jogonba2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#  GradientDescent.py
#  
#  Copyright 2015 Overxflow

def h0(sample,theta0,theta1): return theta0+sample*theta1
def derivative_0(train_samples,theta0,theta1): return (1.0/len(train_samples))*sum([h0(sample[0],theta0,theta1)-sample[1] for sample in train_samples])
def derivative_1(train_samples,theta0,theta1): return (1.0/len(train_samples))*sum([(h0(sample[0],theta0,theta1)-sample[1])*sample[0] for sample in train_samples])

jogonba2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#  KNN.py
#  
#  Copyright 2015 Overxflow 
#  

""" Vector difference """
def v_difference(x,y): return [abs(x[i]-y[i]) for i in xrange(len(x))]

jogonba2

/*
 * linearprey.c
 * Authors: Jose & Alberto
 * 2015 
 */
#include <windows.h>
int main(int argc, char **argv)
{
	int x          = 0;
	// Posiciones límite //

jogonba2

#include <windows.h>
#include <time.h>
#include <stdlib.h>

int main(int argc, char **argv)
{
	srand(time(NULL));
	int x          = 0;
	int uppery     = 170;
	int downy      = 600;

jogonba2

breed [predators predator]
breed [bugs bug]
bugs-own [
  speed    ;; either 1, 2, 3, 4, 5, or 6
]

globals [
    total-speed-6-caught         ;; keeps track of the number of bugs caught with speed of 6
    total-speed-5-caught         ;; keeps track of the number of bugs caught with speed of 5
    total-speed-4-caught         ;; keeps track of the number of bugs caught with speed of 4

jogonba2

#!/usr/bin/octave -qf

if(nargin!=1)
    printf("Usage: multinomial.m <data_filename>");
    exit(1);
end

arglist=argv();
datafile=arglist{1};
disp("Loading data...");

jogonba2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# PageRank #
# Author: Overxfl0w13 #

from random import randint

def make_ergodic_chain(access_graph,alpha):
	for i in xrange(len(access_graph)):
		if reduce(lambda x,y: x+y,access_graph[i])==0: access_graph[i] = [1.0/len(access_graph[i]) for x in access_graph[i]]

jogonba2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Overxfl0w13 #
# KNN para representaciones vectoriales #

def p_distance(v1,v2,p=2): 
	
	""" Calcula las distancias de la familia Lp, http://gyazo.com/b4183c93c58575351334366e3c07370b .
		Se asumen parámetros correctamente introducidos.
	

jogonba2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Overxfl0w13 #
# P Systems #

# PI = (O,mu,w1,...,wm,(R1,p1),...,(Rm,pm),i0) #
# O -> Alfabeto de objetos (catalizadores, de entrada y de salida)
# mu -> estructura de membranas en forma parentizada (estructura de árbol)
# w1,...,wm -> Cadenas representación de multiconjuntos asociadas a cada membrana
# (R1,p1)...,(Rm,pm) -> Reglas de evolución y relaciones de prioridad 

jogonba2

# Gauss MLE classifier - 2 classes -  #
# Author: Overxfl0w13 #

function [c] = gauss(X,Y,x)
	[mX,sigmaX] = matcov(X);
    [mY,sigmaY] = matcov(Y);
    pX          = columns(X) / (columns(X) + columns(Y));
    pY          = columns(Y) / (columns(X) + columns(Y));
    g1          = x'*((-1/2)*inv(sigmaX))*x+(inv(sigmaX)*mX)'*x+log(pX)-(1/2)*log(det(sigmaX))-(1/2)*mX'*inv(sigmaX)*mX;
    g2          = x'*((-1/2)*inv(sigmaY))*x+(inv(sigmaY)*mY)'*x+log(pY)-(1/2)*log(det(sigmaY))-(1/2)*mY'*inv(sigmaY)*mY;