text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import numpy as np
from typing import Any, Callable, Iterator, List, Optional, Tuple, Union, cast
from d3rlpy.metrics.scorer import AlgoProtocol, _make_batches
from d3rlpy.dataset import Episode
from rl4rs.policy.policy_model import policy_model
WINDOW_SIZE = 1024
# modify from https://github.com/takuseno/d3rlpy/blo... | {"hexsha": "7593fa1314c392d3f7661163cd03fe7f2984ca9e", "size": 5551, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl4rs/utils/d3rlpy_scorer.py", "max_stars_repo_name": "fuxiAIlab/RL4RS", "max_stars_repo_head_hexsha": "e26ee5d068eaffd0f04779067614e34e313b1200", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#pragma once
#include <boost/program_options.hpp>
#include <iostream>
using namespace boost::program_options;
using namespace std;
namespace utils {
/**
* Prepare command line arguments processing.
*/
options_description prepareCommandLineOptions ();
/**
* Parse command line.
* @param ... | {"hexsha": "60f3b6507db65b1ee9a5b20fc9841252ffbd175a", "size": 1589, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utils/command_line.hpp", "max_stars_repo_name": "char-lie/pop3_client", "max_stars_repo_head_hexsha": "fceb4266443177358e2f0bdf2d3ebc78ab000601", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import yaml
import time
import shutil
import torch
import random
import argparse
import numpy as np
import matplotlib.pyplot as plt
from torch.utils import data
from tqdm import tqdm
from torchvision.utils import save_image, make_grid
from tifffile import imsave
from functools import reduce
from ptsemseg.m... | {"hexsha": "c1dfeae9590b533517225c56a514a8ec5a87ac6a", "size": 20322, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/UnMICST-P/train.py", "max_stars_repo_name": "Yu-AnChen/UnMICST-info", "max_stars_repo_head_hexsha": "9bcc8a408f3c0c8fab2f58778152ae47ee10ad59", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# convert binary into HDF5 data
using HDF5
datasets = [("train", ["data_batch_$i.bin" for i in 1:5]),
("test", ["test_batch.bin"])]
const width = 32
const height = 32
const channels = 3
const batch_size = 10000
mean_model = zeros(Float32, width, height, channels, 1)
for (key, sources) in dat... | {"hexsha": "fa6171de36b914b2e46a9efd8b492a5c422f89ab", "size": 2067, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/cifar10/convert.jl", "max_stars_repo_name": "baajur/Mocha.jl", "max_stars_repo_head_hexsha": "5e15b882d7dd615b0c5159bb6fde2cc040b2d8ee", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
\section{Nutrition}
It is of my genuine belief that nutrition is of utmost importance. From personal experience, I've felt what a bad nutrition can make you think. Since what we eat is directly correlated with the compounds ours bodies are able to produce, from amino acids to hormones, it becomes clear that a good nutr... | {"hexsha": "ad7b9dd6794ecb95dc6d2d6320c3c4bab2be9add", "size": 2354, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/Nutrition.tex", "max_stars_repo_name": "jmoraispk/TheDocument", "max_stars_repo_head_hexsha": "ef14eaaec34cb09a0945ff4647e87ff77eac6890", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import logging
import os
import pickle
import tempfile
import shutil
import operator
import pandas as pd
import numpy as np
def loadData(currency, interval):
logging.info('Data: loading {0} at {1}...'.format(currency, interval))
df = pd.read_csv(
r'{0}/../../data/{1}e{2}.csv'.format(os.path.realpath(o... | {"hexsha": "9c74b89a295f131310f7100de41d93a0bfb6249d", "size": 4034, "ext": "py", "lang": "Python", "max_stars_repo_path": "16_rf_ma/stable-35-6/main.py", "max_stars_repo_name": "Tjorriemorrie/trading", "max_stars_repo_head_hexsha": "aafa15a6c564bfa86948ab30e33d554172b38a3e", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
'''======================================================
Created by: D. Spencer Maughan
Last updated: May 2015
File name: IRIS_DF_Controller.py
Organization: RISC Lab, Utah State University
Notes:
======================================================'''
import roslib;... | {"hexsha": "40029c8f1caeff06f4611276973391395a0d3d7b", "size": 6110, "ext": "py", "lang": "Python", "max_stars_repo_path": "risc_control/src/IRIS_DF_Controller.py", "max_stars_repo_name": "riscmaster/risc_maap", "max_stars_repo_head_hexsha": "48b0ab79c1938bc3ed36442894dd4bf3091a2942", "max_stars_repo_licenses": ["BSD-2... |
[STATEMENT]
lemma [simp]:
"(- grd (step ClassicMark)) loop = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (- grd (step ClassicMark)) loop = {}
[PROOF STEP]
apply safe
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>a aa ab ac ad b. (a, aa, ab, ac, ad, b) \<in> (- grd (step ClassicMark)) loop \<Longr... | {"llama_tokens": 3135, "file": "GraphMarkingIBP_DSWMark", "length": 8} |
[GOAL]
p : ℕ
inst✝ : Fact (Prime p)
hp : p % 4 ≠ 3
⊢ ∃ a b, a ^ 2 + b ^ 2 = p
[PROOFSTEP]
apply sq_add_sq_of_nat_prime_of_not_irreducible p
[GOAL]
p : ℕ
inst✝ : Fact (Prime p)
hp : p % 4 ≠ 3
⊢ ¬Irreducible ↑p
[PROOFSTEP]
rwa [PrincipalIdealRing.irreducible_iff_prime, prime_iff_mod_four_eq_three_of_nat_prime p]
[GOAL]
R... | {"mathlib_filename": "Mathlib.NumberTheory.SumTwoSquares", "llama_tokens": 14858} |
cdis Forecast Systems Laboratory
cdis NOAA/OAR/ERL/FSL
cdis 325 Broadway
cdis Boulder, CO 80303
cdis
cdis Forecast Research Division
cdis Local Analysis and Prediction Branch
cdis LAPS
cdis
cdis This software and its documentation are in the public domain and
cdis are furnished "as is." ... | {"hexsha": "dcaf96acc585c7e3a71aec0d0861b07d9cc3aff9", "size": 5767, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/newreadobs.f", "max_stars_repo_name": "maxinye/laps-mirror", "max_stars_repo_head_hexsha": "b3f7c08273299a9e19b2187f96bd3eee6e0aa01b", "max_stars_repo_licenses": ["Intel", "Unlicense", "OL... |
Sandra and Joe Proudman are a husband a wife duo that are available throughout Davis and surrounding areas for event, portrait, and pet photography.
