victor-shepardson

from pathlib import Path

from fire import Fire
import torch

def main(model:str, outfile:str=None):
    """
    Args:
        model: path to neutone model file
        outfile: path to write nn~ model file

victor-shepardson

import heapq

class PQDItem:
    def __init__(self, k, v):
        self.v = v
        self.k = k
        self.dead = False
    def __lt__(self, other):
        return self.v < other.v

victor-shepardson

import asyncio

def wrap_coroutine(coroutine):
    def future(*a, **kw):
        return asyncio.ensure_future(coroutine(*a, **kw))
    def sync(*a, **kw):
        return asyncio.get_event_loop().run_until_complete(coroutine(*a, **kw))
    coroutine.sync = sync
    coroutine.future = future
    return coroutine

victor-shepardson

'use strict';

(new Promise((resolve, reject) => {
    resolve('success');
})).then(x => {throw 'thrown error in then, no catch'});

(new Promise((resolve, reject) => {
    resolve('success');
})).then(x => {throw 'thrown error in then, with catch'})
    .catch(err => Promise.reject(err));

victor-shepardson

try:
    from bokeh.io import push_notebook, output_notebook, show
    from bokeh.plotting import figure
    output_notebook()
    def dynamic_image_figure(w,h):
        """create an RGB image figure in current cell and return an update function for it"""
        def im2bokeh(img):
            img = (img*255).astype(np.uint8)
            img = np.dstack([img, np.ones(img.shape[:2], np.uint8) * 255])
            img = np.squeeze(img.view(np.uint32))

victor-shepardson

Code

https://gist.github.com/victor-shepardson/5b3d3087dc2b4817b9bffdb8e87a57c4

Setup

I'm using Ubuntu 16.04 with a GTX 1060

install CUDA

victor-shepardson

from contextlib import contextmanager
import numpy as np
import torch
from torch import Tensor, ByteTensor
import torch.nn.functional as F
from torch.autograd import Variable
import pycuda.driver
from pycuda.gl import graphics_map_flags
from glumpy import app, gloo, gl

victor-shepardson

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm, invgamma

# estimate a univariate gaussian by Gibbs sampling
true_mu = 10
true_sigma = 2
true_v = true_sigma**0.5
N = 50
R = 5000

victor-shepardson

running test
running egg_info
writing dependency_links to protobuf.egg-info/dependency_links.txt
writing protobuf.egg-info/PKG-INFO
writing namespace_packages to protobuf.egg-info/namespace_packages.txt
writing requirements to protobuf.egg-info/requires.txt
writing top-level names to protobuf.egg-info/top_level.txt
reading manifest file 'protobuf.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files found matching 'google/protobuf/internal/*.proto'

victor-shepardson

Wolfram Alpha input to get polynomial approximation to function on an interval

for example to approximate exp(x) with ax^2 + bx + 1 on [0,1]:

solve (partial derivatives wrt a,b of (integral of (exp(x)-(ax^2 +bx+1))^2 from 0 to 1)) for a,b

or properly:

Solve[ D[ Integrate[ (exp(x)-(ax^2 +bx+1))^2, {x,0,1} ], {{a,b}} ] = 0, {a,b}]