repo_licens... |
#!/usr/bin/env python
# Software License Agreement (MIT License)
#
# Copyright (c) 2020, tri_star
# All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction... | {"hexsha": "0d10c0a6b260728a85875fc9f67a82948de0b43f", "size": 9371, "ext": "py", "lang": "Python", "max_stars_repo_path": "tri_star/include/tri_star/file_util.py", "max_stars_repo_name": "ScazLab/Frontiers_Robot_Tool_Use", "max_stars_repo_head_hexsha": "ebace49e88562c18b3b967ec5360a4cec4f8fe56", "max_stars_repo_licens... |
#ifndef MITAMA_PANIC_HPP
#define MITAMA_PANIC_HPP
#include <stdexcept>
#include <boost/format.hpp>
#include <variant>
#include <utility>
#include <string>
#include <string_view>
namespace mitama {
class macro_use_tag_t{};
inline static constexpr macro_use_tag_t macro_use{};
class runtime_panic : public std::runtime... | {"hexsha": "bfd7bb6126805349b97d793ffde72f1ab474a012", "size": 1284, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/mitama/panic.hpp", "max_stars_repo_name": "agate-pris/mitama-cpp-result", "max_stars_repo_head_hexsha": "9d94f3c9b5722892496ee7c63833fe5f12392b89", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""
Created on sun Feb 16 14:39:30 2020
@author: simran kaur
"""
#importing libraries
import numpy as np
import pandas as pd
import sys
import datawig
def missing(data):
if data.shape[0]==0:
return print("empty dataset")
col_null=data.columns[data.isnull().any(... | {"hexsha": "45980d5debee01bd1d715b1b7da511c2a2840496", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "missing_values-101703547-simran_kaur/missing_values.py", "max_stars_repo_name": "simrankaur7575/missing_values-101703547-simran_kaur", "max_stars_repo_head_hexsha": "5d293a7ea8a6aa73e427f4008cf9dc... |
import numpy as np
import cv2
import os
from matplotlib import pyplot as plt
from tqdm import tqdm
from features_palmoil import DS_aux
class PalmOilDataset(DS_aux):
def __init__(self, args,
label_code={'No_OilPalm':0,
'Has_OilPalm':1}):
super().__init__(a... | {"hexsha": "8237b225a5ce58f604be02e55aa81e7cea737276", "size": 4334, "ext": "py", "lang": "Python", "max_stars_repo_path": "palm_oil_ds.py", "max_stars_repo_name": "MartimChaves/glcm_sat_img", "max_stars_repo_head_hexsha": "d56ddb41890f0e63840487ca71f070d62e23b698", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#include "clstm.h"
#include <assert.h>
#include <iostream>
#include <vector>
#include <memory>
#include <math.h>
#include <Eigen/Dense>
#include <string>
#include <sstream>
#include <fstream>
#include <iostream>
#include "multidim.h"
#include "pymulti.h"
#include "extras.h"
using std_string = std::string;
#define str... | {"hexsha": "bc635c48a61aa2df00839ccd12f15868a4ad6f3d", "size": 8015, "ext": "cc", "lang": "C++", "max_stars_repo_path": "OLD/clstmimg.cc", "max_stars_repo_name": "gilteunchoi/clstm", "max_stars_repo_head_hexsha": "e87843c9f32345d899768d801a92871c210a8054", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8... |
import numpy as np
import pandas as pd
import glob
from functools import reduce
def load_df(path : str):
ext = path.split(".")[-1]
try:
if ext == "csv":
df = pd.read_csv(path)
elif ext == "xlsx":
df = pd.read_excel(path, engine="openpyxl")
if "Unnamed: 0" in ... | {"hexsha": "e73cfd9ff45ea1b53cc5c3dfee1f19c207ffc746", "size": 1889, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "bigbreadguy/Data_Sequence_Intepolator", "max_stars_repo_head_hexsha": "8381cd354d7f5b5424451672c9428971856d1579", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from zero_play.game_state import GameState
from zero_play.playout import Playout
from zero_play.tictactoe.state import TicTacToeState
class TakeOneTwiceGame(GameState):
""" Silly game for testing multiple moves in a turn.
