Moved the scripts from dynaml-core to top level directory

tailhq · Dec 20, 2016 · b91cd00 · b91cd00
1 parent 14f8412
commit b91cd00
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Showing 3 changed files with 16 additions and 6 deletions.
diff --git a/...e/scripts/UncertaintyQuantification.scala → scripts/UncertaintyQuantification.scala b/...e/scripts/UncertaintyQuantification.scala → scripts/UncertaintyQuantification.scala
@@ -4,10 +4,12 @@ import io.github.mandar2812.dynaml.analysis.VectorField
 import io.github.mandar2812.dynaml.models.gp.GPRegression
 import io.github.mandar2812.dynaml.optimization.{CoupledSimulatedAnnealing, GridSearch}
 import breeze.stats.distributions.Uniform
-import io.github.mandar2812.dynaml.kernels.{MLPKernel, PeriodicKernel}
+import io.github.mandar2812.dynaml.kernels.{DiracKernel, MLPKernel, PeriodicKernel, RBFKernel}
+import io.github.mandar2812.dynaml.pipes.DataPipe
 import io.github.mandar2812.dynaml.probability._
 import io.github.mandar2812.dynaml.probability.distributions.MultivariateUniform
 import spire.implicits._
+import com.quantifind.charts.Highcharts._
 
 
 val num_features = 1
@@ -19,7 +21,6 @@ val iidXPrior = IIDRandomVarDistr(xPrior) _
 
 val (training, test, noiseLevel) = (100, 500, 0.05)
 
-
 val likelihood = DataPipe((x: Double) => GaussianRV(math.atan(1000.0*x*x*x), noiseLevel))
 
 val model = ProbabilityModel(xPrior, likelihood)
@@ -65,7 +66,7 @@ title("Comparison of Noisy Data versus inferred function")
 legend(List("Test Data samples", "Co-allocation/training data samples", "GP samples"))
 
 
-val rvFTanH = MeasurableFunction(xPrior)(DataPipe((x: Double) => math.atan(1000.0*x*x*x)))
+val rvFTanH = MeasurableFunction(xPrior)((x: Double) => math.atan(1000.0*x*x*x))
 
 //Histogram of test data
 histogram(testData.map(_._2))
@@ -91,7 +92,7 @@ val data2: Stream[(DenseVector[Double], Double)] =
 
 val testData2 = (1 to test).map(_ => model2.sample()).map(c => (DenseVector(c._1), c._2)).toStream
 
-val perKernel = new PeriodicKernel(2.0, 1.5)
+//val perKernel = new PeriodicKernel(2.0, 1.5)
 val noise = new DiracKernel(noiseLevel)
 noise.blocked_hyper_parameters = noise.hyper_parameters
 

diff --git a/dynaml-core/scripts/kernelModels.scala → scripts/kernelModels.scala b/dynaml-core/scripts/kernelModels.scala → scripts/kernelModels.scala
@@ -1,3 +1,9 @@
+import breeze.linalg.DenseVector
+import io.github.mandar2812.dynaml.analysis.VectorField
+import io.github.mandar2812.dynaml.kernels._
+import io.github.mandar2812.dynaml.DynaMLPipe._
+import io.github.mandar2812.dynaml.examples.AbottPowerPlant
+
 implicit val ev = VectorField(6)
 implicit val sp = genericReplicationEncoder[DenseVector[Double]](2)
 

diff --git a/dynaml-core/scripts/probModels.scala → scripts/probModels.scala b/dynaml-core/scripts/probModels.scala → scripts/probModels.scala
@@ -7,12 +7,15 @@ import breeze.stats.distributions._
 import io.github.mandar2812.dynaml.kernels.PeriodicKernel
 import io.github.mandar2812.dynaml.models.gp.GPRegression
 import io.github.mandar2812.dynaml.optimization.GridSearch
-import io.github.mandar2812.dynaml.probability.{GaussianRV, IIDRandomVarDistr, ProbabilityModel, RandomVariable}
+import io.github.mandar2812.dynaml.pipes.DataPipe
+import io.github.mandar2812.dynaml.probability._
 import spire.implicits._
+import com.quantifind.charts.Highcharts._
+
 
 val p = RandomVariable(new Beta(7.5, 7.5))
 
-val coinLikelihood = DataPipe((p: Double) => new BinomialRV(500, p))
+val coinLikelihood = DataPipe((p: Double) => BinomialRV(500, p))
 
 val c_model = ProbabilityModel(p, coinLikelihood)