text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma remove_max_max_lemma:
shows "fst (foldl f (m, t) l) = Max (set (m # l))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fst (foldl f (m, t) l) = Max (SelectionSort_Functional.set (m # l))
[PROOF STEP]
proof (induct l arbitrary: m t rule: rev_induct)
[PROOF STATE]
proof (state)
goal (2 subgoals):... | {"llama_tokens": 1461, "file": "Selection_Heap_Sort_SelectionSort_Functional", "length": 10} |
(* Title: HOL/Analysis/Jordan_Curve.thy
Authors: LC Paulson, based on material from HOL Light
*)
section \<open>The Jordan Curve Theorem and Applications\<close>
theory Jordan_Curve
imports Arcwise_Connected Further_Topology
begin
subsection\<open>Janiszewski's theorem\<close>
lemma Janiszewski_weak:... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Analysis/Jordan_Curve.thy"} |
/**
* The MIT License (MIT)
*
* Copyright © 2018-2020 Ruben Van Boxem
*
* 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
* ... | {"hexsha": "141240df282a95ef0e7a923bf087ce37c0c61674", "size": 4142, "ext": "h++", "lang": "C++", "max_stars_repo_path": "css/grammar/background.h++", "max_stars_repo_name": "1094387012/SKUI", "max_stars_repo_head_hexsha": "c85dd7fb9d30ff15d5d6de184670cb47e46df6de", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from librosa.core import load
from librosa.feature import melspectrogram
import numpy as np
from torch import Tensor, log10
eps = np.finfo(float).eps
def segment_audio(audiopath, f_duration=5, max_frag=6):
total_audio, sr = load(audiopath, sr=44100)
middle_audio = total_audio[int(0.15*len(total_audio)): int... | {"hexsha": "c2e4000e87e4fd00e684cfc4cbfe6236dd168ba6", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "migperfer/Latin-music-genre-recognition", "max_stars_repo_head_hexsha": "6b7270543991d7e83a2020386c91c9cf6250b402", "max_stars_repo_licenses": ["MIT"], "max_stars... |
Require Import Coq.Program.Basics.
Require Import Coq.Logic.FunctionalExtensionality.
Require Import Coq.Program.Combinators.
Require Import Setoid.
Require Import ZArith.
Require Import Psatz.
Require Import FinProof.Common.
Require Import FinProof.CommonInstances.
Require Import FinProof.StateMonad2.
Require Import... | {"author": "Pruvendo", "repo": "depool_contract", "sha": "6afe23011d62f65921ac2493df691ab888ffeb95", "save_path": "github-repos/coq/Pruvendo-depool_contract", "path": "github-repos/coq/Pruvendo-depool_contract/depool_contract-6afe23011d62f65921ac2493df691ab888ffeb95/src/NewProofs/DePoolContract_completeRound.v"} |
CONFIG = Dict(
"target.project_dir" => (@__DIR__) |> dirname |> abspath,
"reporter.use_dataframe" => true,
)
ON_TRAVIS = get(ENV, "TRAVIS", "false") == "true"
if ON_TRAVIS
BENCHMARK_FILES = [
"dummy.jl",
"gdemo.jl",
"mvnormal.jl",
]
else
BENCHMARK_FILES = [
"dummy.j... | {"hexsha": "2550673eade578799b16120ede7e866a93a79d0c", "size": 797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmarks/benchmark_config.jl", "max_stars_repo_name": "andreasnoack/Turing.jl", "max_stars_repo_head_hexsha": "7cd1800905d51e9cd4f2db5072b96a79e9c6c74f", "max_stars_repo_licenses": ["MIT"], "max_s... |
from distutils.core import setup
from Cython.Build import cythonize
import numpy
import os
os.environ['CFLAGS'] = '-O3 -ffast-math -std=c99 -march=native'
setup(name='candid',
version='0.3.1',
py_modules=['candid'],
author='Antoine Merand',
author_email='antoine.merand@gmail.com',
url='h... | {"hexsha": "b7159f40f5608fe8ae58b46174ce8a6650905d6b", "size": 515, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "amerand/CANDID", "max_stars_repo_head_hexsha": "8b54dc66b1fe6c19f15974826cb018adf0fc1d79", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 7, "max_st... |
import numpy as np
import multiprocessing
import lazy_property
from mb_api.analytics import MinCollusionSolver, MinCollusionResult
class IteratedSolver:
max_best_sol_index = 2500
_solution_threshold = 0.01
_solver_cls = MinCollusionSolver
def __init__(self, data, deviations, metric, plausibility_con... | {"hexsha": "77a7d1ca06ddda237445fdf44a2428ddd532082a", "size": 4742, "ext": "py", "lang": "Python", "max_stars_repo_path": "mb_api/solvers.py", "max_stars_repo_name": "chassang/missing_bids", "max_stars_repo_head_hexsha": "eec0b1318c607244c60ce8355f45cd0e9c0b1d16", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import division
from __future__ import print_function
import argparse
import os
import shutil
import time
import warnings
import chainer
from chainer import optimizers
import numpy as np
import six
from lib import iproc
from lib import srcnn
from lib import utils
from lib.dataset_sampler import Datas... | {"hexsha": "81f529065696d79a1f92b0a8b1a77176dbc2ace8", "size": 8747, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "nkxingxh/waifu2x-chainer", "max_stars_repo_head_hexsha": "fa91111d6a3c985d45608942c11643cb884255e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 150, ... |
[STATEMENT]
lemma elementsAppend [simp]:
shows "elements (a @ b) = elements a @ elements b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. elements (a @ b) = elements a @ elements b
[PROOF STEP]
by (induct a) auto | {"llama_tokens": 80, "file": "SATSolverVerification_Trail", "length": 1} |
# Tutorial 2.7. Spin textures
# ===========================
#
# Physics background
# ------------------
# - Spin textures
# - Skyrmions
#
# Kwant features highlighted
# --------------------------
# - operators
# - plotting vector fields
sigma_0 = [1 0; 0 1]
sigma_x = [0 1; 1 0]
sigma_y = [0 -1im; 1im 0]
sigma_z = ... | {"hexsha": "1ec1b3a5d5bff2fdd2d54ef07037d593e5b79b5c", "size": 3050, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/magnetic_texture.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Kwant.jl-ed3f9b50-51cd-11e9-3ece-75fc8af922bb", "max_stars_repo_head_hexsha": "b44d1b78212576ea1337a88a47ffcd9... |
[STATEMENT]
lemma walk_2 [simp]: "v\<rightarrow>w \<Longrightarrow> walk [v,w]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. v \<rightarrow> w \<Longrightarrow> walk [v, w]
[PROOF STEP]
by (simp add: edges_are_in_V(2) walk.intros(3)) | {"llama_tokens": 96, "file": "Menger_Graph", "length": 1} |
section "Solution to Day 8 of AoC 2020"
theory day8
imports Main "HOL.Code_Numeral" string_utils list_natural_utils natural_utils list_utils HOL.Option
begin
text "This is a solution to the puzzle for day 8"
subsection "Input parsing"
datatype instr =
Nop integer
|Acc integer
|Jmp integer
type_synonym prog... | {"author": "lexbailey", "repo": "AOC2020_isabelle", "sha": "c08c347793814e9cc3e9d9638dd889d2ada2eb1d", "save_path": "github-repos/isabelle/lexbailey-AOC2020_isabelle", "path": "github-repos/isabelle/lexbailey-AOC2020_isabelle/AOC2020_isabelle-c08c347793814e9cc3e9d9638dd889d2ada2eb1d/day8.thy"} |
import dace
import numpy as np
N = dace.symbol('N')
@dace.program
def plus_1(X_in: dace.float32[N], num: dace.int32[1], X_out: dace.float32[N]):
@dace.map
def p1(i: _[0:num[0]]):
x_in << X_in[i]
x_out >> X_out[i]
x_out = x_in + 1
if __name__ == '__main__':
X = np.random.rand(10... | {"hexsha": "4c2b6a0e3ab8cb50ec63fbef7cff797fb3669792", "size": 680, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/map_indirect_array_test.py", "max_stars_repo_name": "gronerl/dace", "max_stars_repo_head_hexsha": "886e14cfec5df4aa28ff9a5e6c0fe8150570b8c7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import os
from itertools import product
import time
import torch
import random
import numpy as np
import datetime
# import pathlib
from glob import glob
import matplotlib.pyplot as plt
from torch.utils.data.dataset import Dataset
from torchvision import transforms
import torchvision
from torchvision.utils import save_i... | {"hexsha": "2b22febeeaced3e36141e8bbcf2093c84c2b045a", "size": 11369, "ext": "py", "lang": "Python", "max_stars_repo_path": "AEGEAN/utils.py", "max_stars_repo_name": "laurentperrinet/AEGeAN", "max_stars_repo_head_hexsha": "03e2c6c38c7ae77610d9796f1484abf0694b3a62", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
