Thanks a lot Burak, that helped.
On Fri, Mar 13, 2015 at 1:55 PM, Burak Yavuz <brk...@gmail.com> wrote:
> Hi,
>
> I would suggest you use LBFGS, as I think the step size is hurting you.
> You can run the same thing in LBFGS as:
>
> ```
> val algorithm = new LBFGS(new LeastSquaresGradient(), new SimpleUpdater())
> val initialWeights = Vectors.dense(Array.fill(3)(
> scala.util.Random.nextDouble()))
>
> val weights = algorithm.optimize(parsedData,initialWeights)
> ```
>
> Note that weights will be a Vector and not a model. You can then generate
> the model with:
>
> val w = weights.toArray
> val intercept = w.takeRight(1).head()
> val model = new LinearRegressionModel(Vectors.dense(w.dropRight(1)),
> intercept)
>
>
> Best,
> Burak
>
> On Wed, Mar 11, 2015 at 11:59 AM, EcoMotto Inc. <ecomot...@gmail.com>
> wrote:
>
>> Hello,
>>
>> I am trying to run LinearRegression on a dummy data set, given below.
>> Here I tried all different settings but I am still failing to reproduce
>> desired coefficients.
>>
>> Please help me out, as I facing the same problem in my actual dataset.
>> Thank you.
>>
>> This dataset is generated based on the simple equation: Y = 4 + (2 * x1)
>> + (3 * x2)
>>
>> *Data:*
>> y,x1,x2
>> 6.3,1,0.1
>> 8.6,2,0.2
>> 10.9,3,0.3
>> 13.8,4,0.6
>> 16.4,5,0.8
>> 19.6,6,1.2
>> 22.8,7,1.6
>> 25.7,8,1.9
>> 28.3,9,2.1
>> 31.2,10,2.4
>> 34.1,11,2.7
>>
>> *Spark Code:*
>> val data = sc.textFile("Data/tempData_1.csv" )
>>
>> val parsedData = data.mapPartitions(_.drop(1)).map {
>> line =>
>> val parts = line.split(',')
>> LabeledPoint(parts(0).toDouble,Vectors.dense(Array(1.0,parts(1).toDouble,parts(2).toDouble)))
>> }.cache()
>>
>> var numIterations = 400
>> val step = 0.01
>> val algorithm = new LinearRegressionWithSGD()
>> algorithm.setIntercept(false) //Even tried with intercept(True) and just
>> (x1,x2) features
>> algorithm.optimizer.setStepSize(step)
>> algorithm.optimizer.setNumIterations(numIterations)
>> .setUpdater(new SimpleUpdater())
>> //.setRegParam(0.1)
>> .setMiniBatchFraction(1.0)
>>
>> val initialWeights =
>> Vectors.dense(Array.fill(3)(scala.util.Random.nextDouble()))
>>
>> val model = algorithm.run(parsedData,initialWeights)
>> println(s">>>> Model intercept: ${model.intercept}, weights:
>> ${model.weights}")
>>
>>
>>
>> Regards,
>> Arun
>>
>
>
