piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = git@github.com:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

MikeInnes

# Repeat an operation n times, e.g.
# @dotimes 100 println("hi")

macro dotimes(n, body)
  quote
    for i = 1:$(esc(n))
      $(esc(body))
    end
  end
end

rasmusab

# Test of Significance, takes the same arguments as t.test() .
signif.test <- function(x, ...) {
  p <- t.test(x, ...)$p.value
  
  # List of p excuses retrieved from http://mchankins.wordpress.com/2013/04/21/still-not-significant-2/
  p_excuses <- c(
    "(barely) not statistically significant <p>",
    "a barely detectable statistically significant difference <p>",
    "a borderline significant trend <p>",
    "a certain trend toward significance <p>",

0xjac

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

cormullion

#!/usr/bin/env julia

using Luxor

function makerandomepitrochoid(n)
    hlist = []
    plist = []
    for i in 1:n
        R = 20
        r = rand(30:3:91)

vankesteren

module Adamopt

# This is a module implementing vanilla Adam (https://arxiv.org/abs/1412.6980).
export Adam, step!

# Struct containing all necessary info
mutable struct Adam
  theta::AbstractArray{Float64} # Parameter array
  loss::Function                # Loss function
  grad::Function                # Gradient function

mschauer

using LinearAlgebra
using Random
using GaussianDistributions
using GaussianDistributions: logpdf

pair(u) = u[1], u[2]
pair(p::Gaussian) = p.μ, p.Σ
skiplast(r) = r[1:end-1]

# time grid

Keno

mschauer

# Fit a Brownian motion with drift to and scale to
# Georgia election data:
# fraction of total count of votes vs difference in votes

data = [0.9222672139181572 103896.10389610389
    0.9267802009311443 87662.33766233765
    0.9277217593727027 85714.28571428571
    0.9285683655966674 83116.88311688312
    0.9299797843665768 79220.77922077922
    0.9310169076206811 78571.42857142857

mschauer

d = 100
ts_ = range(0, 1, length=d+1)[2:end]
Γ = SymTridiagonal([2.0ones(d-1); 1.0], -ones(d-1))/ts_[1]

# Note the covariance matrix of B.M is 
ts = range(0, 1, length=d+1)[2:end]
Γ0 = inv([min(i,j) for i in ts_, j in ts_])
@test norm(Γ0 - Γ) < 1e-7