temporary fix for raspberry pi builds

Author: daelsepara

SVMClassifier/Common.cs

@@ -75,7 +75,6 @@ public static uint C2I(Color color)
 
     public static Color I2C(uint color)
     {
-        byte a = (byte)(color >> 24);
         byte r = (byte)(color >> 16);
         byte g = (byte)(color >> 8);
         byte b = (byte)(color >> 0);

SVMClassifier/Conrec.cs

@@ -121,9 +121,9 @@ public static void Contour(double[,] d, double[] x, double[] y, double[] z, Rend
                                     }
                                     else
                                     {
-                                        h[0] = (double)1 / 4 * (h[1] + h[2] + h[3] + h[4]);
-                                        xh[0] = (double)1 / 2 * (x[i] + x[i + 1]);
-                                        yh[0] = (double)1 / 2 * (y[j] + y[j + 1]);
+                                        h[0] = 0.25 * (h[1] + h[2] + h[3] + h[4]);
+                                        xh[0] = 0.5 * (x[i] + x[i + 1]);
+                                        yh[0] = 0.5 * (y[j] + y[j + 1]);
                                     }
 
                                     if (h[m] > 0)

SVMClassifier/KernelFunction.cs

@@ -121,11 +121,11 @@ public static double Fourier(ManagedArray x1, ManagedArray x2, ManagedArray k)
 
             for (var i = 0; i < x1.Length(); i++)
             {
-                z[i] = Math.Sin(m + (double)1 / 2) * 2;
+                z[i] = Math.Sin(m + 0.5) * 2;
 
                 var d = x1[i] - x2[i];
 
-                z[i] = Math.Abs(d) > 0 ? Math.Sin(m + (double)1 / 2) * d / Math.Sin(d * (double)1 / 2) : z[i];
+                z[i] = Math.Abs(d) > 0 ? Math.Sin(m + 0.5) * d / Math.Sin(d * 0.5) : z[i];
 
                 prod = (i == 0) ? z[i] : prod * z[i];
             }

SVMClassifier/MainWindow.cs

@@ -484,8 +484,8 @@ protected void InitializeUserInterface()
         ToggleUserControls(Paused);
 
         Kernels.Add(new KernelClass("Polynomial", KernelType.POLYNOMIAL, new List<double> { 0, 2 }, new List<string> { "bias", "exponent" }));
-        Kernels.Add(new KernelClass("Gaussian", KernelType.GAUSSIAN, new List<double> { (double)1 / 100 }, new List<string> { "sigma" }));
-        Kernels.Add(new KernelClass("Radial", KernelType.RADIAL, new List<double> { (double)1 / 100 }, new List<string> { "sigma" }));
+        Kernels.Add(new KernelClass("Gaussian", KernelType.GAUSSIAN, new List<double> { 0.01 }, new List<string> { "sigma" }));
+        Kernels.Add(new KernelClass("Radial", KernelType.RADIAL, new List<double> { 0.01 }, new List<string> { "sigma" }));
         Kernels.Add(new KernelClass("Sigmoid", KernelType.SIGMOID, new List<double> { 1, 0 }, new List<string> { "slope", "intercept" }));
         Kernels.Add(new KernelClass("Linear", KernelType.LINEAR, new List<double> { 1, 0 }, new List<string> { "slope", "intercept" }));
         Kernels.Add(new KernelClass("Fourier", KernelType.FOURIER, new List<double> { 1 }, new List<string> { "scaling factor" }));

SVMClassifier/Model.cs

@@ -72,7 +72,7 @@ int Cols(ManagedArray x)
             return x.x;
         }
 
-        public void Setup(ManagedArray x, ManagedArray y, double c, KernelType kernel, ManagedArray param, double tolerance = (double)1 / 1000, int maxpasses = 5, int category = 1)
+        public void Setup(ManagedArray x, ManagedArray y, double c, KernelType kernel, ManagedArray param, double tolerance = 0.001, int maxpasses = 5, int category = 1)
         {
             ManagedOps.Free(dx, dy);
             dx = new ManagedArray(x);
@@ -399,7 +399,7 @@ public void Generate()
         // Converted to R by: SD Separa (2016/03/18)
         // Converted to C# by: SD Separa (2018/09/29)
         //
-        public void Train(ManagedArray x, ManagedArray y, double c, KernelType kernel, ManagedArray param, double tolerance = (double)1 / 1000, int maxpasses = 5, int category = 1)
+        public void Train(ManagedArray x, ManagedArray y, double c, KernelType kernel, ManagedArray param, double tolerance = 0.001, int maxpasses = 5, int category = 1)
         {
             Setup(x, y, c, kernel, param, tolerance, maxpasses, category);
 

SVMClassifier/SVMClassifier.csproj

@@ -90,4 +90,7 @@
     <None Include="packages.config" />
   </ItemGroup>
   <Import Project="$(MSBuildBinPath)\Microsoft.CSharp.targets" />
+  <PropertyGroup Condition = " '$(OS)' == 'Unix' ">
+	  <CscToolExe>mcs</CscToolExe>
+  </PropertyGroup>
 </Project>