DuaneNielsen

//
// Created by duane on 10/2/20.
//

#include <torch/torch.h>

int main(int arg, char *argv[]){

    auto x = torch::randn(3, torch::requires_grad());
    auto z = torch::randn(3, torch::requires_grad());

DuaneNielsen

cmake_minimum_required(VERSION 3.5)
project(ros2_cpp_pkg)

# Default to C99
if(NOT CMAKE_C_STANDARD)
  set(CMAKE_C_STANDARD 99)
endif()

# Default to C++14
if(NOT CMAKE_CXX_STANDARD)

DuaneNielsen

from vpython import sphere, vector, rate, color, arrow, canvas, cross, triangle, vertex
from math import sqrt


G = 6.67e-11  # N kg^-2 m^2
au = 1.495978707e11
day = 60 * 60 * 24
year = day * 365.25

earth_mass = 5.972e24  # kg

DuaneNielsen

import glfw
from OpenGL.GL import *
from OpenGL.GL.shaders import compileProgram, compileShader
import numpy as np
import pyrr


def init_window(width, height, title="My OpenGL window"):
    # initializing glfw library
    if not glfw.init():

DuaneNielsen

import torch
from torch import tensor
from torch.distributions.multivariate_normal import MultivariateNormal
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import matplotlib.patches as mpatches
from math import acos, degrees
from matplotlib.pyplot import cm

"""

DuaneNielsen

import torch
from torch.distributions.normal import Normal
from torch.distributions.multivariate_normal import MultivariateNormal


"""
Example of computing c batched Normal and Multivariate distributions from data
and sampling batches from them
"""

DuaneNielsen

import torch
from torch.distributions.normal import Normal
import matplotlib.pyplot as plt

"""
EM algo demo, in pytorch

"""

n = 40 # must be even number

DuaneNielsen

import torch
from torch.distributions.normal import Normal
import matplotlib.pyplot as plt

"""
Using Bayes to estimate the relative probability of 2 Hypotheses given the value of a single data point

Both hypothesis given equal prior probability of being correct
"""

DuaneNielsen

import torch
from torch.distributions.normal import Normal
from torch.distributions.multivariate_normal import MultivariateNormal
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

d = Normal(0.0, 1.0)
x = torch.linspace(-4, 4.0, 50)
y = torch.exp(d.log_prob(x))
plt.plot(x, y)

DuaneNielsen

import multiprocessing as mp
import os
import numpy as np
import multiprocessing.spawn

def info(title):
    print(title)
    print('module name:', __name__)
    print('parent process:', os.getppid())
    print('process id:', os.getpid())