Dear All,
I have some program as below which makes me very much confused and inscrutable,
it is about multiple dimension linear regression mode, the weight / coefficient
is always perfect while the dimension is smaller than 4, otherwise it is wrong
all the time.Or, whether the LinearRegressionWithSGD would be selected for
another one?
public class JavaLinearRegression { public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Linear Regression Example");
JavaSparkContext sc = new JavaSparkContext(conf); SQLContext jsql = new
SQLContext(sc);
//Ax = b, x = [1, 2, 3, 4] would be the only one output about weight
//x1 + 2 * x2 + 3 * x3 + 4 * x4 = y would be the multiple linear mode
List<LabeledPoint> localTraining = Lists.newArrayList( new
LabeledPoint(30.0, Vectors.dense(1.0, 2.0, 3.0, 4.0)), new
LabeledPoint(29.0, Vectors.dense(0.0, 2.0, 3.0, 4.0)), new
LabeledPoint(25.0, Vectors.dense(0.0, 0.0, 3.0, 4.0)), new
LabeledPoint(16.0, Vectors.dense(0.0, 0.0, 0.0, 4.0)));
JavaRDD<LabeledPoint> training = sc.parallelize(localTraining).cache();
// Building the model int numIterations = 1000; //the number could be
reset large final LinearRegressionModel model =
LinearRegressionWithSGD.train(JavaRDD.toRDD(training), numIterations);
//the coefficient weights are perfect while dimension of LabeledPoint is
SMALLER than 4. //otherwise the output is always wrong and inscrutable.
//for instance, one output is //Final w:
[2.537341836047772E25,-7.744333206289736E24,6.697875883454909E23,-2.6704705246777624E22]
System.out.print("Final w: " + model.weights() + "\n\n"); }} I would
appreciate your kind help or guidance very much~~
Thank you!Zhiliang
