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add minmax scaling and tidy up especially gradient descent app
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,83 +1,41 @@ | ||
| package main.scala.util | ||
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| import main.scala.model.{HeightWeight, House, SimplePoint} | ||
| import main.scala.model.SimplePoint | ||
| import org.specs2.mutable.Specification | ||
| import main.scala.util.GradientDescent.LearnedParameterSet | ||
|
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| class GradientDescentTest extends Specification{ | ||
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| "theta0Iter" should { | ||
| "probably not be zero" in { | ||
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| val expected = List( | ||
| HeightWeight("Male",174.0,96.0), | ||
| HeightWeight("Male",189.0,87.0) | ||
| "updatedTheta0" should { | ||
| "be updated from the value passed in" in { | ||
| val normalisedData = List( | ||
| SimplePoint(0.22352941176470573, 0.3949999999999999), | ||
| SimplePoint(-0.4529411764705884, -0.6050000000000001), | ||
| SimplePoint(-0.48235294117647076, -0.45500000000000007), | ||
| SimplePoint(0.19411764705882337, 0.2699999999999999), | ||
| SimplePoint(0.5176470588235292, 0.3949999999999999) | ||
| ) | ||
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| val data = expected.map(dataPoint => SimplePoint(dataPoint.height, dataPoint.weight)) | ||
| val gmm: Double = GradientDescent.theta0Updated(data, 1, 1, 1) | ||
| val updatedTheta0: Double = GradientDescent.theta0Updated(normalisedData, 1, 1, 0.01) | ||
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| gmm shouldEqual (-90) | ||
| updatedTheta0 shouldEqual (0.99) | ||
| } | ||
| } | ||
|
|
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| "theta1iter" should { | ||
| "probably not be zero" in { | ||
| "updatedTheta1" should { | ||
| "be updated from the value passed in" in { | ||
|
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| val expected = List( | ||
| HeightWeight("Female",185.0,110.0), | ||
| HeightWeight("Female",195.0,104.0) | ||
| val normalisedData = List( | ||
| SimplePoint(0.22352941176470573, 0.3949999999999999), | ||
| SimplePoint(-0.4529411764705884, -0.6050000000000001), | ||
| SimplePoint(-0.48235294117647076, -0.45500000000000007), | ||
| SimplePoint(0.19411764705882337, 0.2699999999999999), | ||
| SimplePoint(0.5176470588235292, 0.3949999999999999) | ||
| ) | ||
|
|
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| val data = expected.map(dataPoint => SimplePoint(dataPoint.height, dataPoint.weight)) | ||
| val gmm: Double = GradientDescent.theta1Updated(data, 1, 1, 1) | ||
| val updatedTheta1: Double = GradientDescent.theta1Updated(normalisedData, 1, 1, 0.01) | ||
|
|
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| gmm shouldEqual (-15999) | ||
| updatedTheta1 shouldEqual (1.0000905017301038) | ||
| } | ||
| } | ||
|
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| "gradientDescent" should { | ||
| // "get closer to the right answer" in { | ||
| // | ||
| // def transformer(strings: Array[String]) = House(strings(0), strings(4).toDouble, strings(80).toDouble) | ||
| // val housePrices = CsvReader.asCaseClassList("house-prices-training-resources.data-test.csv", true, transformer) | ||
| // | ||
| // val resources.data = housePrices.map(dataPoint => SimplePoint(dataPoint.lotArea, dataPoint.salePrice)) | ||
| // val gmm = GradientDescent.gradientDescent(resources.data, 0, 1, 2, 1000) | ||
| // | ||
| // gmm shouldEqual (LearnedParameterSet(5, 5)) | ||
| // } | ||
|
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| "get closer to the right answer" in { | ||
|
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| def transformer(strings: Array[String]) = HeightWeight(strings(0), strings(1).toDouble, strings(2).toDouble) | ||
| val heightWeights = CsvReader.asCaseClassListFromTestResource("height-weight-test.csv", true, transformer) | ||
|
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| val data = heightWeights.map(dataPoint => SimplePoint(dataPoint.height, dataPoint.weight)) | ||
| val learnedParams = GradientDescent.gradientDescent(data, 0, 1, 0.001, 1000) | ||
|
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| learnedParams.theta0 shouldEqual (-0.009308274850941753) | ||
| learnedParams.theta1 shouldEqual (0.9889333098394358) | ||
|
|
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| } | ||
|
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| // "pass the coursera test case" in { | ||
| // | ||
| // //[1 5; 1 2; 1 4; 1 5],[1 6 4 2]',[0 0]',0.01,1000 | ||
| // | ||
| // def transformer(strings: Array[String]) = House(strings(0), strings(4).toDouble, strings(80).toDouble) | ||
| // val housePrices: Seq[House] = CsvReader.asCaseClassList("house-prices-training-resources.data-test.csv", true, transformer) | ||
| // | ||
| // val resources.data = List( | ||
| // SimplePoint(1, 5), | ||
| // SimplePoint(1, 2), | ||
| // SimplePoint(1, 4), | ||
| // SimplePoint(1, 5), | ||
| // ) | ||
| //// val resources.data: Seq[SimplePoint] = housePrices.map(dataPoint => SimplePoint(dataPoint.lotArea, dataPoint.salePrice)) | ||
| // val gmm = GradientDescent.gradientDescent(resources.data, 0, 1, 2, 1000) | ||
| // | ||
| // gmm shouldEqual (LearnedParameterSet(5, 5)) | ||
| // } | ||
| } | ||
| } |
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