awidegreen

Introduction

• C-a == Ctrl-a
• M-a == Alt-a

General

:q        close
:w        write/saves
:wa[!]    write/save all windows [force]
:wq       write/save and close

patriciogonzalezvivo

Trigonometry

const float PI = 3.1415926535897932384626433832795;
const float PI_2 = 1.57079632679489661923;
const float PI_4 = 0.785398163397448309616;

float PHI = (1.0+sqrtf(5.0))/2.0;

patriciogonzalezvivo

Extracted from https://github.com/rpavlik/maya2osg/tree/master/src/GLSL

Vertex Shader

varying vec2 vTexCoord;
varying vec3 vNormal;
varying vec3 vEye;

void main() {

galek

in vec2 v_texcoord; // texture coords
in vec3 v_normal;   // normal
in vec3 v_binormal; // binormal (for TBN basis calc)
in vec3 v_pos;      // pixel view space position

out vec4 color;

layout(std140) uniform Transforms
{
    mat4x4 world_matrix;  // object's world position

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

pachadotdev

# Option 1: Use apt-get

# keys taken from https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo
cd ~/GitHub/r-with-intel-mkl/
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB

sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update && sudo apt-get install intel-mkl-64bit

xeekworx

#include <SDL.h>
#include <glad\glad.h>
#include <nanovg.h>
#define NANOVG_GL3_IMPLEMENTATION
#include <nanovg_gl.h>
#include <nanovg_gl_utils.h> // For framebuffer utilities not shown in this code
#include <string>
#include <iostream>
#include <iomanip> // for setw

chaiyujin

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

rxwei

Make Swift AD support all numpy functions via Autograd/JAX

In the Python module:

let autograd = Python.import("jax.grad")

extension PythonObject {
  @differentiable(wrt: args, vjp: _vjpDynamicallyCall)
  @discardableResult

mkocabas

import numpy as np
import torch

def compute_similarity_transform(S1, S2):
    '''
    Computes a similarity transform (sR, t) that takes
    a set of 3D points S1 (3 x N) closest to a set of 3D points S2,
    where R is an 3x3 rotation matrix, t 3x1 translation, s scale.
    i.e. solves the orthogonal Procrutes problem.
    '''