text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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%==============================================================================
% This code is part of the Matlab-based toolbox
% FAIR - Flexible Algorithms for Image Registration.
% For details see
% - https://github.com/C4IR and
% - http://www.siam.org/books/fa06/
%==================================================... | {"author": "C4IR", "repo": "FAIR.m", "sha": "975edebd37b833ae76696792870de5c05efcb9cb", "save_path": "github-repos/MATLAB/C4IR-FAIR.m", "path": "github-repos/MATLAB/C4IR-FAIR.m/FAIR.m-975edebd37b833ae76696792870de5c05efcb9cb/kernel/data/contents.m"} |
from __future__ import division, absolute_import, print_function
from .finders import Location
from .vision import best_convolution, grey_scale, find_edges
from .colour import rgb_to_hsv
from .ocr import Classifier
from .matchers import fuzzy_match
import numpy
from scipy.ndimage.measurements import (
label,
f... | {"hexsha": "4498d29482c07be21f8abfd95ee5781b81b3adcb", "size": 7174, "ext": "py", "lang": "Python", "max_stars_repo_path": "geist/visualfinders.py", "max_stars_repo_name": "ant1441/Geist", "max_stars_repo_head_hexsha": "c3f6cfc8d7b420e0d1836da30981b7d7563d775b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5,... |
#@ Not autoload
s_kochman_max := 10; # max s in H_s(BP;\pi_t(S))
t_kochman_max := 11; # max t in H_s(BP;\pi_t(S))
v_kochman_max := 10; # work mostly mod this power of 2
E_kochman := table():
| {"hexsha": "231db2c90d747b10d439287552275f993a8b5b54", "size": 197, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "lib/chromatic/kochman.mpl", "max_stars_repo_name": "NeilStrickland/maple_lib", "max_stars_repo_head_hexsha": "afdc262a183c56959a7c013e38a166824f7fc3d5", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#define BOOST_TEST_DYN_LINK
#include <canard/net/ofp/v13/message/switch_config.hpp>
#include <boost/test/unit_test.hpp>
#include <cstdint>
#include <vector>
#include <canard/net/ofp/v13/openflow.hpp>
#include <canard/net/ofp/v13/io/openflow.hpp>
#include "../../test_utility.hpp"
namespace of = canard::net::ofp;
names... | {"hexsha": "d1df190d03fda04245ab8d18d0c06160474ba79b", "size": 9611, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/v13/message/switch_config_test.cpp", "max_stars_repo_name": "amedama41/bulb", "max_stars_repo_head_hexsha": "2e9fd8a8c35cfc2be2ecf5f747f83cf36ffbbdbb", "max_stars_repo_licenses": ["BSL-1.0"], "... |
section \<open>Examples for Nameful WS1S Formulas\<close>
(*<*)
theory WS1S_Nameful_Examples
imports Formula_Derivatives.WS1S_Nameful Show.Show_Instances
begin
(*>*)
lift_definition x :: fo is "''x''" by simp
lift_definition y :: fo is "''y''" by simp
lift_definition z :: fo is "''z''" by simp
lift_definition X :: so... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Formula_Derivatives/Examples/WS1S_Nameful_Examples.thy"} |
#!/usr/bin/env python
"""
Title: Solving the Given Integral and Numerically calculating Stefan-Boltzmann Constant using Romberg Integration.
Solution to Problem Set 3, Problem 1
~ Arsh R. Nadkarni
To run:
python romberg_sb_3_1.py
- Can change the no. of points by changing N in romberg()
- Can change the tolerance b... | {"hexsha": "f8f8c160f9a72ef679aecef2b0ac734af607481f", "size": 2192, "ext": "py", "lang": "Python", "max_stars_repo_path": "Problem Sets/Problem Set 3/romberg_sb_3_1.py", "max_stars_repo_name": "astroarshn2000/PHYS305S20", "max_stars_repo_head_hexsha": "18f4ebf0a51ba62fba34672cf76bd119d1db6f1e", "max_stars_repo_license... |
subroutine SMESS (MACT, TEXT, IDAT, FDAT)
c Copyright (c) 1996 California Institute of Technology, Pasadena, CA.
c ALL RIGHTS RESERVED.
c Based on Government Sponsored Research NAS7-03001.
c>> 2009-09-27 SMESS Krogh Same as below, in another place.
c>> 2009-07-23 SMESS Krogh Changed ,1x to :1x in write to FMTF.... | {"hexsha": "b49758cf12b0454b1e0a699dd9296504f9a946ac", "size": 16773, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/MATH77/smess.f", "max_stars_repo_name": "jacobwilliams/math77", "max_stars_repo_head_hexsha": "b562d09e191e99eba8a5bedfec45acf7461203b1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import numpy as np
from inferelator.postprocessing.precision_recall import RankSummaryPR
from inferelator.postprocessing import (TARGET_COLUMN, REGULATOR_COLUMN, CONFIDENCE_COLUMN,
F1_COLUMN, PRECISION_COLUMN, RECALL_COLUMN)
import matplotlib.pyplot as plt
class RankSummaryF1... | {"hexsha": "930cc0f24759da615b5324d92c52e14cd6c44321", "size": 3640, "ext": "py", "lang": "Python", "max_stars_repo_path": "inferelator/postprocessing/f1_score.py", "max_stars_repo_name": "bgorissen/inferelator", "max_stars_repo_head_hexsha": "20474e35e79f00a3510da79bd61fed6d5ee220a3", "max_stars_repo_licenses": ["BSD-... |
# vim: set fileencoding=<utf-8> :
'''General utility functions for data read/writing/manipulation in PopPUNK'''
# universal
import os
import sys
# additional
import pickle
from collections import defaultdict
from tempfile import mkstemp
import numpy as np
import pandas as pd
import sharedmem
def storePickle(rlist, ql... | {"hexsha": "8dc92cf379c75e282cffd9d7fbe7cef4f114b327", "size": 10096, "ext": "py", "lang": "Python", "max_stars_repo_path": "PopPUNK/utils.py", "max_stars_repo_name": "bede/PopPUNK", "max_stars_repo_head_hexsha": "7b421aa39e1076429177a69b0a2a116d8cabeec6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
import numpy as np
from sklearn.metrics import precision_score, recall_score, accuracy_score
import dataloader4ml100kIndexs
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch
from torch import nn
import sys
class embedding_CNN( nn.Module ):
def __init__( self, n_user_features, n_... | {"hexsha": "b49252e2861165829aee2b1a620321ab30da5a86", "size": 3839, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepRec1/CNN_rec.py", "max_stars_repo_name": "meannoharm/movie_recommend", "max_stars_repo_head_hexsha": "aa1b82c82d3e66db62e50568414c5b4a1cfa92b6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import litebird_sim as lbs
import matplotlib.pyplot as plt
import numpy as np
import astropy
import healpy
import logging as log
import os
import inspect
from modules import utils, objects, scanningstrategy as ss
import astropy.time
import astropy.units as u
from astropy.coordinates import (
ICRS,
get_body_bary... | {"hexsha": "96e4948c82467b5b4e688da81a7a2b0d4469ef2c", "size": 3093, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/main.py", "max_stars_repo_name": "lorycontixd/LiteBIRD-Simulation", "max_stars_repo_head_hexsha": "4dbf9273bfebccf053a8d7db9fd1e755d3b98b5d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
## Task 20 - Motor Control
### Introduction to modeling and simulation of human movement
https://github.com/BMClab/bmc/blob/master/courses/ModSim2018.md
Desiree Miraldo
* Task (for Lecture 20):
