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huckl3b3rry87 / segfault
Created December 10, 2016 04:00
this is pretty cool
febbo@febbo-HP-ZBook-17-G2:~$ julia
_
_ _ _(_)_ | A fresh approach to technical computing
(_) | (_) (_) | Documentation: http://docs.julialang.org
_ _ _| |_ __ _ | Type "?help" for help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 0.5.0 (2016-09-19 18:14 UTC)
_/ |\__'_|_|_|\__'_| | Official http://julialang.org/ release
|__/ | x86_64-pc-linux-gnu
@huckl3b3rry87
huckl3b3rry87 / new_error
Created December 10, 2016 04:15
after putting in the print
module DiffEqProblemLibrary
using DiffEqBase, ParameterizedFunctions,
FiniteElementDiffEq, AlgebraicDiffEq, JLD
print("1")
include("ode_premade_problems.jl")
print("2")
include("dae_premade_problems.jl")
print("3")
include("sde_premade_problems.jl")
@huckl3b3rry87
huckl3b3rry87 / test
Last active February 16, 2017 04:02
code
Pkg.clone("https://github.com/huckl3b3rry87/PrettyPlots.jl")
Pkg.clone("https://github.com/huckl3b3rry87/VehicleModels.jl")
pa = Vpara(x_min=x_min,x_max=x_max,y_min=y_min,y_max=y_max,sm=0.01);
@NLconstraint(mdl, obs_con[j=1:Q,i=2:N+1], 1 <= ((x[i]-X_obs[j,i])^2)/((a[j]+sm)^2) + ((y[i]-Y_obs[j,i])^2)/((b[j]+sm)^2))
<?xml version="1.0" ?>
<launch>
<arg name="paused" default="false"/>
<arg name="use_sim_time" default="true"/>
<arg name="gui" default="true"/>
<arg name="headless" default="false"/>
<arg name="debug" default="false"/>
<arg name="verbose" default="false"/>
<arg name="world_name" default="worlds/empty.world"/> <!-- Note: the world_name is with respect to GAZEBO_RESOURCE_PATH environmental variable -->
@huckl3b3rry87
huckl3b3rry87 / subexpressions.jl
Created June 8, 2019 03:03
fix applied to ReverseDiffSparse
# returns the list of subexpressions which a given tape depends on directly
function list_subexpressions(nd::Vector{NodeData})
# TODO: code overlap with compute_gradient_sparsity
indices = Set{Int}()
for k in 1:length(nd)
nod = nd[k]
if nod.nodetype == SUBEXPRESSION
@huckl3b3rry87
huckl3b3rry87 / vgg.py
Created April 20, 2020 21:25
This script will demonstrate how to use a pretrained model, in PyTorch, to make predictions.
"""
This script will demonstrate how to use a pretrained model, in PyTorch,
to make predictions. Specifically, we will be using VGG16 with a cat
image.
References used to make this script:
PyTorch pretrained models doc:
http://pytorch.org/docs/master/torchvision/models.html
PyTorch image transforms example:
http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#transforms
/*
* Copyright (c) 2018 NVIDIA Corporation.  All rights reserved.        
* This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0
* International License.  (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
*/
#pragma once
#include "DomainRandomizationDNNPCH.h"
#include "RandomComponentBase.h"
{
"SettingsVersion": 1.0,
"SimMode": "UrdfBot",
"LocalHostIp": "127.0.0.1",
"ViewMode": "GroundObserver",
#pragma once
#include "NVSceneCapturerModule.h"
#include "Components/ActorComponent.h"
#include "Components/StaticMeshComponent.h"
#include "IImageWrapper.h"
#include "Runtime/Core/Public/Misc/FileHelper.h"
#include "Kismet/BlueprintFunctionLibrary.h"
#include "Serialization/JsonSerializerMacros.h"
#include "Serialization/JsonTypes.h"
#include "Paths.h"