how to manipulate Recursive function parameters ?

Question

wejden hammami 2020-5-31

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/538827-how-to-manipulate-recursive-function-parameters

评论： Thiago Henrique Gomes Lobato ，2020-5-31

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');

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Answer 1

Thiago Henrique Gomes Lobato 2020-5-31

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/538827-how-to-manipulate-recursive-function-parameters#answer_443471

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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Thiago Henrique Gomes Lobato 2020-5-31

Yes and no. If you have a function defined, for example, as:

def Test(A,B):
    return A+1
Test(1)

You will have an error even if you not give B even though it is useless. An alternative is to use standard values:

def Test(A=0,B=0):
    return A+1
Test(1)

This will work, so you would have standard values for all your inputs and could pass only the ones you want to. This, I beleive, would be the best way for your function. Another more complicate solution is to use multipledispatch, but then you will have to define all functions variations.

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