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) |
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