function [C, R, w, info, conf] = rbf_rt_2(X, y, conf)%% Hybrid radial basis function network and regression tree.%% Solves a regression problem with inputs X and outputs y using a% regression tree and an RBF network selected using standard% forward subset selection. Returns the hidden unit centres C,% their radii R, the hidden-to-output weights w, some additional% information info and a fully instantiated configuration% structure conf.%% For further details of the function see:%%  'Matlab Routines for RBF Networks', 1999.%% For a description of the algorithm see:%%  'Further Advances in RBF Networks', 1999.%% Program name (for error messages).prog = 'rbf_rt_2';% Configuration specification.spec(1) = struct( ...  'comment', 'Scale length trial values', ...  'name',    'scales', ...  'type',    {{'vector', 'positive'}}, ...  'options', [], ...  'default', [1 2]);spec(2) = struct( ...  'comment', 'Minimum membership trial values', ...  'name', 'minmem', ...  'type', {{'vector', 'positive', 'integer'}}, ...  'options', [], ...  'default', 5);spec(3) = struct( ...  'comment', 'Placement of centres', ...  'name', 'edge', ...  'type', 'number', ...  'options', {{0, 1}}, ...  'default', 0);spec(4) = struct( ...  'comment', 'Which cells get translated into RBFs', ...  'name', 'trans', ...  'type', 'string', ...  'options', {{'all', 'leaves'}}, ...  'default', 'all');spec(5) = struct( ...  'comment', 'Basis function type', ...  'name', 'type', ...  'type', 'string', ...  'options', [], ...  'default', 'g');spec(6) = struct( ...  'comment', 'Basis function sharpness', ...  'name', 'exp', ...  'type', 'number', ...  'options', [], ...  'default', 2);spec(7) = struct( ...  'comment', 'Configuration for rbf_fs_2', ...  'name', 'fsc', ...  'type', 'structure', ...  'options', [], ...  'default', '[]');spec(8) = struct( ...  'comment', 'Display graphical timer', ...  'name', 'timer', ...  'type', 'string', ...  'options', [], ...  'default', '');spec(9) = struct( ...  'comment', 'Verbose output', ...  'name', 'verb', ...  'type', 'number', ...  'options', {{0 1}}, ...  'default', 0);spec(10) = struct( ...  'comment', 'Model selection criterion', ...  'name', 'msc', ...  'type', 'string', ...  'options', {{'uev', 'fpe', 'gcv', 'bic'}}, ...  'default', 'gcv');spec(11) = struct( ...  'comment', 'Bias unit', ...  'name', 'bias', ...  'type', 'number', ...  'options', {{0 1}}, ...  'default', 0);% Check number of arguments. Take special action if only one.switch nargincase 1  if isstring(X)    switch X    case 'conf'      conf_print(prog, spec)      return    case 'demo'      [mydemo, myclean] = eval(['demo_' prog]);      run_demo(mydemo, myclean)      return    otherwise      error([prog ': ''' X ''' unrecognised for single string argument'])    end  else    error([prog ': unrecognised type for single argument'])  endcase 2  if isstring(X) & isstring(y)    switch X    case 'conf'      conf_print(prog, spec, y)      return    otherwise      error([prog ': ''' X ''' unrecognised for double string argument'])    end  else    conf = [];  endcase 3otherwise  error([prog ': illegal number of arguments'])end% Check type of input arguments.if ~isnumeric(X) | ndims(X) ~= 2  error([prog ': first argument (X) should be a matrix'])endif ~isnumeric(y) | ndims(y) ~= 2 | size(y,2) ~= 1  error([prog ': second argument (y) should be a column vector'])end% Check for consistent size between X and y.[d,p] = size(X);if size(y,1) ~= p  error([prog ': number of samples inconsistent between X and y'])end% Check the configuration is okay and set defaults (if required).conf = conf_check(conf, spec, prog);% Scales and minimum memberships.scales = conf.scales;nscale = length(scales);mins = conf.minmem;nmin = length(mins);% Initialisation for flops and time.stats.comps = flops;stats.ticks = clock;% Setup tree growing configuration.conf1.verb = conf.verb;conf1.rprt = conf.verb;if conf.edge  conf1.place = 'edge';else  conf1.place = 'centre';end% Setup tree translating configuration.conf2.trans = conf.trans;% Setup forward selection configuration.conf3 = conf.fsc;% Overide conf.fsc by parameters set up in conf.conf3.type = conf.type;conf3.exp = conf.exp;conf3.msc = conf.msc;conf3.bias = conf.bias;% Set up a timer for minmem (if required).if ~strcmp(conf.timer,'')  minm_tmr = get_tmr(struct('name', [prog ' minmem ' conf.timer], 'n', nmin));end% Loop over mins.berr = Inf;for m = 1:nmin  % Increment the timer for minmem.  if ~strcmp(conf.timer,'')    inc_tmr(minm_tmr)  end  % Grow a tree for this minmem.  conf1.minm = mins(m);  tree = grow_tree(X, y, conf1);  % Set up a timer for scales (if required).  if ~strcmp(conf.timer,'')    scale_tmr = get_tmr(struct('name', [prog ' scales ' conf.timer], 'n', nscale));  end      for s = 1:nscale      % Increment the timer for scale.    if ~strcmp(conf.timer,'')      inc_tmr(scale_tmr)    end    % Grow an RBF on the current tree.    conf2.scale = scales(s);    [C, R] = tree_rbf(tree, conf2);    % Run forward selection.    conf3.cen = C;    conf3.rad = R;    [C, R, w, info, conf4] = rbf_fs_2(X, y, conf3);      % Is this the best so far.    if info.err < berr      berr = info.err;      btree = tree;      bscale = conf2.scale;      bminm = conf1.minm;      bC = C;      bR = R;      bw = w;      binfo = info;      bconf = conf4;    end  end  % Close the timer for scales.  if ~strcmp(conf.timer,'')    close(scale_tmr)  endend% Close the timer for minm.if ~strcmp(conf.timer,'')  close(minm_tmr)end% Record the computations and time taken.stats.comps = flops - stats.comps;stats.ticks = etime(clock,stats.ticks);% Get the best centres, radii and weights.C = bC;R = bR;w = bw;% Replace conf.fsc with the best rbf_fs_2 conf.conf.fsc = bconf;% Other potentially useful results go in 'info'.info.err = berr;info.scale = bscale;info.minm = bminm;info.tree = btree;info.stats = stats;info.info = binfo;info.bias = binfo.bias;% Configuration for rbf_dm also goes in info.info.dmc = struct('type', conf.type, 'exp', conf.exp, 'bias', binfo.bias);