matlab编写的程序输⼊参数怎么写，运⾏程序：参数如何输⼊function [results] = AntminerPlus(trainingfile, testfile, outputfile, options)
%Performs a single run of the AntMiner+ algorithm on a given training and
function怎么记忆
%test set.
%
% * trainingfile:  file containing the training set (and validation set in
%                  case of Early stop)
% * testfile:      file containing the test set
% * output:        detailed information is printed to this file when using
%                  verbose mode (-V)
% * options:        options string
%  -ES:        (1)    use early stop (1) or not (0)
%  -limit:    (200)  maximum iterations for a single rule
%  -stop:      (0.01)  fraction of non-majority class data
%  -heuristic: (K)    specifies the rule evaluation function
%                      'K',F','M','RCM','A+' or 'SS'
%  -heuristicParameter: specifies the parameter used in the rule
%                      evaluation function (0.44 - 0.28 - 7 - 0.028 resp.)
%  -ants:      (1000)  number of ants
%  -rho:      (0.85)  evaporation factor in pheromone update
%  -p:        (0.1)  MAX-MIN AS parameter
%  -epsilon:  (0.05)  sensitivity of the convergence check
%  -disc:      ('fay') the discretization method - 'kon','efb,'eib','fay'
%  -bins:      (10)    the number of bins in 'efb' or 'eib' discretization
%  -AS:        (0)    use attribute selection (1) or not (0)
%  -ASmethod:  (rel)  attribute selection method. 'cfs','con', 'chi',
%                      'svm', 'gai', 'inf','1R', 'rel', 'sym'
%  -ASnumAttr: (10)    specify number of attributes to select
%  -s:        (time)  the seed of the random number generator
%  -V:        (0)    verbose mode: print data to 'output' (1) or not (0)
%Returns: results      Contains several fields with key statistics
%  results.accuracy    test set accuracy
%  results.rules      number of rules
%  s      number of terms per rule
%  results.time        runtime of the algorithm
%WARNING: due the a rounding error in the output, the seed in the output
%file does not match that used by the random number generator. If
%recreating an experiment is required, always specify the seed.
%Written by Bart Minnaert
%parse the 'options' string
parse_options
RandStream.setDefaultStream(RandStream('mt19937ar','seed',seed));
import java.util.Random;
import java.lang.System;
gen = Random(seed);
% Read the dataset
Instances;
shufflefile = testfile;
read_data %reads 'shufflefile' and returns 'wekadata' - also gathers metadata
testset = wekadata;
shufflefile = trainingfile;
read_data
trainingset = wekadata;
if Estop == 0 %do not use early stop => no validation set
validationset = trainingset; %validationset can't be empty in next steps - explicitly declare it empty at the end of preprocessing
else %early stop: use a validation set
validationset = stCV(3,0); %take one third of the training set for validation - note that this is not randomized!
trainingset = ainCV(3,0); %this reduces the training set however.
end
trainingPointsStart = trainingset.numInstances(); %number of training data at the start of the algorithm
nbTraining = trainingPointsStart;
nbValidation = validationset.numInstances();
nbTest = testset.numInstances();
if Estop == 0
nbValidation = 0;
end
nbDatapoints = nbTraining+nbValidation+nbTest;
nbDatapointsStart = nbDatapoints;
% Preprocessing and removal of majority class
modifydata
% After modifydata, the data is no longer in the weka-format % Initialize the search
init_measurements
% Run the AntMiner+ algorithm
timestart = tic; %measure runtime
AntminerCore
runtime = toc(timestart);
% Save raw output
testAccuracy = finalAccuracy;
fname = outputfile;
%print the parameters, measurements and the ruleset to a file if(verbose)
save_raw_output
end
fclose('all');
stats = [finalAccuracy,nbRules,avgRL,runtime];
results.accuracy = finalAccuracy;
results.rules = nbRules;
results.time = runtime;

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