# coding: utf-8
# # Example: Exporting to $\LaTeX$
#
# The first code block contains the imports needed and defines a flag which determines whether the
# output $\LaTeX$ should be compiled.
# In[ ]:
# imports
import numpy as np
import subprocess
# Flag to compile output tables
compile_latex... | {"hexsha": "719023c90217f1159f0cb877b37e55c5e174f188", "size": 3699, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example-latex-output.py", "max_stars_repo_name": "ColdTeapot273K/python-for-econometrics-statistics-data-analysis", "max_stars_repo_head_hexsha": "f8aa79e400c68cfec23fb5a6a99b4ee1172e9218... |
from kivy.graphics import Color, Line, Rectangle, RoundedRectangle, Ellipse, PushMatrix, PopMatrix, Rotate
from kivy.core.image import Image
from kivy.core.window import Window
from kivy.uix.label import Label
from kivy.graphics.instructions import InstructionGroup
from util import *
import random
import numpy as np
... | {"hexsha": "8498716f70768f0d01f90c369b9326f6dbfbb041", "size": 17435, "ext": "py", "lang": "Python", "max_stars_repo_path": "util_interface.py", "max_stars_repo_name": "magj3k/DrawingVectorizer", "max_stars_repo_head_hexsha": "6cf9f98e042c6667ccfb5af9da2ec3e32e06c385", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import sm
import numpy
def qdot(q1,q2):
return numpy.dot(sm.quatPlus(q1),q2)
def qinv(q):
return sm.quatInv(q)
def qlog(q):
return sm.quat2AxisAngle(q)
def qexp(a):
return sm.axisAngle2quat(a) | {"hexsha": "30503a07462e9dffa0bcc2876745c7fd6fe37405", "size": 213, "ext": "py", "lang": "Python", "max_stars_repo_path": "aslam_nonparametric_estimation/bsplines/interp_rotation/quaternions/__init__.py", "max_stars_repo_name": "PushyamiKaveti/kalibr", "max_stars_repo_head_hexsha": "d8bdfc59ee666ef854012becc93571f96fe5... |
from tensorflow import keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Some code is taken from:
# https://www.kaggle.com/ashusma/training-rfcx-tensorflow-tpu-effnet-b2.
class WarmUpCosine(keras.optimizers.schedules.LearningRateSchedule):
def __init__(
self, learning_rat... | {"hexsha": "2b0eeac84137cf27bd90f0b080a8815e31845c99", "size": 3849, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/model/training_utils.py", "max_stars_repo_name": "avitrost/MAE-Crowd-Counting", "max_stars_repo_head_hexsha": "8f8fad851fc495e4112a0c708636105298a9bc0b", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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": "a3dcc2f93f0e5a2c2abbaca547f048b033d94d7a", "size": 1838, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/cv/gan/export.py", "max_stars_repo_name": "leelige/mindspore", "max_stars_repo_head_hexsha": "5199e05ba3888963473f2b07da3f7bca5b9ef6dc", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
from skimage.measure import compare_ssim
from skimage.color import rgb2gray
import numpy as np
import cv2
import skimage
def test_quality():
real_path = "places_rgb_test//"
#real_path = "resized//"
fake_path = "snapshots//default//images//result_final//"
ssim_scores = []
psnr_scores = ... | {"hexsha": "072fc383a04dc5295304cb655e1734aa951bf6df", "size": 1303, "ext": "py", "lang": "Python", "max_stars_repo_path": "quality_test.py", "max_stars_repo_name": "wangning-001/MANet", "max_stars_repo_head_hexsha": "5c09d1d57b482dc6a1d8d6e2fb6b57af491d6641", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, ... |
! Modifications for optimised local copy in c_redist_22 and c_redist_32
! (and their inverse routines):
! (c) The Numerical Algorithms Group (NAG) Ltd, 2012
! on behalf of EPSRC for the HECToR project
module redistribute
!
! Redistribute distributed (integer, real, complex or logical)
! (1, 2, 3, or 4) dimensional ar... | {"hexsha": "4efffb7d44d96242d84b303444c6077e0ef4449c", "size": 277127, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "utils/redistribute.f90", "max_stars_repo_name": "nicolaschristen/gs2_ndc_git", "max_stars_repo_head_hexsha": "ec0295f0726d7e2f372a02cadad0e375f7cd1c31", "max_stars_repo_licenses": ["MIT"], "ma... |
# a!/b!
factorial_ratio(a::I, b::I) where {I<:Integer} = gamma(a+1)/gamma(b+1) #binomial(a,b)*factorial(a-b)
function coulomb_analytical(k::I, γ::R, ℓ::I, r̃::R) where {I<:Integer, R<:Real}
e⁻ᵞʳ = exp(-γ*r̃)
# S = -gamma(k+ℓ+1)/r̃^(ℓ+1)*(e⁻ᵞʳ-1)/γ^(k+ℓ+1)
S = 2gamma(k+ℓ+1)*exp(-γ*r̃/2 -(ℓ+1)*log(r̃))*... | {"hexsha": "4a8654c6dff54fce7b172624d3e5e4d95a3863ea", "size": 556, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/analytical.jl", "max_stars_repo_name": "jagot/CoulombIntegrals.jl", "max_stars_repo_head_hexsha": "d3be310cd936ea820f46813969bf9dd67f79b633", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy
from dedupe import predicates
from .base import FieldType
class PriceType(FieldType):
_predicate_functions = [predicates.orderOfMagnitude,
predicates.wholeFieldPredicate,
predicates.roundTo1]
type = "Price"
@staticmethod
def compara... | {"hexsha": "4804a4e7b8fdaf68d642b336d4ab99a53925e5c4", "size": 535, "ext": "py", "lang": "Python", "max_stars_repo_path": "dedupe/variables/price.py", "max_stars_repo_name": "cilopez/dedupe", "max_stars_repo_head_hexsha": "d6567dc22be42174bbe80c43236acc1e845c1d91", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import imageio
import numpy as np
from generator import base_numbers
from generator.Generator import Generator
from ocr.ocr_detector import get_detector
from ocr.ocr_recognizer import get_recognizer
def load_model():
detector_model_h5 = "/Users/wdavis4/__pycache__/lecture0/sudoku_solver/solver/ocr_detector.h5"
... | {"hexsha": "916c3dad01bc399c2ab56b3b5e39878b0903078f", "size": 5415, "ext": "py", "lang": "Python", "max_stars_repo_path": "solver/utils.py", "max_stars_repo_name": "w1ll1am-davis/lecture0", "max_stars_repo_head_hexsha": "0c1ae34c69c9ff236457f52897af14839a4839e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import tactic
import data.real.basic
universe u
--local attribute [instance] classical.prop_decidable
noncomputable def absVal (a : ℝ) : ℝ := if a < 0 then -a else a
theorem triIneqInt (a : ℝ) (b : ℝ)
: (absVal(b - a) ≤ absVal(a) + absVal(b)) :=
begin
repeat {rw absVal},
split_ifs,
repeat {linarith},
end
def absVal... | {"author": "AlexKontorovich", "repo": "Spring2020Math492", "sha": "659108c5d864ff5c75b9b3b13b847aa5cff4348a", "save_path": "github-repos/lean/AlexKontorovich-Spring2020Math492", "path": "github-repos/lean/AlexKontorovich-Spring2020Math492/Spring2020Math492-659108c5d864ff5c75b9b3b13b847aa5cff4348a/triIneq_v2.lean"} |
"""
Project: dncnn
Author: khalil MEFTAH
Date: 2021-11-26
