server/examples/mllib/lr_example.js
function run(sc) {
    var lines = sc.textFile(directory);
    var points = lines.map(function (line, LabeledPoint, Vectors) {
        var parts = line.split(",");
        var y = parseFloat(parts[0]);
        var tok = parts[1].split(" ");
        var x = [];
        for (var i = 0; i < tok.length; ++i) {
            x[i] = parseFloat(tok[i]);
        }

        return new LabeledPoint(y, Vectors.dense(x));

    }, [LabeledPoint, Vectors]).cache();


    // Another way to configure LogisticRegression
    //
    // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
    // lr.optimizer().setNumIterations(iterations)
    //               .setStepSize(stepSize)
    //               .setMiniBatchFraction(1.0);
    // lr.setIntercept(true);
    // var model = lr.train(points.rdd());

    var model = LogisticRegressionWithSGD.train(points, iterations, stepSize);

    return model.weights();

}
function run(sc) {
    var lines = sc.textFile(directory);
    var points = lines.map(function (line, LabeledPoint, Vectors) {
        var parts = line.split(",");
        var y = parseFloat(parts[0]);
        var tok = parts[1].split(" ");
        var x = [];
        for (var i = 0; i < tok.length; ++i) {
            x[i] = parseFloat(tok[i]);
        }

        return new LabeledPoint(y, Vectors.dense(x));

    }, [LabeledPoint, Vectors]).cache();


    // Another way to configure LogisticRegression
    //
    // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
    // lr.optimizer().setNumIterations(iterations)
    //               .setStepSize(stepSize)
    //               .setMiniBatchFraction(1.0);
    // lr.setIntercept(true);
    // var model = lr.train(points.rdd());

    var model = LogisticRegressionWithSGD.train(points, iterations, stepSize);

    return model.weights();

}
function run ( sc ) { var lines = sc . textFile ( directory ) ; var points = lines . map ( function ( line , LabeledPoint , Vectors ) { var parts = line . split ( "," ) ; var y = parseFloat ( parts [ 0 ] ) ; var tok = parts [ 1 ] . split ( " " ) ; var x = [ ] ; for ( var i = 0 ; i < tok . length ; ++ i ) { x [ i ] = parseFloat ( tok [ i ] ) ; } return new LabeledPoint ( y , Vectors . dense ( x ) ) ; } , [ LabeledPoint , Vectors ] ) . cache ( ) ; // Another way to configure LogisticRegression // // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD(); // lr.optimizer().setNumIterations(iterations) //               .setStepSize(stepSize) //               .setMiniBatchFraction(1.0); // lr.setIntercept(true); // var model = lr.train(points.rdd()); var model = LogisticRegressionWithSGD . train ( points , iterations , stepSize ) ; return model . weights ( ) ; }
Logistic regression based classification using ML Lib.
Logistic regression based classification using ML Lib .