Solve problemas 3 and 4 of the notebook [Optimization (Marcos Duarte)](http://nbviewer.jupyter.org/github/BMClab/bmc/blob/m... | {"hexsha": "0748941e041e6324a7f5e2ec80b7a6331d8258b3", "size": 81981, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "courses/modsim2018/tasks/Desiree/Task20_MotorControl.ipynb", "max_stars_repo_name": "desireemiraldo/bmc", "max_stars_repo_head_hexsha": "5e50d806ea2e2d7494035ba120c3cd3a620a156a", "m... |
from collections import Counter
from datetime import date, timedelta
from typing import Annotated, Callable, Optional, Sequence, TypeVar
import matplotlib as mpl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn
from scipy.stats import poisson
from... | {"hexsha": "80a1e059461a8120c76601be3ae2bb555453ddd1", "size": 9671, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "braedynl/DasCrazy", "max_stars_repo_head_hexsha": "02a3e41631929eaf402116d25299ec252f6fee2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars... |
import datetime
import pandas
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from params import Params as param
print ("Enter the model_path_values name:")
model_path = raw_input()
print ("Enter the datetime in YYYYMMDD_HH_MM format:")
folder_name = raw_input()
start_date = datetime.datetim... | {"hexsha": "fcd74eec4326a17996eb323a1b25a113abd58899", "size": 3282, "ext": "py", "lang": "Python", "max_stars_repo_path": "STResNet/plotting_results.py", "max_stars_repo_name": "vtsuperdarn/deep_leaning_on_GSP_TEC", "max_stars_repo_head_hexsha": "f5989d1742be9c02edbcab37433f468cb2c5f771", "max_stars_repo_licenses": ["... |
import numpy as np
def table_check(DataTable, print_statement=True):
data_list = DataTable.columns.values
if 'Order' not in data_list:
raise ValueError("Data Table does not contain the required 'Order' column")
if DataTable.Order.isnull().values.any() == True:
raise ValueError("Order colu... | {"hexsha": "76903630ee884a4c8bf5149fe4fd85fe65a5e35a", "size": 1455, "ext": "py", "lang": "Python", "max_stars_repo_path": "qcrsc/table_check.py", "max_stars_repo_name": "KevinMMendez/qcrsc", "max_stars_repo_head_hexsha": "15352db3d2414935c02712c72b6b10a6a9628dc5", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
[STATEMENT]
lemma DynProcStaticSpec:
assumes adapt: "P \<subseteq> {s. s \<in> S \<and> (\<exists>Z. init s \<in> P' Z \<and>
(\<forall>\<tau>. \<tau> \<in> Q' Z \<longrightarrow> return s \<tau> \<in> R s \<tau>) \<and>
(\<forall>\<tau>. \<tau> \<in> A' Z \<long... | {"llama_tokens": 2191, "file": "Simpl_HoareTotal", "length": 15} |
#
# Author : Marcos Teixeira
# SkyNet is watching you
#
# common imports
import numpy as np
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score
import matplotlib.pyplot as plt
import lightgbm as lgb
d... | {"hexsha": "cc1d95b06d3d67a5a337323a08a79e4f85cda60d", "size": 4030, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer.py", "max_stars_repo_name": "marcostx/fraud_detection", "max_stars_repo_head_hexsha": "c83da57f9b406844152b8a4fce43c68bcbda247a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import fft
def GCD(A, B):
""" Greatest common divisor """
while B:
A, B = B, A % B
return A
def LCM(A, B):
""" Lowest common denominator """
return A * B / GCD(A, B)
Fs = 1200 # Sample frequency
f1 = 130 # Freque... | {"hexsha": "5b87c340264745db071500223292949e310f2508", "size": 911, "ext": "py", "lang": "Python", "max_stars_repo_path": "01_task/02_subtask.py", "max_stars_repo_name": "SKantar/SignalProcessing", "max_stars_repo_head_hexsha": "c8e5e9a45c92e1d337086b60bf7eed131756dcaf", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from numpy import arcsin, exp
def _comp_point_coordinate(self):
"""Compute the point coordinates needed to plot the Slot.
Parameters
----------
self : SlotW27
A SlotW27 object
Returns
-------
point_dict: dict
A dict of the slot point coordinates
"""
Rbo = self.get... | {"hexsha": "967a2f3a1107040316170b6a6e2b8369f6449c6e", "size": 1353, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Methods/Slot/SlotW27/_comp_point_coordinate.py", "max_stars_repo_name": "IrakozeFD/pyleecan", "max_stars_repo_head_hexsha": "5a93bd98755d880176c1ce8ac90f36ca1b907055", "max_stars_repo_lic... |
[STATEMENT]
lemma sim_valI[intro]:
"(\<And>u. u \<in> worlds M \<Longrightarrow> valuation M u = valuation M' (f u))
\<Longrightarrow> sim_val M M' f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>u. u \<in> worlds M \<Longrightarrow> valuation M u = valuation M' (f u)) \<Longrightarrow> sim_val M M' f
... | {"llama_tokens": 227, "file": "KBPs_Kripke", "length": 2} |
SUBROUTINE MB03CZ( A, LDA, B, LDB, D, LDD, CO1, SI1, CO2, SI2,
$ CO3, SI3 )
C
C SLICOT RELEASE 5.5.
C
C Copyright (c) 2002-2012 NICONET e.V.
C
C PURPOSE
C
C To compute unitary matrices Q1, Q2, and Q3 for a complex 2-by-2
C regular pencil aAB - bD, with A, B, D ... | {"hexsha": "3d3f837a07b5e551c45c57483f4d95137494d3ce", "size": 4551, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "External/SLICOT/MB03CZ.f", "max_stars_repo_name": "bgin/MissileSimulation", "max_stars_repo_head_hexsha": "90adcbf1c049daafb939f3fe9f9dfe792f26d5df", "max_stars_repo_licenses": ["MIT"], "max_stars... |
%!TEX root = ../Thesis.tex
\chapter{Preface}
This bachelor's project was prepared at the department of Applied Mathematics and Computer Science at the Technical University of Denmark in fulfillment of the requirements for acquiring a bachelor's degree in Physics and Nanotechnology.
\vfill
{
\centering
\thesisloca... | {"hexsha": "f45440744a7bcf2bd1689f8453c216799f2d7ada", "size": 461, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/frontmatter/Preface.tex", "max_stars_repo_name": "GandalfSaxe/leto", "max_stars_repo_head_hexsha": "d27c2a4a04518f4230a80ce83d0252257247a512", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from builtins import str
from builtins import zip
from builtins import range
from sys import stdout
import multiprocessing as mp
import numpy as np
from vsm.split import split_documents
from vsm.model.ldafuncti... | {"hexsha": "20a26adb40873c85b4c2534d8d52cd65e53111c0", "size": 16063, "ext": "py", "lang": "Python", "max_stars_repo_path": "vsm/model/ldacgsmulti.py", "max_stars_repo_name": "inpho/vsm", "max_stars_repo_head_hexsha": "d5fc930ccc95f275e10e151c8f05db2c05aba01f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31,... |
import time
import atexit
from io import BytesIO
from threading import Thread, Event, Lock
from collections import namedtuple, deque
import cv2
import imutils
from pidevices.sensors import Sensor
import numpy as np
# Dimensions tuple
Dims = namedtuple('Dims', ['width', 'height'])
# Camera data tuple
CameraData = n... | {"hexsha": "2e6b2fbbed6929d33b6c8c5dfa77e685543ff8da", "size": 4523, "ext": "py", "lang": "Python", "max_stars_repo_path": "pidevices/sensors/cv2_camera.py", "max_stars_repo_name": "robotics-4-all/tektrain-robot-sw", "max_stars_repo_head_hexsha": "3a420f1c47e1cdcca76361c0a921a678f31e1ec1", "max_stars_repo_licenses": ["... |
import numpy as np
import torch
import shutil
import matplotlib.pyplot as plt
import os
from PIL import Image
def angles_to_matrix(angles):
"""Compute the rotation matrix from euler angles for a mini-batch.