DnCNN: Deep Neural Convolutional Network for Image Denoising data loader implementation
"""
# Imports
import numpy as np
from PIL import Image, UnidentifiedImageError
from pathlib import Path
from random import randint
import torch
from torch.utils.data impo... | {"hexsha": "bb4b56a91264089659b503498902eaeb950fda5b", "size": 2015, "ext": "py", "lang": "Python", "max_stars_repo_path": "dncnn/loader.py", "max_stars_repo_name": "kTonpa/DnCNN", "max_stars_repo_head_hexsha": "aca7e07ccbe6b75bee7d4763958dade4a8eee609", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
> module Double.Predicates
> import Data.So
> %default total
> %access public export
> %auto_implicits on
* EQ
> |||
> data EQ : Double -> Double -> Type where
> MkEQ : {x : Double} -> {y : Double} -> So (x == y) -> EQ x y
* LT
> |||
> data LT : Double -> Double -> Type where
> MkLT : {x : Double} -> {y : Do... | {"hexsha": "5d40cba40cc48b6e0609d73c471c3c4b24829016", "size": 887, "ext": "lidr", "lang": "Idris", "max_stars_repo_path": "Double/Predicates.lidr", "max_stars_repo_name": "zenntenn/IdrisLibs", "max_stars_repo_head_hexsha": "a81c3674273a4658cd205e9bd1b6f95163cefc3e", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
import numpy as np
def most_frequent_class(y):
labels, counts = np.unique(y, return_counts=True)
return labels[np.argmax(counts)]
| {"hexsha": "c9eab26c05440a7ddf602b7428fd6dd76064fc80", "size": 140, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "Bellator95/gradient_boosting", "max_stars_repo_head_hexsha": "679851e107093e181aadc1106c02d4857eaf753e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
from matplotlib import pyplot as plt
from .basewidget import BaseWidget
from .utils import get_unit_colors
from .unitprobemap import plot_unit_probe_map
from .unitwaveformdensitymap import plot_unit_waveform_density_map
from .amplitudes import plot_amplitudes_timeseries
from .unitwaveforms import p... | {"hexsha": "c90db0a609b55696a41b7716159235d4d0cf03e3", "size": 3122, "ext": "py", "lang": "Python", "max_stars_repo_path": "spikeinterface/widgets/unitsummary.py", "max_stars_repo_name": "vncntprvst/spikeinterface", "max_stars_repo_head_hexsha": "dd5ae94f85fe5d9082b45321d2c96ba316eb4b77", "max_stars_repo_licenses": ["M... |
using Images, TestImages, Colors, ZernikePolynomials, FFTW
using NumberTheoreticTransforms
image_float = channelview(testimage("cameraman"))
image_int = map(x -> x.:i, image_float) .|> Int64
blur_float = evaluateZernike(LinRange(-41,41,512), [12, 4, 0], [1.0, -1.0, 2.0], index=:OSA)
blur_float ./= (sum(blur_float))
b... | {"hexsha": "82cf84acaef8d7c7fb2a932313e3c3e5c3c3261a", "size": 1267, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "doc/src/fnt/cameraman.jl", "max_stars_repo_name": "jakubwro/NumberTheoreticTransforms.jl", "max_stars_repo_head_hexsha": "5e933d4edcbb05926be2ede4e2c2105d381ebd7d", "max_stars_repo_licenses": ["MIT... |
"""This module contains the code for approximate solutions to the DCDP."""
import warnings
import numba as nb
import numpy as np
from respy.config import MAX_LOG_FLOAT
from respy.parallelization import parallelize_across_dense_dimensions
from respy.shared import calculate_expected_value_functions
from respy.shared im... | {"hexsha": "fb7977076f8c410647d9129d351ff4d90a729b2f", "size": 14502, "ext": "py", "lang": "Python", "max_stars_repo_path": "respy/interpolate.py", "max_stars_repo_name": "restudToolbox/respy", "max_stars_repo_head_hexsha": "19b9602c6f34f39034b00a88f36219ed3c4cfe5a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
subroutine near3 (xg, yg, zg, node)
!***********************************************************************
! $Id: near3.f,v 1.1 2006/05/17 15:23:22 zvd Exp $
!***********************************************************************
! Copyright, 1993, 2005, The Regents of the University of California.
! This... | {"hexsha": "ee047f3b72e97152a5cbd3b878ddf98735935e4f", "size": 3284, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/near3.f", "max_stars_repo_name": "lanl/PLUMECALC", "max_stars_repo_head_hexsha": "70a15749a1f21b8d4289d696a401b283265d49a9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3, ... |
Add LoadPath "D:\sfsol".
Require Export Types.
Module STLC.
Inductive ty : Type :=
| TBool : ty
| TArrow : ty -> ty -> ty.
Inductive tm : Type :=
| tvar : id -> tm
| tapp : tm -> tm -> tm
| tabs : id -> ty -> tm -> tm
| ttrue : tm
| tfalse : tm
| tif : tm -> tm -> tm -> tm.
Tactic Notation "t_cases"... | {"author": "mmalone", "repo": "sfsol", "sha": "5888f4532a1ec1ababa21bef39e25eb26279f0e4", "save_path": "github-repos/coq/mmalone-sfsol", "path": "github-repos/coq/mmalone-sfsol/sfsol-5888f4532a1ec1ababa21bef39e25eb26279f0e4/Stlc.v"} |
#-----------------------------------------------------------------------------------------------------------------------------
discret_data_normalized <- function(x, inter){
re <- rep((1/(length(inter)-1)),length(inter)-1)
for(i in 2:length(inter)){
re[i-1] <- (re[i-1] + length(which(x >= inter[i-1] & x <... | {"hexsha": "036f5bc283cfc45c22218e469fc6e8ff0f246cd2", "size": 2956, "ext": "r", "lang": "R", "max_stars_repo_path": "DySyn_synthetic/functions/discret_data.r", "max_stars_repo_name": "andregustavom/icdm21_paper", "max_stars_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_stars_repo_licenses": ["MIT... |
! <module_mc_wind_domain.for - A component of the City-scale
! Chemistry Transport Model EPISODE-CityChem>
!*****************************************************************************!
!*
!* EPISODE - An urban-scale air quality model
!* ==========================================
!* Copyright (C) 201... | {"hexsha": "376df3b7b1dc953760d5cd0d9e12bd11fc5695bb", "size": 9963, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "JPS_CITY_CHEM/citychem-1.3/preproc/mcwind/src/module_mc_wind_domain.for", "max_stars_repo_name": "mdhillmancmcl/TheWorldAvatar-CMCL-Fork", "max_stars_repo_head_hexsha": "011aee78c016b76762eaf511... |
[STATEMENT]
lemma (in pre_digraph) subgraphI_max_subgraph: "max_subgraph P x \<Longrightarrow> subgraph x G"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. max_subgraph P x \<Longrightarrow> subgraph x G
[PROOF STEP]
by (simp add: max_subgraph_def) | {"llama_tokens": 95, "file": "Graph_Theory_Digraph_Component", "length": 1} |
Require Export ProjectiveGeometry.Dev.matroid_properties.
Require Export ProjectiveGeometry.Dev.projective_space_rank_axioms.
(*****************************************************************************)
(** Rank space or higher properties **)
Section s_rankProperties_1.
Context `{M : RankProjectiveSpace}.
