Train Exhaustive Nearest Neighbor Searcher

Load Fisher's iris data set.

loadfisheriris

Remove five irises randomly from the predictor data to use as a query set.

rng('default');% For reproducibilityn = size(meas,1);% Sample sizeqIdx = randsample(n,5);% Indices of query dataX = meas(~ismember(1:n,qIdx),:); Y = meas(qIdx,:);

Prepare an exhaustive nearest neighbor searcher using the training data. Specify the'Distance'and'P'name-value pair arguments to use the Minkowski distance with an exponent of 1 for finding the nearest neighbor.

Mdl = ExhaustiveSearcher(X,'Distance','minkowski','P',1);

Find the index of the training data (X) that is the nearest neighbor of each point in the query data (Y).

Idx = knnsearch(Mdl,Y);

Generate Code UsingsaveLearnerForCoderandloadLearnerForCoder

Generate code that loads an exhaustive searcher, takes query data as an input argument, and then finds the nearest neighbor.

Save the exhaustive searcher to a file usingsaveLearnerForCoder.

saveLearnerForCoder(Mdl,'searcherModel')

saveLearnerForCodersaves the model to the MATLAB binary file searcherModel.mat as a structure array in the current folder.

Define the entry-point functionmyknnsearch1这需要查询数据作为输入参数。在the function, load the searcher object by usingloadLearnerForCoder, and then pass the loaded model toknnsearch.

typemyknnsearch1.m% Display contents of myknnsearch1.m file

function idx = myknnsearch1(Y) %#codegen Mdl = loadLearnerForCoder('searcherModel'); idx = knnsearch(Mdl,Y); end

Note:If you click the button located in the upper-right section of this page and open this example in MATLAB®, then MATLAB® opens the example folder. This folder includes the entry-point function files,myknnsearch1.m,myknnsearch2.m, andmyknnsearch3.m.

Generate code formyknnsearch1by usingcodegen. Specify the data type and dimension of the input argument by usingcoder.typeofso that the generated code accepts a variable-size array.

codegenmyknnsearch1-args{coder.typeof(Y,[Inf,4],[1,0])}

For a more detailed code generation example that usessaveLearnerForCoderandloadLearnerForCoder, seeCode Generation for Prediction of Machine Learning Model at Command Line. For more details about specifying variable-size arguments, seeSpecify Variable-Size Arguments for Code Generation.

Pass the query data (Y) to verify thatmyknnsearch1and the MEX file return the same indices.

myIdx1 = myknnsearch1(Y); myIdx1_mex = myknnsearch1_mex(Y);

ComparemyIdx1andmyIdx1_mexby usingisequal.

verifyMEX1 = isequal(Idx,myIdx1,myIdx1_mex)

verifyMEX1 =logical1

isequalreturns logical 1 (true) if all the inputs are equal. This comparison confirms thatmyknnsearch1and the MEX file return the same results.

Generate Code with Constant Folded Model Object

Nearest neighbor searcher objects can be an input argument of a function you define for code generation. The-argsoption ofcodegenaccept a compile-time constant searcher object.

Define the entry-point functionmyknnsearch2that takes both an exhaustive searcher model and query data as input arguments instead of loading the model in the function.

typemyknnsearch2.m% Display contents of myknnsearch2.m file

function idx = myknnsearch2(Mdl,Y) %#codegen idx = knnsearch(Mdl,Y); end

To generate code that takes the model object as well as the query data, designate the model object as a compile-time constant by usingcoder.Constantand include the constant folded model object in the-argsvalue ofcodegen.

codegenmyknnsearch2-args{coder.Constant(Mdl),coder.typeof(Y,[Inf,4],[1,0])}

The code generation workflow with a constant folded model object follows general code generation workflow. For details, seeGeneral Code Generation Workflow.

Verify thatmyknnsearch2and the MEX file return the same results.

myIdx2 = myknnsearch2(Mdl,Y); myIdx2_mex = myknnsearch2_mex(Mdl,Y); verifyMEX2 = isequal(Idx,myIdx2,myIdx2_mex)

verifyMEX2 =logical1

Generate Code with Name-Value Pair Arguments

Define the entry-point functionmyknnsearch3that takes a model object, query data, and name-value pair arguments. You can allow for optional name-value arguments by specifyingvararginas an input argument. For details, seeCode Generation for Variable Length Argument Lists(MATLAB Coder).

typemyknnsearch3.m% Display contents of myknnsearch3.m file

function idx = myknnsearch3(Mdl,Y,varargin) %#codegen idx = knnsearch(Mdl,Y,varargin{:}); end

To generate code that allows a user-defined exponent for the Minkowski distance, include{coder.Constant('P'),0}in the-argsvalue ofcodegen. Usecoder.Constantbecause the name of a name-value pair argument must be a compile-time constant.

codegenmyknnsearch3-args{coder.Constant(Mdl),coder.typeof(Y,[Inf,4],[1,0]),coder.Constant('P'),0}

Verify thatmyknnsearch3and the MEX file return the same results.

newIdx = knnsearch(Mdl,Y,'P',2); myIdx3 = myknnsearch3(Mdl,Y,'P',2); myIdx3_mex = myknnsearch3_mex(Mdl,Y,'P',2); verifyMEX3 = isequal(newIdx,myIdx3,myIdx3_mex)

verifyMEX3 =logical1