mblondel

import numpy as np

def _logsum(logx, logy):
    """
    Return log(x+y), avoiding arithmetic underflow/overflow.

    logx: log(x)
    logy: log(y)

    Rationale:

mblondel

# adapted from http://roscidus.com/desktop/node/413

import socket
import gobject

def server(host, port):
    '''Initialize server and start listening.'''
    sock = socket.socket()
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    sock.bind((host, port))

mblondel

def recv_count():
    try:
        while True:
             n = (yield)
             print "T-minus", n
    except GeneratorExit:
        print "Kaboom!"

def ex1():
    r = recv_count()

mblondel

% (C) Mathieu Blondel, July 2010

\documentclass[a4paper,10pt]{article}

\usepackage[english]{babel}
\usepackage[T1]{fontenc}
\usepackage[ansinew]{inputenc}

\usepackage{lmodern}
\usepackage{amsmath}

mblondel

#!/usr/bin/env python
"""
Find the solution for the second order differential equation

u'' = -u

with u(0) = 10 and u'(0) = -5

using the Euler and the Runge-Kutta methods.

mblondel

#!/usr/bin/env python

"""
Find the operations needed to sum up to TARGET by using all 4 numbers in NUMBERS.
"""

from itertools import permutations, product

NUMBERS = ["3","4","7","8"]
TARGET = 10.0

mblondel

from random import random

"""
Find pi by the Monte-Carlo method.

area of a circle = pi r^2
area of a square = (2r)^2 = 4 r^2

Perform random uniform sampling between -1 and 1.
The proportion of points in the unit circle is:

mblondel

"""
Exercises for the Markov Chain Monte-Carlo (MCMC) course available at

http://users.aims.ac.za/~ioana/
"""

import numpy as np
import numpy.linalg as la
import pylab
from scipy import stats

mblondel

"""
(C) Mathieu Blondel - 2010
License: BSD 3 clause

Implementation of the collapsed Gibbs sampler for
Latent Dirichlet Allocation, as described in

Finding scientifc topics (Griffiths and Steyvers)
"""

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)