Contex... | {"author": "ProjectiveGeometry", "repo": "ProjectiveGeometry", "sha": "4f7f4e6c14580833c91fdef38d048259fb454b88", "save_path": "github-repos/coq/ProjectiveGeometry-ProjectiveGeometry", "path": "github-repos/coq/ProjectiveGeometry-ProjectiveGeometry/ProjectiveGeometry-4f7f4e6c14580833c91fdef38d048259fb454b88/Dev/rank_sp... |
'''
This file applies the GMM_IVQR method to the Fulton fish market data
'''
import numpy as np
import pandas as pd
from IVQR_GMM import IVQR_GMM
from math import log
from decimal import Decimal as Dec
from decimal import getcontext
getcontext().prec = 50
df = pd.read_csv('NYFishMarket.csv')
#print(df)... | {"hexsha": "53fb7fabb40d4dd07b5e4cfce5fcc35b07e4aaa2", "size": 1126, "ext": "py", "lang": "Python", "max_stars_repo_path": "Empirical_FishMarket.py", "max_stars_repo_name": "zizhe-xia/IVQR-GMM-Python", "max_stars_repo_head_hexsha": "4c6d5e94bee255317951c1d73745e9ec63d8b1f4", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import cv2
import numpy as np
from PIL import Image
import os, glob
# 画像が保存されているルートディレクトリのパス
root_dir = "../score"
# 画像名
types = [
"score_16_9",
]
# 画像データ用配列
X = []
# ラベルデータ用配列
Y = []
# 画像データごとにadd_sample()を呼び出し、X,Yの配列を返す関数
def make_sample(files):
global X, Y
X = []
Y = []
for cat, fname in file... | {"hexsha": "d9aef43c9d7e636bcd1676bca42f9d94bc509643", "size": 1171, "ext": "py", "lang": "Python", "max_stars_repo_path": "mask_maker/score_data_to_mask_16_9.py", "max_stars_repo_name": "hibibol/PriLog_web", "max_stars_repo_head_hexsha": "d15a8111424e3b3b5bd8d786ef8bb8949c9c8d90", "max_stars_repo_licenses": ["MIT"], "... |
function sys = probability(c)
% PROBABILITY Create basis for chance constraint
%
% EXAMPLE:
% The following example computes the largest value t such that the
% probability that a zero mean unit variance of a Gaussian variable is
% larger than t, is larger than 0.9
%
% w = sdpvar(1,1);
% t = sdpvar(1);
% Model = [... | {"author": "yalmip", "repo": "YALMIP", "sha": "f6d5a6d4222a4d722de30bffb43cae4b3e13b860", "save_path": "github-repos/MATLAB/yalmip-YALMIP", "path": "github-repos/MATLAB/yalmip-YALMIP/YALMIP-f6d5a6d4222a4d722de30bffb43cae4b3e13b860/extras/@probability/probability.m"} |
from airflow import DAG
# Operator imports
from airflow.operators.mssql_operator import MsSqlOperator
from airflow.operators.python_operator import PythonOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.hooks.mssql_hook import MsSqlHook
# Utils imports
from airflow.macros import datetim... | {"hexsha": "5a2a36abca82136e09c0af551e085f8d9b5bc2b5", "size": 3621, "ext": "py", "lang": "Python", "max_stars_repo_path": "dags/db_setup_dag.py", "max_stars_repo_name": "PFreitas91/b2b-ecommerce", "max_stars_repo_head_hexsha": "d8a693c45f4fc78b39146ab4af85e6b134bba5ae", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
------------------------------------------------------------------------
-- The Agda standard library
--
-- Type(s) used (only) when calling out to Haskell via the FFI
------------------------------------------------------------------------
{-# OPTIONS --without-K #-}
module Foreign.Haskell where
open import Level
... | {"hexsha": "6243ca498b84a0ac5aaac53b31595994acc7df6f", "size": 1493, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "agda-stdlib/src/Foreign/Haskell.agda", "max_stars_repo_name": "DreamLinuxer/popl21-artifact", "max_stars_repo_head_hexsha": "fb380f2e67dcb4a94f353dbaec91624fcb5b8933", "max_stars_repo_licenses": [... |
import os.path as op
import numpy as np
import pandas as pd
from sklearn.dummy import DummyRegressor
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import RidgeCV
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, cross_val_score
import mne
import config ... | {"hexsha": "eb89ef878a238469a5c5c85da1e601c92aeab67e", "size": 4270, "ext": "py", "lang": "Python", "max_stars_repo_path": "debug/compute_scores_models_nips.py", "max_stars_repo_name": "DavidSabbagh/meeg_power_regression", "max_stars_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_stars_repo_license... |
import onnx
import onnxruntime
import numpy as np
import argparse
import coloredlogs
import logging
import onnx.numpy_helper as onh
from onnx import helper
coloredlogs.install(level='INFO')
logging.basicConfig(level=logging.INFO)
class Quantizer():
def __init__(self, model):
self.fractional_part = 8
s... | {"hexsha": "a332ef9aacf5ccdd1f44472d783386a62f3f7ae1", "size": 4661, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/analysis/quantization.py", "max_stars_repo_name": "ptoupas/mmaction2", "max_stars_repo_head_hexsha": "1e1911295b63cffeba4c6f4809cb74d291c4505b", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import os
import time
import numpy as np
import torch
from ray import tune
from logging import getLogger
from torch.utils.tensorboard import SummaryWriter
from libcity.executor.abstract_executor import AbstractExecutor
from libcity.utils import get_evaluator, ensure_dir
from libcity.model import loss
from functools imp... | {"hexsha": "e1f1c2060914c8027a0ffc1bea303536f86df40d", "size": 18614, "ext": "py", "lang": "Python", "max_stars_repo_path": "libcity/executor/traffic_state_executor.py", "max_stars_repo_name": "moghadas76/test_bigcity", "max_stars_repo_head_hexsha": "607b9602c5b1113b23e1830455e174b0901d7558", "max_stars_repo_licenses":... |
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
return 1.0/(1+np.exp(-x))
"""
Predicts the outcome for the input x and the weights w
x_0 is 1 and w_0 is the bias
"""
def predict(x,w):
return sigmoid(np.sum([w[p]*x[p] for p in range(len(x))]))
"""
Determine the cost of the pred... | {"hexsha": "40bd8d7a5cbddc4d6e485c7047e94873e188f1a7", "size": 2091, "ext": "py", "lang": "Python", "max_stars_repo_path": "and.py", "max_stars_repo_name": "openmachinesblog/learning-sigmoid-neuron", "max_stars_repo_head_hexsha": "1daadc69a34627fe72d130da84722aca32c4b491", "max_stars_repo_licenses": ["MIT"], "max_stars... |
__all__ = ['getCurDir', 'getDatasetPath', 'getKaggleJson', 'getModelPath', 'r_mse', 'm_rmse', 'rf', 'rf_feat_importance', 'plot_fi', 'get_oob', 'normalize']
import fastbook
from fastbook import *
from fastai.tabular.all import Path
from sklearn.ensemble import RandomForestRegressor
from scipy.special import erfinv
d... | {"hexsha": "b4bb75cb4857c8f45eb49d64ca3a5d6b52550596", "size": 2205, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/func.py", "max_stars_repo_name": "JimBeam2019/Prudential-Life-Insurance-Assessment", "max_stars_repo_head_hexsha": "352d4d9c24a57888ef6bfd24c791d38cbc5b7b65", "max_stars_repo_licenses": ["MI... |
## @file pipeline.py
# @authir Andre N. Zabegaev <speench@gmail.com>
# pipeline for lane line finding on video
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
## Class for calibration adn correction of camera distortion
class CameraCorrector(object)... | {"hexsha": "23b69a9d602110b263265a9720b6203d005e6193", "size": 18173, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipeline.py", "max_stars_repo_name": "Spinch/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "618d19b4fc3394809c6d6bfa6872527e129d856f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from copy import deepcopy
import logging
import numpy as np
from wepy.reporter.reporter import FileReporter
from wepy.hdf5 import WepyHDF5
from wepy.walker import Walker, WalkerState
from wepy.util.json_top import json_top_atom_count
class WepyHDF5Reporter(FileReporter):
"""Reporter for generating an HDF5 format... | {"hexsha": "e1c2556c842b426c193501586e53947d906a887a", "size": 26112, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wepy/reporter/hdf5.py", "max_stars_repo_name": "edeustua/wepy", "max_stars_repo_head_hexsha": "f1a2ef5c8cc368d5602c9d683983b3af69a48ce2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import faculty
import drawFigure
from flask import Flask , render_template , redirect , request
app = Flask(__name__)
with open('feedback1.json') as file:
json_string = file.read()
documents1 = json.loads(json_string)
w... | {"hexsha": "e44819f48f746e273a6923dcca972807b385c9bd", "size": 10430, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "rishabh99-rc/Student-Feedback-Sentimental-Analysis", "max_stars_repo_head_hexsha": "fb1d228bae9ef6d5ff077f6555c18ac36983d8ff", "max_stars_repo_licenses": ["MIT"],... |
! { dg-do run }
!
