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# slc_prj.py
import os
import os.path as osp
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import astropy.units as au
import astropy.constants as ac
from matplotlib.colors import Normalize, LogNorm
from mpl_toolkits.axes_grid1 import ImageGrid
import xarray as xr
from ..load_sim import L... | {"hexsha": "c25ede03b630a0a1e586e7cfe54d386d5356ed20", "size": 12406, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyathena/tigress_ncr/slc_prj.py", "max_stars_repo_name": "jeonggyukim/pyathena", "max_stars_repo_head_hexsha": "f3c983d5c0a3f36e28134a4a6d3eb80ac26c2a8e", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
from keras_pretrained_models.imagenet_utils import preprocess_input
from keras.models import Model
from keras.preprocessing import image
from keras_pretrained_models.vgg19 import VGG19
base_model = VGG19(weights='imagenet')
model = Model(input=base_model.input, output=base_model.get_layer('fc2').ou... | {"hexsha": "f56f284092c849f808dfac76f220b00f503006bd", "size": 690, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract_image_features/keras_pretrained_models/extract_VGG19.py", "max_stars_repo_name": "schen496/auditory-hallucinations", "max_stars_repo_head_hexsha": "31b89df838a9f3c4558c7c3b69dbcd43c7f9de19"... |
[STATEMENT]
lemma getFresh: "finite V \<Longrightarrow> getFresh V \<in> var \<and> getFresh V \<notin> V"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite V \<Longrightarrow> getFresh V \<in> var \<and> getFresh V \<notin> V
[PROOF STEP]
by (metis (no_types, lifting) finite_subset getFresh_def infinite_var so... | {"llama_tokens": 124, "file": "Syntax_Independent_Logic_Syntax", "length": 1} |
# -*- coding: utf-8 -*-
from numpy import pi
from ....Methods.Slot.Slot.check import SlotCheckError
def check(self):
"""Check that the HoleM54 object is correct
Parameters
----------
self : HoleM54
A HoleM54 object
Returns
-------
None
Raises
-------
H54_W0CheckErr... | {"hexsha": "a8542b53f1023fe504bca1879cd8f79f30c0a0ba", "size": 727, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Methods/Slot/HoleM54/check.py", "max_stars_repo_name": "helene-t/pyleecan", "max_stars_repo_head_hexsha": "8362de9b0e32b346051b38192e07f3a6974ea9aa", "max_stars_repo_licenses": ["Apache-2.... |
import json
import os
import subprocess
import sys
from contextlib import contextmanager
from datetime import datetime
from pathlib import Path
from time import sleep
from typing import List, Union
import numpy as np
import torch
import torch.nn as nn
def create_logdir(root: Union[str, Path] = None):
if (root i... | {"hexsha": "f7c570c8e0928247da08e94c9dce16712108981a", "size": 4273, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "mil-ad/prospr", "max_stars_repo_head_hexsha": "a92177989f4480f1f2b43a48b3e18a6597ebba6d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_re... |
// Copyright Gavin Band 2008 - 2012.
// 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)
#ifndef GENFILE_COHORTINDIVIDUALSOURCE_HPP
#define GENFILE_COHORTINDIVIDUALSOURCE_HPP
#include <string>... | {"hexsha": "4b485022dfe359225cb32dd9fcfd06061844cefd", "size": 5264, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "genfile/include/genfile/CohortIndividualSource.hpp", "max_stars_repo_name": "gavinband/bingwa", "max_stars_repo_head_hexsha": "d52e166b3bb6bc32cd32ba63bf8a4a147275eca1", "max_stars_repo_licenses": [... |
import subprocess, operator, random, msgpack, nltk, math, sys, os
from prettytable import PrettyTable
from nltk.corpus import stopwords
from datetime import datetime
from tqdm import tqdm
from PIL import Image
from collections import Counter
import numpy as np
import dateutil.parser
from utils import START_TIME, se... | {"hexsha": "b4b1827fdabeff37edae2dbc09f094cdd6b7f0e2", "size": 8571, "ext": "py", "lang": "Python", "max_stars_repo_path": "mention_graph.py", "max_stars_repo_name": "cfwelch/longitudinal_dialog", "max_stars_repo_head_hexsha": "9f2de780026565df6447301a134a3f2126b0e64b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from unittest import TestCase
from esbo_etc.classes.optical_component.Mirror import Mirror
from esbo_etc.classes.SpectralQty import SpectralQty
from esbo_etc.classes.target.FileTarget import FileTarget
import astropy.units as u
import numpy as np
class TestMirror(TestCase):
wl = np.arange(201, 205, 1) << u.nm
... | {"hexsha": "1069283408e7b63cf5fe4aa129a7940e1f4c3056", "size": 1144, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/optical_component/test_Mirror.py", "max_stars_repo_name": "LukasK13/ESBO-ETC", "max_stars_repo_head_hexsha": "d1db999f1670f2777c5227d79629d421f03e5393", "max_stars_repo_licenses": ["Apache-2... |
import numpy as np
import data_loader
import decision_tree
###############
# Toy example #
###############
'''
Toy example
dim_1
┃
╋ ○
┃
╋ × ○
┃
╋ ×
┃
━╋━━━╋━━━╋━━━╋━ dim_0
Print the tree and check the result by yourself!
'''
# data
features, labels = data_loader.toy_data_3(... | {"hexsha": "a0cf573ab6c8913c32dd00e4ccd113ef80df81f1", "size": 434, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment-3/decision_tree_check.py", "max_stars_repo_name": "ZhangShiqiu1993/CSCI-567-machine-learning", "max_stars_repo_head_hexsha": "07144b299aeb9f29c304798549ef2d44fe1f4083", "max_stars_repo_l... |
import numpy as np
import cv2
from .skeleton import _Skeleton
class Skeleton2D(_Skeleton):
"""
Class to visualise 2D skeletons on neutral background or original RGB.
"""
###########################################################################
# 2D drawing functions
#######################... | {"hexsha": "4b8c604adfb74884e4a8a467c291a7cfb3e6195b", "size": 5748, "ext": "py", "lang": "Python", "max_stars_repo_path": "humanpose/visualise/skeleton2d.py", "max_stars_repo_name": "kschlegel/HumanPose", "max_stars_repo_head_hexsha": "2976116bbc276d7c5aa75b3f7f5708284c70d30f", "max_stars_repo_licenses": ["Apache-2.0"... |
import os
import numpy as np
from demo_utils import plot_image
import svmbir
"""
This file demonstrates the generation of a 3D microscopy phantom followed by sinogram projection and reconstruction using MBIR.
The phantom, sinogram, and reconstruction are then displayed.
"""
# Simulated image parameters
num_rows = 2... | {"hexsha": "6a0ed8bae09bd5c113982402898cf88e6f11ee2f", "size": 1897, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/demo_3D_microscopy.py", "max_stars_repo_name": "Mohammad-Chowdhury-31/svmbir", "max_stars_repo_head_hexsha": "05665eb2a65b7aa951e26dd3691955e737f16c06", "max_stars_repo_licenses": ["BSD-3-Cla... |
#!/usr/bin/env python
import _init_paths
import os, sys, cv2, json
import math, PIL, cairo
import numpy as np
import pickle, random
import os.path as osp
from time import time
from copy import deepcopy
from glob import glob
import matplotlib.pyplot as plt
from collections import OrderedDict
import torch, torchtext
fr... | {"hexsha": "948e73bf1da640592066b5ecd9a65e2d2c76afe9", "size": 1010, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/eval_region.py", "max_stars_repo_name": "uvavision/DrillDown", "max_stars_repo_head_hexsha": "58fb4f382afda8460a7d0971c45a76d3d0bbe22d", "max_stars_repo_licenses": ["Unlicense", "MIT"], "max... |
using IterTools
using ProgressMeter
function find_first_invalid_number(input_numbers, window_length)
@showprogress for (j, i) in enumerate(window_length + 1:length(input_numbers))
input_subset = input_numbers[j:j + window_length - 1]
valid_sums = Set([sum(subset) for subset in subsets(input_subset... | {"hexsha": "821529b9d201e3c8a033b80e838c0646da4e12fa", "size": 1751, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Day9/day9_solution.jl", "max_stars_repo_name": "FedericoV/JuliaAdventOfCode2020", "max_stars_repo_head_hexsha": "30426fdee9f32fa15f4dd219462efff3de2d0fc9", "max_stars_repo_licenses": ["MIT"], "max_... |
-- ---------------------------------------------------------------- [ Core.idr ]
-- Module : Lightyear.Core
-- Description : Central Definitions and Instances
--
-- This code is distributed under the BSD 2-clause license.
