function [sameSf, diffSf, featNames, sameSfOther, diffSfOther] = ...
  formNewerFeatureMatrix(dataMat, columnNames)
%formNewerFeatureMatrix - form feature matrices from "newer" data.

%Written by Luca Cazzanti
%Copyright 2005
%$Id$
 

%% Quick error check
nNames = length(columnNames);
nCols = size(dataMat, 2);
if nNames ~= nCols
  error('Number of column names must be the same as the number of columns');
end


%% First eliminate the data points for which the contact_order is 0
%% as per email exhange with Lars, 5-11 and 5-12 2005.

colIdx = strmatch('prediction_contact_order', columnNames, 'exact');
eliminateIdx = find( dataMat(:,colIdx) == 0);
dataMat(eliminateIdx,:) = [];

colIdx = strmatch('experiment_contact_order', columnNames, 'exact');
eliminateIdx = find( dataMat(:,colIdx) == 0);
dataMat(eliminateIdx,:) = [];


%% Form feature matrix
featMat = [];

%  ... ratio feature ...
colIdx1 = strmatch('prediction_sequence_length', columnNames, 'exact');
colIdx2 = strmatch('experiment_sequence_length', columnNames, 'exact');
featMat = [featMat dataMat(:, colIdx1)./dataMat(:,colIdx2)];
featNames{1} = 'ratio';

%  ... difference in percent alpha ...
colIdx1 = strmatch('prediction_percent_alpha', columnNames, 'exact');
colIdx2 = strmatch('experiment_percent_alpha', columnNames, 'exact');
featMat = [featMat diff(dataMat(:, [colIdx1 colIdx2]), 1, 2)];
featNames{2} = 'difference_percent_alpha';

%  ... difference in percent beta ...
colIdx1 = strmatch('prediction_percent_beta', columnNames, 'exact');
colIdx2 = strmatch('experiment_percent_beta', columnNames, 'exact');
featMat = [featMat diff(dataMat(:, [colIdx1 colIdx2]), 1, 2)];
featNames{3} = 'difference_percent_beta';

%  ... Mammoth zscore feature ...
colIdx = strmatch('zscore', columnNames, 'exact');
featMat = [featMat dataMat(:, colIdx)];
featNames{4} = 'zscore';


% ... experiment contact order (THIS MAY CHANGE) ...
% Why the experimet contact order? It should be the prediction, right?
%colIdx = strmatch('experiment_contact_order', columnNames, 'exact');
%featMat = [featMat dataMat(:, colIdx)];
%featNames{5} = 'experiment_contact_order';
colIdx = strmatch('prediction_contact_order', columnNames, 'exact');
featMat = [featMat dataMat(:, colIdx)];
featNames{5} = 'prediction_contact_order';


% additional features not used in classifiers
otherFeatMat = [];
otherFeatCount = 1;
colIdx = strmatch('prob2', columnNames, 'exact');
tmp = dataMat(:, colIdx);
otherFeatMat = [otherFeatMat tmp];
otherFeatNames{otherFeatCount} = 'prob2';
otherFeatCount = otherFeatCount + 1;

colIdx = strmatch('prediction_sequence_length', columnNames, 'exact');
tmp = dataMat(:, colIdx);
otherFeatMat = [otherFeatMat tmp];
otherFeatNames{otherFeatCount} = 'prediction_sequence_length';
otherFeatCount = otherFeatCount + 1;

colIdx = strmatch('convergence', columnNames, 'exact');
tmp = dataMat(:, colIdx);
otherFeatMat = [otherFeatMat tmp];
otherFeatNames{1} = 'convergence';
otherFeatCount = otherFeatCount + 1;

%% Separate same superfamily/different superfamily
colIdx = strmatch('correct_superfamily', columnNames, 'exact');
tmp = dataMat(:, colIdx);
sameSfIdx = find(tmp == 1);
sameSf = featMat(sameSfIdx,:);
diffSf = featMat;
clear featMat;
diffSf(sameSfIdx,:) = [];

sameSfOther = otherFeatMat(sameSfIdx,:);
diffSfOther = otherFeatMat;
clear otherFeatMat;
diffSfOther(sameSfIdx,:) = [];

return