This is a PyTorch implementation computed by myself for calculating
R = Rz(inp) Rx(ele - pi/2) Rz(-a... | {"hexsha": "8ed9cb76dd0e9cc0513810cf80e40f38e5df316e", "size": 2455, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/rotation_eval.py", "max_stars_repo_name": "brian220/Sketch2PointCloud", "max_stars_repo_head_hexsha": "65ce7b9921a7bef9f21ca2fc72820ca7f6fd6648", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma powrat_mult_pos_neg:
assumes "0 < x" and "0 < r" and "s < 0"
shows "x pow\<^sub>\<rat> (r * s) = (x pow\<^sub>\<rat> r) pow\<^sub>\<rat> s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x pow\<^sub>\<rat> (r * s) = (x pow\<^sub>\<rat> r) pow\<^sub>\<rat> s
[PROOF STEP]
proof -
[PROOF STATE]
pr... | {"llama_tokens": 828, "file": "Real_Power_RatPower", "length": 8} |
#Python 3.x
import face_recognition
import numpy as np
import cv2
from settings import *
def numberOfMatches(faceEncoding, knownFaceEncodings):
"""
Compare face encoding to all known face encodings for this person and
find the close matches and return their count
"""
#Get the distances from this ... | {"hexsha": "9385f7aff54054a648513b33fcbcf847aebb016d", "size": 1469, "ext": "py", "lang": "Python", "max_stars_repo_path": "facialRecognition.py", "max_stars_repo_name": "evvanErb/facialRecognitionCommandLineProgram", "max_stars_repo_head_hexsha": "2a4404626795bf835883bda89bc5adc106f794ac", "max_stars_repo_licenses": [... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn... | {"hexsha": "4fd6d443357043e731483de7671dbfd88d25f2bc", "size": 5687, "ext": "py", "lang": "Python", "max_stars_repo_path": "Part_1__Data_Preprocessing/data_preProcessing.py", "max_stars_repo_name": "nandanvasudevan/MachineLearning_A_Z", "max_stars_repo_head_hexsha": "ad5e36df404c9911caa835f0b7850ceafffa0186", "max_star... |
program main
implicit none
call sub_main('./input/HC_1.dat','ene_HC_1_03-06.dat',3,6)
call sub_main('./input/HC_2.dat','ene_HC_2_06-07.dat',6,7)
call sub_main('./input/HC_3.dat','ene_HC_3_07-08.dat',7,8)
call sub_main('./input/K_1.dat','ene_K_1_07-08.dat',7,8)
call sub_main('./input/K_2.dat','ene_K_2_10-12.... | {"hexsha": "e8ac1503492956461e2ba38c6f5ac5e42aa91568", "size": 2460, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/main_exp.f90", "max_stars_repo_name": "ueda1984/SQCF90", "max_stars_repo_head_hexsha": "6e47e2801089c9129681f680bdfa18812c76d19e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# ------------ Reading parameters and choices ------------ #
read_parameter(ctx::K, addr::Address) where K <: BackpropagationContext = read_parameter(ctx, ctx.params, addr)
read_parameter(ctx::K, params::Store, addr::Address) where K <: BackpropagationContext = getindex(ctx.initial_params, addr)
Zygote.@adjoint funct... | {"hexsha": "4576095e7b362045b2a62b07f4c23f9abb18e523", "size": 12147, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pipelines/gfi/backpropagate.jl", "max_stars_repo_name": "femtomc/Jaynes.jl", "max_stars_repo_head_hexsha": "ab6e3993713fbdea2d732ef7dc217a1f79b54675", "max_stars_repo_licenses": ["Apache-2.0"]... |
import sys
from pandas.errors import EmptyDataError
from CartDecisionTreeClassifier import CartDecisionTreeClassifier
import numpy as np
import pandas as pd
import argparse
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.model_selection import train_test_split
from sklearn import metrics
from skle... | {"hexsha": "e2a664af480fbf649e383e1d32b44e101bd6a6c1", "size": 5351, "ext": "py", "lang": "Python", "max_stars_repo_path": "DecisionTree.py", "max_stars_repo_name": "ian0/decision-tree-sfrom-scratch", "max_stars_repo_head_hexsha": "bc771bb01c961acb6e635b73d2d51da5ee4cbe1c", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <tuple>
#include <Eigen/Core>
#include <iostream>
#include <npe.h>
npe_function(mutate_sparse_matrix)
npe_arg(a, sparse_double, sparse_float)
npe_begin_code()
a.coeffRef(0, 0) = 2.0;
return npe::move(a);
npe_end_code()
| {"hexsha": "6779b4ec95673762223738249e97367224845b90", "size": 233, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/mutate_sparse_matrix.cpp", "max_stars_repo_name": "ivansipiran/numpyeigen", "max_stars_repo_head_hexsha": "48c6ccc5724572c6107240fa472d3c1ff04d679d", "max_stars_repo_licenses": ["MIT"], "max_st... |
function [ structout ] = bz_CollapseStruct( structin,dim,combine,NEST )
%structout = CollapseStruct( structin,dim,combine,NEST ) Combines elements in a
%structure array
%
%INPUT
% structin struct(N).fields structure array with N elements where each
% of the N elements has the same fields and field st... | {"author": "buzsakilab", "repo": "buzcode", "sha": "2d700a38b3c2a860ad1333be90f14d7a37a72815", "save_path": "github-repos/MATLAB/buzsakilab-buzcode", "path": "github-repos/MATLAB/buzsakilab-buzcode/buzcode-2d700a38b3c2a860ad1333be90f14d7a37a72815/utilities/bz_CollapseStruct.m"} |
import pickle
import urllib.request
from datetime import datetime as dt
from glob import glob as ls
from os import path, remove
import numpy as np
from astropy.time import Time
from bs4 import BeautifulSoup
from scipy.interpolate import interp1d
from scipy.io import readsav
def iris_get_response(date=dt.strftime(dt.... | {"hexsha": "cb37a1b70eb7a5aa31661e8b2d6ef6ef511a02e9", "size": 13413, "ext": "py", "lang": "Python", "max_stars_repo_path": "irispreppy/radcal/iris_get_response.py", "max_stars_repo_name": "OfAaron3/irispreppy", "max_stars_repo_head_hexsha": "a826c6cffa4d7ac76f28208dc71befc8601424d2", "max_stars_repo_licenses": ["MIT"]... |
using PlanarMaps
using Test
# write your own tests here
N = NeighborCycle([3,7,9,4])
@assert length(N) == 4
@assert over(N,9,3) == 7
@assert rotate(N,9) == [9,4,3,7]
@assert NeighborCycle([1,4,2,7]) == NeighborCycle([2,7,1,4])
@assert NeighborCycle([1,4,2,7]) ≠ NeighborCycle([1,4,7,2])
@assert Face([2,1,5,4,2,3]) ==... | {"hexsha": "f14102ad45e28134f951741251840c953281ff89", "size": 1501, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/PlanarMaps.jl-291fd964-e446-5d75-9412-e8e0eb420fa7", "max_stars_repo_head_hexsha": "57da45205da61f1a8552a97be82bbe4545dcb21... |
(**************************************************
* Author: Ana Nora Evans (ananevans@virginia.edu)
**************************************************)
Require Import Coq.Arith.Arith.
Require Import Coq.ZArith.ZArith.
Require Import Coq.omega.Omega.
Require Coq.Bool.Bool.