-- See the file LICENSE in the root directory for its full text.
-- -------------------------... | {"hexsha": "b6db21ca41f9d47bcf7719e10ff04a5eeb8e2608", "size": 6252, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Lightyear/Core.idr", "max_stars_repo_name": "david-christiansen/lightyear", "max_stars_repo_head_hexsha": "3d4f025a77159af3a2d12c803d783405c9b0a04b", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
import unittest
import tensorflow as tf
import numpy as np
from DeepQNetwork import DeepQnetwork
from ExperienceReplay import ExperienceReplay
from PreProcessor import PreProcessor
from ResultsRecorder import ResultsRecorder
# Test the functionality of the Deep Q Network
class TestDQN(unittest.TestCase):
# Ensur... | {"hexsha": "081e77281223e7f27adca0cce1afa4be58cb76df", "size": 9943, "ext": "py", "lang": "Python", "max_stars_repo_path": "UnitTests.py", "max_stars_repo_name": "ChristopherHaynes/Atari-2600-Deep-Learning-Agent", "max_stars_repo_head_hexsha": "02ccd83701a4eeb7af160b0ff2cdb258a2338048", "max_stars_repo_licenses": ["Unl... |
module TestFirstOrder2
using ModiaLang
using DifferentialEquations
@usingModiaPlot
using Test
# using RuntimeGeneratedFunctions
# RuntimeGeneratedFunctions.init(@__MODULE__)
inputSignal(t) = sin(t)
FirstOrder1 = Model(
T = 0.2,
x = Var(init=0.3),
equations = :[u = inputSignal(time/u"s"),
... | {"hexsha": "3516cc44ce42884b385466805e723a78c0ef4761", "size": 1841, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TestFirstOrder2.jl", "max_stars_repo_name": "ModiaSim/ModiaLang", "max_stars_repo_head_hexsha": "6f8fc420f86f9af51eb897cfd9d7069c6ccc9659", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
@testset "benchmark_normals" begin
p, q = synthetic_gradient(SynthSphere(50))
p2, q2 = synthetic_gradient(SynthSphere(51))
error = benchmark_normals(p, q, p2, q2)
@test error ≈ 1.4866545112360603
end
| {"hexsha": "2a5f9f0b725a9a2f6a9b6147bd388fccc00daddf", "size": 216, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/benchmark_normals.jl", "max_stars_repo_name": "betttris13/ShapeFromShading.jl", "max_stars_repo_head_hexsha": "c486ad60d1675a65aacfe61dc1ef4d308bd534e1", "max_stars_repo_licenses": ["MIT"], "ma... |
/*******************************************************************************
* ARICPP - ARI interface for C++
* Copyright (C) 2017-2021 Daniele Pallastrelli
*
* This file is part of aricpp.
* For more information, see http://github.com/daniele77/aricpp
*
* Boost Software License - Version 1.0 - August 17th, ... | {"hexsha": "33e7c485f55ac6a5d4d1ce9f0d1d2a4144f274b9", "size": 8881, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/play_and_record.cpp", "max_stars_repo_name": "daniele77/aricpp", "max_stars_repo_head_hexsha": "5af798197ff8bf81619c00cb1c21e9576b853dab", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
import os
import sys
import argparse
import torch
import numpy as np
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import models
import utils
from utils import alignment, data, attack
import definitions
parser = argparse.ArgumentParser(description='Aligns two GoogLeNets using cros... | {"hexsha": "e8d8c94d0074de805bdf0e92ba7e8f68caa8a24b", "size": 5394, "ext": "py", "lang": "Python", "max_stars_repo_path": "alignment/align_googlenet.py", "max_stars_repo_name": "IBM/NeuronAlignment", "max_stars_repo_head_hexsha": "5b82b60666db1fac72e53db07529a3328ee549c4", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import logging
import numpy
from neodroid.utilities.unity_specifications import (
Configuration,
Motion,
Reaction,
ReactionParameters,
) # Motion,; EnvironmentDescription,
__author__ = "Christian Heider Nielsen"
__doc__ = r"""
Created on 9/4... | {"hexsha": "f3befada4dd50fb61251b6188608a7c0d25ac730", "size": 6029, "ext": "py", "lang": "Python", "max_stars_repo_path": "neodroid/factories/configuration_reactions.py", "max_stars_repo_name": "sintefneodroid/neo", "max_stars_repo_head_hexsha": "0999f1dff95c4a8c5880a9b3add532d74f38586a", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 14 15:59:11 2017
@author: af5u13
"""
# Usage for debugging from raw Python console
#exec(open("/Users/af5u13/dev/visr/src/python/scripts/rsao/reverbObjectBinauralisation.py").read())
#exec(open("/home/andi/dev/visr/src/python/scripts/rsao/reverbObjectBinauralisation... | {"hexsha": "abc151aa54dd326a2a86e59e32f75d39178dd47e", "size": 8637, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/scripts/rsao/reverbObjectBinauralisation_simple.py", "max_stars_repo_name": "s3a-spatialaudio/VISR", "max_stars_repo_head_hexsha": "55f6289bc5058d4898106f3520e1a60644ffb3ab", "max_stars... |
// Copyright (c) 2016
// Author: Chrono Law
#include <stack>
#include <std.hpp>
using namespace std;
#include <boost/array.hpp>
#include <boost/range.hpp>
using namespace boost;
///////////////////////////////////////
void case1()
{
assert(has_range_iterator<vector<int>>::value);
assert(has_range_iterator<s... | {"hexsha": "bf6abff173a28bd3124b2d5481661307e4b984c7", "size": 816, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "range/traits.cpp", "max_stars_repo_name": "MaxHonggg/professional_boost", "max_stars_repo_head_hexsha": "6fff73d3b9832644068dc8fe0443be813c7237b4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
using AbstractPlotting.PlotUtils, AbstractPlotting.Colors
################################################################################
# Colormap reference #
################################################################################
function colors_... | {"hexsha": "e26849356e74718c5a6b5a627d04aed4016dc097", "size": 3044, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/documentation.jl", "max_stars_repo_name": "pauljurczak/MakieGallery.jl", "max_stars_repo_head_hexsha": "3ba5534180d2314729aeb5a7d2d6a7afa1ffcdff", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#!/usr/bin/env python3
PKG = 'tfg'
import roslib; roslib.load_manifest(PKG)
#import rosbag
import numpy as np
import rospy
from rospy.numpy_msg import numpy_msg
from sensor_msgs.msg import Image
from sensor_msgs.msg import CompressedImage
import os
import cv2
from cv_bridge import CvBridge, CvBridgeError
from utiliti... | {"hexsha": "7a04cfb2a2fa45404c66706747d8a78cf8810091", "size": 1858, "ext": "py", "lang": "Python", "max_stars_repo_path": "tfg/src/camera_publisher.py", "max_stars_repo_name": "lccatala/tfg_ros", "max_stars_repo_head_hexsha": "d8da2bc6b1e0036e34460d174e708764a3c6f4ca", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
@testset "Covering" begin
@testset "Rectangle" begin
r = cover(RegularGrid{Float64}(100, 200), RectangleCoverer())
@test r == RectangleRegion((0.,0.), (99.,199.))
r = cover(PointSet([0. 1. 2.; 0. 2. 1.]), RectangleCoverer())
@test r == RectangleRegion((0.,0.), (2.,2.))
