Training (constructing PCA components from input data) and prediction (performing PCA To save memory on the device to which you deploy generated code, you can separate The output dimensions are commensurate with corresponding The generated code does not treat an input matrix X thatĪs a special case. Returns the sixth output mu as a row vector.Ī 1-by-0 array. Returns the fifth output explained as a column in theĪrguments must be compile-time constants. Name-value pair argument in the generated code, include The value for the 'Economy' name-value pair argument must beĪ compile-time constant. 'VariableWeights' name-value pair arguments must be Missing data at random, but might not perform well Work well for data sets with a small percentage of With missing values without listwise deletion Uses an iterative method starting with randomĪLS is designed to better handle missing values. TheĮIG algorithm is faster than SVD when the number of observations, n,Įxceeds the number of variables, p, but is lessĪccurate because the condition number of the covariance is the squareĪlternating least squares (ALS) algorithm. Singular value decomposition (SVD) of X.Įigenvalue decomposition (EIG) of the covariance matrix. For details, see Specify Variable-Size Arguments for Code Generation.ĭefault. If the number of observations is unknown at compile time, you can also specify the input as variable-size by using coder.typeof (MATLAB Coder). To specify the data type and exact input array size, pass a MATLAB® expression that represents the set of values with a certain data type and array size by using the -args option. Because C and C++ are statically typed languages, you must determine the properties of all variables in the entry-point function at compile time. Generate code by using codegen (MATLAB Coder). This folder includes the entry-point function file. 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. In this way, you do not pass training data, which can be of considerable size. 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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