brendano

"""ajaxgoogle.py - Simple bindings to the AJAX Google Search API
(Just the JSON-over-HTTP bit of it, nothing to do with AJAX per se)
http://code.google.com/apis/ajaxsearch/documentation/reference.html#_intro_fonje
brendan o'connor - gist.github.com/28405 - anyall.org"""
try:
  import json
except ImportError:
  import simplejson as json

import urllib, urllib2

jacobian

### Install some packages. Ignore the errors.

aptitude install binutils libgdal1-1.5.0 postgresql-8.3-postgis postgresql-server-dev-8.3 python-psycopg2 python-setuptools

### Make connecting to postgres easier

echo "local all all trust" > /etc/postgresql/8.3/main/pg_hba.conf 
invoke-rc.d postgresql-8.3 reload

### Become the Postgres user to create a spatial template database:

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, October 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg

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

def polynomial_kernel(x, y, p=3):

joskid

import json, requests, simplegeo
from simplegeo.models import Record

client = simplegeo.Client('your-key', 'your-secret')

firehose = 'http://api.plancast.com/02/plans/firehose.json?extensions=place'
resp = requests.get(url=firehose)
data = json.load(resp)

plans = data['plans']

joskid

### Install some packages. Ignore the errors.

aptitude install binutils libgdal1-1.5.0 postgresql-8.3-postgis postgresql-server-dev-8.3 python-psycopg2 python-setuptools

### Make connecting to postgres easier

echo "local all all trust" > /etc/postgresql/8.3/main/pg_hba.conf 
invoke-rc.d postgresql-8.3 reload

### Become the Postgres user to create a spatial template database:

jimeh

Let's not localize programming languages. Please >_<

Feel free to fork and expand and/or add more languages as an example to why this would be horrible, and I'll add them here :)

brusic

// define inside file timetest.m
function blank = timetest(x, y, MAX_ITR)
    m = length(y);
    x = [ones(m, 1) x];
    theta = zeros(size(x(1,:)))'; % initialize fitting parameters
    alpha = 0.07;
    
    for num_iterations = 1:MAX_ITR
        grad = (1/m).* x' * ((x * theta) - y);
        theta = theta - alpha .* grad;

vgoklani

#!/usr/bin/python
# http://en.wikipedia.org/wiki/Viterbi_algorithm

'''
Consider two friends, Alice and Bob, who live far apart from each other and who talk together daily over the telephone about what they did that day. Bob is only interested in three activities: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. Alice has no definite information about the weather where Bob lives, but she knows general trends. Based on what Bob tells her he did each day, Alice tries to guess what the weather must have been like.
Alice believes that the weather operates as a discrete Markov chain. There are two states, "Rainy" and "Sunny", but she cannot observe them directly, that is, they are hidden from her. On each day, there is a certain chance that Bob will perform one of the following activities, depending on the weather: "walk", "shop", or "clean". Since Bob tells Alice about his activities, those are the observations. The entire

retnuh

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
%   taking num_iters gradient steps with learning rate alpha

% Initialize some useful values
m = length(y); % number of training examples
n = size(X, 2); % number of features (+ 1)
J_history = zeros(num_iters, 1);