end
end
| {"hexsha": "a4be7a0de6bddcfddb3e61351f358ad148e7a96d", "size": 299, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/covering.jl", "max_stars_repo_name": "briochemc/GeoStatsBase.jl", "max_stars_repo_head_hexsha": "59ce064df9bcdc5c022edd80bd72125c2ca7819d", "max_stars_repo_licenses": ["ISC"], "max_stars_count"... |
import argparse
import datetime
import imutils
import time
import cv2
import numpy as np
import numpy
import string, random
import os
import SkinDetector
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", help="path to the video file")
ap.add_argument... | {"hexsha": "37c4b52b890f0dd1689107f14e1f5c24995350f7", "size": 3093, "ext": "py", "lang": "Python", "max_stars_repo_path": "redact.py", "max_stars_repo_name": "timothyclemansinsea/bodycamredaction", "max_stars_repo_head_hexsha": "e9917021059aa819be173975076614a2932ca555", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# Implementation of an elementary cellular automata
# according to https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
# uses random initialization and uses wolfram codes to specify the rule
# Asynchronous update of the 1D lattice
using Agents, Random
using CairoMakie
using InteractiveDynamics
using CSV
""... | {"hexsha": "5a2011b46d71dc764508d48835ceb6fa343e23d7", "size": 2645, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "elementary_ca_async.jl", "max_stars_repo_name": "astenuz/cellular_automata", "max_stars_repo_head_hexsha": "f98815b9678d308a5bd31e70efa92e2fb5309bf3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as num
from direct.showbase import DirectObject
from direct.task.Task import Task
from panda3d.core import LVector3f, NodePath, WindowProperties
# from Hardware import HardwareHandler
# from Meshes import Arrow
from Engine.Utils.utils import get_hpr, get_distance
TO_RAD = 0.017453293
TO_DEG = 57.29577951... | {"hexsha": "89938a944f2367df1f3b227b4c823a78b53ff160", "size": 11314, "ext": "py", "lang": "Python", "max_stars_repo_path": "Engine/GraphicEngine/Shuttle.py", "max_stars_repo_name": "pdefromont/SpaceBusGame", "max_stars_repo_head_hexsha": "629f6aa58a11756edeb85735a98504d1aadff586", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import scipy.sparse as sparse
from typing import Any
from torch.utils.checkpoint import checkpoint
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_max
from .. import register_model, BaseModel
from cogdl.utils import mul_edge_softmax, spmm, get_a... | {"hexsha": "4af3d4bb1190be6fbe80860108d9b52cb30ba440", "size": 9165, "ext": "py", "lang": "Python", "max_stars_repo_path": "cogdl/models/nn/pyg_deepergcn.py", "max_stars_repo_name": "xssstory/cogdl", "max_stars_repo_head_hexsha": "ae8de495c365993f19f04774f083960fd282c2a3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
'''ShuffleNetV2 in PyTorch.
See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
######
CODE_SIZE = 16
SLICE_SHAPE = [16,16,1,1]
#########
class ShuffleBlock(nn.Module):
... | {"hexsha": "5262fddd6ae4bbc3185a8daf72b3983dd9726734", "size": 12529, "ext": "py", "lang": "Python", "max_stars_repo_path": "Implementations/CIFAR10/models/shufflenetv2.py", "max_stars_repo_name": "hamedomidvar/associativeconv", "max_stars_repo_head_hexsha": "9930915abd3625871354df676865fc44eb92abf3", "max_stars_repo_l... |
[STATEMENT]
lemma ucast_s2: "(AND) w 0b00000000000000000000000010000000 = 0
\<Longrightarrow> (((get_S w))::word1) = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. w AND 128 = 0 \<Longrightarrow> get_S w = 0
[PROOF STEP]
by (simp add: get_S_def) | {"llama_tokens": 137, "file": "SPARCv8_SparcModel_MMU_Sparc_Properties", "length": 1} |
[STATEMENT]
lemma LLs_LLq:
"t1 \<in> atrm \<Longrightarrow> t2 \<in> atrm \<Longrightarrow>
LLs t1 t2 = cnj (LLq t1 t2) (neg (eql t1 t2))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>t1 \<in> atrm; t2 \<in> atrm\<rbrakk> \<Longrightarrow> LLs t1 t2 = cnj (LLq t1 t2) (neg (eql t1 t2))
[PROOF STEP]
by (si... | {"llama_tokens": 171, "file": "Syntax_Independent_Logic_Syntax_Arith", "length": 1} |
"""
compute partial correlation
"""
import numpy
def pcor_from_precision(P,zero_diagonal=1):
# given a precision matrix, compute the partial correlation matrix
# based on wikipedia page: http://en.wikipedia.org/wiki/Partial_correlat
#Using_matrix_inversion
pcor=numpy.zeros(P.shape)
for i in range(... | {"hexsha": "7c25f566ee1fd8f0f0909c72c892c0a2fb679839", "size": 512, "ext": "py", "lang": "Python", "max_stars_repo_path": "statistics/pcor_from_precision.py", "max_stars_repo_name": "poldrack/poldracklab-base", "max_stars_repo_head_hexsha": "d4c573aca032b67362fc25252779997dacb4a166", "max_stars_repo_licenses": ["Apache... |
[STATEMENT]
lemma zero_vector_1:
"zero_vector x \<longleftrightarrow> (\<forall>y . x * y = x * bot)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. zero_vector x = (\<forall>y. x * y = x * bot)
[PROOF STEP]
by (metis top_right_mult_increasing zero_vector_def zero_vector_left_zero) | {"llama_tokens": 114, "file": "Correctness_Algebras_Boolean_Semirings", "length": 1} |
using Test
# using Revise
using PolynomialBasis
PB = PolynomialBasis
function allequal(v1,v2)
return all(v1 .≈ v2)
end
function allequal(v1,v2,tol)
np = length(v1)
f = length(v2) == np
return f && all([isapprox(v1[i],v2[i],atol=tol) for i = 1:np])
end
p = [-1.0 1.0]
@test_throws AssertionError PB.t... | {"hexsha": "c7136be3a1ec61eaf634db0e85cd38ce7aa1c979", "size": 7891, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_lagrange_tensor_product_basis.jl", "max_stars_repo_name": "ArjunNarayanan/PolynomialBasis.jl", "max_stars_repo_head_hexsha": "09a3479154639c45a285d559508aa0092dfedad6", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 22 10:46:09 2019
@author: lwg
"""
# http://www.numpy.org/
import numpy as np
import matplotlib.pyplot as plt
def relu(x):
return np.maximum(0, x)
x = np.arange(-5.0, 5.0, 0.1)
y = relu(x)
plt.plot(x, y)
plt.ylim(-1, 6) # y轴范围
plt.show()
| {"hexsha": "69cd96e2424ab4db8f1a4ad57ba0a97f796aef76", "size": 296, "ext": "py", "lang": "Python", "max_stars_repo_path": "deeplearning_python/chapter3/ReLU.py", "max_stars_repo_name": "lwg82/DeepLearningPython", "max_stars_repo_head_hexsha": "a36d80a84ff05ea2e7e3cbd5cc868aa2929ebb99", "max_stars_repo_licenses": ["Apac... |
# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2021
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hun... | {"hexsha": "1c491ddc1083a7330a21f38ee5180e48205db0d8", "size": 5606, "ext": "py", "lang": "Python", "max_stars_repo_path": "vidbench/data/process.py", "max_stars_repo_name": "melaniebeck/video-classification", "max_stars_repo_head_hexsha": "eeb879605f8265ce28a007d5239f0e85aeed0719", "max_stars_repo_licenses": ["Apache-... |
@testset "ModelParameters" begin
@test mP_1.U ≈ 1.1
@test mP_1.μ ≈ 1.2
@test mP_1.β ≈ 1.3
@test mP_1.n ≈ 1.4
end
@testset "SimulationParameters" begin
@test sP_1.n_iω == 1
@test sP_1.n_iν == 2
@test sP_1.shift == false
@test sP_1.tc_type_f == :nothing
@test sP_1.tc_type_b == :nothin... | {"hexsha": "e3b5e4be18fbdfd2b7bd0cf589fafb3f57cb0387", "size": 653, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Config.jl", "max_stars_repo_name": "Atomtomate/LadderDGA.jl", "max_stars_repo_head_hexsha": "8cd39fe2ae2aa1130bff706171266d3cf2d4c8e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5,... |
section {* Backwards Compatibility for Version 1 *}
theory CollectionsV1
imports Collections
begin
text {*
This theory defines some stuff to establish (partial) backwards
compatibility with ICF Version 1.
