![]() MyPCAPredict applies PCA to new data using coeff and mu, and then predicts ratings using the transformed data. ![]() ScoreTest = Load trained classification model Generating C/C++ code requires MATLAB® Coder™.įunction label = myPCAPredict(XTest,coeff,mu) %#codegen % Transform data using PCA Use pca in MATLAB® and apply PCA to new data in the generated code on the device. ![]() To save memory on the device, you can separate training and prediction. In this workflow, you must pass training data, which can be of considerable size. Because pca supports code generation, you can generate code that performs PCA using a training data set and applies the PCA to a test data set. This example also describes how to generate C/C++ code. To test the trained model using the test data set, you need to apply the PCA transformation obtained from the training data to the test data set. For example, you can preprocess the training data set by using PCA and then train a model. This procedure is useful when you have a training data set and a test data set for a machine learning model.
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