add random data point corruption ability, change to scatter

src/Tutorials/GaussianProcessDataGenerator.cs

@@ -13,6 +13,12 @@ using System.Threading;
 
 namespace Microsoft.ML.Probabilistic.Tutorials
 {
+    /// <summary>
+    /// This script generates a dataset which has the following pipeline:
+    /// 1) randomly sample a 1D function from a GP;
+    /// 2) pick a random subset of 'numData' points;
+    /// 3) pick a further random proportion 'propCorrupt' of 'numData' to corrupt according to a uniform distribution with a range of -3 to 3
+    /// </summary>
     [Example("Applications", "A Gaussian Process regression example")]
     class GaussianProcessDataGenerator
     {
@@ -25,8 +31,14 @@ namespace Microsoft.ML.Probabilistic.Tutorials
                 return;
             }
 
+            // Number of datapoints
+            int numData = 30;
+
+            // The proportion of points to randomly corrupt
+            double propCorrupt = 0.3;
+
             // The points to evaluate
-            Vector[] inputs = this.VectorLinSpace(-5, 5, 51);
+            Vector[] inputs = this.VectorRange(-5, 5, numData, true);
 
             // Set up the GP prior, a distribution over functions, which will be filled in later
             Variable<SparseGP> prior = Variable.New<SparseGP>().Named("prior");
@@ -42,7 +54,7 @@ namespace Microsoft.ML.Probabilistic.Tutorials
             Variable<double> score = Variable.FunctionEvaluate(f, x[j]).Named("score");
 
             // The basis
-            Vector[] basis = VectorLinSpace(-5, 5, 6);
+            Vector[] basis = this.VectorRange(-5, 5, 6, false);
 
             // The kernel
             IKernelFunction kf;
@@ -57,18 +69,26 @@ namespace Microsoft.ML.Probabilistic.Tutorials
             var randomFunc = sgp.Sample();
 
             // plotting boilerplate
-            var p1 = new OxyPlot.Series.LineSeries
+            var p1 = new OxyPlot.Series.ScatterSeries
             {
                 Title = "Random Function"
             };
 
-            Console.WriteLine("");
-            Console.WriteLine("Random function evaluations:");
+            Random rng = new Random();
+
+            // get random data
             for (int i = 0; i < inputs.Length; i++)
             {
                 double post = randomFunc.Evaluate(inputs[i]);
-                Console.WriteLine("f({0}) = {1}", inputs[i], post);
-                p1.Points.Add(new DataPoint(inputs[i][0], post));
+                // corrupt data point if it we haven't exceed the proportion we wish to corrupt
+                if (i < propCorrupt * numData)
+                {
+                    double sign = rng.NextDouble() > 0.5 ? 1 : -1;
+                    double distance = rng.NextDouble() * 3;
+                    post = (sign * distance) + post;
+                }
+
+                p1.Points.Add(new OxyPlot.Series.ScatterPoint(inputs[i][0], post));
             }
 
             var model = new PlotModel();
@@ -77,6 +97,8 @@ namespace Microsoft.ML.Probabilistic.Tutorials
             Thread thread = new Thread(() => DisplayPNG(model));
             thread.SetApartmentState(ApartmentState.STA);
             thread.Start();
+
+            Console.WriteLine("Plotting complete: Generated {0} points with {1} corrupted", numData, (int)System.Math.Ceiling(numData * propCorrupt));
         }
 
         private void DisplayPNG(PlotModel model)
@@ -86,13 +108,26 @@ namespace Microsoft.ML.Probabilistic.Tutorials
             PngExporter.Export(model, outputToFile, 600, 400, OxyColors.White);
         }
 
-        private Vector[] VectorLinSpace(int min, int max, int len)
+        /// <summary>
+        /// Generates a 1D vector with length len having a min and max; data points are randomly distributed and ordered if specified
+        /// </summary>
+        private Vector[] VectorRange(double min, double max, int len, bool random)
         {
             Vector[] inputs = new Vector[len];
+            Random rng = new Random();
 
             for (int i = 0; i < len; i++)
             {
-                double num = i / (double)(len - 1);
+                double num = new double();
+
+                if (random)
+                {
+                    num = rng.NextDouble();
+                }
+                else
+                {
+                    num = i / (double)(len - 1);
+                }
                 num = num * (max - min);
                 num += min;
                 inputs[i] = Vector.FromArray(new double[1] { num });