*}
(*
TODO: Dirty hack to workaround a problem that occurs with sublocale here:
When decla... | {"author": "andredidier", "repo": "phd", "sha": "113f7c8b360a3914a571db13d9513e313954f4b2", "save_path": "github-repos/isabelle/andredidier-phd", "path": "github-repos/isabelle/andredidier-phd/phd-113f7c8b360a3914a571db13d9513e313954f4b2/thesis/Collections/ICF/CollectionsV1.thy"} |
# -*- coding:utf-8 -*-
"""
A local image scale tool
Licensed under The MIT License
Writen by Shaowu Wu, 20190926
"""
import cv2.cv2 as cv
import numpy as np
import os
LINE_COLOR = (0, 255, 0) # 获取在原图上画的线的颜色
LINE_WIDTH = 2 # 在原图上线的宽度
SCALE = 2 # 对选取区域的放大倍数
ADD_BBOX = True # 是否对要保存的图像增加边框
BBOX_WIDTH = 4 # 增加的边框的宽度... | {"hexsha": "a2c3253428f5e21102f3ecb0d061d4057e0bbc7d", "size": 5157, "ext": "py", "lang": "Python", "max_stars_repo_path": "scale_tool/enlarge_image_local.py", "max_stars_repo_name": "wshaow/enlarge_local_image_tool", "max_stars_repo_head_hexsha": "11b6eacf69dde5e0c6e06d57401a8c8d7c0dd4b9", "max_stars_repo_licenses": [... |
/*!
@file
Forward declares `boost::hana::Pair`.
@copyright Louis Dionne 2015
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#ifndef BOOST_HANA_FWD_PAIR_HPP
#define BOOST_HANA_FWD_PAIR_HPP
#include <boost/hana/fwd/core/make... | {"hexsha": "b2d7451fedf7a9a3697d7b8132ab2190974789b0", "size": 2606, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/hana/fwd/pair.hpp", "max_stars_repo_name": "josephwinston/hana", "max_stars_repo_head_hexsha": "a8586ec1812e14e43dfd6867209412aa1d254e1a", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
REAL FUNCTION URAND(IY)
INTEGER IY
C
C URAND IS A UNIFORM RANDOM NUMBER GENERATOR BASED ON THEORY AND
C SUGGESTIONS GIVEN IN D.E. KNUTH (1969), VOL 2. THE INTEGER IY
C SHOULD BE INITIALIZED TO AN ARBITRARY INTEGER PRIOR TO THE FIRST CALL
C TO URAND. THE CALLING PROGRAM SHOULD NOT... | {"hexsha": "acd7361d30cd121d30def414abd2159f88111776", "size": 1666, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Modules/ThirdParty/VNL/src/vxl/v3p/netlib/laso/urand.f", "max_stars_repo_name": "nalinimsingh/ITK_4D", "max_stars_repo_head_hexsha": "95a2eacaeaffe572889832ef0894239f89e3f303", "max_stars_repo_lic... |
# Copyright (C) 2019-2020 Intel Corporation
#
# SPDX-License-Identifier: MIT
# pylint: disable=exec-used
import cv2
import logging as log
import numpy as np
import os.path as osp
import shutil
from openvino.inference_engine import IECore
from datumaro.components.cli_plugin import CliPlugin
from datumaro.components... | {"hexsha": "7c64d6fa44995f7b6976125c0e7161c529ea5950", "size": 7048, "ext": "py", "lang": "Python", "max_stars_repo_path": "datumaro/plugins/openvino_plugin/launcher.py", "max_stars_repo_name": "einstonlabs/datumaro", "max_stars_repo_head_hexsha": "9eb5246febb4b4ae10c321fae80413bb87fb1a7d", "max_stars_repo_licenses": [... |
import numpy as np
MAX_ROUNDS = 18
RC = np.array([
0x01, 0x82, 0x8a, 0x00, 0x8b, 0x01, 0x81, 0x09, 0x8a, 0x88, 0x09, 0x0a,
0x8b, 0x8b, 0x89, 0x03, 0x02, 0x80
],
dtype=np.uint8)
RHO_OFFSETS = np.array([[0, 1, 6, 4, 3], [4, 4, 6, 7, 4], [3, 2, 3, 1, 7],
[1, 5, 7, 5, 0], [2... | {"hexsha": "3e1cd198f3d286dcba86c0e9c341a540b9de83b8", "size": 4147, "ext": "py", "lang": "Python", "max_stars_repo_path": "npcrypto/keccak.py", "max_stars_repo_name": "timoi-Lucypher/npCrypto", "max_stars_repo_head_hexsha": "10156482d70503fa01880421aba4a4a3d171bd98", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Measures of anisotropy
"""
Au(C) -> au
Return the Universal Elastic Anisotropy Index, `au`, of the tensor `C`.
See: Ranganathan & Ostoja-Starzewksi, Universal elastic anisotropy index,
Phys Rev Lett (2008) vol. 101 (5) pp. 055504
"""
function Au(C)
Kv, Gv, Kr, Gr = VoigtK(C), VoigtG(C), ReussK(C), Reu... | {"hexsha": "df207394fa98547d7a9c7406ad0f5106ef19458a", "size": 357, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/measures.jl", "max_stars_repo_name": "anowacki/CIJ.jl", "max_stars_repo_head_hexsha": "f6e7b0fac22048f9c20652c6d36c3e4d48d31d39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
/- Author: E.W.Ayers
This should be in mathlib. Some simp and extensionality lemmas for comma and over. -/
import category_theory.comma
namespace category_theory
section
universes v₁ v₂ v₃ u₁ u₂ u₃ -- declare the `v`'s first; see `category_theory.category` for an explanation
variables {A : Type u₁} [𝒜 : category... | {"author": "Or7ando", "repo": "lean", "sha": "d41169cf4e416a0d42092fb6bdc14131cee9dd15", "save_path": "github-repos/lean/Or7ando-lean", "path": "github-repos/lean/Or7ando-lean/lean-d41169cf4e416a0d42092fb6bdc14131cee9dd15/.github/workflows/geo/src/comma.lean"} |
# ---
# title: 424. Longest Repeating Character Replacement
# id: problem424
# author: Tian Jun
# date: 2020-10-31
# difficulty: Medium
# categories: Two Pointers, Sliding Window
# link: <https://leetcode.com/problems/longest-repeating-character-replacement/description/>
# hidden: true
# ---
#
# Given a string `s` tha... | {"hexsha": "d9346880f583cbb5f28721635285b92f721d2eb2", "size": 1322, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/424.longest-repeating-character-replacement.jl", "max_stars_repo_name": "noob-data-analaysis/LeetCode.jl", "max_stars_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "... |
[STATEMENT]
lemma sign_r_pos_sgnx_iff:
"sign_r_pos p a \<longleftrightarrow> sgnx (poly p) (at_right a) > 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sign_r_pos p a = (0 < sgnx (poly p) (at_right a))
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. sign_r_pos p a \<Longrightarrow> 0 < sg... | {"llama_tokens": 2947, "file": "Winding_Number_Eval_Cauchy_Index_Theorem", "length": 35} |
#!/usr/bin/env python
"""
Copyright 2019 Daryl Gohl
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": "5444a7d4b8bc49f0495a378e72a0db33d045dfd6", "size": 13108, "ext": "py", "lang": "Python", "max_stars_repo_path": "ConcatMap_v1.2.py", "max_stars_repo_name": "darylgohl/ConcatMap", "max_stars_repo_head_hexsha": "b00377f772c39f794606d4d1b4455fe5e0437552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import glob
import json
import logging
import matplotlib.patheffects as path_effects
import numpy as np
import os
import pandas as pd
import re
import matplotlib as mpl
mpl.use('Agg')
from os.path import basename
from matplotlib import pyplot as plt
from shutil import copyfile