! PR fortran/18918
!
! this_image(coarray) run test,
! expecially for num_images > 1
!
! Tested are values up to num_images == 8,
! higher values are OK, but not tested for
!
implicit none
integer :: a(1)[2:2, 3:4, 7:*]
integer :: b(:)[:, :,:]
allocatable :: b
integer :: i
if (this_image(A, dim=1) /=... | {"hexsha": "9ee4b153231663c53fd91323daf70ed389ca2637", "size": 5796, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "gcc-gcc-7_3_0-release/gcc/testsuite/gfortran.dg/coarray/this_image_1.f90", "max_stars_repo_name": "best08618/asylo", "max_stars_repo_head_hexsha": "5a520a9f5c461ede0f32acc284017b737a43898c", "ma... |
[STATEMENT]
lemma Ord_linear2:
assumes o: "Ord(k)" "Ord(l)"
obtains (lt) "k\<^bold>\<in>l" | (ge) "l \<le> k"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>k \<^bold>\<in> l \<Longrightarrow> thesis; l \<le> k \<Longrightarrow> thesis\<rbrakk> \<Longrightarrow> thesis
[PROOF STEP]
by (metis Ord_linear ... | {"llama_tokens": 143, "file": "HereditarilyFinite_Ordinal", "length": 1} |
using ComponentArrays
using DifferentialEquations
using UnPack: @unpack
tspan = (0.0, 20.0)
## Lorenz system
function lorenz!(D, u, p, t; f=0.0)
@unpack σ, ρ, β = p
@unpack x, y, z = u
D.x = σ*(y - x)
D.y = x*(ρ - z) - y - f
D.z = x*y - β*z
return nothing
end
function lorenz_jac!(D, u, ... | {"hexsha": "8568cd515e3f35c918e628207dc3d1bdc1e41780", "size": 2313, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/ODE_jac_example.jl", "max_stars_repo_name": "torfjelde/ComponentArrays.jl", "max_stars_repo_head_hexsha": "37e3e4d9d0c0ab12e672984188cf5bf23e10b8e7", "max_stars_repo_licenses": ["MIT"], "m... |
""" binning/bootstrap/reweighting """
import numpy as np
def binned(data, rwt=None, binsize=None, nbins=None):
"""bin data along axis=0"""
assert (binsize is None) or (nbins is None)
if binsize is not None:
nbins = data.shape[0] // binsize
if nbins is not None:
binsize = data.shape[0... | {"hexsha": "a9052dfd994c45559e173018151b3e7d8c11e654", "size": 2718, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/puck/statistics.py", "max_stars_repo_name": "krox/puck", "max_stars_repo_head_hexsha": "3760f697049790549d6bb5c9b134416d756c2d6d", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": ... |
[STATEMENT]
lemma hdomain_hunion [simp]: "hdomain (f \<squnion> g) = hdomain f \<squnion> hdomain g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. hdomain (f \<squnion> g) = hdomain f \<squnion> hdomain g
[PROOF STEP]
by (simp add: hdomain_def) | {"llama_tokens": 105, "file": "HereditarilyFinite_HF", "length": 1} |
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import os.path
import math
# user defined functions
from t2nnls import T2NNLS
from getT2LogMean import getT2LogMean
from getLambdaFromRMSE import getLambdaFromRMSE
from pltLcurve import pltLambdaRMS, pltLcurve
from pltRTD import pltT2dist
def... | {"hexsha": "f29f801a9f0288f0fdf3c40e187e3dcab3215f16", "size": 2974, "ext": "py", "lang": "Python", "max_stars_repo_path": ".ipynb_checkpoints/invRCA-checkpoint.py", "max_stars_repo_name": "pengyonghui/invRCA", "max_stars_repo_head_hexsha": "2247dcb1709dba442573b05c63c58d3b6a7f72c0", "max_stars_repo_licenses": ["MIT"],... |
import numpy
import math
class IntigerToStringIdConverter:
def convert(self,id):
ALPHABET = numpy.array(
["G", "k", "v", "s", "y", "4", "g", "3", "j", "b", "x", "r", "A", "o", "l", "6", "R", "f", "0", "F", "m",
"B", "U", "p", "D", "i", "t", "7", "8", "S", "L", "2", "w", "d", "Z", ... | {"hexsha": "320c113eb681c8b5d93669618df968bc8ec77f64", "size": 745, "ext": "py", "lang": "Python", "max_stars_repo_path": "CommonCode/intigerToStringIdConvertor.py", "max_stars_repo_name": "prodProject/WorkkerAndConsumerServer", "max_stars_repo_head_hexsha": "95496f026109279c9891e08af46040c7b9487c81", "max_stars_repo_l... |
using Ejemplo
using Test
@testset "Ejemplo.jl" begin
@test f(2, 1) == 7
@test f(2, 3) == 13
@test f(1, 3) == 11
end
@testset "Derivada de f" begin
@test ∂ₓf(124.24, 245.245) == 2
end | {"hexsha": "b7f21497db64a29d4e470541d041e23edde23453", "size": 200, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_f.jl", "max_stars_repo_name": "Erasmo98/Ejemplo.jl", "max_stars_repo_head_hexsha": "e4fc709506a4321880db4ca3fbfcd20569125a8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import time
import random
import datatable as dt
import pandas as pd
import numpy as np
def genom_multilat(n=2000, k=50, urdata="../../sample_data/mydata", refdata="", refmaf=""):
# Import Data (Reference / Your own parsed data)
print(" Import Reference Data... | {"hexsha": "bf8293b14a8a49826212263ee6d190599523efe9", "size": 3627, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/1.DV_Generator/dv_gen.py", "max_stars_repo_name": "hanlab-SNU/GenomicGPS", "max_stars_repo_head_hexsha": "adff68e3d6e04104d032768e3cca3b12d08ffb1d", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <mpi.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <unistd.h>
#include <gsl/gsl_rng.h>
#ifdef SUBFIND
#include "fof.h"
#include "allvars.h"
#include "proto.h"
#include "domain.h"
#include "subfind.h"
static struct id_li... | {"hexsha": "938b93e7d74b8d378222ef6fac0d25b1f5273ecd", "size": 26420, "ext": "c", "lang": "C", "max_stars_repo_path": "testing/icgen/random_verschillende_resoluties_N-GenIC/gadget3_64/subfind.c", "max_stars_repo_name": "egpbos/egp", "max_stars_repo_head_hexsha": "5e82c2de9e6884795b4ee89f2b15ed5dde70388f", "max_stars_re... |
/Users/remywang/metalift/txl/stng/stng_labeled_cloverleaf/stencil/kernel//viscosity_kernel.f90
/Users/remywang/metalift/txl/stng/stng_labeled_cloverleaf/stencil/kernel//update_halo_kernel.f90
/Users/remywang/metalift/txl/stng/stng_labeled_cloverleaf/stencil/kernel//field_summary_kernel.f90
/Users/remywang/metalift/txl/... | {"hexsha": "3075e87eacd06dd3ded58feb8ee45ce4e92aa836", "size": 5855, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "fp.f90", "max_stars_repo_name": "remysucre/sloth-fortran", "max_stars_repo_head_hexsha": "24e4e362135bd9a5e0ee0508af3d156d33880efd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
from styx_msgs.msg import TrafficLight
import cv2
import numpy as np
import tensorflow as tf
# Based on Team Vulture's guide on how to train a Traffic Light Detector & Classifier with TF Object Detection API:
class TLClassifierSite(object):
def __init__(self):
# Handling cuDNN issues when using the model:... | {"hexsha": "11e2ec9c4fa03f872befcdce3da3c385a229ea29", "size": 3306, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/tl_detector/light_classification/tl_classifier_site.py", "max_stars_repo_name": "SamuelHazak/SDCE_T3_P3_capstone", "max_stars_repo_head_hexsha": "fde856f924522f800162f73a06379643d29c11db",... |
[STATEMENT]
lemma has_derivative_at':
"(f has_derivative f') (at x)
\<longleftrightarrow> bounded_linear f' \<and>
(\<forall>e>0. \<exists>d>0. \<forall>x'. 0 < norm (x' - x) \<and> norm (x' - x) < d \<longrightarrow>
norm (f x' - f x - f'(x' - x)) / norm (x' - x) < e)"
[PROOF STATE]
proof (prove)
... | {"llama_tokens": 504, "file": null, "length": 2} |
"""
This code contains support code for formatting L1B products for the LP DAAC.
