Package Exports
- ml-pls
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Readme
Partial Least Squares (PLS)
PLS regression algorithm based on the Yi Cao Matlab implementation:
Partial Least-Squares and Discriminant Analysis
installation
$ npm install ml-pls
Methods
new PLS()
Constructor that takes no arguments.
Example
var pls = new PLS();fit(trainingSet, predictions)
Fit the PLS model to the given training set and predictions
Arguments
trainingSet- A matrix of the training set.predictions- A matrix of predictions with the same size of rows of the trainingSet.
Example
var training = [[0.1, 0.02], [0.25, 1.01] ,[0.95, 0.01], [1.01, 0.96]];
var predicted = [[1, 0], [1, 0], [1, 0], [0, 1]];
pls.fit(trainingSet, predictions);predict(dataset)
Predict the values of the dataset.
Arguments
dataset- A matrix that contains the dataset.
Example
var dataset = [[0, 0], [0, 1], [1, 0], [1, 1]];
var ans = pls.predict(dataset);getExplainedVariance()
Returns the explained variance on training
export()
Exports the actual PLS to an Javascript Object.
load(model)
Returns a new PLS with the given model.
Arguments
model- Javascript Object generated from export() function.