| {"hexsha": "eb9c8b2347393d6381d7aa0526ba55fefd178498", "size": 157, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Sandra_and_Joe_Proudman_Photography.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["M... |
from functools import reduce
import numpy as np
def _fired_rules(instance, rule_list, threshold=0.001):
"""Returns the rules fired by the instance given a threshold
Parameters
----------
instance : dict, {feature: {set_1: pert_1, set_2: pert_2, ...}, ...}
Fuzzy representation of the instance ... | {"hexsha": "94ec192b23ef4f554adf431266a95a6399ab0953", "size": 5949, "ext": "py", "lang": "Python", "max_stars_repo_path": "teacher/explanation/_factual.py", "max_stars_repo_name": "Kaysera/fuzzy-lore", "max_stars_repo_head_hexsha": "128131e0f41f480d509b63c5e75d0ce58f07bae4", "max_stars_repo_licenses": ["MIT"], "max_st... |
clear
clc
close all
addpath('data')
addpath('src')
dataset = {'1_mECS', '2_Kolod', '3_Pollen', '4_Usoskin'}
for i = 1:4
% perform the analysis for the current dataset
load(['Test_' dataset{i}]);
C = max(true_labs); %%% number of clusters
rng(i,'twister'); %%% for reproducibility
[y, S, F, yda... | {"author": "BatzoglouLabSU", "repo": "SIMLR", "sha": "bf44967cd40d9d4c789ecf866b3aae15ae6190f5", "save_path": "github-repos/MATLAB/BatzoglouLabSU-SIMLR", "path": "github-repos/MATLAB/BatzoglouLabSU-SIMLR/SIMLR-bf44967cd40d9d4c789ecf866b3aae15ae6190f5/MATLAB/Matlab_main_demo_SIMLR.m"} |
#!/usr/bin/env python
from __future__ import division
from builtins import range
from .._externals.srm import SRM
from .procrustes import procrustes
import numpy as np
from .format_data import format_data as formatter
from .._shared.helpers import memoize
import warnings
@memoize
def align(data, align='hyper', normal... | {"hexsha": "846a7423e82a5385e61f8007de3d707e81b644dd", "size": 5648, "ext": "py", "lang": "Python", "max_stars_repo_path": "hypertools/tools/align.py", "max_stars_repo_name": "mewbak/hypertools", "max_stars_repo_head_hexsha": "bc2947737be8bd5a6e2a3bdca84132f6fee8989c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
###########################################################################################
## Nighttime light 1992-2018 in Mexican states' ##
## ##
## Code to clip worldwide files using Mexica... | {"hexsha": "61b505738d32392550ad430c0e370d52f0cba06f", "size": 12433, "ext": "r", "lang": "R", "max_stars_repo_path": "code/clip-estados.r", "max_stars_repo_name": "emagar/luminosity", "max_stars_repo_head_hexsha": "db40e79902ed9bf6ad517317f296f0fb82fd01d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
module TestSweep
using TimeZoneLookup
using TimeZoneLookup: V
using Test
@testset "Points comparison" begin
@test V(1, 2) > V(1, 3)
@test V(1, 2) < V(2, 2)
end
end
| {"hexsha": "db92a2c8fec4a09aa8d18a884b93b325c4e2fe77", "size": 175, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test03_sweep.jl", "max_stars_repo_name": "Arkoniak/TimeZoneLookup.jl", "max_stars_repo_head_hexsha": "68c18ef9f4875c7c836b7fdb70b4c13c00de6b7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
struct RestrictedMeasure{F,M} <: AbstractMeasure
f::F
base::M
end
@inline function logdensity(d::RestrictedMeasure, x)
d.f(x) || return -Inf
return 0.0
end
function density(d::RestrictedMeasure, x)
d.f(x) || return 0.0
return 1.0
end
basemeasure(μ::RestrictedMeasure) = μ.base
| {"hexsha": "5500405a8d7c52ab73cf1ff3fb0d0005642d6af2", "size": 304, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/combinators/restricted.jl", "max_stars_repo_name": "theogf/MeasureBase.jl", "max_stars_repo_head_hexsha": "917304dc4f13e8d353306d599ffc89b2f8b2acac", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import jax
import jax.numpy as jnp
import chex
from typing import Tuple
from ..strategy import Strategy
class SimAnneal(Strategy):
def __init__(self, num_dims: int, popsize: int):
"""Simulated Annealing (Rasdi Rere et al., 2015)
Reference: https://www.sciencedirect.com/science/article/pii/S1877050... | {"hexsha": "671c4abe24a91a76f60fb0c9435d564d2fb1a565", "size": 3496, "ext": "py", "lang": "Python", "max_stars_repo_path": "evosax/strategies/sim_anneal.py", "max_stars_repo_name": "RobertTLange/evosax", "max_stars_repo_head_hexsha": "2646be9053f08c068347cda27736c1399454eedd", "max_stars_repo_licenses": ["Apache-2.0"],... |
#!/usr/bin/env python
import numpy as np
import rospy
import matplotlib.pyplot as plt
from sensor_msgs.msg import NavSatFix
f = plt.figure()
filter_points = np.empty((0, 2), float)
def callback1()
def callback(nav_sat_fix):
global filter_points
lat = nav_sat_fix.latitude
lon = nav_sat_fix.longitude
... | {"hexsha": "0d50973596ea1a9208402b647dd88a9854aab568", "size": 751, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_sat.py", "max_stars_repo_name": "HMellor/LIO-SAM", "max_stars_repo_head_hexsha": "bf08441fee7de68b5d3a0efe8f0ea2e4d70ca2e9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
# Set up and load data
# Includes
import sys
import os
import numpy as np
import json
import os
# Setup paths containing utility
curr_folder = os.getcwd()
sys.path.insert(0, os.path.join(curr_folder,'../app'))
# Load the data
from utils import load_SQuAD_train
arts = load_SQuAD_train()
| {"hexsha": "d5b9050d95886e046bdb433fac150e234f745d31", "size": 290, "ext": "py", "lang": "Python", "max_stars_repo_path": "play/test.py", "max_stars_repo_name": "davestanley/animated-succotash", "max_stars_repo_head_hexsha": "174f08063c222ead153bf9db67c75e2843301912", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
!************************************************************************
MODULE si3d_procedures
!************************************************************************
!
! Purpose: Procedures for the semi-implicit 3-D (si3d) hydrodynamic
! model.
!
!------------------------------... | {"hexsha": "da6bb31a4bb98eac86075d5fd6ae7286c14d8c59", "size": 162079, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "PSi3DL/si3d_procedures.f90", "max_stars_repo_name": "savalbuena/2021WR030666_3D_Flow_Structures_During_Upwelling_Events_in_Lakes_of_Moderate_Size", "max_stars_repo_head_hexsha": "3f066a270da84... |
# AUTOGENERATED! DO NOT EDIT! File to edit: 09_utils.ipynb (unless otherwise specified).
__all__ = ['checkIsListOfStr', 'checkUnique', 'checkNoRepeated', 'checkValidArray', 'checkValidDict', 'checkDictArray']
# Cell
def checkIsListOfStr(l):
"Make sure that l is a list containing only strings"
if not isinstanc... | {"hexsha": "3d472b99ac97aa51c1e6fecdc46fac79189f9876", "size": 1695, "ext": "py", "lang": "Python", "max_stars_repo_path": "nangs/utils.py", "max_stars_repo_name": "smatkovi/nangs", "max_stars_repo_head_hexsha": "b9ab6f32fe3632d9ee403f197742cc203670217d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2,... |
#!/usr/bin/python
#
# Copyright 2018 Google LLC
#
# 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 ag... | {"hexsha": "8a8100390c92b3d7acd7f0594e0ceb0746314459", "size": 10375, "ext": "py", "lang": "Python", "max_stars_repo_path": "sg2im/bilinear.py", "max_stars_repo_name": "peter-rich/Conditional-Imitation-bedroom", "max_stars_repo_head_hexsha": "f3ee95f64d4e27c67cbcbadd08754f7bcdd0699e", "max_stars_repo_licenses": ["Apach... |
import numpy as np
from scipy.interpolate import interp1d
from scipy import integrate
from scipy.stats import norm
from sphericosmo.cosmocontainer import *
from sphericosmo.sphericalpower import *
def SetupPiTau(piOption,zLimits,cosmoCont):
zCurve=cosmoCont.zCurve
tauCurve=cosmoCont.taus
with... | {"hexsha": "f675b53dc66c17ff85f68bfc46c03c6a75f3982c", "size": 3747, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphericosmo/pitau.py", "max_stars_repo_name": "beckrob/SpheriCosmo", "max_stars_repo_head_hexsha": "a961c70763ce29112cfc2d69bd330601608d55e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python3
import os
import time
import cv2
import pycuda.autoinit # For initializing CUDA driver
import pycuda.driver as cuda
from utils.yolo_classes import get_cls_dict
from utils.display import open_window, set_display, show_fps
from utils.visualization import BBoxVisualization
from utils.yolo_with_p... | {"hexsha": "4c0439fdfb595b2edea247c0e03847d0968f88e4", "size": 5969, "ext": "py", "lang": "Python", "max_stars_repo_path": "trt_yolo_v4.py", "max_stars_repo_name": "privvyledge/yolov4_trt_ros", "max_stars_repo_head_hexsha": "502a8b6cc61bf18e04033496eca11f39c242f439", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import pandas as pd
import pickle
from scipy.integrate import odeint
from scipy.integrate import solve_ivp
import matplotlib
import matplotlib.pyplot as plt
np.random.seed(10)
#Function to compute equilibrium constant
def compute_K(vi, Ai ,Bi, Ci, Di, Gi, Hi, T_K):
#Inputs:
# - vi... | {"hexsha": "96b84de971c70f52a12f2fe9523cfb9addd8eda2", "size": 10854, "ext": "py", "lang": "Python", "max_stars_repo_path": "In_Silico.py", "max_stars_repo_name": "MuRE-group/ANN_Reforming_Modeling", "max_stars_repo_head_hexsha": "88aa586a4003fff1ff7116bc33611c4c69e8655e", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
module Sampling
export ListSampler, RejectionSampler, UniformSampler
export create_sampler
using Random
using DataStructures
using QXContexts.Contexts
# Module containing sampler objects which provide different levels of sampling features.
# Each sampler has a constructor which takes a context to perform sampling i... | {"hexsha": "28fdedf0354b2287d2ca154b2d6a0857291778b3", "size": 7735, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sampling.jl", "max_stars_repo_name": "JuliaQX/QXRun.jl", "max_stars_repo_head_hexsha": "9d1a271a56372d79a78fdeccd00d6efb45078702", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max... |
import numpy as np
import pygame
import sys
import math
def main():
ROW_COUNT = 6
COLUMN_COUNT = 7
BLUE = (0, 0 ,230)
BLACK = (0,0,0)
RED = (255, 0, 0)
YELLOW = (255, 255, 0)
def create_board():
board = np.zeros((ROW_COUNT,COLUMN_COUNT))
return board
def drop_peice(col, board, row, peice):
board[row][... | {"hexsha": "3c19fb5b97957f5ae9cc71172ab433cc97a531f3", "size": 4110, "ext": "py", "lang": "Python", "max_stars_repo_path": "connect4 copy.py", "max_stars_repo_name": "yijiehuang0/connect4AI", "max_stars_repo_head_hexsha": "5a134ded8009fd51210a96ba2169920bf1b19aa8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#/usr/bin/env python
import math
import numpy as np
import time
import torch
import torch.nn as nn
NEG_INF = -float("inf")
def logsumexp(*args):
if all(a==NEG_INF for a in args):
return NEG_INF
a_max = max(args)
lsp = math.log(sum(math.exp(a - a_max) for a in args))
return a_max + lsp
def l... | {"hexsha": "fd12a8fe18a68b47d91919cc89e962495b5ee98a", "size": 6346, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctc.py", "max_stars_repo_name": "robinn37/my_ctc", "max_stars_repo_head_hexsha": "616c0fa3963a4307907480e387af85f48428933a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_s... |
seed!(1337)
x = randn(10000)
@testset "default params" begin
p = @inferred histogram(x)
@test_reference(
"references/histogram/default.txt",
@io2str(print(IOContext(::IO, :color=>true), p)),
render = BeforeAfterFull()
)
@test_reference(
"references/histogram/default_noco... | {"hexsha": "c6faab209645d9b248123fcddd673fd3ae0e49a7", "size": 4584, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/tst_histogram.jl", "max_stars_repo_name": "simonbyrne/UnicodePlots.jl", "max_stars_repo_head_hexsha": "625ba36b2dc402839a1401a6e90a650c7668eb06", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import io
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from flask import Flask, req... | {"hexsha": "b94302ca5181d94d729be9c2305647cbea931bc7", "size": 1589, "ext": "py", "lang": "Python", "max_stars_repo_path": "Cloud Computing Development/ml-flask-deployment/main.py", "max_stars_repo_name": "emnopal/bangkit-final-project", "max_stars_repo_head_hexsha": "b0642cb64d916a01101e35844fbf244bafdea4a2", "max_sta... |
[STATEMENT]
lemma set_child_nodes_pointers_preserved:
assumes "w \<in> set_child_nodes_locs object_ptr"
assumes "h \<turnstile> w \<rightarrow>\<^sub>h h'"
shows "object_ptr_kinds h = object_ptr_kinds h'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. object_ptr_kinds h = object_ptr_kinds h'
[PROOF STEP]
using... | {"llama_tokens": 373, "file": "Core_SC_DOM_common_Core_DOM_Functions", "length": 2} |
import unittest
from theano import theano, tensor as tt
import numpy as np
import pymc3 as pm
from pymc3.distributions import HalfCauchy, Normal
from pymc3 import Potential, Deterministic
from pymc3.theanof import generator
class NewModel(pm.Model):
def __init__(self, name='', model=None):
super(NewModel,... | {"hexsha": "232d1a8090a2273791e9cdeb2d4f61e824501f8b", "size": 6059, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymc3/tests/test_model.py", "max_stars_repo_name": "vpolisky/pymc3", "max_stars_repo_head_hexsha": "87cdd712c86321121c2ed3150764f3d847f5083c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
/*************************************************************************
* Copyright (C) 2017-2019 Barcelona Supercomputing Center *
* Centro Nacional de Supercomputacion *
* All rights reserved. *
* ... | {"hexsha": "8ff386624badf8711bc8f23ed918bbd1a494fc00", "size": 7143, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/io/task-info.cpp", "max_stars_repo_name": "bsc-ssrg/NORNS", "max_stars_repo_head_hexsha": "4fd2d181019eceadb8b1b04a94e3756476326239", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, ... |
theory HOL_Specific
imports Base "~~/src/HOL/Library/Old_Datatype" "~~/src/HOL/Library/Old_Recdef"
"~~/src/Tools/Adhoc_Overloading"
begin
chapter \<open>Higher-Order Logic\<close>
text \<open>Isabelle/HOL is based on Higher-Order Logic, a polymorphic
version of Church's Simple Theory of Types. HOL can be best
... | {"author": "Josh-Tilles", "repo": "isabelle", "sha": "990accf749b8a6e037d25012258ecae20d59ca62", "save_path": "github-repos/isabelle/Josh-Tilles-isabelle", "path": "github-repos/isabelle/Josh-Tilles-isabelle/isabelle-990accf749b8a6e037d25012258ecae20d59ca62/src/Doc/Isar_Ref/HOL_Specific.thy"} |
### A Pluto.jl notebook ###
# v0.14.5
using Markdown
using InteractiveUtils
# ╔═╡ f11023e5-8f7b-4f40-86d3-3407b61863d9
begin
using PlutoUI, Viznet, Compose, Plots
function shrink(a, b, da, db)
d = b .- a
r = sqrt(sum(abs2, d))
unitd = d ./ r
a .+ unitd .* da, b .- unitd .* db
end
end;
# ╔═╡ ce44f8bd-692e-... | {"hexsha": "b3b02773fa886d7899ecb38fb4f5b221268b998b", "size": 51355, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/autodiff.jl", "max_stars_repo_name": "GiggleLiu/NiLang.jl", "max_stars_repo_head_hexsha": "f24036c478ace467ae71f630e1db2698e38fd64d", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
## This script generates a curves for fluid particle trajectories
## This is intended as the script of a 'Programmable Source'
## Author: Kelton Halbert
## Institution: University of Wisconsin - Madison
## Department: Atmospheric and Oceanic Sciences
## Research Group: Cooperative Institute for Meteorological Satellit... | {"hexsha": "7517cdd54c1a70362b92df06022092d4ce272326", "size": 4379, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/pvplugin.py", "max_stars_repo_name": "keltonhalbert/LOFT", "max_stars_repo_head_hexsha": "23a242dd23036a50a932a25ecb85116ce3194177", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
# -*- coding: utf-8 -*-
import numpy as np
import torch as th
def is_indexable(data):
if isinstance(data, tuple):
return True
elif isinstance(data, list):
return True
elif isinstance(data, np.ndarray):
return True
elif isinstance(data, th._TensorBase):
return True
... | {"hexsha": "ab8b064feaec20c0eceb9698677d4cadb85e4842", "size": 532, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/flare/util/iter.py", "max_stars_repo_name": "mountain/planetarium", "max_stars_repo_head_hexsha": "14c5a75f9ac0be36f28d059c7bf7a77635d617da", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
This script is organized like so:
+ `if __name__ == "__main__" sets up the Streamlit UI elements
+ `generate_image` houses interactions between UI and the CLIP image
generation models
+ Core model code is abstracted in `logic.py` and imported in `generate_image`
"""
import streamlit as st
from pathlib import Path
... | {"hexsha": "d5fdac1838719736239d9b0bc5363ffd34a4045e", "size": 19174, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "marklr/vqgan-clip-app", "max_stars_repo_head_hexsha": "23edb7ae6234ab177a91865c02be160151fcf566", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import torch
import numpy as np
def squash(tensor):
"""
Squash function, defined in [1]. Works as a non-linearity for CapsNets.
