karlnapf

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2011 Heiko Strathmann
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

karlnapf

% samples from N(mu, C) in case type=='cov', and from N(mu, C^(-1)) in case
% type=='prec'. z is optional and must be drawn from N(0,I), inds is a
% permutations vector that is applied to C before sampling (doesnt effect
% the output, just efficiency, smyamd is used as default)
function sample=gaussian_sampler_symamd(mu, C, type, z, inds)
if nargin<3
    type='prec';
end

if nargin <4

karlnapf

#include <shogun/io/streaming/StreamingAsciiFile.h>
#include <shogun/features/streaming/StreamingSparseFeatures.h>

using namespace shogun;

int main(int argc, char *argv[])
{
	init_shogun();

	CStreamingAsciiFile *file=new CStreamingAsciiFile("trivial-crash-example.dat");

karlnapf

from numpy.ma.core import reshape, mean
from shogun.Features import RegressionLabels, RealFeatures
from shogun.Regression import LeastAngleRegression, LinearRidgeRegression, LeastSquaresRegression
from shogun.Evaluation import CrossValidation, CrossValidationResult, \
    CrossValidationSplitting, MeanSquaredError
import time

n=250
n_class=6
dim=274 * 100 * 12

karlnapf

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2013 Heiko Strathmann
 */

#include <shogun/lib/config.h>

karlnapf

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2013 Heiko Strathmann
 */

#include <shogun/lib/config.h>

karlnapf

function [V,colours] = probe( colours)
%% function [V] = probe(A,p)
%   Creates a probing matrix based on a graph colouring of A using the
%   algorithm in "J. Tang and Y. Saad, A probing method for computing the diagonal of the matrix
%   inverse" (2010).
%
%   REQUIRES: Matgraph toolbox http://www.ams.jhu.edu/~ers/matgraph/
%
%    Input:  colours   - A graph colouring (of A^p probably)
%            

karlnapf

function [d,V] = det_Krylov_preconditioned(A,P,precomputed, tol,maxiter)
n = length(A);
 V = probe(precomputed.colouring);
 
d= 0.0;
parfor iii=1:size(V,2)
    workerNo = get(getCurrentTask,'ID');
    fprintf('det_Krylov_preconditioned %d/%d\n', iii, size(V,2));
     [tmp,iter]=ratKrylov01_preconditioned(A,V(:,iii),P,precomputed.wsq,precomputed.dzdt,precomputed.const,tol,maxiter);
    d = d + V(:,iii)'*tmp;

karlnapf

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2013 Heiko Strathmann
 */

#include <shogun/lib/config.h>

karlnapf

{
 "metadata": {
  "name": "shogun_gp_classification"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {