scala-lr
scala-lr is a logistic regression library written in Scala.
Work in progress!
Example usage
object LogisticRegressionExample {
type TrainData = Seq[DataPoint[DenseVector[Double],Double]]
type TestData = Seq[DenseVector[Double]]
val N = 10000 // Number of data points
val D = 10 // Number of dimensions
val R = 0.7 // Scaling factor
val LR = 1.0 // Learning rate
val ITERATIONS = 1000
val rand = new Random(42)
def generateTrainAndTestData: (TrainData,TestData) = {
def generatePoint(i: Int): DataPoint[DenseVector[Double], Double] = {
val y = if(i % 2 == 0) -1.0 else 1.0
val x = DenseVector.fill(D)(rand.nextGaussian + y * R)
DataPoint(x,y)
}
val train = Seq.tabulate(9 * N / 10)(generatePoint)
val test = Seq.tabulate(N / 10)(generatePoint).map(_.featureVector)
(train,test)
}
def main(args: Array[String]) = {
val (trainData,testData) = generateTrainAndTestData
val lr = LogisticRegression(ITERATIONS, LR)
val lrModel = lr.train(trainData)
val results = lrModel.predict(testData)
println(results)
}
}MIT License
Copyright (c) 2011-2013
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