johnhw

import matplotlib.pyplot as plt
import numpy as np
import fractions

def lcm(a,b): 
	return abs(a * b) / fractions.gcd(a,b) if a and b else 0


def hex_rgb_to_float(hex):
	r = int(hex[0:2],16)/255.0

johnhw

p |link_ptr|name_ptr|class_ptr|doc_ptr|data_words...

execute:
mov x1, x0+D_DATA
ldr x0, [x0+D_CLASS]
b x0

bytecode:
mov x0, [IP]

johnhw

johnhw

-- assign logical name to macro keyboard
lmc_assign_keyboard('MACROS');

view_mode = false
-- define callback for whole device
lmc_set_handler('MACROS',function(button, direction)

print(button)
  if (direction == 1) then return end  -- ignore down
  if     (button == 33) then lmc_send_keys("{ESC}{UP}")

johnhw

div.cell.text_cell.rendered.selected
{
        border: 4px solid orange;        
}

div.cell.code_cell.rendered.selected
{
        border: 4px solid orange;
}

johnhw

$$\newcommand{\vec}[1]{{\bf #1} } 
\newenvironment{examinable}{\hspace{16em}}{{\hspace{16em}\LARGE[\spadesuit]}}
\newcommand{\real}{\mathbb{R}}
\newcommand{\expect}[1]{\mathbb{E}[#1]}
\DeclareMathOperator*{\argmin}{arg\,min}
\begin{examinable}
\vec{x}
\real
\end{examinable}
$$

johnhw

# BSD 3-Clause License

# Copyright (c) 2018, John H. Williamson
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.

johnhw

johnhw

# This implements the ideas in the paper "Neural Ordinary Differential Equations"
# in as simple a form as possible, using only autograd. It is not efficient.
# It is not useful for any practical purpose. 
# Use [torchdiffeq](https://github.com/rtqichen/torchdiffeq) for any real use.
#
# > [1] Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud. 
# "Neural Ordinary Differential Equations." *Advances in Neural Processing Information Systems.* 2018. 
# [[arxiv]](https://arxiv.org/abs/1806.07366)
#
# The implementation is based on the 

johnhw