Input tensor will be of format (bs, units, C, H, W) or (bs, units, C)
Norm should be computed on the axis representing the number of units.
Parameters
----------
... | {"hexsha": "f36202abd36716cf3e5b3f4142628b339ffbcd01", "size": 1245, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "apsvieira/Capsule-Networks", "max_stars_repo_head_hexsha": "18bb4429bbcec0508f7760a14c312eb9fdcdd117", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 22 00:39:18 2020
@author: nikbakht
"""
import tensorflow as tf
from tensorflow.keras.layers import Layer
import numpy as np
class Data(Layer):
def __init__(self,Nuser, **kwargs):
super(Data, self).__init__(**kwargs)
self.EX=100
self.EY=100
... | {"hexsha": "65c875d98cbc12f75d6a7b98658d78d7f27c454a", "size": 4818, "ext": "py", "lang": "Python", "max_stars_repo_path": "Cellular/Uplink/lib/Data0.py", "max_stars_repo_name": "FerdinandHannequart/Nikbakht", "max_stars_repo_head_hexsha": "85e1b3ec400338de0dea6ad37ce773024d4cd571", "max_stars_repo_licenses": ["MIT"], ... |
from pytest import raises
from numpy import arange, prod, array, full
from hypothesis import given, example
from hypothesis.strategies import integers, one_of
from ..ndindex import ndindex
from ..tuple import Tuple
from ..integer import Integer
from .helpers import ndindices, check_same, short_shapes
@example(..., ... | {"hexsha": "bd6022a64c659c112d5499ba77092d7ca3811c36", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "ndindex/tests/test_newshape.py", "max_stars_repo_name": "Quansight/ndindex", "max_stars_repo_head_hexsha": "5957c70d1ab5fab66f7c87aba8030a45f858085c", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule Cubature_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("Cubature")
JLLWrappers.@generate_main_file("Cubature", UUID("7bc98958-0e37-5d67-a6ac-a3a19030071a"))
end # module Cubature_jll
| {"hexsha": "14f9e0436600d5f688230d28dc637903a42b685f", "size": 310, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Cubature_jll.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Cubature_jll.jl", "max_stars_repo_head_hexsha": "b8ebd00e185c66d6f20613dbb30ad469f8d144e7", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma has_derivative_power[simp, derivative_intros]:
fixes f :: "'a :: real_normed_vector \<Rightarrow> 'b :: real_normed_field"
assumes f: "(f has_derivative f') (at x within S)"
shows "((\<lambda>x. f x^n) has_derivative (\<lambda>y. of_nat n * f' y * f x^(n - 1))) (at x within S)"
[PROOF STATE]
pro... | {"llama_tokens": 376, "file": null, "length": 2} |
import os.path as osp
import Image
from scipy.misc import fromimage
import numpy as np
from ImageProcessing import thresholdNDArray
from DefinitionsAndUtils import *
from GraphAndHistogramUtilities import countQuantiles
from CurrentLM import applyCurrentLM, iles, ileNames
def applyPredThresh(pixels):
# zero remo... | {"hexsha": "1e06435b59cdbeef586efd393b88f7746919e2c7", "size": 1782, "ext": "py", "lang": "Python", "max_stars_repo_path": "thresholdAnImage.py", "max_stars_repo_name": "mfenner1/py_coloc_utils", "max_stars_repo_head_hexsha": "1d98c8e9934928ced9d92f8dcef64471aa4b9dbc", "max_stars_repo_licenses": ["Unlicense", "BSD-3-Cl... |
# -*- coding: utf-8 -*-
from scipy.constants import Avogadro
from pymatgen.core.structure import Structure as Structure_PMG
# from pymatgen.analysis.prototypes import AflowPrototypeMatcher
from simmate.database.base_data_types import (
DatabaseTable,
table_column,
Spacegroup,
)
# TODO:
# Explore polymo... | {"hexsha": "0da110f43f4e8c419cf5f32ced055a04a438d10c", "size": 14381, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/simmate/database/base_data_types/structure.py", "max_stars_repo_name": "sionab/simmate", "max_stars_repo_head_hexsha": "6dedea7310829aae425bf3393e7923e454a0129f", "max_stars_repo_licenses": [... |
import numpy as np
import heapq
import os
import time
import random
import csv
import scipy as sp
import scipy.stats
# Global Variables for easier use in the simulation.
# ----------------------------------- Parameters -----------------------------------
pm = 0 # Number of parallel simulations
k = 0 # Num... | {"hexsha": "d7db0c2e5199d9d2fb56960ce1d32f4c90281879", "size": 29974, "ext": "py", "lang": "Python", "max_stars_repo_path": "5_server_queue.py", "max_stars_repo_name": "LaughMachine/Summer-Internship-Project", "max_stars_repo_head_hexsha": "9f4a0560853de2b988d08716479b3e62f092f85b", "max_stars_repo_licenses": ["MIT"], ... |
from wtpy import BaseCtaStrategy
from wtpy import CtaContext
import numpy as np
import statsmodels.tsa.stattools as ts
# 我们首先创建一个函数用来协整检验
def cointegration_check(series01, series02):
urt_1 = ts.adfuller(np.array(series01), 1)[1]
urt_2 = ts.adfuller(np.array(series02), 1)[1]
# 同时平稳或不平稳则差分再次检验
... | {"hexsha": "e0def8c442ba4bd30c1e801bb4c8867caa3c5a59", "size": 4543, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/cta_arbitrage_bt/Strategies/T1.py", "max_stars_repo_name": "systemtrader/wtpy", "max_stars_repo_head_hexsha": "5654662618b7281d12eedc4a782251838e7a9048", "max_stars_repo_licenses": ["MIT"], ... |
#=~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~=#
# Problem set 5 solutions
# Written by Tyler Ransom
# Commented by Giuseppe Grasso
# Recording available in Notability
#=~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~=#
using Random
using LinearAlgebra
using Statistics
using Optim
using DataFrames
using DataFramesM... | {"hexsha": "66898fa3781458f6f7d842c082c4bdedec263a1c", "size": 20103, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ProblemSets/PS5-ddc/PS5solutions_GG.jl", "max_stars_repo_name": "peppegrass/fall-2020", "max_stars_repo_head_hexsha": "03f90548ca4d800146bbeaf9dceca917a21c1195", "max_stars_repo_licenses": ["MIT"]... |
import gc
import numba
from numba import jit
import numpy as np
import sklearn
import tqdm
import warnings
@jit(nopython=True, nogil=True, fastmath=True)
def _update_wgrad_clipped(learning_rate, loss, w1, w2):
"""same as above, clamped in unit sphere"""
for k in range(w1.size):
grad = loss * w2[k]
... | {"hexsha": "461d48046f6c4e9b6e5625441ff1a85c3ac324f4", "size": 7732, "ext": "py", "lang": "Python", "max_stars_repo_path": "csrgraph/ggvec.py", "max_stars_repo_name": "netrias/CSRGraph", "max_stars_repo_head_hexsha": "b35460c8d84906d203f66b511b8eb553a97a622b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 79, ... |
"""librarian: Librarian and Settings classes.
