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train(trainingSet, trainingValues) {
if (this.checkLabels) {
/* eslint-disable camelcase */
this.options.num_class = new Set(trainingValues).size.toString();
/* eslint-enable camelcase */
}
var X = Matrix.checkMatrix(trainingSet);
var rows = X.rows;
var cols = X.columns;
var flattenData = X.to1DArray();
this.model = create_model(new Uint8Array(Float32Array.from(flattenData).buffer), new Uint8Array(Float32Array.from(trainingValues).buffer), rows, cols);
var variables = Object.keys(this.options);
for (var i = 0; i < variables.length; ++i) {
var variable = variables[i];
if (variable === 'iterations') {
continue;
}
var value = this.options[variable];
set_param(this.model, variable, value);
}
predict(toPredict) {
var Xtest = Matrix.checkMatrix(toPredict);
var predictions = new Array(Xtest.rows);
for (var i = 0; i < Xtest.rows; i++) {
var current = Xtest.getRow(i);
predictions[i] = predict_one(this.model, new Uint8Array(Float32Array.from(current).buffer), Xtest.columns);
}
return predictions;
}