MATLAB: How to manipulate Recursive function parameters

MATLAB

Hello, i'm have some confusion in terms of recursive functions. Can someone explain to me why this recursive function calls itself without the same number of arguments initially defined ? is that possible? What's the goal of not taking the other inputs into cosideration ? 
function [output1 output2 output3] = TimeCourse(input, mode, geneIndex, dataForCurrentGene, newLogHypers, newCovarianceMatrixInverses)
switch mode
    case 'init'
        data              = input.data;
        nGenes            = input.nGenes;
        nFeatures         = input.nFeatures;
        sparseMatrix      = zeros(nGenes,nFeatures);
        sparseVector      = false(1,nGenes);
        maxNumberOfComponents = input.maxNumberOfComponents;
        
        featureNames      = input.featureNames;
        featureNames      = cellfun(@str2num,featureNames);
        [X, Y] = meshgrid(featureNames);
        timeDiffs         = (-(X - Y).^2);
       
        hyperPriorParameters = [0, 1; 0, 1; 0, 1]; % [mean s.d.; ...]
        lowerTriangularLogicalMatrix = logical(tril(ones(nFeatures)));
        % Define the cluster structure
        clusterStruct(1,(maxNumberOfComponents+1)) = struct(...
            'nFeatures', [], ...
            'nGenesOverall', [], ...
            'timeDiffs', [],...
            'logHypers', [], ...
            'logPriorOfLogHypers', [], ...
            'squaredHypers', [], ...
            'hyperPriorParams', [], ...
            'lowerTriangularPartOfCovarianceMatrix', [], ...
            'covarianceMatrixInverses', [], ...
            'nGenes', [], ...
            'logMarginalLikelihood', [],...
            'dataCounts', [], ...
            'squaredDataCounts', [], ...
            'logicalGeneIDs', [], ...
            'lowerTriangularLogicalMatrix', [], ...
            'N', []);
        
        [clusterStruct.nFeatures       ] = deal(nFeatures);
        [clusterStruct.nGenesOverall   ] = deal(nGenes);
        [clusterStruct.hyperPriorParams] = deal(hyperPriorParameters);
        [clusterStruct.timeDiffs] = deal(timeDiffs);
        [clusterStruct.lowerTriangularLogicalMatrix] = deal(lowerTriangularLogicalMatrix);
        
        [clusterStruct.logMarginalLikelihood] = deal(0);
        
        [clusterStruct.nGenes] = deal(0);
        [clusterStruct.logicalGeneIDs] = deal(sparseVector);
        
        % Initialise clusters:
        nStartingClusters = ceil(log(nGenes));
        clusterIDs        = random('unid', nStartingClusters, 1, nGenes); %row vector
        uniqueIDs         = unique(clusterIDs);
        
        for i = 1:maxNumberOfComponents
            clusterStruct(i).covarianceMatrixInverses(1,nGenes) =...
                struct('invertedCovarianceMatrix', [], 'determinant', []);
        end
        
        
        for i = uniqueIDs
            logicalIndices                   = clusterIDs == i;
            indices                          = find(logicalIndices);
            nGenesInCluster                  = length(indices);
            dataInCluster                    = sparseMatrix;
            dataInCluster(indices,:)         = data(logicalIndices,:);
            currentCluster                   = clusterStruct(i);
            currentCluster.logicalGeneIDs    = logicalIndices;
            currentCluster.dataCounts        = sum(dataInCluster,1);
            currentCluster.squaredDataCounts = sum(dataInCluster.^2,1);
            currentCluster.nGenes            = nGenesInCluster;
            currentCluster.N                 = nFeatures*nGenesInCluster;
            
            logHypers = TimeCourse(currentCluster, 'sampleHypers');

Best Answer

You didn't post the whole function, but regardless of this: It is possible to call a function without passing all the parameters as long as you handle this in the code itself. There are many reasons to do this, but all go around using a somewhat different function depending of the parameters without having to create many different functions with very similar code.
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