Require Import Coq.Lists.List. Import List... | {"author": "secure-compilation", "repo": "when-good-components-go-bad", "sha": "7bef0fa18780f1e9699abcdadd61e15bf3aba95d", "save_path": "github-repos/coq/secure-compilation-when-good-components-go-bad", "path": "github-repos/coq/secure-compilation-when-good-components-go-bad/when-good-components-go-bad-7bef0fa18780f1e9... |
using Test
import ArchGDAL; const AG = ArchGDAL
import GeoFormatTypes; const GFT = GeoFormatTypes
# Tests high level convert methods
@testset "convert point format" begin
point = AG.createpoint(100, 70)
json = convert(GFT.GeoJSON, point)
kml = convert(GFT.KML, point)
gml = convert(GFT.GML, point)
... | {"hexsha": "de576cc71183559357544b8afaff12fe5877d1ae", "size": 1145, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_convert.jl", "max_stars_repo_name": "pritamd47/ArchGDAL.jl", "max_stars_repo_head_hexsha": "47cc5564d23a7cde0591311908270444bc60643a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import torch
import json
from torch import nn
from torch import optim
import numpy as np
import torch.nn.functional as F
import ann_utils as au
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.modeling.rpn import rpn
from maskrcnn_be... | {"hexsha": "61073ea2ef5c15363a6053ec06a0edc91f575c9d", "size": 6775, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/mrcnn.py", "max_stars_repo_name": "ElementAI/wise_ils", "max_stars_repo_head_hexsha": "f0897b4aae042c2085eeb13b38c2a26b43cb8eaf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
# coding: utf-8
# # Artucus Spectra
#
# Infrared Arcturus Atlas (Hinkle+ 1995)
# These are currently not telluric corrected but you can find some that are
# Resolving power of 100000,
# In[1]:
import glob
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import string
import numpy... | {"hexsha": "a3cbf70bf582b59de1c13c0042fc9b2f0fd81ff0", "size": 1128, "ext": "py", "lang": "Python", "max_stars_repo_path": "Artucus Notebook.py", "max_stars_repo_name": "jason-neal/phoenix_notes", "max_stars_repo_head_hexsha": "210891542ab942969b123755756aae957979056e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#' Get data for particular ORCID's
#'
#' @export
#'
#' @param orcid (character) A single Orcid identifier, of the
#' form XXXX-XXXX-XXXX-XXXX
#' @param ... Curl options passed on to [crul::HttpClient()]
#'
#' @return A named list of results - from a call to [orcid_person()]
#'
#' @examples \dontrun{
#' res <- orcid_id... | {"hexsha": "60bff7d883a95e727525e326790c56f78812693a", "size": 1078, "ext": "r", "lang": "R", "max_stars_repo_path": "R/orcid_id.r", "max_stars_repo_name": "awconway/rorcid", "max_stars_repo_head_hexsha": "098a02074546a70a143fbce884043b3edcfdbd5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 107, "max_stars_... |
[STATEMENT]
lemma in_progress_med_progress:
"x \<in> {xnx, xny, xgnx, xgny, xsk, xEnd}
\<Longrightarrow> in_progress (med_progress r R) x \<longleftrightarrow> in_progress (r R) x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> {xnx, xny, xgnx, xgny, xsk, xEnd} \<Longrightarrow> in_progress (med_progre... | {"llama_tokens": 177, "file": "Key_Agreement_Strong_Adversaries_sklvl1", "length": 1} |
Our purpose is to raise awareness around the nuances and politics of bi, pan, omni, pomo, and nonmonosexual or otherwise unlabeled, fluid, or flexible sexualities and how they intersect with our many other identities. In doing so, we hope to diminish stereotypes and make the B in LGBT more visible.
What we do:
Educ... | {"hexsha": "c4b3f23dbfce59dd13d33f7be5b12b8c11050a35", "size": 1472, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/The_Bivisibility_Project.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max... |
(*
* Copyright 2020, Data61, CSIRO (ABN 41 687 119 230)
*
* SPDX-License-Identifier: GPL-2.0-only
*)
(*
Results about CNode Invocations, particularly the
recursive revoke and delete operations.
*)
theory CNodeInv_R
imports Ipc_R Invocations_R
begin
unbundle l4v_word_context
context begin interpretation Arch... | {"author": "seL4", "repo": "l4v", "sha": "9ba34e269008732d4f89fb7a7e32337ffdd09ff9", "save_path": "github-repos/isabelle/seL4-l4v", "path": "github-repos/isabelle/seL4-l4v/l4v-9ba34e269008732d4f89fb7a7e32337ffdd09ff9/proof/refine/RISCV64/CNodeInv_R.thy"} |
[STATEMENT]
lemma divisors_pos_funD: "divisors_pos_fun df \<Longrightarrow> x \<noteq> 0 \<Longrightarrow> d dvd x \<Longrightarrow> d > 0 \<Longrightarrow> d \<in> set (df x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>divisors_pos_fun df; x \<noteq> (0::'a); d dvd x; (0::'a) < d\<rbrakk> \<Longrightar... | {"llama_tokens": 297, "file": "Polynomial_Factorization_Prime_Factorization", "length": 2} |
#ifndef BOOST_THREAD_PTHREAD_MUTEX_HPP
#define BOOST_THREAD_PTHREAD_MUTEX_HPP
// (C) Copyright 2007-8 Anthony Williams
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <pthread.h>
#include <boost/utility... | {"hexsha": "3389debe9dbf745ee93fba7f23734dd2efad75e6", "size": 5872, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost-1.47.0/boost/thread/pthread/mutex.hpp", "max_stars_repo_name": "basho-labs/riak-cxx-client", "max_stars_repo_head_hexsha": "88f7196f8c30f35ec784c6e051e82082927acc77", "max_stars_repo_lice... |
import multiprocessing
import sys
import time
from unittest import TestCase, main, skipIf
import numpy
from cogent3.util import parallel
__author__ = "Sheng Han Moses Koh"
__copyright__ = "Copyright 2007-2020, The Cogent Project"
__credits__ = ["Gavin Huttley", "Sheng Han Moses Koh"]
__license__ = "BSD-3"
__versio... | {"hexsha": "83c46b0e269ca1946345b5af7dbe96982f698400", "size": 2778, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_util/test_parallel.py", "max_stars_repo_name": "GavinHuttley/c3test", "max_stars_repo_head_hexsha": "c5bf7f8252b4f7b75a851e28275536a8c378897a", "max_stars_repo_licenses": ["BSD-3-Clause... |
using ITensors, Test, Random
using ITensors: nsite, set_nsite!
@testset "Basic DMRG" begin
@testset "Spin-one Heisenberg" begin
N = 10
sites = siteinds("S=1", N)
os = OpSum()
for j in 1:(N - 1)
add!(os, "Sz", j, "Sz", j + 1)
add!(os, 0.5, "S+", j, "S-", j + 1)
add!(os, 0.5, "S-", ... | {"hexsha": "5c05456a7be18354522effebcd147effbb0d65da", "size": 11008, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dmrg.jl", "max_stars_repo_name": "LinjianMa/ITensors.jl", "max_stars_repo_head_hexsha": "579bd97f45e1723367ba569f094dd1569817b8d7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
'''
Main wrapper for SAC training
'''
import numpy as np
from training.SAC.sacAgent import sacAgent
from training.seller_utils import ydiff2action
def initialize_agents(seller_info, buyer_info, train_config, logger, evaluate=False):
# get required parameters for WolFPHC algorithm
aux_price_min = 1 / seller_i... | {"hexsha": "287fdc21f7cc7f248caf3a9e58c39e0f8943a47f", "size": 3883, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/SAC/sacTrainer.py", "max_stars_repo_name": "prasoonpatidar/multiagentRL-resource-sharing", "max_stars_repo_head_hexsha": "e63ba7fc3c7ab019e9fd109cd45b739e3322152f", "max_stars_repo_licens... |
[STATEMENT]
lemma find_base_vectors_transfer: assumes [transfer_rule]: "(R ===> R ===> (=)) (=) (=)"
shows "((R ===> R) ===> R ===> R ===> mat_rel R
===> list_all2 (vec_rel R)) find_base_vectors_gen find_base_vectors_gen"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((R ===> R) ===> R ===> R ===> mat_rel R ==... | {"llama_tokens": 8483, "file": "Berlekamp_Zassenhaus_Matrix_Record_Based", "length": 58} |
import numpy as np
from numba import njit
# consav
from consav import linear_interp # for linear interpolation
@njit
def compute(t,sol,par,G2EGM=True):
# unpack
w = sol.w[t]
wa = sol.wa[t]
if G2EGM:
wb = sol.wb[t]
# loop over outermost post-decision state
for i_b in range(par.Nb_pd):... | {"hexsha": "b247432798b29b0ec926ba4b25d7788ec68ec281", "size": 2880, "ext": "py", "lang": "Python", "max_stars_repo_path": "03. G2EGM/post_decision.py", "max_stars_repo_name": "alanlujan91/ConsumptionSavingNotebooks", "max_stars_repo_head_hexsha": "4455500d17fed4dd1f3f4844aeb5dd5d3b89903f", "max_stars_repo_licenses": [... |
(*
Copyright 2018
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 in writing, software
distributed und... | {"author": "ssrg-vt", "repo": "Luce-src", "sha": "f7f1ef0fd07bba48bcb3d5e32404db6013a5f1bc", "save_path": "github-repos/isabelle/ssrg-vt-Luce-src", "path": "github-repos/isabelle/ssrg-vt-Luce-src/Luce-src-f7f1ef0fd07bba48bcb3d5e32404db6013a5f1bc/safecomp2019_artifact/current_work/examples/hermitcore/string/strlen_mem.t... |
#! format: off
@deprecate imfill(img::AbstractArray{Bool}, interval::Tuple{Real,Real}, dims::Union{Dims, AbstractVector{Int}}) imfill(img, interval; dims=dims)
@deprecate dilate!(img; kwargs...) dilate!(img, copy(img); kwargs...)