Contains the Librarian class and the Settings class. The Librarian class is the
main Librarian class sets up and runs the librarian program. It reads settings
from the librarian.yaml file.
Copyright (c) 2017 by Jeff Bass.
License: MIT, see LICENSE for more details.
"""
... | {"hexsha": "b45d7a6f1e3403d1d60af3d7afba31a4d9dd5090", "size": 8342, "ext": "py", "lang": "Python", "max_stars_repo_path": "librarian-prototype/librarian/helpers/library.py", "max_stars_repo_name": "jeffbass/yin-yang-ranch", "max_stars_repo_head_hexsha": "234a3120d4f134a65ab8f064fb5805a436874498", "max_stars_repo_licen... |
from src import data_generator
from io import StringIO
import numpy as np
import pytest
class TestFetchDataset:
def test_fetch_dataset_from_right_formatted_data(self):
source = StringIO(
"species,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g\n"
"0,0.25454545454545... | {"hexsha": "645ee5d60e02040ee8d88fe23132e882eb201b66", "size": 979, "ext": "py", "lang": "Python", "max_stars_repo_path": "kfp/components/data_generator/tests/test_main.py", "max_stars_repo_name": "hotchpotch/lab_sample_pipelines", "max_stars_repo_head_hexsha": "7c266d807eff861140d4b51c267b0bfba8c50263", "max_stars_rep... |
import numpy as np
import random
data = np.loadtxt('env_sorter.cfg',skiprows=10)
print(data)
print(np.shape(data), type(data))
print(data[3][3], type(data[3][3]))
# print(data[73][69], data[88][38])
file1 = open("addverb2.scen","a")
count = 0
for i in range(1000):
x_i = random.randint(1,87)
y_i = random.rand... | {"hexsha": "df6b6ab2bb58c979685d6b85f3258753ebbe1141", "size": 659, "ext": "py", "lang": "Python", "max_stars_repo_path": "addverb_random_benchmarks.py", "max_stars_repo_name": "Aakriti05/ORCA_Astar-warehouse-navigation", "max_stars_repo_head_hexsha": "8699b30c25cacf1a7be1f56dc34db90d5a757b36", "max_stars_repo_licenses... |
#Author Lucas Saraiva
import re
import networkx as nx
import sys
def compute_triangle_and_balance(G):
triangles = {}
ballanced = 0
unballanced = 0
print len(G.edges())
print "Calculating triangles status, it may take a while"
triadsVisited = set()
i = 0
for u in G.nodes():
... | {"hexsha": "056b39be683944c473089ec3d1809a2737f648b9", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Triangles/triangles_python27.py", "max_stars_repo_name": "saraiva3/Shape-of-War", "max_stars_repo_head_hexsha": "454f7d77f919742420dfa4cdc44820f0c88f91ea", "max_stars_repo_licenses": ["MIT"], ... |
! $UWHPSC/codes/fortran/ifelse1.f90
program ifelse1
implicit none
real(kind=8) :: x
integer :: i
i = 3
if (i<2) then
print *, "i is less than 2"
else
print *, "i is not less than 2"
endif
if (i<=2) then
print *, "i is less or equal to 2"
else if (i/=5) the... | {"hexsha": "1720c37ebf970a5e151401c681304f788afe54f4", "size": 456, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "uwhpsc/codes/fortran/ifelse1.f90", "max_stars_repo_name": "philipwangdk/HPC", "max_stars_repo_head_hexsha": "e2937016821701adb80ece5bf65d43d1860640c0", "max_stars_repo_licenses": ["MIT"], "max_st... |
\section{Padding}
\label{sec:padding}
The \fw{Padding} module provides a wrapper widget type, \fw{Padded},
which wraps another widget with a specified amount of padding on any
or all four of its sides.
We create padded widgets with the \fw{padded} function, which takes a
child of type \fw{Widget a} and a padding valu... | {"hexsha": "441a0659ca9ce71b657420892351b2745efabdc0", "size": 2248, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/ch4/Padded.tex", "max_stars_repo_name": "erikd/vty-ui", "max_stars_repo_head_hexsha": "250474a8d9dc5e22b8dc80cfa871d9ac4c12ce04", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": ... |
import pickle
import numpy as np
import re
counts_list = []
price_list = []
for i in range(9):
with open('info/info-{}.pkl'.format(i+1), 'rb') as f:
temp = pickle.load(f)
counts_list.append(temp[0])
price_list.append(temp[1])
# Aggergate information
counts = counts_list[0]
for i in range... | {"hexsha": "1f8a90f8bd00cec4cb11ff06ba7e2afb6a7f7651", "size": 1065, "ext": "py", "lang": "Python", "max_stars_repo_path": "info.py", "max_stars_repo_name": "HarangDev/ka-1", "max_stars_repo_head_hexsha": "065f4fa7966fbb0a5e97a696e9890ee2d291826b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "ma... |
import numpy as np
import scipy as sp
#Given a data matrix X, this picks
| {"hexsha": "902c20b9996c6009179a55121303fe7e88966cd3", "size": 74, "ext": "py", "lang": "Python", "max_stars_repo_path": "bandwidth_selection.py", "max_stars_repo_name": "bubble-07/FETISH3", "max_stars_repo_head_hexsha": "243429acd16d55b30a0de4f1f5d72b0cd7dc84b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
// (C) Copyright 2015 - 2018 Christopher Beck
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE or copy at http://www.boost.org/LICENSE_1_0.txt)
#ifndef SPIRIT_PO_EXCEPTIONS_HPP_INCLUDED
#define SPIRIT_PO_EXCEPTIONS_HPP_INCLUDED
#include <boost/spirit/include/support_... | {"hexsha": "603ae6fcfc2de9067571482382aea9319c35e97c", "size": 2212, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/spirit_po/exceptions.hpp", "max_stars_repo_name": "fcojavmc/spirit-po", "max_stars_repo_head_hexsha": "5f88675757a44f86eae845a703c697f93d9cae39", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
'''
Created on Jul 3, 2014
@author: roj-idl71
'''
import os
import datetime
import numpy
try:
from gevent import sleep
except:
from time import sleep
from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader
from schainpy.model.data.jrodata import Voltage
from schainpy.model.proc.jropro... | {"hexsha": "1ac362b7ed779c7ddd1f7f913f9af6d1c368946c", "size": 19528, "ext": "py", "lang": "Python", "max_stars_repo_path": "schainpy/model/io/jroIO_usrp.py", "max_stars_repo_name": "LuisRondoCuevas/schainpy", "max_stars_repo_head_hexsha": "ef41efe03993a6ae56e587334a1bfc529fccc2df", "max_stars_repo_licenses": ["BSD-3-C... |
#
# Princeton University licenses this file to You 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 writ... | {"hexsha": "e04beea9d192547fa647471f0760b4ec2da65d3b", "size": 177428, "ext": "py", "lang": "Python", "max_stars_repo_path": "psyneulink/core/components/functions/nonstateful/transferfunctions.py", "max_stars_repo_name": "MetaCell/PsyNeuLink", "max_stars_repo_head_hexsha": "aeddf3e8ea62504a5d928b100b59aa18e593156c", "m... |
'''
Created on Sep 19, 2013
@author: johannes
'''
# create some test cases
import scipy as SP
from limix_legacy.ensemble import lmm_forest_utils as utils
import h5py
