gaussianKdePredict
Syntax
gaussianKdePredict(model,X)
Arguments
model is a dictionary indicating the model generated by
gaussianKde
.
X is a numeric vector, matrix, tuple, or table indicating the data to be
predicted. Its dimensions must be the same as those of the dataset used in
gaussianKde
.
Details
Predict the probability density of the input data based on the model generated by
gaussianKde
.
Return value: A floating-point vector of the same size as the number of rows in X, indicating the prediction result of each data point in X.
Examples
The following example first uses the gaussianKde
function to
estimate the probability density of the input trainset.txt. based on the
Gaussian kernel estimation and generate a model. Then, the
gaussianKdePredict
function is called to apply this model
to another input file, testset.txt, to predict its probability density
results.
trainData = loadText("trainset.txt"," ");
testData = loadText("testset.txt"," ");
model = gaussianKde(trainData)
gaussianKdePredict(model, testData)
Output:
->[0.0623,0.0730,0.0336,0.0030,0.0001,0.0552....]
Related Function: gaussianKde