# Configure logging
logging.basicConfi... | {"hexsha": "71fc4001d1fa6b1c569bc8963806797374516cdf", "size": 16747, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment4/experiments/plotting.py", "max_stars_repo_name": "manishmalik/CS-7641-assignments", "max_stars_repo_head_hexsha": "f8a7de0aac0e53931ef7c364b9a752dbb4664d40", "max_stars_repo_licenses"... |
import numpy as np
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# --------
# overhead
# --------
rootdir = 'my/path/somewhere/'
subs = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
ROI_list = ['ROI1', 'ROI2', 'ROI3', 'ROI4']
condition_list = ['pre', 'post']
hemi_li... | {"hexsha": "e212213c0c1d1898b24531b944507d6ef63cae12", "size": 1163, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_visualization/example_catplot.py", "max_stars_repo_name": "NicoleEic/projects", "max_stars_repo_head_hexsha": "028a4bb4b49539fc98b442f0a2f9434e95c94561", "max_stars_repo_licenses": ["MIT"], "... |
"""
Simple way to control the torso through a ui
Author: Patrick Gmerek
"""
import sys
sys.path.append("../robot_drivers/")
import Adafruit_PCA9685
import numpy as np
import cv2 as cv
import time
from hex_walker_driver import *
def main():
torso = initialize_torso()
slider_names = ["Waist",
... | {"hexsha": "afa48d64731d3ca827ae46838c9d7ca53f1beda8", "size": 2846, "ext": "py", "lang": "Python", "max_stars_repo_path": "resources/robot/project_files/testing/interactive_control_torso.py", "max_stars_repo_name": "ramk94/Thief_Policemen", "max_stars_repo_head_hexsha": "557701909a20f9a50c9bebed8532873a1910e599", "max... |
{-# OPTIONS --safe #-}
module Cubical.Algebra.CommRing.QuotientRing where
open import Cubical.Foundations.Prelude
open import Cubical.Data.Nat
open import Cubical.Data.FinData
open import Cubical.HITs.SetQuotients as SQ renaming (_/_ to _/sq_)
open import Cubical.HITs.PropositionalTruncation as PT
open import Cubic... | {"hexsha": "057300ee3915a28511c57482b620746d9ea9cc0b", "size": 3555, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Algebra/CommRing/QuotientRing.agda", "max_stars_repo_name": "xekoukou/cubical", "max_stars_repo_head_hexsha": "b6fbca9e83e553c5c2e4a16a2df7f9e9039034dc", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma asEnv_pickE:
assumes "goodEnv rho" shows "asEnv (pickE rho) xs x = rho xs x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. asEnv (pickE rho) xs x = rho xs x
[PROOF STEP]
using assms asTerm_pick
[PROOF STATE]
proof (prove)
using this:
goodEnv rho
good ?X \<Longrightarrow> asTerm (pick ?X) = ?X
go... | {"llama_tokens": 205, "file": "Binding_Syntax_Theory_Transition_QuasiTerms_Terms", "length": 2} |
module TestDefComposite
using Test
using Mimi
using MacroTools
import Mimi: ComponentPath, build, @defmodel
@defcomp Comp1 begin
par_1_1 = Parameter(index=[time]) # external input
var_1_1 = Variable(index=[time]) # computed
foo = Parameter()
function run_timestep(p, v, d, t)
v.var... | {"hexsha": "7d9ec9ab0876207c26b62b1ba895009159679754", "size": 1512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_defcomposite.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Mimi.jl-e4e893b0-ee5e-52ea-8111-44b3bdec128c", "max_stars_repo_head_hexsha": "c9336f1076996dca728c30befd561280dfc1... |
# Help Document
# -------------
#
# By convention, variable `i` is used to represent the index (or position) of a
# bit vector and `j` is used to represent the count (or cardinality) of a bit
# vector.
"""
rank0(rb, i)
Count the number of 0s (`false`s) within `bv[1:i]`.
"""
rank0
"""
rank1(bv, i)
Count the ... | {"hexsha": "208ecb4dea810160c31a29864eae1af930fe3b69", "size": 1353, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/help.jl", "max_stars_repo_name": "bicycle1885/IndexedBitVectors.jl", "max_stars_repo_head_hexsha": "5f3a69a85bf9db2e274ca0a470341547711fbdc6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Han is a badass.
Users/HelenWang i love my boyfriend because he is a badass. <3h
| {"hexsha": "d05c560bbb8126fc5507b2837360b58a7a92ebc3", "size": 82, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/HanLwin.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
%% LyX 2.2.3 created this file. For more info, see http://www.lyx.org/.
%% Do not edit unless you really know what you are doing.
\documentclass{article}
\usepackage[latin9]{inputenc}
\usepackage{listings}
\renewcommand{\lstlistingname}{Listing}
\begin{document}
\part{Introduction}
CSCN files are easily editable te... | {"hexsha": "0271de3178182e296c60961de64141c3a4f0c483", "size": 42354, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/Castor3D/wiki_scene_files-en.tex", "max_stars_repo_name": "Mu-L/Castor3D", "max_stars_repo_head_hexsha": "7b9c6e7be6f7373ad60c0811d136c0004e50e76b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
Require Export Coq.NArith.NArith.
Require Export Bedrock.Memory Bedrock.Word.
Require Export
Fiat.Narcissus.Automation.SolverOpt
Fiat.Narcissus.BinLib.Bool
Fiat.Narcissus.BinLib.Core
Fiat.Narcissus.BinLib.Enum
Fiat.Narcissus.BinLib.FixInt
Fiat.Narcissus.Common.Compose
... | {"author": "mit-plv", "repo": "fiat", "sha": "4c78284c3a88db32051bdba79202f40c645ffb7f", "save_path": "github-repos/coq/mit-plv-fiat", "path": "github-repos/coq/mit-plv-fiat/fiat-4c78284c3a88db32051bdba79202f40c645ffb7f/src/CertifiedExtraction/Extraction/BinEncoders/Basics.v"} |
"""
This example of the double integrator demonstrates how to pass constraints to PyTrajectory.
"""
# imports
from pytrajectory import TransitionProblem
import numpy as np
def f(xx, uu, uuref, t, pp):
""" Right hand side of the vectorfield defining the system dynamics
:param xx: state
:param uu: ... | {"hexsha": "f1f06b246afbf25ebd7c21903de9f68209aef724", "size": 2170, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ex6_ConstrainedDoubleIntegrator.py", "max_stars_repo_name": "TUD-RST/pytrajectory", "max_stars_repo_head_hexsha": "fa3c7e89450748d1d75800f89a831e608cec1d8f", "max_stars_repo_licenses": ["... |
for (study in c("SDY212", "SDY400", "SDY404")) {
fn.ge = file.path(PROJECT_DIR, "generated_data", "HIPC",
paste0(study, "_GE_matrix_gene.txt"))
dat = fread(fn.ge, data.table = F)
fn.si = file.path(PROJECT_DIR, "generated_data", "HIPC",
paste0(study, "_sample_info.txt... | {"hexsha": "116d686e353ec87d92a9b9ded5f5b0aabdef1361", "size": 963, "ext": "r", "lang": "R", "max_stars_repo_path": "R/hipc_dataprep/hipc_sample_filtering.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""Training routine for models."""