from limix_legacy.ensemble.lmm_forest import Forest as MF
import os
import unittest
class TestMixedForest(unittest.TestCase):
def setUp(self, n=10... | {"hexsha": "7f95d5fbd911415024080d45b6f958627f9b4fb3", "size": 8456, "ext": "py", "lang": "Python", "max_stars_repo_path": "limix_legacy/test/lmm_forest/test_lmm_forest.py", "max_stars_repo_name": "michoel-lab/limix-legacy", "max_stars_repo_head_hexsha": "cd6c9887a2c411372beeddde3a86979b2aa21837", "max_stars_repo_licen... |
import numpy as np
import pandas as pd
import random
import subprocess
from termcolor import colored
import matplotlib.pyplot as plt
MIN_NUMBERS = 8
MAX_NUMBERS = 24
NUMBER_STEP = 1
TEST_REPEAT = 5
MIN_RANGE = 0
MAX_RANGE = 100
FLOAT_MIN = -3.40282e+38
def compute_angles(numbers):
angles = [round(np.arctan( (numb... | {"hexsha": "3e64f2c66628474f8a76cf12e98c699e62cda4be", "size": 4104, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "xstupi00/Line-of-Sight", "max_stars_repo_head_hexsha": "1d4b00d655e34017cd152c088a7329a12cd3baaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# coding=utf-8
# Copyright 2018 The Dopamine 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 law... | {"hexsha": "c70b3687ccdd277d412fcd8a7f5f08bebc97526c", "size": 4265, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/dopamine/discrete_domains/atari_lib_test.py", "max_stars_repo_name": "aghinsa/dopamine", "max_stars_repo_head_hexsha": "e7d780d7c80954b7c396d984325002d60557f7d1", "max_stars_repo_licenses": ... |
import os
import numpy as np
import argparse
from sklearn.model_selection import StratifiedKFold
#from data.image_folder import make_dataset
#from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import json
import pandas as pd
import numpy as np
import os, sys
import glob
import... | {"hexsha": "d1fc417939f9071ab9558bc0b04de9acb77ad0f7", "size": 6734, "ext": "py", "lang": "Python", "max_stars_repo_path": "versions/v1/v1_tb/aux_kfold.py", "max_stars_repo_name": "otavares93/rxpix2pix", "max_stars_repo_head_hexsha": "cc72ff165769bc4f0c312372fe7c3b52ecda45a0", "max_stars_repo_licenses": ["BSD-3-Clause"... |
[GOAL]
α : Type u_1
β : Type u_2
γ : Type u_3
δ : Type u_4
inst✝¹ : AddCommMonoid α
inst✝ : TopologicalSpace α
f g : β → α
a b : α
s : Finset β
⊢ HasSum (fun x => 0) 0
[PROOFSTEP]
simp [HasSum, tendsto_const_nhds]
[GOAL]
α : Type u_1
β : Type u_2
γ : Type u_3
δ : Type u_4
inst✝² : AddCommMonoid α
inst✝¹ : TopologicalSp... | {"mathlib_filename": "Mathlib.Topology.Algebra.InfiniteSum.Basic", "llama_tokens": 75911} |
import os
import json
import xml.etree.ElementTree as ET
from PIL import Image
from collections import defaultdict
import torch
import numpy as np
import pycocotools.mask as mask_util
from torchvision import transforms
from .generalized_dataset import GeneralizedDataset
VOC_CLASSES = (
"aeroplane",
"bicycle"... | {"hexsha": "816154425db7b762d5213474d772bd601b9a696f", "size": 5772, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch_mask_rcnn/datasets/voc_dataset.py", "max_stars_repo_name": "JinchengHeRyan/STATS402_Final_MaskRcnn", "max_stars_repo_head_hexsha": "c8103751008afb2f969c7e321a7e843a50c0f681", "max_stars_re... |
import keras
import numpy as np
import AxonDeepSeg.ads_utils as ads
from scipy import ndimage
from skimage import exposure
import AxonDeepSeg.ads_utils
from AxonDeepSeg.ads_utils import convert_path
class DataGen(keras.utils.Sequence):
"""Generates data for Keras"""
def __init__(
self,
ids... | {"hexsha": "913375dc88d7d8c51ea4bdd6cf418d804d1798fc", "size": 4728, "ext": "py", "lang": "Python", "max_stars_repo_path": "AxonDeepSeg/data_management/input_data.py", "max_stars_repo_name": "mariehbourget/axondeepseg", "max_stars_repo_head_hexsha": "23c3f7355f85b9800e16acca980be1f5cae79b21", "max_stars_repo_licenses":... |
/**
* \file dcs/testbed/constant_signal_generator.hpp
*
* \brief Generates constant signals.
*
* \author Marco Guazzone (marco.guazzone@gmail.com)
*
* <hr/>
*
* Copyright 2012 Marco Guazzone (marco.guazzone@gmail.com)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this... | {"hexsha": "e50b6e7e6504fed6a6e4d737f7605124bc6b4e87", "size": 2348, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "inc/dcs/testbed/constant_signal_generator.hpp", "max_stars_repo_name": "sguazt/dcsxx-testbed", "max_stars_repo_head_hexsha": "e7210f0c7f54256d5bf0c90297e0c4f9eaf82da0", "max_stars_repo_licenses": ["... |
# coding: utf-8
def load_pickle(fname):
with open(fname, 'rb') as f:
return pickle.load(f)
## time
def aexp2zred(aexp):
return [1.0/a - 1.0 for a in aexp]
def zred2aexp(zred):
return [1.0/(1.0 + z) for z in zred]
def lbt2aexp(lts):
import astropy.units as u
from astropy.cosmology import W... | {"hexsha": "6ccafc0da1910ad8584e619f6b3913eb1e54324d", "size": 8611, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyram/analysis/save_mpgs3.py", "max_stars_repo_name": "Hoseung/pyRamAn", "max_stars_repo_head_hexsha": "f9386fa5a9f045f98590039988d3cd50bc488dc2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from __future__ import division
from psychopy.visual import TextStim, Window
from psychopy import core, event, gui, data, logging
import numpy as np
import pandas as pd
import os
from routines import Routine
# Code for the choice titration experiment of Weber and Chapman (2005) https://doi.org/10.1016/j.obhdp.2005.01... | {"hexsha": "2c85e8a230e5861025c4e5d171799a8b00fda215", "size": 3495, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_2.py", "max_stars_repo_name": "laurafontanesi/psych-routines", "max_stars_repo_head_hexsha": "fe3a1ff055fd5b32bc1ca666a2d86ee19b3a2f49", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include "rotation.h"
#include <Eigen/Dense>
#include <Eigen/Geometry>
#include <iostream>
namespace cpt {
Eigen::Matrix3f get_angle_axis_rotation_matrix(const Eigen::Vector3f& angleAxis) {
return Eigen::AngleAxisf(angleAxis.norm(), angleAxis.normalized()).toRotationMatrix();
}
Eigen::Matrix3f get_euler_xyz_ro... | {"hexsha": "05314a00d4e969315f3c3bd9668fc77d9527f72b", "size": 913, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/math/rotation.cpp", "max_stars_repo_name": "b3h47pte/cuda-path-tracing", "max_stars_repo_head_hexsha": "b874b86f15b4aca18ecd40e9eb962996298f5fa8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""Script to calulate the value of pre-flop hands for n_players
Examples:
Call this file like so to get the help:
```bash
$ python monte_carlo_rank.py --help
Usage: monte_carlo_rank.py [OPTIONS]
Multithreaded monte carlo pre-flop hand equity calculation.
