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    Metadata details:
    filename: phow_caltech101.m
    path of file: ./repos/Image-Harmonization-Dataset-iHarmony4/Lalonde and Efros/utilsPrivate/3rd_party/vlfeat-0.9.14/apps
    
    the code of the file until where you have to start completion: function phow_caltech101
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
%   This program demonstrates how to use VLFeat to construct an image
%   classifier on the Caltech-101 data. The classifier uses PHOW
%   features (dense SIFT), spatial histograms of visual words, and a
%   Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT,
%   kd-trees, and homogeneous kernel map. The program also
%   demonstrates VLFeat PEGASOS SVM solver, although for this small
%   dataset other solvers such as LIBLINEAR can be more efficient.
%
%   By default 15 training images are used, which should result in
%   about 64% performance (a good performance considering that only a
%   single feature type is being used).

    
function phow_caltech101
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
%   This program demonstrates how to use VLFeat to construct an image
%   classifier on the Caltech-101 data. The classifier uses PHOW
%   features (dense SIFT), spatial histograms of visual words, and a
%   Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT,
%   kd-trees, and homogeneous kernel map. The program also
%   demonstrates VLFeat PEGASOS SVM solver, although for this small
%   dataset other solvers such as LIBLINEAR can be more efficient.
%
%   By default 15 training images are used, which should result in
%   about 64% performance (a good performance considering that only a
%   single feature type is being used).
%
%   Call PHOW_CALTECH101 to train and test a classifier on a small
%   subset of the Caltech-101 data. Note that the program
%   automatically downloads a copy of the Caltech-101 data from the
%   Internet if it cannot find a local copy.
%
%   Edit the PHOW_CALTECH101 file to change the program configuration.
%
%   To run on the entire dataset change CONF.TINYPROBLEM to FALSE.
%
%   The Caltech-101 data is saved into CONF.CALDIR, which defaults to
%   'data/caltech-101'. Change this path to the desired location, for
%   instance to point to an existing copy of the Caltech-101 data.
%
%   The program can also be used to train a model on custom data by
%   pointing CONF.CALDIR to it. Just create a subdirectory for each
%   class and put the training images there. Make sure to adjust
%   CONF.NUMTRAIN accordingly.
%
%   Intermediate files are stored in the directory CONF.DATADIR. All
%   such files begin with the prefix CONF.PREFIX, which can be changed
%   to test different parameter settings without overriding previous
%   results.
%
%   The program saves the trained model in
%   <CONF.DATADIR>/<CONF.PREFIX>-model.mat. This model can be used to
%   test novel images independently of the Caltech data.
%
%     load('data/baseline-model.mat') ; # change to the model path
%     label = model.classify(model, im) ;
%

% AUTORIGHTS

conf.calDir = 'data/caltech-101' ;
conf.dataDir = 'data/' ;
conf.autoDownloadData = true ;
conf.numTrain = 15 ;
conf.numTest = 15 ;
conf.numClasses = 102 ;
conf.numWords = 600 ;
conf.numSpatialX = [2 4] ;
conf.numSpatialY = [2 4] ;
conf.quantizer = 'kdtree' ;
conf.svm.C = 10 ;
conf.svm.solver = 'pegasos' ;
conf.svm.biasMultiplier = 1 ;
conf.phowOpts = {'Step', 3} ;
conf.clobber = false ;
conf.tinyProblem = true ;
conf.prefix = 'baseline' ;
conf.randSeed = 1 ;

if conf.tinyProblem
  conf.prefix = 'tiny' ;
  conf.numClasses = 5 ;
  conf.numSpatialX = 2 ;
  conf.numSpatialY = 2 ;
  conf.numWords = 300 ;
  conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ;
end

conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ;
conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ;
conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ;
conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ;

randn('state',conf.randSeed) ;
rand('state',conf.randSeed) ;
vl_twister('state',conf.randSeed) ;

% --------------------------------------------------------------------
%                                            Download Caltech-101 data
% --------------------------------------------------------------------

if ~exist(conf.calDir, 'dir') || ...
   (~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ...
    ~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes')))
  if ~conf.autoDownloadData
    error(...
      ['Caltech-101 data not found. ' ...
       'Set conf.autoDownloadData=true to download the required data.']) ;
  end