@deprecate erode!(img; kwargs...) erode!(img, copy(img); kwargs...)
@deprecate opening!(... | {"hexsha": "e9ec49f4d747239638a41f2c9bdf1976ef54c445", "size": 815, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/deprecations.jl", "max_stars_repo_name": "ThomasRetornaz/ImageMorphology.jl", "max_stars_repo_head_hexsha": "3181b69eab153fec9eeb0eaa55e97000252fa895", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
# Copyright 2021
#
# 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, publ... | {"hexsha": "ee30f652be3f9c365e4304bbcda3dfbfaf5cf1c4", "size": 8480, "ext": "py", "lang": "Python", "max_stars_repo_path": "streamcyber/utils.py", "max_stars_repo_name": "gditzler/streaming-cyber-analysis", "max_stars_repo_head_hexsha": "6d314a6301127a7d048afc989a1a9f9e20de730d", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma \<L>_intersect: "\<L> (reg_intersect R L) = \<L> R \<inter> \<L> L"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<L> (reg_intersect R L) = \<L> R \<inter> \<L> L
[PROOF STEP]
by (auto simp: intersect_ta_gta_lang \<L>_def reg_intersect_def) | {"llama_tokens": 118, "file": "Regular_Tree_Relations_Tree_Automata_Tree_Automata", "length": 1} |
import cv2
import numpy as np
from numpy import clip
def cut_roi(frame, roi):
p1 = roi.position.astype(int)
p1 = clip(p1, [0, 0], [frame.shape[-1], frame.shape[-2]])
p2 = (roi.position + roi.size).astype(int)
p2 = clip(p2, [0, 0], [frame.shape[-1], frame.shape[-2]])
return np.array(frame[:... | {"hexsha": "5e3fb75a448ce2b389a45f8fedef9c1204b36855", "size": 1017, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "shashwat623/Computer-Pointer-Controller-With-Face-Authentication", "max_stars_repo_head_hexsha": "c78e7a34a2c8b8c79d4c9b4a4e7e46ad790bf74e", "max_stars_repo_lice... |
[STATEMENT]
lemma Rat_interval_closure:
fixes x :: real
assumes "x < y"
shows "closure ({x<..<y} \<inter> \<rat>) = {x..y}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. closure ({x<..<y} \<inter> \<rat>) = {x..y}
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
x < y
goal (1 subgoal):
1. cl... | {"llama_tokens": 237, "file": "Kuratowski_Closure_Complement_KuratowskiClosureComplementTheorem", "length": 2} |
[STATEMENT]
lemma inj_on_mult':
assumes coprime: "coprime x (q::nat)"
shows "inj_on (\<lambda> b. x*b mod q) ({..<q} - {0})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. inj_on (\<lambda>b. x * b mod q) ({..<q} - {0})
[PROOF STEP]
apply(auto simp add: inj_on_def)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
... | {"llama_tokens": 328, "file": "Sigma_Commit_Crypto_Uniform_Sampling", "length": 3} |
import logging
import argparse
import sys
import os
import json
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import sys
import time
import datetime
timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y_%m_%d_%H_%M_%S')
from util import create_log, mnist_loader, shape_2d
fr... | {"hexsha": "a0682a617c9bed19b2930df836568f386de17f07", "size": 11944, "ext": "py", "lang": "Python", "max_stars_repo_path": "histogram_attack.py", "max_stars_repo_name": "zwc662/On_Manifold_Counterexample", "max_stars_repo_head_hexsha": "0cd2de85083d60d73542dc59f33cbce53798cbf8", "max_stars_repo_licenses": ["MIT"], "ma... |
import pickle
# from sklearn.ensemble import VotingRegressor
import pandas as pd
import numpy as np
from util import clustering
seed = 123
np.random.seed(seed)
# TODO
n_rows = 10000
# data_all = pd.read_csv(
# 'data/training_set_VU_DM_clean.csv', sep=';', nrows=n_rows)
# data_all_clusters = pd.read_csv(
# 'dat... | {"hexsha": "ca940205d4e4b36467ee267431ee6ff986e0e4f7", "size": 2604, "ext": "py", "lang": "Python", "max_stars_repo_path": "clean_data_2c_svd.py", "max_stars_repo_name": "voschezang/Data-Mining", "max_stars_repo_head_hexsha": "0762df1d9a63f81d6f44d8a35cc61802baad4c37", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Thu 02 Aug 2018 01:22:32 PM +0430
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import numpy as np
import os
import gi
from numpy import array
from matplotlib import cm
import math
from .fileExtract import *
gi.require_version('... | {"hexsha": "2cb9726d6ef6dc84bdae7da9c82dbf9edf503a5c", "size": 22199, "ext": "py", "lang": "Python", "max_stars_repo_path": "func/plot3DFunc.py", "max_stars_repo_name": "majidzarephysics/python-plotter", "max_stars_repo_head_hexsha": "14ba8f66b0553d84040186e8eb5d4e188e6a35c8", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 27 00:00:56 2017
Frequent Set Mining
@author: luminous
"""
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
# import data
def importData(inFile):
data = pd.read_csv(inFile)
out = {}
out["ID"] = []... | {"hexsha": "f8a6901e087bf0b63e8049b7731646befceefb95", "size": 947, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw3/q2_frequentSet.py", "max_stars_repo_name": "1452712/DataMining_course_project", "max_stars_repo_head_hexsha": "043cc7851c391f6b99f692f5a9dd843db34a27ca", "max_stars_repo_licenses": ["MIT"], "ma... |
# **********************************************************************************************************************
#
# brief: Mask R-CNN
# Configurations and data loading code for the elevator dataset.
#
# author: Lukas Reithmeier
# date: 22.04.2020
#
# *****************************************... | {"hexsha": "c43140af0ec6a2f1c4233fead26c110f5998b344", "size": 15680, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/elevator/elevator_rgb.py", "max_stars_repo_name": "reithmeier/Mask_RCNN", "max_stars_repo_head_hexsha": "4e7d93adf8c244dc541c7fcc959d5e994c8dd9b1", "max_stars_repo_licenses": ["MIT"], "ma... |
function foot_clearance(nlp, bounds, frame)
% constraints for swing foot clearance
domain = nlp.Plant;
x = domain.States.x;
pos = getCartesianPosition(domain, frame);
constraint_func = SymFunction(['foot_clearance_',domain.Name], pos(3), {x});
% Foot Clearance M... | {"author": "ayonga", "repo": "frost-dev", "sha": "e5dc0624d834520872bfa588dd3eda5643da71de", "save_path": "github-repos/MATLAB/ayonga-frost-dev", "path": "github-repos/MATLAB/ayonga-frost-dev/frost-dev-e5dc0624d834520872bfa588dd3eda5643da71de/example/atlas/+opt/+constraint/foot_clearance.m"} |
import CompactBasisFunctions: Lagrange
import LinearAlgebra
@doc raw"""
The s-stage Lobatto nodes are defined as the roots of the following polynomial of degree $s$:
```math
\frac{d^{s-2}}{dx^{s-2}} \big( (x - x^2)^{s-1} \big) .