Over `n_threads` threads, rank the pre-flop hands accord... | {"hexsha": "8fda9163a2c3393cc74bef909c9c6f1aacd09efd", "size": 8483, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/preflop_hand_ranking/monte_carlo_rank.py", "max_stars_repo_name": "keithlee96/pluribus-poker-AI", "max_stars_repo_head_hexsha": "15e52fe73dd09570e782dd0e7b9069865eb5823d", "max_stars_repo... |
# Elaine Laguerta (github: @elaguerta)
# LBNL GIG
# File created: 19 February 2021
# Create Circuit class to mirror a dss Circuit object
# used by Solution objects to solve powerflow
import numpy as np
import pandas as pd
from . bus_group import BusGroup
from . line_group import LineGroup
from . load_group import Loa... | {"hexsha": "5d83041491983c7cebe106cba0a3c8173462fc7c", "size": 7955, "ext": "py", "lang": "Python", "max_stars_repo_path": "gigpower/src/gigpower/circuit.py", "max_stars_repo_name": "elaguerta/gigpower", "max_stars_repo_head_hexsha": "22e0a6152fa8d7a04f6067f3d500bfee042a98f9", "max_stars_repo_licenses": ["Unlicense"], ... |
#include <gtest/gtest.h>
#include <boost/math/quaternion.hpp>
#include "test_util.h"
#include "ApproachCube.hpp"
#include "SwarmieSensors.hpp"
#include "Tag.hpp"
class ApproachCubeTest : public testing::Test
{
protected:
SwarmieSensors sensors;
ApproachCube approach;
boost::math::quaternion<double> defaultO... | {"hexsha": "64466cfa9e4e5adf145dc3647cd7e5cef44da987", "size": 5223, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/behaviours/test/approach_cube_test.cpp", "max_stars_repo_name": "BCLab-UNM/SwarmBaseCode-Modular-Public", "max_stars_repo_head_hexsha": "2061796570baf65deeb74f29444fcaf3b6464aa1", "max_stars_rep... |
from itertools import combinations_with_replacement as cwr
import numpy as np
from rpy2.robjects.packages import importr
import rpy2.robjects as ro
import scipy.sparse as sp
import anndata2ri
from anndata._core.sparse_dataset import SparseDataset
from controller.cellar.utils.exceptions import UserError
from ._neighbo... | {"hexsha": "60f20d5dd89f7f29faa8d269d502d1919c65205a", "size": 6593, "ext": "py", "lang": "Python", "max_stars_repo_path": "controller/cellar/core/_spatial_scores.py", "max_stars_repo_name": "euxhenh/cellar", "max_stars_repo_head_hexsha": "679387216043f3d287ea29a15f78868f412d2948", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma equivalent_complements:
assumes \<open>complements F G\<close>
assumes \<open>equivalent_registers G G'\<close>
shows \<open>complements F G'\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. complements F G'
[PROOF STEP]
apply (rule complementsI)
[PROOF STATE]
proof (prove)
goal (2 subg... | {"llama_tokens": 270, "file": "Registers_Laws_Complement_Quantum", "length": 3} |
# This file was generated, do not modify it. # hide
using HTTP
using MLJ
using PyPlot
import DataFrames: DataFrame, describe
using UrlDownload
MLJ.color_off() # hide
url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data"
header = ["Class", "Alcool", "Malic acid", "Ash", "Alcalinity of ash",
... | {"hexsha": "75f90ce643128b2e63bd80be5a3013f2e7411e7b", "size": 561, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/end-to-end/wine/code/ex1.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licen... |
(* 1st-order unification did not work when in competition with pattern unif. *)
Set Implicit Arguments.
Lemma test : forall
(A : Type)
(B : Type)
(f : A -> B)
(S : B -> Prop)
(EV : forall y (f':A->B), (forall x', S (f' x')) -> S (f y))
(HS : forall x', S (f x'))
(x : A),
S (f x).
Proof.
intros. eappl... | {"author": "mattam82", "repo": "Coq-misc", "sha": "60bc3cbe72083f4fa1aa759914936e4fa3d6b42e", "save_path": "github-repos/coq/mattam82-Coq-misc", "path": "github-repos/coq/mattam82-Coq-misc/Coq-misc-60bc3cbe72083f4fa1aa759914936e4fa3d6b42e/test-suite/bugs/closed/shouldsucceed/2244.v"} |
"""
Provides functions for processing a video file into numpy arrays
for RGB data and optical flow data, which can be used with the I3D model.
"""
import cv2
import numpy as np
def _raw_numpy_array(video_file, nframes=None):
"""
Loads a video from the given file. Will set the number
of frames to `nframes` if t... | {"hexsha": "284896f1a986946cea2667aa9b467c23ce2dd02f", "size": 3839, "ext": "py", "lang": "Python", "max_stars_repo_path": "video-classifier/process_video.py", "max_stars_repo_name": "Sben05/Sportable", "max_stars_repo_head_hexsha": "70a7c315b24a72e32a957978ec901d35507a0456", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
from dataclasses import dataclass
from typing import Dict, List
import numpy as np
import numpy.testing as npt
from caffe2.python import schema, workspace
from ml.rl import types as rlt
from ml.rl.preprocess... | {"hexsha": "62d9e03ebfc781345b411924ee43757b6c4201f3", "size": 62107, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml/rl/test/preprocessing/test_feature_extractor.py", "max_stars_repo_name": "michaeltashman/Horizon", "max_stars_repo_head_hexsha": "ee310b34adeb807bbae379a6e1703d0f725f26a9", "max_stars_repo_lic... |
import pandas as pd
from os import path
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.ticker as mticker
from cartopy.feature.nightshade import Nightshade
from datetime import datetime
import numpy as np
import shape... | {"hexsha": "e6c6a5a8b83ab4e4449445ff1101893df71b76e9", "size": 3880, "ext": "py", "lang": "Python", "max_stars_repo_path": "freshmapping.py", "max_stars_repo_name": "JacobParrott/OccultationProfiler", "max_stars_repo_head_hexsha": "f537555d44239e29bbba69c83d2413eec14f9c88", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import MySQLdb
import os, sys, anydbm, time
#from config import datb, dataloc
#db = anydbm.open("./db/" + cluster,'c')
import lib
#lib.galextinct(cluster, db)
#db[sys.argv[0][:-3]] = 'Started/' + time.asctime()
spectype = 'full'
if len(sys.argv) > 2:
if sys.argv[2] == 'spec': spectype = 'spec'
listfile... | {"hexsha": "5047f4fe4c83b950445227a5fb82de08f547793c", "size": 2197, "ext": "py", "lang": "Python", "max_stars_repo_path": "mkplotsspecme.py", "max_stars_repo_name": "deapplegate/wtgpipeline", "max_stars_repo_head_hexsha": "9693e8562022cc97bf5a96427e22965e1a5e8497", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import json
import csv
import codecs
from collections import Counter, namedtuple
import numpy as np
from correlations import Correlations
#from analyzer import SortedThetas
SortedThetas = namedtuple('SortedThetas', 'thetas labels histogram correlations')
class ComplexDecoder(object):
'''Decodes json complex array... | {"hexsha": "6f876350811ec7c6ebdaf94080bc717bda476a1b", "size": 7457, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dao.py", "max_stars_repo_name": "DogeMajor/GDFT", "max_stars_repo_head_hexsha": "bd84a8cef8d68f88c3c80de9936ee65ce85fcb40", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
import time
import argparse
import traceback
import numpy as np
import torch
from torch.utils.data import DataLoader
import networkx as nx
import dgl
from models import MLP, InteractionNet, PrepareLayer
from dataloader import MultiBodyGraphCollator, MultiBodyTrainDataset,\
MultiBodyValidDataset, MultiBodyTestData... | {"hexsha": "103aa2fe2e1217139be35631e990fa4d9d552cd5", "size": 7027, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pytorch/graphsim/train.py", "max_stars_repo_name": "ketyi/dgl", "max_stars_repo_head_hexsha": "a1b859c29b63a673c148d13231a49504740e0e01", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# Activation normalization from Kingma & Dhariwal (2018)
# Author: Philipp Witte, pwitte3@gatech.edu
# Date: January 2020
using InvertibleNetworks, LinearAlgebra, Test
# Input
nx = 64
ny = 64
k = 10
batchsize = 4
# Input image: nx x ny x k x batchsize
X = randn(Float32, nx, ny, k, batchsize)
Y = randn(Float32, nx, n... | {"hexsha": "dd7f4f049d21c2b5c332a7a67bab3d73af26de9d", "size": 572, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/layers/layer_actnorm.jl", "max_stars_repo_name": "PetersBas/InvertibleNetworks.jl", "max_stars_repo_head_hexsha": "c53dacf426ecd1381f79f297f6954e6695c515b3", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python3
# -*- coding: latin-1 -*-
# HEREHEREHERE
# MAINMAINMAIN
#############################################################################
# fits2psqlraw
#
# ls -1 *fits > input.txt # 7421 files
# fits2psqlraw --list input.txt -D wayne -t myfits -c
#
# /home/git/clones/NGSL/data/stis_xxx/fits2psql... | {"hexsha": "3afcd69afafc11987da8a83ec26f5ae14e388198", "size": 20802, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/fits2psqlraw.py", "max_stars_repo_name": "The-SMTSci/NGSL", "max_stars_repo_head_hexsha": "dbdad80ef521811387eeaafd90821b07df2fe6b0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
from functools import partial
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pycpd import deformable_registration
import numpy as np
import time
def visualize(iteration, error, X, Y, ax):
plt.cla()
ax.scatter(X[:,0], X[:,1], X[:,2], color='red', label='Target')
ax.scatter(Y[:... | {"hexsha": "0a5779d986326eef36e40101288e5afed58116e8", "size": 1457, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/fish_deformable_3D.py", "max_stars_repo_name": "KingDeng005/pycpd", "max_stars_repo_head_hexsha": "a2d383d5aee96b2e0fc4d5efa238efb57f8536c8", "max_stars_repo_licenses": ["MIT"], "max_star... |
!==============================================================================!