Authors: Philip G. Brodrick, philip.brodrick@jpl.nasa.gov
Nimrod Carmon, nimrod.carmon@jpl.nasa.gov
"""
import argparse
from netCDF4 import Dataset
from emit_utils import daac_converter
from emit_utils.file_checks import netcdf... | {"hexsha": "b2801200fbd74b2a4f5a4797c59724de18333d44", "size": 5752, "ext": "py", "lang": "Python", "max_stars_repo_path": "output_conversion.py", "max_stars_repo_name": "emit-sds/emit-sds-l1b", "max_stars_repo_head_hexsha": "be5307fe6821a043971becdd33609b4cf89b1974", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from config import alpha_dir, figure_dir, gamma_dir
from config import f_alpha, mae_offset, mse_offset, mae_v_gamma, mse_v_gamma
from config import width, height, pad_inches
from config import p_label, s_label, d_label
from config import colors, markers, linestyles, p_index, s_index, d_index
from config import line_wid... | {"hexsha": "dd89cc117d0ca6262360436abda11f377138f5b0", "size": 3804, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/double_robust_estimators/draw_gamma.py", "max_stars_repo_name": "xiaojiew1/surprise", "max_stars_repo_head_hexsha": "74d8f15d01b6a6e4e82852a0d35eb04c83bf4e9f", "max_stars_repo_licenses": ... |
import cv2
import numpy as np
import sys
def resize(dst,img):
width = img.shape[1]
height = img.shape[0]
dim = (width, height)
resized = cv2.resize(dst, dim, interpolation = cv2.INTER_AREA)
return resized
video = cv2.VideoCapture(0)
oceanVideo = cv2.VideoCapture("ocean.mp4")
success, ref_img = video.read()
flag ... | {"hexsha": "d79a08e115586f50f773cc3aaa15bff6dfe6251e", "size": 1569, "ext": "py", "lang": "Python", "max_stars_repo_path": "bg_removal/backgroundRemoval_video.py", "max_stars_repo_name": "shantanukaushik97/Background-Removal", "max_stars_repo_head_hexsha": "b462e87be32d70195640b688028c99ca1b57d42d", "max_stars_repo_lic... |
module puresaturation
use nonlinear_solvers
use thermopack_var, only: nc, get_active_thermo_model, thermo_model, &
base_eos_param, get_active_alt_eos
! use utilities, only: get_thread_index
implicit none
private
save
public :: PureSat, PureSatLine
contains
!-------------------------------------... | {"hexsha": "7c558dc62ee78e1cf146634d1fb568e8e5ea19ba", "size": 8824, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/puresaturation.f90", "max_stars_repo_name": "SINTEF/Thermopack", "max_stars_repo_head_hexsha": "63c0dc82fe6f88dd5612c53a35f7fbf405b4f3f6", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from __future__ import print_function, division, absolute_import
import numpy as np
from .sort_driver import RocRadixSortDriver
from timeit import default_timer as timer
def speed(compare_driver, nelem, dtype=np.intp):
data = np.random.randint(0, 0xffffffff, nelem).astype(dtype)
sorter = compare_driver()
... | {"hexsha": "7d0ba8de8d8ff895a35f130b59c0ab8b26fe032e", "size": 1210, "ext": "py", "lang": "Python", "max_stars_repo_path": "numba_roc_examples/radixsort/benchmark.py", "max_stars_repo_name": "numba/roc-examples", "max_stars_repo_head_hexsha": "752391b1f014df8e8f6919279ffa382d278f3b4b", "max_stars_repo_licenses": ["BSD-... |
(*
雪江明彦「代数学1群論入門」日本評論社
本文に沿って、coqにて展開する。
2011_03_19 Sectionを使い、群に共通の定義をまとめて定義した。
2011_03_20 Ltac でtacticsをまとめた。
*)
Require Import Setoid. (* rewrite at *)
Section Group.
Variable G : Set.
(* 演算子 *)
Variable App : G -> G -> G.
Infix "**" := App (at level 61, left assoc... | {"author": "elle-et-noire", "repo": "coq", "sha": "fd253f245131883ee55ff9f1824d4bb417b6e7b7", "save_path": "github-repos/coq/elle-et-noire-coq", "path": "github-repos/coq/elle-et-noire-coq/coq-fd253f245131883ee55ff9f1824d4bb417b6e7b7/algebra/yukie_group_theory.v"} |
# -*- coding: utf-8 -*-
## Minimal 3-node example of PyPSA linear optimal power flow
#
# Available as a Jupyter notebook at <https://pypsa.readthedocs.io/en/latest/examples/minimal_example_lopf.ipynb>.
import numpy as np
import pypsa
network = pypsa.Network()
# add three buses
for i in range(3):
network.add("Bu... | {"hexsha": "985fc2d081712f2565ea154e591da0d542c07157", "size": 1912, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/minimal_example_lopf.py", "max_stars_repo_name": "p-glaum/PyPSA", "max_stars_repo_head_hexsha": "a8cfdf1acd9b348828474ad0899afe2c77818159", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import IMLearn.learners.regressors.linear_regression
from IMLearn.learners.regressors import PolynomialFitting
from IMLearn.utils import split_train_test
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: str) -> p... | {"hexsha": "e7bdd97cb6b26b53d395ee57651d7135c60eac73", "size": 3260, "ext": "py", "lang": "Python", "max_stars_repo_path": "exercises/city_temperature_prediction.py", "max_stars_repo_name": "chaimgross7/IML.HUJI", "max_stars_repo_head_hexsha": "5e79c723c67663f5f7899a7250428eb2ffa8db98", "max_stars_repo_licenses": ["MIT... |
import argparse
from datasets import PhototourismDataset, NotreDameDataset
import numpy as np
import os
import pickle
def get_opts():
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', type=str, required=True,
help='root directory of dataset')
parser.add_argument(... | {"hexsha": "1d41775bfd0cae45c7bcd0621c9b59eef69fd196", "size": 2506, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepare_phototourism.py", "max_stars_repo_name": "AlphaPlusTT/nerf-w", "max_stars_repo_head_hexsha": "c56589df46b80077eb9e0bfb29b023490b0a7fa1", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma llist_all2_lSupI:
assumes "Complete_Partial_Order.chain (rel_prod (\<sqsubseteq>) (\<sqsubseteq>)) Y" "\<forall>(xs, ys)\<in>Y. llist_all2 P xs ys"
shows "llist_all2 P (lSup (fst ` Y)) (lSup (snd ` Y))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. llist_all2 P (lSup (fst ` Y)) (lSup (snd ` Y)... | {"llama_tokens": 12891, "file": "Coinductive_Coinductive_List", "length": 51} |
# -*- coding: utf-8 -*-
"""
Es 2
Il metodo senza pivot non funziona poiche abbiamo un elemento sulla diagonale di A1 nullo
"""
import funzioni_Sistemi_lineari as fz
import numpy as np
A1 = np.array([1,2,3,0,0,1,1,3,5], dtype=float).reshape((3,3))
b1 = np.array([6,1,9], dtype=float)
A2 = np.array([1,1,0,3,2,1... | {"hexsha": "cedcef3ff9f3065f0a17e63f4ade7d34345ca0cb", "size": 1198, "ext": "py", "lang": "Python", "max_stars_repo_path": "sistemi_lineari/esercizi/Test2.py", "max_stars_repo_name": "luigi-borriello00/Metodi_SIUMerici", "max_stars_repo_head_hexsha": "cf1407c0ad432a49a96dcd08303213e48723c57a", "max_stars_repo_licenses"... |
import os
import sys
import json
import torch
from parse import parse
import pickle as pkl
from gm_hmm.src.genHMM import load_model
from gm_hmm.src.utils import append_class, accuracy_fun, accuracy_fun_torch, divide, parse_, get_freer_gpu
from functools import partial
import time
import numpy as np
if __name__ == "__m... | {"hexsha": "cc52429801dd92512ce6e7ba34d09ccaec7bff8d", "size": 3023, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/compute_accuracy_class.py", "max_stars_repo_name": "FirstHandScientist/genhmm", "max_stars_repo_head_hexsha": "95954794a48c40486c9df4644a654c541866df4c", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma finfun_Ex_Ex: "finfun_Ex P = Ex (($) P)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finfun_Ex P = Ex (($) P)
[PROOF STEP]
unfolding finfun_Ex_def finfun_All_All
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<not> All (($) (Not \<circ>$ P))) = Ex (($) P)
[PROOF STEP]
by simp | {"llama_tokens": 143, "file": "FinFun_FinFun", "length": 2} |
abstract type DiagnosticsGroupParams end
"""
DiagnosticsGroup
Holds a set of diagnostics that share a collection interval, a filename
prefix, an output writer, an interpolation, and any extra parameters.
