DGriffin91

// License: Apache-2.0 / MIT

use bevy::core_pipeline::{
    draw_3d_graph, node, AlphaMask3d, Opaque3d, RenderTargetClearColors, Transparent3d,
};
use bevy::prelude::*;
use bevy::reflect::TypeUuid;
use bevy::render::camera::{ActiveCamera, CameraTypePlugin, RenderTarget};
use bevy::render::render_asset::RenderAssets;
use bevy::render::render_graph::{NodeRunError, RenderGraph, RenderGraphContext, SlotValue};

jix

import networkx as nx
import itertools


def transitive_closure_compression(g):
    # For a given DAG return a smaller (edge count) DAG with additional nodes
    # and a transitive closure that has the transitive closure of the original
    # DAG as induced subgraph.

    # It does this by generating a contraction hierachy (CH) and building a new

alsrgv

from __future__ import print_function

import argparse
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import datasets, transforms, models
import horovod.torch as hvd
import tensorboardX

BigEpsilon

import os

{.link: "/usr/local/lib/libopencv_core.so".} #pass arguments to the linker
{.link: "/usr/local/lib/libopencv_highgui.so".}
{.link: "/usr/local/lib/libopencv_imgproc.so".}

const # headers to include
    std_vector = "<vector>"
    cv_core = "<opencv2/core/core.hpp>" 
    cv_highgui = "<opencv2/highgui/highgui.hpp>"

zacharycarter

Binding to C Libraries with Nim

---

Table of Contents

• Introduction
• C2Nim
• The C Preprocessor
• Pragmas

ElijahLynn

# These examples assume you have a container currently running.

# 1 Pipe from a file
sudo docker exec --interactive CONTAINER_NAME /bin/bash < the_beginning.sh | tee the_beginning_output.txt`

#2a Pipe by piping
echo "echo This is how we pipe to docker exec" | sudo docker exec --interactive CONTAINER_NAME /bin/bash - 

chiral

#include "ceres/ceres.h"
#include "glog/logging.h"

using ceres::CostFunction;
using ceres::AutoDiffCostFunction;
using ceres::Problem;
using ceres::Solver;
using ceres::Solve;

struct CostFunctor {

tokestermw

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.