subroutine Backup_Mod_Write_Variable(fh, disp, vc, var_name, var)
!------------------------------------------------------------------------------!
! Writes a whole variable to backup file. !
!--------... | {"hexsha": "81d5d1d2a17b042e0848a5e432dd9a72831ca44b", "size": 1578, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/Process/Backup_Mod/Write_Variable.f90", "max_stars_repo_name": "MassimoBorrelliPhysicist/T-Flows", "max_stars_repo_head_hexsha": "48c330f1e9576bf0ef79edbee1b7bc09092e0706", "max_stars_re... |
from typing import Optional
import gym
import numpy as np
import pytest
from gym.spaces import Box, Dict, Discrete
from gym.utils.env_checker import check_env
class ActionDictTestEnv(gym.Env):
action_space = Dict({"position": Discrete(1), "velocity": Discrete(1)})
observation_space = Box(low=-1.0, high=2.0,... | {"hexsha": "32dd2f7b89c3e6f9a70cbd84209c9c886bf94507", "size": 1073, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/gym/tests/utils/test_env_checker.py", "max_stars_repo_name": "maxgold/icml22", "max_stars_repo_head_hexsha": "49f026dd2314091639b52f5b8364a29e8000b738", "max_stars_repo_licenses": ["MIT"], "m... |
%%%%% CPLOP %%%%%
\section{\cploplong{}}\label{sec:background:cplop}
This section details the aspects of \cploplong{} (\cplop{}) relevant to \mstlong{} (\mst{}).
It explains the nature of \pyros{} and the \pyro{}ing process, including what segments of \ecoli{} \dna{} \cplop{} researchers use and how they collect the \... | {"hexsha": "fd2335f0312c779c2914dc9d2f118109237d42e8", "size": 22319, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/background/cplop.tex", "max_stars_repo_name": "jmcgover/thesis", "max_stars_repo_head_hexsha": "25664684158d00864dbe697276d2691ba84461cb", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma compE1_eq_Call [simp]:
"compE1 Vs e = obj\<bullet>M(params) \<longleftrightarrow> (\<exists>obj' params'. e = obj'\<bullet>M(params') \<and> compE1 Vs obj' = obj \<and> compEs1 Vs params' = params)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (compE1 Vs e = obj\<bullet>M(params)) = (\<exists>o... | {"llama_tokens": 182, "file": "JinjaThreads_Compiler_Compiler1", "length": 1} |
import pandas as pd
import numpy as np
from tqdm import trange
from time import sleep
import glob
import os
import matplotlib.pyplot as plt
# Import module to get a current time and date used to name the files containing normalization information
from datetime import datetime
import csv
try:
# Use gitpython to ... | {"hexsha": "bef2e58abaff75959cd129834ffbca719868b106", "size": 19147, "ext": "py", "lang": "Python", "max_stars_repo_path": "SI_Toolkit/load_and_normalize.py", "max_stars_repo_name": "jhuebotter/CartpoleSNNdemo", "max_stars_repo_head_hexsha": "d18a85cbc45bff48295c46c9cd8c9fc00192318c", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 4 12:25:18 2019
@author: liujinyang
"""
import zlib
import pandas as pd
import sys
import os
import tarfile
import glob
import multiprocessing as mp
import re
import json
import pickle
import time
import itertools
import shutil
import lzma
import ... | {"hexsha": "0c45cf3fb9a9c5ca823cff1e8a22a1325241193d", "size": 13361, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/logzip/zipper_longgest.py", "max_stars_repo_name": "JinYang88/LogZip", "max_stars_repo_head_hexsha": "796bd632623d010989fb7dfa61f51c72ea6b68b4", "max_stars_repo_licenses": ["MIT"], "max_stars... |
(* Title: HOL/Auth/KerberosV.thy
Author: Giampaolo Bella, Catania University
*)
section\<open>The Kerberos Protocol, Version V\<close>
theory KerberosV imports Public begin
text\<open>The "u" prefix indicates theorems referring to an updated version of the protocol. The "r" suffix indicates theorems wh... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Auth/KerberosV.thy"} |
"""
Collection of generic numpy array functions
"""
import math
import warnings
import numpy as np
from margrie_libs.margrie_libs.signal_processing.exceptions import BadRandomError, PeakDetectionError
def _get_decimate_new_n_pnts(trace, window_width, end_method):
methods = ("drop", "strict", "pad")
... | {"hexsha": "e0485bec65d56b984c8b4e363e80c4ab4a1d4cbe", "size": 10436, "ext": "py", "lang": "Python", "max_stars_repo_path": "margrie_libs/margrie_libs/signal_processing/mat_utils.py", "max_stars_repo_name": "Sepidak/spikeGUI", "max_stars_repo_head_hexsha": "25ae60160308c0a34e7180f3e39a1c4dc6aad708", "max_stars_repo_lic... |
%==============================================================================
% This code is part of the Matlab-based toolbox
% LagLDDDM - A Lagrangian Gauss--Newton--Krylov Solver for Mass- and
% Intensity-Preserving Diffeomorphic Image Registration
%
% For details and license info see
% -... | {"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/add-ons/LagLDDMM/mfDiffusionST.m"} |
# importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib
import numpy as np
import cv2
from warnings import warn
from collections import deque
from sklearn.cluster import KMeans as ClusterFinder
def grayscale(img):
"""Applies the Grayscale transform
... | {"hexsha": "a69fe4fa0b1f8bd0d0c577eebbb2844bf6887405", "size": 17506, "ext": "py", "lang": "Python", "max_stars_repo_path": "laneLines.py", "max_stars_repo_name": "tsbertalan/CarND-LaneLines-P1", "max_stars_repo_head_hexsha": "ba7bb0e1f60bb5c84425021b6bdb0d7162171fa8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env julia
push!(LOAD_PATH, ".")
using HelloWorld
using Test
@test HelloWorld.greet("John") == "Hello, John"
| {"hexsha": "c01fac05c52b2f113d92cc358d3234ac43c75d37", "size": 120, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hello_world/test.jl", "max_stars_repo_name": "vtavernier/jomw", "max_stars_repo_head_hexsha": "3de1c99579381be4485825f2942fb555da45be4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import logging
import numpy as np
import xobjects as xo
import xtrack.linear_normal_form as lnf
import xpart as xp # To get the right Particles class depending on pyheatail interface state
logger = logging.getLogger(__name__)
def _check_lengths(**kwargs):
length = None
for nn, xx in kwargs.items():
... | {"hexsha": "61205360fe00af0d87992b8aa534023f062d2ee3", "size": 17110, "ext": "py", "lang": "Python", "max_stars_repo_path": "xpart/build_particles.py", "max_stars_repo_name": "pkicsiny/xpart", "max_stars_repo_head_hexsha": "cddf3eb65ffc198c22dd37204139ce3177a9bd96", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
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