"""
mutable struct DiagnosticsGroup{DGP <: Union{Nothing, DiagnosticsGroupParams}}
name::String
init::... | {"hexsha": "ff82d7b70aab629acf834c19c737bb30c9e984f7", "size": 2091, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Diagnostics/groups.jl", "max_stars_repo_name": "mwarusz/CLIMA", "max_stars_repo_head_hexsha": "af1bb8e2865bca9df9cf97c9bc1540080169676c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
""" Implementation of spiking activation maps (SAM). Original paper: https://arxiv.org/pdf/2103.14441.pdf"""
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import cv2
from spikingjelly.clock_driven import neuron
import math
impo... | {"hexsha": "21bb34c1995fada843471d6bad6b55ae86b0fed8", "size": 5307, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/utils/sam.py", "max_stars_repo_name": "Barchid/snn-sod", "max_stars_repo_head_hexsha": "719508be981125432cdf61b4047c9750964eb9db", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import os
import sys
sys.path.insert(
0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
)
# External:
from sklearn import datasets
import copy
import math
import numpy as np
import pandas as pd
import random
# Arboreal:
from core.dataset import Metadata, Dataset
from core.arboreal_tree import Dec... | {"hexsha": "432f7757d5712a8d6f7ed9dbe0be4fa1654a252b", "size": 3447, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scratch_pandas.py", "max_stars_repo_name": "PietrOz/arboreal", "max_stars_repo_head_hexsha": "3fda601a9af21a11bef3284a11d0bffe804cb3cc", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
@testset "ch08_fastslam02345" begin
dt = 0.1
# environment
xlim = [-5.0, 5.0]
ylim = [-5.0, 5.0]
# id of landmark must start from 0 with 1 step
landmarks =
[Landmark([2.0, -3.0], 0), Landmark([3.0, 3.0], 1), Landmark([-4.0, 2.0], 2)]
envmap = Map()
push!(envmap, landmarks)
wo... | {"hexsha": "2f94b966ea94c7fda32ea1e23bd6e495b08eb72d", "size": 2643, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ch08_test.jl", "max_stars_repo_name": "soblin/JuliaProbo", "max_stars_repo_head_hexsha": "bb206e19dd350af7f82b90e7c5062e5a088eff2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
from functools import partial, partialmethod
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import gridspec
try:
from gatspy import periodic
except ImportError:
raise ImportError('Please, pip install gatspy')
from astropy.timeseries import LombScargle
from .utils ... | {"hexsha": "cda5b2bd058c25d9460162fb520c95a3bcac2b4e", "size": 19299, "ext": "py", "lang": "Python", "max_stars_repo_path": "vera/_periodograms.py", "max_stars_repo_name": "j-faria/vera", "max_stars_repo_head_hexsha": "96cbdb61c98c3527416611155b29a03a2bc66b15", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
||| An approach to intrinsically-typed STLC with types as terms.
|||
||| We use this razor to demonstrate succintly how Type universes are
||| used to separate descriptions of how types are formed and their
||| use to type values.
|||
||| Standard constructions are used to represent the language as an
||| EDSL, togethe... | {"hexsha": "397a9bf8e8e95c5dc4b4fc24dff0272bce162ed3", "size": 13628, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Razor/STLC.idr", "max_stars_repo_name": "border-patrol/pearly-razors", "max_stars_repo_head_hexsha": "bc2677dda9171f8043e6cfb62ef4153e92c15714", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"],... |
# coding=utf-8
# Copyright 2021 The init2winit Authors.
#
# 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 la... | {"hexsha": "6cdd0abdd0699576bfff98fecc85b7e5490ffd92", "size": 4806, "ext": "py", "lang": "Python", "max_stars_repo_path": "init2winit/dataset_lib/test_ogbg_molpcba.py", "max_stars_repo_name": "google/init2winit", "max_stars_repo_head_hexsha": "62ec9fd31bd7b38bb7c220f15d4187bf0706506d", "max_stars_repo_licenses": ["Apa... |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule libigc_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("libigc")
JLLWrappers.@generate_main_file("libigc", UUID("94295238-5935-5bd7-bb0f-b00942e9bdd5"))
end # module libigc_jll
| {"hexsha": "e22e4397475720caefc433bcb0a991079582fae8", "size": 302, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/libigc_jll.jl", "max_stars_repo_name": "troels/igc.jl", "max_stars_repo_head_hexsha": "546804a0c01e5e7ca3967337cbc7f9e300c5e8e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import os
from unittest.mock import patch
import numpy as np
import pytest
import xarray as xr
from pharedox import experiment
from pharedox import image_processing as ip
from pharedox import pio
class TestExperiment:
@pytest.fixture(scope="function")
def paired_imgs(self, shared_datadir):
return p... | {"hexsha": "5bcd5a710390a1bb61eaab028ba2759cc32b7878", "size": 1915, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_experiment.py", "max_stars_repo_name": "omarvaneer/pharynx_redox", "max_stars_repo_head_hexsha": "ffcd5733fd0823244f50590951e9af0bc9ae2518", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from matplotlib import pyplot as plt
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from protosc.pipeline import BasePipeElement
from pathlib import Path
class FourierFeatures(BasePipeElement):
def __init__(self, n_angular=8, n_spatial=7, cut_circle=True,
... | {"hexsha": "cf1f198e628983a99968058f9f4e0dfd79d966d6", "size": 5778, "ext": "py", "lang": "Python", "max_stars_repo_path": "protosc/feature_extraction/fourier_features.py", "max_stars_repo_name": "UtrechtUniversity/protosc", "max_stars_repo_head_hexsha": "cc78f25c0ffed8bbfb8b9edf7fac544fb9bfe2ca", "max_stars_repo_licen... |
import numpy as np
from Network import utils
class Input:
def __init__(self, n):
self.output = None
self.next_layer = n
def get_image(self, img):
self.output = np.array(img) / 255.0
def forward_pass(self,img):
self.get_image(img)
self.next_layer.forw... | {"hexsha": "1768e20446d0b0128e0edb0eafe8a0b1a41bf041", "size": 508, "ext": "py", "lang": "Python", "max_stars_repo_path": "CNN_from_scratch/Network/Input.py", "max_stars_repo_name": "pr0deep/ML-AI-from-scratch", "max_stars_repo_head_hexsha": "2d06056a7dc6cca9f06c87fb69a4ee3e3bec924f", "max_stars_repo_licenses": ["MIT"]... |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 25 13:50:13 2018
@author: shams
"""
# importing libraries
import numpy as np
import pandas as pd
from keras.preprocessing import sequence
from keras.models import load_model
from keras.layers import Dense, Input, LSTM, GRU, BatchNormalization
from keras.models import ... | {"hexsha": "52b84db10046a2da69c11b106e926ee41e0b0108", "size": 3494, "ext": "py", "lang": "Python", "max_stars_repo_path": "HPC_code/train_graham.py", "max_stars_repo_name": "nasim-shams/SlackTrack", "max_stars_repo_head_hexsha": "09d9d4522679ac2f95efc2d7653d7d1e432326b6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#include "UGCPopularity.hpp"
#include "ContentElement.hpp"
#include <boost/random/mersenne_twister.hpp>
#include <boost/random/uniform_01.hpp>
#include <boost/random/gamma_distribution.hpp>
#include "boost/random/uniform_real_distribution.hpp"
#include <boost/math/distributions/lognormal.hpp>
#include "boost/random/u... | {"hexsha": "e8735ef178714a9e6c78f033e9c9ab76d99069a6", "size": 6932, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/UGCPopularity.cpp", "max_stars_repo_name": "manuhalo/PLACeS", "max_stars_repo_head_hexsha": "1574a34a2a98468e72d072cc9d1f2b32fcee38f2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
"""
pp_prob_plot(sepp::SEPP)
Plot the probability plot for the point process.