from os.path import join
import json
from itertools import chain
import numpy as np
import tensorflow as tf
from typing import Callable
from tensorflow.keras.callbacks import (
CSVLogger, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard)
from .callbacks import (
... | {"hexsha": "b9fc820460bcc13fd667795f3cb43cb1bf9154ab", "size": 12606, "ext": "py", "lang": "Python", "max_stars_repo_path": "seft/training_routine.py", "max_stars_repo_name": "daniel-trejobanos/seft-hypoxia", "max_stars_repo_head_hexsha": "77f46086f44fde696bb18885549d559056a49714", "max_stars_repo_licenses": ["BSD-3-Cl... |
////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2011 Bryce Lelbach
// Copyright (c) 2007-2013 Hartmut Kaiser
//
// 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)
/... | {"hexsha": "f89071bad7a47dee46c5d9602bb9c704b844e060", "size": 5389, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "hpx/runtime/agas/big_boot_barrier.hpp", "max_stars_repo_name": "atrantan/hpx", "max_stars_repo_head_hexsha": "6c214b2f3e3fc58648513c9f1cfef37fde59333c", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
import time
import subprocess
from io import BytesIO
import numpy as np
from PIL import Image
def cmd(command):
subp = subprocess.Popen(command,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8")
subp.wait(100)
if subp.poll() == 0:
print(subp.communicate()[0])
else:
... | {"hexsha": "2bbd45af3aa0a354e2b631b660ba0e4fa0ffdce8", "size": 503, "ext": "py", "lang": "Python", "max_stars_repo_path": "serve_model_yolov5.py", "max_stars_repo_name": "4nuragk/Yolov5-Facemask", "max_stars_repo_head_hexsha": "dce9c34d5edd092e8082fd00cf9d97748387a602", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
''' new Network definitions using the functional API from keras.
first part: model settings like input variables, outputs and transformations
second part: model definition, name must be def model(input_shape):
'''
import numpy as np
import keras
import keras.layers
from keras import backend as K
from keras import regu... | {"hexsha": "fe23a0440fa60b191aca23030628611228f6a020", "size": 6764, "ext": "py", "lang": "Python", "max_stars_repo_path": "i3deepice/models/mu_energy_reco_full_range/model.py", "max_stars_repo_name": "tglauch/DeepIceLearning_Module", "max_stars_repo_head_hexsha": "8c05929ec97226f07ab9e13a1dfc539d0e47a2b1", "max_stars_... |
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
@testset "build" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../instances/s1.json")
graph = RELOG.build_gr... | {"hexsha": "27d0e7f92c86141bd27adbdec7a4b2db3fa0e804", "size": 1297, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/model/build_test.jl", "max_stars_repo_name": "ANL-CEEESA/RELOG", "max_stars_repo_head_hexsha": "92d30460b9f2c227770dd426415c1ee34dac5300", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import numpy as np
from numpy import sqrt, real, conj
from glob import glob
from apertools.utils import take_looks
import apertools.sario as sario
from apertools.log import get_log
logger = get_log()
EPS = np.finfo(np.float32).eps
def abs2(x):
# Weird, but it seems to be faster...
# %timeit np.abs(b)**2
... | {"hexsha": "f3c1dbdc9b8a2d7b2ce3fb10a7d32691d66e0777", "size": 8234, "ext": "py", "lang": "Python", "max_stars_repo_path": "insar/form_igrams.py", "max_stars_repo_name": "scottstanie/insar", "max_stars_repo_head_hexsha": "61724be3cef7faf1e977e1b0ffad89dcae342761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
export searchvtx
# recursive function for applying search criteria
function keycheck(data::Dict{<:Any,<:Any},str::Array{String,1},mode::Array{Symbol,1})
found = false
for key in keys(data)
if :deps in mode
for s in str
(key == s) && (found = true)
end
end... | {"hexsha": "b605be815a270c04241f6537fa843729762f945f", "size": 1886, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/search.jl", "max_stars_repo_name": "UnofficialJuliaMirror/VerTeX.jl-cc48e778-429c-5593-b60f-2bcf41d5649c", "max_stars_repo_head_hexsha": "41ddb48918c789767511ceeaf135790eeab86d68", "max_stars_r... |
import argparse
import time
import numpy as np
import os
def main():
parser = argparse.ArgumentParser(description = "WGAN-GP")
# Saving parameters
parser.add_argument("--name", "-n", "-id", type = str, default = str(int(time.time())),
help = "Name/ID of the current training model")
parser.add_argument("--re... | {"hexsha": "d7497318d119a6f029e99d49c39b8ccdedf38f31", "size": 5845, "ext": "py", "lang": "Python", "max_stars_repo_path": "launcher.py", "max_stars_repo_name": "saundersp/wgan-gp", "max_stars_repo_head_hexsha": "27f1afbee348a71edb0275e2dbb7c57f29b74adf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
#!/usr/bin/env python3
"""
Simple exercise to construct a controller that controls the simulated Duckiebot using pose.
"""
import time
import sys
import argparse
import math
import numpy as np
import gym
from gym_duckietown.envs import DuckietownEnv
parser = argparse.ArgumentParser()
parser.add_argument('--env-name... | {"hexsha": "0ca4bbfd36593fb28354dec59a8d08498c71b6ab", "size": 1818, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym-duckietown/exercises/basic_control.py", "max_stars_repo_name": "lyf44/CS4278-5478-Project-Materials", "max_stars_repo_head_hexsha": "685419c65847e72450e99586e9e0f3794369b4a3", "max_stars_repo_... |
import itertools
import operator
import numpy as np
from sklearn import cross_validation
from sklearn import neighbors
train = np.load('train.npy')
# Remove the labels
test = np.load('test_distribute.npy')[:,1:]
data = train[:,1:]
target = train[:,0]
np.set_printoptions(threshold='nan')
print target
#print neighbo... | {"hexsha": "62c5106f638f3e87763773985d405ff121f5d9bc", "size": 599, "ext": "py", "lang": "Python", "max_stars_repo_path": "Black-Box/opt.py", "max_stars_repo_name": "bcspragu/Machine-Learning-Projects", "max_stars_repo_head_hexsha": "b6832cbb9bb27d7e8253300f97a3ab84b1a555dc", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
Licensed under the Unlicense License;
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://unlicense.org
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BAS... | {"hexsha": "9124e3de78258a3a2126a65e27a8a30b612a0e04", "size": 7160, "ext": "py", "lang": "Python", "max_stars_repo_path": "LR5/LR5.py", "max_stars_repo_name": "XxOinvizioNxX/NSvZTZiU", "max_stars_repo_head_hexsha": "6eeab20503cddf299c1258969fba6e94915112fb", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count":... |
import numpy as np
from time import time
import datetime
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import h5py
import sys
from os import listdir, remove
from os.path import isfile, join, exists, basename, splitext
#from laspy.file import File
from random import randint
from enum import Enum
from math import *... | {"hexsha": "89d058df08112781643cac812829df70e275950d", "size": 6617, "ext": "py", "lang": "Python", "max_stars_repo_path": "imports.py", "max_stars_repo_name": "eglrp/ConvPoint_Keras", "max_stars_repo_head_hexsha": "66c94479ff8dc8ad174ed4da8e6bb1d641a8a8c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
[STATEMENT]
lemma inorder_eq_mset: "mset (inorder t) = relations_mset t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. mset (inorder t) = relations_mset t
[PROOF STEP]
by(induction t) (auto) | {"llama_tokens": 84, "file": "Query_Optimization_JoinTree", "length": 1} |
# -*- coding: utf-8 -*-
"""
@Time:Created on 2019/5/20 19:40
@author: LiFan Chen
@Filename: model_glu.py
@Software: PyCharm
"""
# -*- coding: utf-8 -*-
"""
@Time:Created on 2019/5/7 13:40
@author: LiFan Chen
@Filename: model.py
@Software: PyCharm
"""
import torch
import torch.nn as nn
import torch.optim as optim
import... | {"hexsha": "fe80781ff85830f912f1f4b2b50d08a871664187", "size": 12216, "ext": "py", "lang": "Python", "max_stars_repo_path": "Human,C.elegans/model_glu.py", "max_stars_repo_name": "nepp1d0/transformerCPI", "max_stars_repo_head_hexsha": "a84c1e9b23b35ba3f02ad13621a1413f0ae7c62a", "max_stars_repo_licenses": ["Apache-2.0"]... |
from collections import deque
from copy import deepcopy
from typing import Any, Deque, Dict, List, Optional, Tuple
import numpy as np
from abides_core import Message, NanosecondTime
from abides_core.generators import ConstantTimeGenerator, InterArrivalTimeGenerator
from abides_core.utils import str_to_ns
from abides_... | {"hexsha": "cb2e032a5085c9671aba01fecb5c65998077669c", "size": 13819, "ext": "py", "lang": "Python", "max_stars_repo_path": "abides-markets/abides_markets/agents/background_v2/core_background_agent.py", "max_stars_repo_name": "jpmorganchase/ABIDES-jpmc-gym", "max_stars_repo_head_hexsha": "198736a1b1316190072356c9804125... |
import numpy as np
from scipy.optimize import curve_fit
def exp_func(x, a, b, c):
"""
An exponential function.
