jg-you

// Test data
std::vector< std::vector<int> > jagged_array(3);
jagged_array[0] = {0};
jagged_array[1] = {0, 1, 2, 3};
jagged_array[2] = {0, 1, 2};

hvl_t * X = (hvl_t *)malloc(jagged_array.size() * sizeof(hvl_t))
for (unsigned int i = 0; i < jagged_array.size(); ++i) {
    X[i].len = jagged_array[i].size();
    int * ptr = (int *) malloc (X[i].len * sizeof(int));

jg-you

import networkx as nx

# Mean function (no statistics module in python2)
mean = lambda l: sum(l)/len(l)

# Declare some graph
G = nx.erdos_renyi_graph(200,0.1)

# Prepare raw results container
result = dict()

jg-you

diri <- function(alpha) {
    # Sample from the Dirichlet distribution with parameter (vector) alpha
    k <- length (alpha)
    Z <- rep(0,k)
    for(i in 1:k) {
        Z[i]  <- rgamma(n=1,shape=alpha[i], rate =1)
        S  <- sum(Z)
        P  <- Z/S
    }
    return(P)

jg-you

collection = [{1, 2, 3, 4, 5},
              {1, 2},
              {1, 2, 3, 4, 5, 6},
              {3, 4, 8},
              {3, 4, 11},
              {3},
              {3},
              {12},
              {1, 2, 3, 4, 5, 6},
              {1, 2, 3, 4, 7, 9},

jg-you

#!/usr/bin/env python3
# Author: Jean-Gabriel Young
# Email: jean.gabriel.young@gmail.com
# -*- coding: utf-8 -*-
import argparse
import subprocess
import os
from PIL import Image

jg-you

import matplotlibt.pyplot as plt
import numpy as np

plt.figure(figsize=(5,4))
X, Y = np.meshgrid(np.linspace(0,1), np.linspace(0,1))
plt.pcolormesh(X,Y,graphon_val(X,Y,p,n))
plt.colorbar()

jg-you

# The newest version of graph-tool plots directly in a gtk window, by default. 
# The following allow you to add inline plots in a jupyter-notebook (this was previously trivial).

# [The following code must appear in a notebook, obviously]

import graph_tool as gt
import graph_tool.draw
import graph_tool.collection

import matplotlib.pyplot as plt

jg-you

#include <iostream>

class master_virtual
{
public: 
    virtual void msg() {return;}
};

class derived_hi : public master_virtual
{

jg-you

functions {
  /**
  * Computes the value of a Bernstein polynomial of degree N at point t,
  * using De Casteljau's algorithm.
  *
  * @param t      Point in [0, 1] where the the polynomial will be evaluated.
  * @param beta   Vector of the real N + 1 coefficients of the polynomial.
  * @param N      Degree of the polynomial.
  *
  * @return Value of the polynomial at point t.

jg-you

#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @author: Jean-Gabriel Young <jean.gabriel.young@gmail.com>
"""Generate TOC for a markdown file."""
import re

# Match between 1 and 4 #
section = re.compile('^\s*(#){1,4}\s?')
strip_url = re.compile('[\W_]+', re.UNICODE)