display : visualization, 'on' or 'off'.Ī class named SvddVisualization is defined to visualize the training and test results.variableType: variable type, specified as 'real' (real variable), 'integer' (integer variable).More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects. variableName: variables that are to be optimized, including 'cost', 'degree', 'offset', and 'gamma'. To associate your repository with the image-encryption topic, visit your repo's landing page and select 'manage topics.' GitHub is where people build software.method: optimization methods, only supported for 'bayes', 'pso', and 'ga'.m file named mytest.m, then the resulting file is mytest.p. Please see the demonstration □ demo_ParameterOptimization.m for details. m file or folder on the search path and produces P-code files with the extension. First define an optimization setting structure, then add it to the svdd parameter structure.The parameter optimization of the polynomial kernel function can only be achieved by using Bayesian optimization. Specifically, if the data does not have labels, please change the inputs for training or testing to ain(trainData) and results = svdd.test(testData).Ī class named SvddOptimization is defined to optimized the parameters.trainData, trainLabel, testData, and testLabel. BinaryDataset is designed to validate the svdd model only, you can use your data and please be careful to keep the naming of variables consistent, e.g.Kernel = BaseKernel( 'type ', 'gaussian ', 'gamma ', 0.04)
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