cesarsouza

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cesarsouza

#!/bin/sh
for file in */*.tar.bz2
do
     tar xvkjf ${file} --strip-components 1 -C videos
done

cesarsouza

 
        // Creating the model
        let kernel = Linear()
        let svm = new MulticlassSupportVectorMachine(features, kernel, classes)
        
        // Creating the teacher    
        let learner = MulticlassSupportVectorLearning(svm, observations, labels)
 
        learner.Algorithm <- fun svm x y _ _ -> 
                SequentialMinimalOptimization(svm, x, y)

cesarsouza

        /// <summary>
        ///   Sorts the columns of a matrix by sorting keys.
        /// </summary>
        /// 
        /// <param name="keys">The key value for each column.</param>
        /// <param name="values">The matrix to be sorted.</param>
        /// <param name="comparer">The comparer to use.</param>
        /// 
        public static TValue[][] Sort<TKey, TValue>(TKey[] keys, TValue[][] values, IComparer<TKey> comparer)
        {

cesarsouza

// Accord Statistics Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2015
// cesarsouza at gmail.com
//
//    This library is free software; you can redistribute it and/or
//    modify it under the terms of the GNU Lesser General Public
//    License as published by the Free Software Foundation; either

cesarsouza

// Let's say we have 2 meteorological sensors gathering data
// from different time periods of the day. Those periods are
// represented below:

double[][][] data =
{
    new double[][] // first sequence (we just repeated the measurements 
    {              //  once, so there is only one observation sequence)

        new double[] { 1, 2 }, // Day 1, 15:00 pm