Inputs:
x : (1D array) x-values to be input into the exponential function.
a : (float) multiplicative factor for the exponential.
b : (float) multiplicative factor for the exponentiated x.
c... | {"hexsha": "59cbcd0dca5b8ea30b6c7e4da7bee40aabe4916b", "size": 1044, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/template/fit_line.py", "max_stars_repo_name": "arjunsavel/template-repo", "max_stars_repo_head_hexsha": "4dcffb2fe0cd748c76304e872a2fced5ea2b597d", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import re
import string
from math import pi
import numpy as np
import pandas as pd
from bokeh.models import ColumnDataSource, NumeralTickFormatter
from bokeh.plotting import figure
from bokeh.transform import cumsum
from bokeh.palettes import Category10
from numpy import histogram
from sklearn import metrics
from sklea... | {"hexsha": "c8632628f84c05459260defc31ee0e3b632d136e", "size": 10140, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/graphs.py", "max_stars_repo_name": "profilator/profilator", "max_stars_repo_head_hexsha": "6161efffee00b95216c8d60fcbd637171fae6980", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import cv2
import tensorflow as tf
import numpy as np
OUTPUT_PATH = "../events/"
NUM_FILTERS = 10
FILTER_SIZE = (3, 3)
STRIDES = (1, 1)
def nn(input_node):
with tf.variable_scope('nn'):
w = tf.get_variable(
name='weight',
shape=[FILTER_SIZE[0], FILTER_SIZE[1], 3, NUM_FILTERS],
... | {"hexsha": "81dc4f11e5576ffd637d347eccc3eb3c8593cb6c", "size": 2010, "ext": "py", "lang": "Python", "max_stars_repo_path": "05/conv2d.py", "max_stars_repo_name": "jason9075/ithome_tensorflow_series", "max_stars_repo_head_hexsha": "e8f92de2a73a88e7b03a9ac58ece4c4a604f066e", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
[STATEMENT]
lemma is_strict_if:
assumes "\<And>f. ide f \<Longrightarrow> f \<star> src f = f"
and "\<And>f. ide f \<Longrightarrow> trg f \<star> f = f"
and "\<And>a. obj a \<Longrightarrow> ide \<i>[a]"
and "\<And>f g h. \<lbrakk>ide f; ide g; ide h; src f = trg g; src g = trg h\<rbrakk> \<Longrightar... | {"llama_tokens": 4317, "file": "Bicategory_Strictness", "length": 44} |
import os
os.environ['OMP_NUM_THREADS'] = '1'
import dgl
import sys
import numpy as np
import time
from scipy import sparse as spsp
from numpy.testing import assert_array_equal
from multiprocessing import Process, Manager, Condition, Value
import multiprocessing as mp
from dgl.graph_index import create_graph_index
from... | {"hexsha": "62c3f69b117167cdebc522346d1837d857cfcf6c", "size": 3704, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/distributed/test_dist_graph_store.py", "max_stars_repo_name": "m30m/dgl", "max_stars_repo_head_hexsha": "2190c39d674f76c65db9ee8da7b43d3021f19c29", "max_stars_repo_licenses": ["Apache-2.0"],... |
# -*- coding: utf-8 -*-
from zaifapi import ZaifPublicApi, ZaifTradeApi
from decimal import Decimal, ROUND_DOWN
from TickChanger import Tick_int
import numpy
import time
import traceback
import re
import datetime
class EXCaccess:
def __init__(self):
self.investment = 10000 # 投資制限額
# 予想利益額の閾値（閾値以上... | {"hexsha": "59d9fc3c02acfcd8dd496358c5283fde3d44b387", "size": 33487, "ext": "py", "lang": "Python", "max_stars_repo_path": "EXCaccess.py", "max_stars_repo_name": "v2okimochi/AutoTA-TriangularArbitrage", "max_stars_repo_head_hexsha": "1b00cc672ed688d833a37611c934da2bb29154ad", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
This file constructs the functions to find lower and upper bound of optimal
set of sourcing countries according to Jia's algorithm.
"""
## Define module and things to be exported
module JiaAlgorithm
export lowerbound_setup, lowerbound, upperbound_setup, upperbound, optimalset
## Load packages
using LinearAlgebr... | {"hexsha": "0ab26d09aed979ae49e35062298d2dfeac85bf21", "size": 4807, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Code/JiaAlgorithm.jl", "max_stars_repo_name": "loforteg/AFT2017-Replication", "max_stars_repo_head_hexsha": "4b2abdc4584550c8c31d210d4cfa8cb7c3d20400", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from torch.autograd import Function
try:
import expansion_penalty
except:
pass
import math
import sys
from numbers import Number
from collections import Set, Mapping, deque
def square_distance(src, dst):
"""
... | {"hexsha": "f058d4131e3dc9aa303ac5c79dd01b9243dfd880", "size": 9837, "ext": "py", "lang": "Python", "max_stars_repo_path": "loss.py", "max_stars_repo_name": "benedictlee21/FYP_SCSE21_0204", "max_stars_repo_head_hexsha": "b5fdefac0fbec1291def5d47c780e8e7dced3b50", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
from __future__ import division
import argparse
import os
import glob
import time
from datetime import datetime
import torch.distributed as dist
import torch
import utils
import logging
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data.distributed import DistributedSampler
import torchvis... | {"hexsha": "54f42456ef2101d14b97370affe2b111d506642b", "size": 7967, "ext": "py", "lang": "Python", "max_stars_repo_path": "DSNAS/eval_imagenet.py", "max_stars_repo_name": "cwlacewe/SNAS-Series", "max_stars_repo_head_hexsha": "92ac8031f718235aecaefb9967851f8f355dbca0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
!#################################################################################################!
!BSD 3-Clause License
!
!Copyright (c) 2017, Ricardo Torres
!All rights reserved.
!
