View local_regression.py
"""Local regression""" | |
# Author: Mathieu Blondel <mathieu@mblondel.org> | |
# License: BSD 3 clause | |
import numpy as np | |
from sklearn.base import BaseEstimator, RegressorMixin | |
from sklearn.metrics.pairwise import pairwise_kernels | |
from sklearn.linear_model import Ridge |
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 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 regression_lp.py
# (C) 2011 Mathieu Blondel | |
# License: BSD 3 clause | |
import numpy as np | |
import numpy.linalg as linalg | |
import pylab as pl | |
from cvxopt import matrix, solvers | |
np.random.seed(0) |
View online_variance.py
def online_mean_variance(iterable): | |
mN = 0 | |
mM = 0.0 | |
mS = 0.0 | |
for x in iterable: | |
mN += 1 | |
nextM = mM + (x - mM) / mN | |
mS += (x - mM) * (x - nextM) |
View lbfgs_nnls.py
# (C) Mathieu Blondel 2012 | |
# License: BSD 3 clause | |
import numpy as np | |
from scipy.optimize import fmin_l_bfgs_b | |
from sklearn.base import BaseEstimator, RegressorMixin | |
from sklearn.utils.extmath import safe_sparse_dot |
View xref.txt
paper.tex: main manuscript | |
supp.tex: supplementary material | |
Cross-referencing | |
----------------- | |
We want to cross-reference equations in paper.tex from supp.tex. | |
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 |
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