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March 16, 2016 20:36
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Logistic Regression
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| package models | |
| import java.util.Random | |
| import breeze.linalg.{SparseVector, DenseVector} | |
| import breeze.numerics.{round, exp} | |
| case class DataPoint(featureVector: DenseVector[Double], label: Double) | |
| // TODO: add defaults | |
| case class LogisticRegression(maxIterations: Int, learningRate: Double) { | |
| type TrainData = Seq[DataPoint] | |
| val rand = new Random(42) | |
| def train(trainData: TrainData): LogisticRegressionModel = { | |
| val dim = trainData.head.featureVector.length | |
| // Initialize the target weight vector | |
| val itw = generateTargetWeights(dim) | |
| // Calculate our hypothesis weight vector using gradient descent | |
| val ftw = gradientDescent(trainData, itw, maxIterations, learningRate) | |
| LogisticRegressionModel(ftw) | |
| } | |
| def generateTargetWeights(dim: Int) = { | |
| // Scale from [0, 1] to [-1, 1] | |
| var itw = DenseVector.fill(dim)(2 * rand.nextDouble - 1) | |
| println("Initial target weights: " + itw) | |
| itw | |
| } | |
| // TODO: check for convergence | |
| def gradientDescent(trainData: TrainData, itw: DenseVector[Double], maxIterations: Int, learningRate: Double) = { | |
| for (i <- 1 to maxIterations) { | |
| println("On iteration " + i) | |
| val gradient: DenseVector[Double] = trainData.map { point => | |
| point.featureVector * point.label * (1 / (1 + exp(itw.dot(point.featureVector) * -point.label))) | |
| }.reduce(_ + _) | |
| itw -= gradient * learningRate | |
| } | |
| println("Final w: " + itw) | |
| itw | |
| } | |
| } | |
| case class LogisticRegressionModel(ftw: DenseVector[Double]) { | |
| type TestData = Seq[DenseVector[Double]] | |
| def sigmoid(signal: Double) = 1 / (1 + exp(-signal)) | |
| def predict(testData: TestData): Seq[Double] = { | |
| testData.map { point => | |
| sigmoid(point.dot(ftw)) | |
| } | |
| } | |
| } |
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