```
"""
function get_lobatto_nodes(::Type{T}, s) where {T}
if s == 1
throw(E... | {"hexsha": "21ea53215d265c2e808c128f3bf09b1d010c50af", "size": 12908, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tableaus/lobatto.jl", "max_stars_repo_name": "JuliaGNI/RungeKutta.jl", "max_stars_repo_head_hexsha": "b6933446c0f76525a2e36f4d94bf7ff9694c7f5b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
using GLVisualize, GeometryTypes, Reactive, GLAbstraction
if !isdefined(:runtests)
window = glscreen()
timesignal = bounce(linspace(0f0, 1f0,360))
end
# last argument can be used to control the granularity of the resulting mesh
sphere = GLNormalMesh(Sphere(Point3f0(0.5), 0.5f0), 24)
c = collect(linspace(0.1f0,... | {"hexsha": "cf360b07c0cc6d1e79251b9c3710a84bf1d92b41", "size": 558, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/particles/sphere1Drange.jl", "max_stars_repo_name": "JuliaPackageMirrors/GLVisualize.jl", "max_stars_repo_head_hexsha": "90bba1fd997194b1caafb5d479de9380dbdfc13f", "max_stars_repo_licenses"... |
# -*- coding: utf-8 -*-
import sys
sys.path.append('..')
import ecopann.ann as ann
import ecopann.coplot.plot_contours as plc
import ecopann.cosmic_params as cosmic_params
import simulator
import matplotlib.pyplot as plt
import numpy as np
#%% obs data
union = np.loadtxt('data/Union2.1.txt')[:,:3]
# %% estimate pa... | {"hexsha": "0298920557b7dc944f9adea17cf46ebecc3cc8db", "size": 1298, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/predict_union2.1.py", "max_stars_repo_name": "Guo-Jian-Wang/ecopann", "max_stars_repo_head_hexsha": "934108d22e4d5ba9489fcfe1d3cc82f7e847b42b", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma spy_method_via_spy_framework_input_completeness :
assumes "observable M2"
and "minimal M2"
and "size M2 \<le> size_r (to_prime M1) + (nat_of_integer additionalStates)"
and "FSM.inputs M2 = FSM.inputs M1"
and "FSM.outputs M2 = FSM.outputs M1"
and "isAlreadyPrime \<Longrightarrow> ... | {"llama_tokens": 2535, "file": "FSM_Tests_Test_Suite_Generator_Code_Export", "length": 4} |
[STATEMENT]
lemma ZFUnionRangeExplode:
assumes "\<And> x . x \<in> A \<Longrightarrow> f x \<in> range explode" and "A \<in> range explode"
shows "(\<Union> x \<in> A . f x) \<in> range explode"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Union> (f ` A) \<in> range explode
[PROOF STEP]
proof-
[PROOF STATE... | {"llama_tokens": 1519, "file": "Category2_Universe", "length": 16} |
"""
Some simple cases for generating data for unit tests.
"""
import numpy as np
from invertH import invertHtrue, invertHsim
def generate_univ_sim_and_obs(m=100, n=10, sig_n=0.1, seed=42):
"""
Generate simple synthetic univariate-output simulation and observation data.
:param m: scalar -- number of obse... | {"hexsha": "c8c0abc19b9670f6c985a7df4e83f9370b2322c8", "size": 6583, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/generate_data.py", "max_stars_repo_name": "lanl/SEPIA", "max_stars_repo_head_hexsha": "0a1e606e1d1072f49e4f3f358962bd8918a5d3a3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
# -*- coding: utf-8 -*-
"""
Cross-validation iterators for GAM
Author: Luca Puggini
"""
from abc import ABCMeta, abstractmethod
from statsmodels.compat.python import with_metaclass
import numpy as np
class BaseCrossValidator(with_metaclass(ABCMeta)):
"""
The BaseCrossValidator class is a base class for all... | {"hexsha": "dca908d5493fd931a4ace76a3054fdf0ddcdab9f", "size": 1664, "ext": "py", "lang": "Python", "max_stars_repo_path": "statsmodels/gam/gam_cross_validation/cross_validators.py", "max_stars_repo_name": "timgates42/statsmodels", "max_stars_repo_head_hexsha": "ab8ff09e3eb8c385214bd1575aa47b81bf53d584", "max_stars_rep... |
import numpy as np
import matplotlib.pyplot as plt
def size_histogram_from_segmentation(segmentation, n_bins=16, histogram_bins=[1], bin_for_threshold=None,
min_size=None, max_size=None, ignore_background=True):
""" Plot size histogram for the objects in the segmentation to fi... | {"hexsha": "8cf0aac28c806a6f26160d4d925b6b81f8d6b22c", "size": 3499, "ext": "py", "lang": "Python", "max_stars_repo_path": "elf/visualisation/size_histogram.py", "max_stars_repo_name": "qin-yu/elf", "max_stars_repo_head_hexsha": "bb8e0a41c1c2539ac6f866271751139271fbeeb1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# coding=utf8
from functools import partial
import numpy as np
class Kmeans(object):
'''
K均值聚类算法主体类-最朴素Kmeans
'''
Metrics = {
'euclidean': 2, # 欧式距离
'manhattan': 1, # 曼哈顿距离
'chebyshev': np.inf, # 切比雪夫距离
}
def __init__(self, k=2, metric='euclidean', p=4):
... | {"hexsha": "e44f7abad173d93ad9f8807953e6eaa2dc892484", "size": 2806, "ext": "py", "lang": "Python", "max_stars_repo_path": "Kmeans/Kmeans.py", "max_stars_repo_name": "ruiyuanlu/MachineLearningImplementation", "max_stars_repo_head_hexsha": "22f3d6869b5a1cb48dd8db4388420ad3e3ef7aa6", "max_stars_repo_licenses": ["MIT"], "... |
"""Spectrogram."""
