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

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

// 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));