!Redistribution and use in source and binary forms, with or without
!modification, are permitted provided that the following conditions ... | {"hexsha": "d48e9c3df391bb6a07945df3b7860221976d062f", "size": 65616, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/H5_OO_mod.f90", "max_stars_repo_name": "rjgtorres/oo_hdf", "max_stars_repo_head_hexsha": "486f7cb0ad6581ed134a9a514da88e58c2f8adf7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
#include <iostream>
#include <armadillo>
#include <cmath>
#include <cstdlib>
#include <time.h>
#include <fstream>
using namespace std;
using namespace arma;
void RHO_A_FILL(vec &rho, mat &A, int N,double rhoN); //rho, kind of like a linespace
//A, Tridiagonal matrix
voi... | {"hexsha": "de0292302ec5175b48f8a9c3707eb398a3dc22ce", "size": 4506, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "project2/code-joseph/jacobi_interact.cpp", "max_stars_repo_name": "frxstrem/fys3150", "max_stars_repo_head_hexsha": "35c0310f48fca07444ec5924267bf646d121b147", "max_stars_repo_licenses": ["MIT"], "m... |
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
import pandas as pd
import datetime
import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters
import sys
""" TODO:
- Add possibility to color different segments of the time series
"""... | {"hexsha": "11f274b04f96d2de39efa6c13a439af1ff1d52cc", "size": 26826, "ext": "py", "lang": "Python", "max_stars_repo_path": "bidaf/TimeSeriesVisualizer.py", "max_stars_repo_name": "RI-SE/BIDAF", "max_stars_repo_head_hexsha": "ebb7c2f96ab65cb9cec0859f49d9dd951b93874c", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
#include <boost/metaparse/get_col.hpp>
| {"hexsha": "067a541dd8775c1f7f1c9be9643f048e753fa10d", "size": 39, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_metaparse_get_col.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"], ... |
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from transformers import GPT2TokenizerFast, GPT2Model
from sklearn.preprocessing import MultiLabelBinarizer
from mitnewsclassify2 import tfidf, tfidf_bi, download
import os
import gc
import ... | {"hexsha": "22aa5463592d13a85eb06dca77cc5fb3424e7d64", "size": 3395, "ext": "py", "lang": "Python", "max_stars_repo_path": "NYT/ensemble/vectorize-first.py", "max_stars_repo_name": "kristjanr/ut-mit-news-classify", "max_stars_repo_head_hexsha": "d85e32256f36d4a22d727e678adfaa7e0a4a3108", "max_stars_repo_licenses": ["MI... |
export Closed, Partial, Open, closure
"""
Trait to indicate that a binary operation • is closed over set S. Only methods
of • with the signature •(x::S, y::S) are to be considered.
The definition of closed is that •(x::S, y::S) shall not throw an error, and
•(x::S, y::S) shall return a result of type S.
"""
abstract ... | {"hexsha": "207d5c551dc5ef92b756e5baa180471ea6b08d1b", "size": 1187, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/closure.jl", "max_stars_repo_name": "TotalVerb/AlgebraicTraits.jl", "max_stars_repo_head_hexsha": "7dc81229d31d2c9afc11003398c6c1fdd3468cde", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from scipy import stats
from matplotlib import pyplot as plt
from pandas import DataFrame
import numpy as np
from abra.utils import dict_to_object
from abra.stats import Samples
NPTS = 100
LABEL_Y_OFFSET_FACTOR = 30.
COLORS = dict_to_object(
{
"blue": "#4257B2",
... | {"hexsha": "8936da7c769ae6d29e3659a964aed726bf10e759", "size": 22475, "ext": "py", "lang": "Python", "max_stars_repo_path": "abra/vis.py", "max_stars_repo_name": "quizlet/abracadabra", "max_stars_repo_head_hexsha": "eda599bd02f14b96efdc521f53132d93c9100ede", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24, "m... |
from sympy import Eq, expand, Function, solve, symbols
from devito import t, time, x, y, z, Dimension
from devito.interfaces import DenseData, TimeData, Forward, Backward
from devito.foreign import Operator
from numpy.random import randint
def acoustic_laplacian(v, rho):
# Derive stencil from symbolic equation
... | {"hexsha": "295cd8f8a7368e6a39761eab40c90b3a0d4c3dd2", "size": 9570, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/operators/acoustic/Jfwi_operators.py", "max_stars_repo_name": "SINBADconsortium/opesciSLIM", "max_stars_repo_head_hexsha": "8c3af2d8e9809fcd4d53fa160c01dbd624b6bb25", "max_stars_repo_licens... |
# Working with Sampling Distributions
Most statistical analysis involves working with distributions - usually of sample data.
## Sampling and Sampling Distributions
As we discussed earlier, when working with statistics, we usually base our calculations on a sample and not the full population of data. This means we nee... | {"hexsha": "23e6479da4671c3e54019de692ca2505d18378d2", "size": 91264, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Statistics and Probability by Hiren/04-05-Sampling Distributions.ipynb", "max_stars_repo_name": "serkin/Basic-Mathematics-for-Machine-Learning", "max_stars_repo_head_hexsha": "ac0ae9... |
#!/usr/bin/env python
# /***************************************************************************
#
# @package: panda_siimulator_examples
# @metapackage: panda_simulator
# @author: Saif Sidhik <sxs1412@bham.ac.uk>
#
# **************************************************************************/
# /**************... | {"hexsha": "2981bd8ba691abca1967085c830d265aca3f97de", "size": 8415, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/simu/scripts/task_space_control.py", "max_stars_repo_name": "Grossbier/simulation_multirobots", "max_stars_repo_head_hexsha": "1fe00bf81932ad6de20709ad85f677f4cf196333", "max_stars_repo_licens... |
import numpy as np
"""
Implementation of the non-separable blending modes as described in
https://www.w3.org/TR/compositing-1/#blendingnonseparable
"""
"""
four non-separable utility functions as described on the aforementioned page
Lum(C) = 0.3 x Cred + 0.59 x Cgreen + 0.11 x Cblue
ClipColor(C)
... | {"hexsha": "7e5dc5bf042ce7414afd9f8cfd32e63a6f7a4d16", "size": 5574, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyora/BlendNonSep.py", "max_stars_repo_name": "FredHappyface/pyora-mirror", "max_stars_repo_head_hexsha": "23d90239183f18be40d1bc47ac89fa4259996cee", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
import matplotlib.pyplot as plt
import numpy as np
import visdom
from tensorboardX import SummaryWriter
TENSORBOARD_DIR = 'tensorboard/runs/'
class Plotter:
def on_new_point(self, label, x, y):
pass
def on_finish(self):
pass
class MatplotlibPlotter(Plotter):
def __init__(sel... | {"hexsha": "7acccb65d0dca05bbb16fdcd03d18899e66d73dc", "size": 2706, "ext": "py", "lang": "Python", "max_stars_repo_path": "zerogercrnn/lib/visualization/plotter.py", "max_stars_repo_name": "zerogerc/rnn-autocomplete", "max_stars_repo_head_hexsha": "39dc8dd7c431cb8ac9e15016388ec823771388e4", "max_stars_repo_licenses": ... |
import numpy as np
try:
import keplertools.Cyeccanom
haveCyeccanom = True
except ImportError:
haveCyeccanom = False
pass
def eccanom(M, e, epsmult=4.01, maxIter=100, returnIter=False, noc=False):
"""Finds eccentric anomaly from mean anomaly and eccentricity
This method uses Newton-Raphson i... | {"hexsha": "02baff70ee2a7c272b70f5a9e764c254a02bae38", "size": 16926, "ext": "py", "lang": "Python", "max_stars_repo_path": "keplertools/fun.py", "max_stars_repo_name": "dsavransky/keplertools", "max_stars_repo_head_hexsha": "52de5f7ec6cb57a6363a6fac1925e39c10391b49", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
'''
intra_blob recursively evaluates each blob for three forks of extended internal cross-comparison and sub-clustering:
- comp_r: incremental range cross-comp in low-variation flat areas of +v--vg: the trigger is positive deviation of negated -vg,
- comp_a: angle cross-comp in high-variation edge areas of... | {"hexsha": "81dc72276b2041b06ce127471003d48bfcf86148", "size": 8351, "ext": "py", "lang": "Python", "max_stars_repo_path": "frame_2D_alg/intra_blob.py", "max_stars_repo_name": "aqibmumtaz/CogAlg", "max_stars_repo_head_hexsha": "36009f456be93833dd44038c8adbd99b6037383c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include "converter.h"
#include <string>
#include <iostream>
#include <sstream>
#include <boost/gil/image.hpp>
#include <boost/gil/typedefs.hpp>
#include <boost/gil/io/io.hpp>
#include <boost/gil/extension/io/jpeg.hpp>
#include <boost/gil/extension/io/png.hpp>
#include "utils.h"
using namespace boost::gil;
using n... | {"hexsha": "03f910cdd3321cdb7cb81c2a3ed7dc27e5435daa", "size": 2244, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/converter.cpp", "max_stars_repo_name": "kopytjuk/wasm-image-converter", "max_stars_repo_head_hexsha": "5f20492a9ce60e942b6432ce251618908afbc4f2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
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