See 4.1 in Li2020.
"""
function pp_prob_plot(sepp::SEPP)
s, p = pp_analysis(sepp)
id = layer(x = p, y = p, color = [color("red")], Geom.line, order = 2)
emp = layer(x = p, y = s, color = [color("black")], Geom.line, order... | {"hexsha": "cd14bd3c057af7db0beba6bb4353a9fe58a2a3c1", "size": 1501, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Plots/ValidationPlots.jl", "max_stars_repo_name": "adam-dvd/SEMPP.jl", "max_stars_repo_head_hexsha": "fb2c0f39b7deddd3864fcd768959d889e55d7606", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding: utf-8 -*-
"""
Created on Wed May 22 16:58:10 2019
@author: sgs4167
"""
import numpy as np
import cv2
import PIL.Image as Image
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
def contrast_brightness(image, c, b): #其中c为对比度，b为每个像素加上的值（调节亮度）
blank = np.zeros(image.shape, image.dt... | {"hexsha": "38af39aad1b98298953747dd7676e7187ca2f7c0", "size": 9561, "ext": "py", "lang": "Python", "max_stars_repo_path": "ai_detection/fire_detection.py", "max_stars_repo_name": "bzy880114/Safety_model_cap", "max_stars_repo_head_hexsha": "6914927a3cd8aecfb217d33d8ae480e4619a6e0a", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma integrable_weighted_\<theta>:
assumes "2 \<le> a" "a \<le> x"
shows "((\<lambda>t. \<theta> t / (t * ln t ^ 2)) integrable_on {a..x})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>t. \<theta> t / (t * (ln t)\<^sup>2)) integrable_on {a..x}
[PROOF STEP]
proof (cases "a < x")
[PROOF ... | {"llama_tokens": 1210, "file": "Prime_Number_Theorem_Prime_Counting_Functions", "length": 9} |
# codinf=utf-8
import numpy as np
import tensorflow as tf
from GNN.Sequencers.GraphSequencers import CompositeMultiGraphSequencer, CompositeSingleGraphSequencer
from GNN.composite_graph_class import CompositeGraphObject
from GNN.graph_class import GraphObject
################################################... | {"hexsha": "4d3055637e28a63125b67c8de687ff71c141481e", "size": 9202, "ext": "py", "lang": "Python", "max_stars_repo_path": "GNN/Sequencers/TransductiveGraphSequencers.py", "max_stars_repo_name": "NickDrake117/GNNkeras", "max_stars_repo_head_hexsha": "17fc756dcc3102c4e33d213cd83f4990659af003", "max_stars_repo_licenses":... |
import tensorflow.keras.backend as K
import numpy as np
#######################################################################################
def categorical_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0):
y_pred = K.constant(y_pred)
y_true = K.cast(y_true, y_pred.dtype)
if label_sm... | {"hexsha": "0669700d0c227b384570b1943c6c7166af2e230f", "size": 1800, "ext": "py", "lang": "Python", "max_stars_repo_path": "losses.py", "max_stars_repo_name": "xAlpharax/TensorFlowTools", "max_stars_repo_head_hexsha": "a03e4be067a8019383436180c7d1e3c3c44bd2a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
import sys
import numpy as np
from scipy.fft import rfftfreq, rfft, irfft
class KdVSolverBaseClass():
def __init__(self, t, x, delta, nSkip=1):
self.nSkip = nSkip
self.delta = delta
self.dt = t[1]-t[0]
self.t = t
self.x = x
self.k = rfftfreq(x.size,d=x[1]-x[0])*2*n... | {"hexsha": "97e48293aeea88661cc0daf1ad06205b12d233ce", "size": 3267, "ext": "py", "lang": "Python", "max_stars_repo_path": "main_KdV_generate_data.py", "max_stars_repo_name": "omelchert/IQOSeminarWeek2020", "max_stars_repo_head_hexsha": "8ecdd9cbbc56fabbfa62b13bff0b4c1634c5a5dd", "max_stars_repo_licenses": ["MIT"], "ma... |
module InfiniteOpt
# Import the necessary packages.
import JuMP
import MathOptInterface
import Distributions
import Random
import MutableArithmetics
const _MA = MutableArithmetics
const MOI = MathOptInterface
const MOIU = MOI.Utilities
const JuMPC = JuMP.Containers
# Import all of the datatpyes, methods, macros, and ... | {"hexsha": "a1759be7d7e356004251c3387543b079b49dd08d", "size": 4509, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/InfiniteOpt.jl", "max_stars_repo_name": "thongisto/InfiniteOpt.jl", "max_stars_repo_head_hexsha": "0f7537b642acb26c3ada5230e54fa96e074f337e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
## Jobs data import (Jinsung Yoon, 10/11/2017)
import numpy as np
from scipy.special import expit
import argparse
import pandas as pd
import initpath_alg
initpath_alg.init_sys_path()
import utilmlab
'''
Input: train_rate: 0.8
Outputs:
- Train_X, Test_X: Train and Test features
- Train_Y: Observable outcomes
- T... | {"hexsha": "a481c73791deb22e63a65af57639d5167fbdcd62", "size": 4738, "ext": "py", "lang": "Python", "max_stars_repo_path": "alg/ganite/data_preprocessing_ganite.py", "max_stars_repo_name": "loramf/mlforhealthlabpub", "max_stars_repo_head_hexsha": "aa5a42a4814cf69c8223f27c21324ee39d43c404", "max_stars_repo_licenses": ["... |
import cv2
import numpy as np
class TPerspectiveTransformer():
""" Perspective transformer class.
"""
# Constants ---------------------------------------------------------------
# Manually captured on straight_lines1.jpg
LEFT_BOTTOM = (193, 719)
LEFT_TOP = (595, 449)
RIGHT_TOP = (685, 449)... | {"hexsha": "9612119259d5a238313da81aedbf1f17e4faa582", "size": 1470, "ext": "py", "lang": "Python", "max_stars_repo_path": "perspective_transform.py", "max_stars_repo_name": "vernor1/carnd_advanced_lane_finding", "max_stars_repo_head_hexsha": "1e9d2fb7b845c647452542e9bb063781d76cd8ec", "max_stars_repo_licenses": ["Apac... |
import numpy as np
from scipy.ndimage import map_coordinates
import cv2
# Based on https://github.com/sunset1995/py360convert
class Equirec2Cube:
def __init__(self, equ_h, equ_w, face_w):
'''
equ_h: int, height of the equirectangular image
equ_w: int, width of the equirectangular image
... | {"hexsha": "16ba8129ba914b4d8a6f6f65c482776ea8760500", "size": 3826, "ext": "py", "lang": "Python", "max_stars_repo_path": "UniFuse/datasets/util.py", "max_stars_repo_name": "HalleyJiang/UniFuse-Unidirectional-Fusion", "max_stars_repo_head_hexsha": "27a4441fe3d3031d1c9f3eb2d72a3624407d19fc", "max_stars_repo_licenses": ... |
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