The game starts with a numerical value, and each player makes two m... | {"hexsha": "b8815e1fcaa8ae70039fd2945bc0d601ff43f710", "size": 4125, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_playout.py", "max_stars_repo_name": "donkirkby/zero-play", "max_stars_repo_head_hexsha": "15e3afa950037cfd1f373ee4943cd8b42d4c82c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import csv
import os
import numpy
#return: dict, key = uniq id
#val: dict, key = column name, val = val
#example: dict: {2066053: {'affiliation': 'KAIST', 'name': 'myname'}}
def load_single_file(input_file, limit_keys=None):
with open(input_file, 'r', encoding='utf-8') as read_file:
reader = csv.reader(read_file)
... | {"hexsha": "ba3e13b7ce24c7a04b344f768b97e73a431ca4eb", "size": 1976, "ext": "py", "lang": "Python", "max_stars_repo_path": "loader.py", "max_stars_repo_name": "leeopop/2015-CS570-Project", "max_stars_repo_head_hexsha": "12cb0dd3e20d8a8861290a095ad64abd6f34d6f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from torch.utils.data import Dataset
from external.vqa.vqa import VQA
import re
import os
# import skimage.io as io
from PIL import Image
import numpy as np
import collections
import pickle
import torch
def _get_majority_ans(answers):
answers = list(map(lambda x: x['answer'], answers))
counter = collections.C... | {"hexsha": "4e85733979a2e5d5e62e38b00f2a8a6898c53cd2", "size": 5879, "ext": "py", "lang": "Python", "max_stars_repo_path": "student_code/vqa_dataset.py", "max_stars_repo_name": "Jmq14/VQA", "max_stars_repo_head_hexsha": "109a426eba8384c8e624f263ff6f52591dfc9153", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5... |
Describe RyanJoseph here.
OK, welllll...he lives in an Fountain Circle apartment with 3 other dudes and a Users/YawenChen girl.
| {"hexsha": "971edf23b10d9a800d4e07a2309b522b7415baae", "size": 128, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/RyanJoseph.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# This is a list of utility functions for dealing with image data in napari.
# todo: As those are not clEsperanto-specific, we may want to split them out
# and ship a separate package
import numpy as np
from napari import Viewer
from typing_extensions import Annotated
import napari
from napari.layers import Imag... | {"hexsha": "0297aea537f7173b341ae74e72865255802067b4", "size": 4062, "ext": "py", "lang": "Python", "max_stars_repo_path": "napari_pyclesperanto_assistant/_convert_to_numpy.py", "max_stars_repo_name": "kevinyamauchi/napari_pyclesperanto_assistant", "max_stars_repo_head_hexsha": "b068b1d89ee21c4448ab6a99c9fb2faabb127456... |
\section{Dataset description}
\subsection{Email example (Malware detection)}
The second scenario evaluated in this article is related to the detection and analysis of malicious emails and therefore the detection of compromised user accounts. We assume having multiple emails, a spam classifier, and a display showing the... | {"hexsha": "6f177d3bb9dbfde4bbc343d0d529191c138fb923", "size": 8399, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Documents/CHIIR2019/Appendix.tex", "max_stars_repo_name": "D3Mlab/visir", "max_stars_repo_head_hexsha": "cd1860984dee8d7aba368857e734ad11c14124c8", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import json
import numpy as np
import re
from collections import defaultdict as dd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import HashingVecto... | {"hexsha": "3319f4052fbd67f018f47df29c8c104458c068be", "size": 2645, "ext": "py", "lang": "Python", "max_stars_repo_path": "createfile.py", "max_stars_repo_name": "abigailyuan/LIDproj", "max_stars_repo_head_hexsha": "3e34c4d78b89c9513182ab064dc4b3858f59a1d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import copy
import faulthandler
import logging
import os
import platform
import sys
from typing import List
import hydra
import numpy as np
import pytorch_lightning as pl
import torch
from hydra.utils import instantiate
from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer
from pytorch_... | {"hexsha": "68eaa2511d30e95b33c6b3700598e8c8102f462d", "size": 7249, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "m-dml/hydra_template_project", "max_stars_repo_head_hexsha": "6186c548ad877232e4a4e0510ca81f49a59f69e2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
from __future__ import print_function
import numpy as np
import os
from .json_utils import write_to_json
class TweenParams(object):
"""A class to store tween parameters and make an output file"""
def __init__(
self,
coords=None,
duration=5,
loop=True,
filename=None):... | {"hexsha": "63ad2508e7058e346578f1421af98e3d5e6f318b", "size": 6550, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/firefly/data_reader/tween.py", "max_stars_repo_name": "agurvich/firefly", "max_stars_repo_head_hexsha": "60c8df088d7ab73071171e9efa6e235a6d072624", "max_stars_repo_licenses": ["MIT"], "max_sta... |
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