View logsum.py
import numpy as np
def _logsum(logx, logy):
"""
Return log(x+y), avoiding arithmetic underflow/overflow.
logx: log(x)
logy: log(y)
Rationale:
View echo_server.py
# 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))
View coroutines.py
def recv_count():
try:
while True:
n = (yield)
print "T-minus", n
except GeneratorExit:
print "Kaboom!"
def ex1():
r = recv_count()
View hmm.tex
% (C) Mathieu Blondel, July 2010
\documentclass[a4paper,10pt]{article}
\usepackage[english]{babel}
\usepackage[T1]{fontenc}
\usepackage[ansinew]{inputenc}
\usepackage{lmodern}
\usepackage{amsmath}
View second_order_ode.py
#!/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.
View number_plate_solver.py
#!/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
View mc_pi.py
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:
View mcmc_exercices.py
"""
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
View lda_gibbs.py
"""
(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)
"""
View svm.py
# 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)