-dev, +user
You’ll need to set the gradient descent step size to something small - a bit of
trial and error shows that 0.00000001 works.
You’ll need to create a LinearRegressionWithSGD instance and set the step size
explicitly:
val lr = new LinearRegressionWithSGD()
lr.optimizer.setStepSize(0.00000001)
lr.optimizer.setNumIterations(100)
val model = lr.run(parsedData)
On 15 Jan 2015, at 16:46, devl.development <devl.developm...@gmail.com> wrote:
> From what I gather, you use LinearRegressionWithSGD to predict y or the
> response variable given a feature vector x.
>
> In a simple example I used a perfectly linear dataset such that x=y
> y,x
> 1,1
> 2,2
> ...
>
> 10000,10000
>
> Using the out-of-box example from the website (with and without scaling):
>
> val data = sc.textFile(file)
>
> val parsedData = data.map { line =>
> val parts = line.split(',')
> LabeledPoint(parts(1).toDouble, Vectors.dense(parts(0).toDouble)) //y
> and x
>
> }
> val scaler = new StandardScaler(withMean = true, withStd = true)
> .fit(parsedData.map(x => x.features))
> val scaledData = parsedData
> .map(x =>
> LabeledPoint(x.label,
> scaler.transform(Vectors.dense(x.features.toArray))))
>
> // Building the model
> val numIterations = 100
> val model = LinearRegressionWithSGD.train(parsedData, numIterations)
>
> // Evaluate model on training examples and compute training error *
> tried using both scaledData and parsedData
> val valuesAndPreds = scaledData.map { point =>
> val prediction = model.predict(point.features)
> (point.label, prediction)
> }
> val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
> println("training Mean Squared Error = " + MSE)
>
> Both scaled and unscaled attempts give:
>
> training Mean Squared Error = NaN
>
> I've even tried x, y+(sample noise from normal with mean 0 and stddev 1)
> still comes up with the same thing.
>
> Is this not supposed to work for x and y or 2 dimensional plots? Is there
> something I'm missing or wrong in the code above? Or is there a limitation
> in the method?
>
> Thanks for any advice.
>
>
>
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