kjlubick

# This is a very simple Python 2.7 implementation of the Information Set Monte Carlo Tree Search algorithm.
# The function ISMCTS(rootstate, itermax, verbose = False) is towards the bottom of the code.
# It aims to have the clearest and simplest possible code, and for the sake of clarity, the code
# is orders of magnitude less efficient than it could be made, particularly by using a 
# state.GetRandomMove() or state.DoRandomRollout() function.
# 
# An example GameState classes for Knockout Whist is included to give some idea of how you
# can write your own GameState to use ISMCTS in your hidden information game.
# 
# Written by Peter Cowling, Edward Powley, Daniel Whitehouse (University of York, UK) September 2012 - August 2013.

mblondel

"""
NMF by coordinate descent, designed for sparse data (without missing values)
"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np
import scipy.sparse as sp
import numba

jamestrimble

# Script to create data for http://ukdataexplorer.com/census/
# Author James Trimble, james.trimble at yahoo.co.uk
# 
# This code is based on Alex Singleton's work:
# http://rpubs.com/alexsingleton/openatlas
# http://www.alex-singleton.com/2011-census-open-atlas-project/
#
# The script takes around an hour to run. It could be made faster!
# The script requires a lookup table and shapefile from
# https://geoportal.statistics.gov.uk/geoportal/catalog/search/browse/browse.page

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

cjbayesian

## Corey Chivers, 2012 ##
sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3)
{
   ## Simulate outcomes in advance   
   outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE)
   success<-cumsum(outcomes)

   for(frame in 1:N)
   {
      png(paste("plots/",1000+frame,".png",sep=""))

blackball

/**
 * Fast non-maximum suppression in C, port from  
 * http://quantombone.blogspot.com/2011/08/blazing-fast-nmsm-from-exemplar-svm.html
 *
 * @blackball (bugway@gmail.com)
 */

#include <stdio.h>
#include <stdlib.h>
#include <limits.h>

mshivers

import scipy as sp
from scipy import optimize as opt
def nnlr(X, y, C):
    """
    Non-negative Logistic Regression with L2 regularizer
    """
    def lr_cost(X, y, theta, C):
        m = len(y)
        return (1./m) * (sp.dot(-y, sp.log(sigmoid(sp.dot(X, theta)))) \

quantombone

function top = nms(boxes, overlap)
% top = nms_fast(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
% but an inner loop has been eliminated to significantly speed it
% up in the case of a large number of boxes
% Tomasz Malisiewicz (tomasz@cmu.edu)

blinks

"""
Scanner: match text to generate tokens.
Adam Blinkinsop <blinks@acm.org>

First, construct a scanner with the tokens you'd like to match described as
keyword arguments, using Python-syntax regular expressions.
WARNING: Group syntax in these expressions has an undefined effect.

>>> simple = Scan(ID=r'\w+')