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
from scipy.io import wavfile
# 讀入單一檔案測試
sampling_rate, frequency = wavfile.read('一分鐘，吸睛說話術-bt-_c9DxQmY.wav')
FFT_SIZE = sampling_rate
time = len(frequency) / sampl... | {"hexsha": "81e9954c64ae57709b58e02a7bde2f58509c4888", "size": 2224, "ext": "py", "lang": "Python", "max_stars_repo_path": "own_practice/spectrogram.py", "max_stars_repo_name": "Ellis0817/Introduction-to-Programming-Using-Python", "max_stars_repo_head_hexsha": "1882a2a846162d5ff56d4d56c3940b638ef408bd", "max_stars_repo... |
/**
* @file SttclBoostSemaphore.cpp
*
* Copyright (c) 2012, Guenther Makulik All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted provided that
* the following conditions are met:
*
* 1) Redistributions of source code must retain the above copyr... | {"hexsha": "b749ac14b945aa36c89030c0c879debda4fcc8fb", "size": 2485, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sttcl/BoostThreads/src/SttclBoostSemaphore.cpp", "max_stars_repo_name": "makulik/sttcl", "max_stars_repo_head_hexsha": "b1be1969b705e9b6a42fadd8581ab678b8485aa4", "max_stars_repo_licenses": ["Unlice... |
#pragma once
#include <functional>
#include <memory>
#include <mutex>
#include <unordered_map>
#include <boost/any.hpp>
#include "blackhole/error.hpp"
#include "blackhole/repository/config/formatter.hpp"
#include "blackhole/repository/config/sink.hpp"
#include "blackhole/repository/factory/frontend/keeper.hpp"
#incl... | {"hexsha": "6930fd0c1a1f9d301d2467db7a6046b78b5cab6f", "size": 2396, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/blackhole/repository/factory/frontend.hpp", "max_stars_repo_name": "bioothod/blackhole", "max_stars_repo_head_hexsha": "2bd242e6027f20019e60b600f50a9e25127db640", "max_stars_repo_licenses": ["MI... |
import numpy as np
from typing import Callable, List
from pypika import functions as fn
from pypika.queries import QueryBuilder
from fireant import utils
from fireant.utils import immutable
from .fields import Field
from .modifiers import FieldModifier
class ReferenceFilter:
def __init__(self, metric: Field, op... | {"hexsha": "20e6a061afb86e2d0d46c60ab5f79c893c1cd0c8", "size": 4794, "ext": "py", "lang": "Python", "max_stars_repo_path": "fireant/dataset/references.py", "max_stars_repo_name": "mikeengland/fireant", "max_stars_repo_head_hexsha": "63c12728c11f1fb252265459f8b8f384d20414b9", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from matplotlib import pyplot as plt
import numpy as np
forceControlData = np.load('force_control_response.npz')
F = forceControlData['force']
U = forceControlData['displacement']
plt.plot(U, F, marker='o')
plt.xlabel('Displacement')
plt.ylabel('Force')
plotComparison = True
if plotComparison:
dispControlData = ... | {"hexsha": "0344c83e25d2c3ceffc1a6d741e3fda76f4fe3a4", "size": 609, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/hemisphere_cap/plot_force_displacement.py", "max_stars_repo_name": "btalamini/optimism", "max_stars_repo_head_hexsha": "023e1b2a0b137900a7517e4c7ac5056255cf7bbe", "max_stars_repo_licenses"... |
.sp
.ne 6i
.rs
.sp 6i
.ft CW
.sp -1
.ps 6
.sp -1
.sp -1
\h'568u'\v'-800u'\D'l 0u 400u'
.sp -1
\h'568u'\v'-400u'
.sp -1
\h'568u'\v'-240u'\h'-0.m'\v'.2m'\h\(ts-\w\(ts1\(tsu\(ts1
.sp -1
\h'568u'\v'-240u'
.sp -1
\h'625u'\v'-800u'\D'l 0u 200u'
.sp -1
\h'625u'\v'-600u'
.sp -1
\h'682u'\v'-800u'\D'l 0u 200u'
.sp -1
\h'682u... | {"hexsha": "8bfaa8b9fbd2c375f9eef54c738ceb3a5caa7ce1", "size": 56618, "ext": "r", "lang": "R", "max_stars_repo_path": "sys/doc/cda/toy.draw.r", "max_stars_repo_name": "henesy/plan9-1e", "max_stars_repo_head_hexsha": "47575dc4a4638a1ee0d9eed78d88a9f1720a4430", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# First import library
from numpy.lib.polynomial import _polyint_dispatcher
import pyrealsense2 as rs
# Import Numpy for easy array manipulation
import numpy as np
# Import OpenCV for easy image rendering
import cv2
# Import argparse for command-line options
import argparse
# Import os.path for file path manipulation
i... | {"hexsha": "8eed46a259eb65eee5851de27d6d8e27402c4ecf", "size": 6295, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy_o3d_convert_depth_to_pc_method.py", "max_stars_repo_name": "guisoares9/realsense_studies", "max_stars_repo_head_hexsha": "47bdcc448591159275647248d5072f097b081d6a", "max_stars_repo_licenses"... |
import os
import sys
import re
import xmltodict
import igraph as ig
import numpy as np
from .utils import *
from collections import defaultdict
from PyBoolNet import FileExchange
from pathlib import Path
#from PyBoolNet import QuineMcCluskey as QMC
from . import QMC
format_classes = {'primes':0,
... | {"hexsha": "2ccddedcc4ad741cef5973eed919caf0fbaf5d5c", "size": 7872, "ext": "py", "lang": "Python", "max_stars_repo_path": "booldog/utils/io.py", "max_stars_repo_name": "NIB-SI/squad-reboot", "max_stars_repo_head_hexsha": "8d32527eddc1ee04d8f0f00b8a744dbd84f811f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from typing import List
import numpy as np
import greedypacker
from model.vegetable import Vegetable
class GardenProposal:
def __init__(self,
width,
height,
vegetables_availables: List[Vegetable],
vegetables_position: List[greedypacker.Item]
... | {"hexsha": "66b7eef281f9935a6d98410c96d54f900da2e413", "size": 1304, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/garden_proposal.py", "max_stars_repo_name": "louisoutin/garden-csp", "max_stars_repo_head_hexsha": "13bd094b75006d2562f6da7b78925848d47899cf", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
(** *This FILE IS DEPRICATED *)
(** *I'm moving some lemmas before erasing it*)
(** * Please see: erasure_signature.v *)
Require Import compcert.common.Memory.
(* The concurrent machinery*)
Require Import VST.concurrency.scheduler.
Require Import VST.concurrency.concurrent_m... | {"author": "ildyria", "repo": "coq-verif-tweetnacl", "sha": "8181ab4406cefd03ab0bd53d4063eb1644a2673d", "save_path": "github-repos/coq/ildyria-coq-verif-tweetnacl", "path": "github-repos/coq/ildyria-coq-verif-tweetnacl/coq-verif-tweetnacl-8181ab4406cefd03ab0bd53d4063eb1644a2673d/packages/coq-vst/coq-vst.2.0/concurrency... |
"""Our mod_wsgi frontend to autoplot generation"""
# pylint: disable=abstract-class-instantiated
from collections import OrderedDict
import sys
import os
import datetime
import tempfile
import imp
import json
import traceback
from io import BytesIO
import numpy as np
import memcache
import pytz
import pandas as pd
fro... | {"hexsha": "9f98ffffadaaaf4d1b77c90e2fc4625c9c14e600", "size": 9070, "ext": "wsgi", "lang": "Python", "max_stars_repo_path": "htdocs/plotting/auto/autoplot.wsgi", "max_stars_repo_name": "jamayfieldjr/iem", "max_stars_repo_head_hexsha": "275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a", "max_stars_repo_licenses": ["MIT"], "max... |
module Prelude.IO
import Builtin
import PrimIO
import Prelude.Basics
import Prelude.Interfaces
import Prelude.Show
%default total
--------
-- IO --
--------
public export
Functor IO where
map f io = io_bind io $ io_pure . f
%inline
public export
Applicative IO where
pure x = io_pure x
f <*> a
= io_bind... | {"hexsha": "de5e3f415825a814cb6e65f6a8c5ce41e56b8503", "size": 4383, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "libs/prelude/Prelude/IO.idr", "max_stars_repo_name": "ska80/idris-jvm", "max_stars_repo_head_hexsha": "66223d026d034578876b325e9fcd95874faa6052", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
#!/usr/bin/env python
import rospy
import robot
import world
import random
import sys
import numpy as np
import copy
# base class... don't use directly
class Planner():
def __init__(self, robot, world ):
self.world = world
self.robot = robot
def plan(self):
raise NotImplementedError()... | {"hexsha": "a6d7db2c24e671250e45812499839a01201b37a4", "size": 7038, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/resurface_dijkstras.py", "max_stars_repo_name": "Shital14Sable/ROB_545_Proj", "max_stars_repo_head_hexsha": "b093e12c2116694dbde2660e1f49415307de5d92", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma wadjust_backto_standard_pos_via_left_Bk[simp]:
"wadjust_goon_left_moving m rs (c, Bk # list) \<Longrightarrow>
wadjust_backto_standard_pos m rs (tl c, hd c # Bk # list)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wadjust_goon_left_moving m rs (c, Bk # list) \<Longrightarrow> wadjust_backto_... | {"llama_tokens": 236, "file": "Universal_Turing_Machine_UTM", "length": 1} |
% !TEX root = ABMCMC.tex
\section{Related Research}\label{sec:background}
\paragraph{Intro to DA}
\todo[inline]{Do we need a paragraph to very briefly explain how DA works? Basically predict, update, and the posterior}
\paragraph{Sampling}
\todo[inline]{What are there other approaches designed to allow efficient sa... | {"hexsha": "897a01065b258468459edecc64c64bd09a63001e", "size": 2944, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/background.tex", "max_stars_repo_name": "jonward1982/AgentBasedMCMC", "max_stars_repo_head_hexsha": "621d32d5e2d9d363c753d42cd1ede6b31195e716", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Software License for MTL
//
// Copyright (c) 2007 The Trustees of Indiana University.
// 2008 Dresden University of Technology and the Trustees of Indiana University.
// 2010 SimuNova UG (haftungsbeschränkt), www.simunova.com.
