sneakers-the-rat

# --------------------------------------------------------------------
# Recursively find pdfs from the directory given as the first argument, 
# otherwise search the current directory.
# Use exiftool and qpdf (both must be installed and locatable on $PATH) 
# to strip all top-level metadata from PDFs.
#
# Note - This only removes file-level metadata, not any metadata
# in embedded images, etc. 
#
# Code is provided as-is, I take no responsibility for its use,

mschauer

n = 1000
x = range(0, 1, length=n)
ς = 1.5 # noise level

μ = 3*x.*sin.(2pi*x) # periodic signal in time domain
#μ = 6*sqrt.(abs.(x .- 0.5)) # this one is difficult to estimate

# Model: Signal distorted by white noise
y = μ + ς*randn(n)  

Keno

mschauer

# Linear state space system forward filtering backward sampling with Zygote

using LinearAlgebra
using GaussianDistributions
using GaussianDistributions: ⊕, logpdf
using StaticArrays

using Statistics

using Zygote

mick001

#Load library mass and set seed
library(MASS)
set.seed(100)

# We are going to use 3 random variables
m <- 3

# Number of samples to be drawn
n <- 2000

gizmaa

Plotting

Last Update: May 13, 2019
Offline Version

Contents

• Translating
• Important Note
Basic Plot

wandernauta

#!/usr/bin/env bash

#
# This is sp, the command-line Spotify controller. It talks to a running
# instance of the Spotify Linux client over dbus, providing an interface not
# unlike mpc.
#
# Put differently, it allows you to control Spotify without leaving the comfort
# of your command line, and without a custom client or Premium subscription.
#

jkeirstead

// Predict from Gaussian Process
// All data parameters must be passed as a list to the Stan call
// Based on original file from https://code.google.com/p/stan/source/browse/src/models/misc/gaussian-process/

data {
int<lower=1> N1;
vector[N1] x1;
vector[N1] y1;
int<lower=1> N2;
vector[N2] x2;