// All rights reserved.
// Authors: Peter Gottschling and And... | {"hexsha": "9c47a15d886972d71717d188119857ea330946c8", "size": 4507, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/mtl4/boost/numeric/mtl/operation/operators.hpp", "max_stars_repo_name": "spraetor/amdis2", "max_stars_repo_head_hexsha": "53c45c81a65752a8fafbb54f9ae6724a86639dcd", "max_stars_repo_licenses": ["... |
From iris.algebra Require Export updates local_updates frac agree.
From iris.algebra Require Import proofmode_classes big_op.
From iris.prelude Require Import options.
(** The view camera with fractional authoritative elements *)
(** The view camera, which is reminiscent of the views framework, is used to
provide a lo... | {"author": "gares", "repo": "iris", "sha": "7b4a04ce0d396cb27eeef22e883a9f3b738e83f4", "save_path": "github-repos/coq/gares-iris", "path": "github-repos/coq/gares-iris/iris-7b4a04ce0d396cb27eeef22e883a9f3b738e83f4/iris/algebra/view.v"} |
[STATEMENT]
lemma P1_cong:
fixes tms :: "trm list"
assumes "\<And>i t x. atom i \<sharp> tms \<Longrightarrow> (P t)(i::=x) = P (subst i x t)" and "H \<turnstile> x EQ x'"
shows "H \<turnstile> P x IFF P x'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. H \<turnstile> P x IFF P x'
[PROOF STEP]
proof -
[PROOF ... | {"llama_tokens": 848, "file": "Robinson_Arithmetic_Robinson_Arithmetic", "length": 9} |
# Kerja Gaya Gesek
Aniesah Akhyar <br>
Program Studi Sarjana Fisika, Institut Teknologi Bandung <br>
Jalan Ganesha 10, Bandung 40132, Indonesia <br>
aniesah.akhyar@gmail.com, https://github.com/Aniesah <br>
Kerja yang dilakukan oleh gaya gesek merupakan bentuk kerja yang tidak diharapkan karena energi yang dikeluarkan... | {"hexsha": "151ce0ad1003eba1ab2d4ce1cf36a34256028fa2", "size": 6002, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "assignments/05/10219033/Kerja Gaya Gesek.ipynb", "max_stars_repo_name": "Aniesah/fi3201-01-2021-2", "max_stars_repo_head_hexsha": "6bba4d92496f74e14149c274ac37d956d999a04d", "max_star... |
"""Plot functions
"""
import os
import logging
from logging import Logger
import numpy as np
import matplotlib.pyplot as plt
#import ipywidgets as widgets
from IPython.display import display
from PIL import Image
from .Utils import joinPath, datestamp, timestamp, setDir
from . import ConvPlotTools
from . import LabelD... | {"hexsha": "bd3c9773782400967c50da4ad84d1098c1e8e5ad", "size": 7480, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyneval/metric/utils/klib/glib/PlotTools.py", "max_stars_repo_name": "SupermeLC/PyNeval", "max_stars_repo_head_hexsha": "2cccfb1af7d97857454e9cbc3515ba75e5d8d4b0", "max_stars_repo_licenses": ["BSD... |
#!/usr/bin/env python
#updating the code
from __future__ import print_function
import tensorflow as tf
import cv2
import sys
sys.path.append("game/")
import wrapped_flappy_bird as game
import random
import numpy as np
from collections import deque
GAME = 'bird' # the name of the game being played for log files
ACTION... | {"hexsha": "8eb9c448fec92ef0edd56f1220cc211e36fab4a8", "size": 14250, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_q_network.py", "max_stars_repo_name": "vishal240597/DeepLearningFlappyBird", "max_stars_repo_head_hexsha": "7fa666176b63b086df7309b472a5efa0ca3c92b2", "max_stars_repo_licenses": ["MIT"], "ma... |
/*<-
Copyright (c) 2016 Barrett Adair
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
->*/
#include <boost/callable_traits/detail/config.hpp>
#ifndef BOOST_CLBL_TRTS_ENABLE_TRANSACTION_SAFE
int main(){}
#else
//[ remove_transa... | {"hexsha": "7d018398fe065e3e1852bfee928ad28f25289ca9", "size": 699, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/external/boost/boost_1_68_0/libs/callable_traits/example/remove_transaction_safe.cpp", "max_stars_repo_name": "Bpowers4/turicreate", "max_stars_repo_head_hexsha": "73dad213cc1c4f74337b905baea2b3a... |
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This is an example illustrating the use of the deep learning tools from the
dlib C++ Library. In it, we will show how to use the loss_metric layer to do
metric learning.
The main reason you might wan... | {"hexsha": "6aba7ff1fea09c4c75eeaceb7f813a2d37e8688b", "size": 5501, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/dnn_metric_learning_ex.cpp", "max_stars_repo_name": "ckproc/dlib-19.7", "max_stars_repo_head_hexsha": "0ca40f5e85de2436e557bee9a805d3987d2d9507", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 18 18:05:03 2021
@author: 알파제로를 분석하며 배우는 인공지능
"""
#%%
# 4-4-4 패키지 임포트
import gym
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from collections import deque
from... | {"hexsha": "6ef120b3a3652a279753c17c06809ad7361c45b2", "size": 5657, "ext": "py", "lang": "Python", "max_stars_repo_path": "AlphaZero_book/chap4/4-4.py", "max_stars_repo_name": "jisuk500/ML_learning", "max_stars_repo_head_hexsha": "4f77eb34bd652753e63fb75fa2be5bd252232f80", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma (in Corps) value_mI_genTr1:"\<lbrakk>0 < n; distinct_pds K n P; ideal (O\<^bsub>K P n\<^esub>) I;
I \<noteq> {\<zero>\<^bsub>O\<^bsub>K P n\<^esub>\<^esub>}; I \<noteq> carrier (O\<^bsub>K P n\<^esub>); j \<le> n\<rbrakk> \<Longrightarrow>
(mprod_exp K (K_gamma j) (Kb\<^bsub>K n P\<^esub>) n)\<^bsub... | {"llama_tokens": 1785, "file": "Valuation_Valuation2", "length": 6} |
INCLUDE 'VICMAIN_FOR'
SUBROUTINE MAIN44
C---- VICAR PROGRAM "RAPIDMOS.
C PURPOSE: TAKES MULTIPLE IMAGES AND MOSAICS THEM
C INTO ONE OUTPUT DATA SET.THE OPERATION IS
C SIMILAR TO THAT OF "FASTMOS", BUT WITH
C REDUCED EXECUTION TIME AND MANY NOT WIDELY
C ... | {"hexsha": "951ea0e2d6b42b8a0efe233e61fcb7d0f185fccc", "size": 5206, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/prog/rapidmos/rapidmos.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#!/usr/bin/env python2
# -*- coding: UTF-8 -*-
# File: utils.py
# Date: Wed Dec 25 20:24:38 2013 +0800
# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
import numpy
from scipy.io import wavfile
kwd_mark = object()
def cached_func(function):
cache = {}
def wrapper(*args, **kwargs):
key = args + (kwd_mark,) + ... | {"hexsha": "bbea5bf0502e0e4b3d29c79a3877fc1a05e6769e", "size": 1302, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "isingmodel/voiceproject", "max_stars_repo_head_hexsha": "f6942946ae452fa6112976c8970b2fbe425a1f36", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
"""
validated C(R)NN structure models,
for classifying ECG arrhythmias
"""
from copy import deepcopy
from itertools import repeat
from collections import OrderedDict
from typing import Union, Optional, Tuple, Sequence, NoReturn
from numbers import Real, Number
import numpy as np
np.set_printoptions(precision=5, suppre... | {"hexsha": "36a0790ee25802ce5771fce58b484b2cde1d4a6e", "size": 13833, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_ecg/models/ecg_crnn.py", "max_stars_repo_name": "busyyang/torch_ecg", "max_stars_repo_head_hexsha": "031d90a32b8a1e202364efe1e5a19a9ba1f0a726", "max_stars_repo_licenses": ["MIT"], "max_star... |
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