Add C# implemenation for 1D Convolutions

contents/convolutions/1d/1d.md

@@ -53,7 +53,9 @@ With this in mind, we can almost directly transcribe the discrete equation into
 
 {% method %}
 {% sample lang="jl" %}
-[import:29-48, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:27-46, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:63-84, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 The easiest way to reason about this code is to read it as you might read a textbook.
@@ -184,30 +186,38 @@ Here it is again for clarity:
 
 {% method %}
 {% sample lang="jl" %}
-[import:29-48, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:27-46, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:63-84, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 Here, the main difference between the bounded and unbounded versions is that the output array size is smaller in the bounded case.
 For an unbounded convolution, the function would be called with a the output array size specified to be the size of both signals put together:
 
 {% method %}
 {% sample lang="jl" %}
-[import:60-61, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:58-59, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:96-97, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 On the other hand, the bounded call would set the output array size to simply be the length of the signal
 
 {% method %}
 {% sample lang="jl" %}
-[import:63-64, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:61-62, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:98-99, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 Finally, as we mentioned before, it is possible to center bounded convolutions by changing the location where we calculate the each point along the filter.
 This can be done by modifying the following line:
 
 {% method %}
 {% sample lang="jl" %}
-[import:37-37, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:35-35, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:71-71, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 Here, `j` counts from `i-length(filter)` to `i`.
@@ -239,7 +249,9 @@ In code, this typically amounts to using some form of modulus operation, as show
 
 {% method %}
 {% sample lang="jl" %}
-[import:4-27, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:4-25, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import:38-61, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 This is essentially the same as before, except for the modulus operations, which allow us to work on a periodic domain.
@@ -255,6 +267,8 @@ For the code associated with this chapter, we have used the convolution to gener
 {% method %}
 {% sample lang="jl" %}
 [import, lang:"julia"](../code/julia/1d_convolution.jl)
+{% sample lang="cs" %}
+[import, lang:"csharp"](../code/csharp/1DConvolution.cs)
 {% endmethod %}
 
 At a test case, we have chosen to use two sawtooth functions, which should produce the following images:

contents/convolutions/code/csharp/1DConvolution.cs

@@ -0,0 +1,109 @@
+using System;
+using System.IO;
+
+namespace Convolution1D
+{
+    public class Convolution1D
+    {
+        // Creates a sawtooth function with the given length
+        static double[] CreateSawtooth(int length)
+        {
+            var array = new double[length];
+            for (var i = 0; i < length; i++)
+                array[i] = (i + 1) / 200f;
+            return array;
+        }
+
+        // Normalizes the given array
+        static void Normalize(double[] array)
+        {
+            var norm = Norm(array);
+            for (var i = 0; i < array.Length; i++)
+                array[i] /= norm;
+        }
+
+        // Calculates the norm of the array
+        static double Norm(double[] array)
+        {
+            var sum = 0.0;
+            for (var i = 0; i < array.Length; i++)
+                sum += Math.Pow(array[i], 2);
+            return Math.Sqrt(sum);
+        }
+
+        // Julia like modulus function which handles negative values properly
+        // Assumes that y >= 0
+        static int Mod(int x, int y) => ((x % y) + y) % y;
+
+        static double[] ConvolveCyclic(double[] signal, double[] filter)
+        {
+            var outputSize = Math.Max(signal.Length, filter.Length);
+
+            // Convolutional output
+            var output = new double[outputSize];
+            var sum = 0.0;
+
+            for (var i = 0; i < outputSize; i++)
+            {
+                for (var j = 0; j < outputSize; j++)
+                {
+                    if (Mod(i - j, outputSize) < filter.Length)
+                    {
+                        sum += signal[Mod(j - 1, outputSize)] * filter[Mod(i - j, outputSize)];
+                    }
+                }
+
+                output[i] = sum;
+                sum = 0.0;
+            }
+
+            return output;
+        }
+
+        static double[] ConvolveLinear(double[] signal, double[] filter, int outputSize)
+        {
+            // Convolutional output
+            var output = new double[outputSize];
+            var sum = 0.0;
+
+            for (var i = 0; i < outputSize; i++)
+            {
+                for (var j = Math.Max(0, i - filter.Length); j <= i; j++)
+                {
+                    if (j < signal.Length && (i - j) < filter.Length)
+                    {
+                        sum += signal[j] * filter[i - j];
+                    }
+                }
+
+                output[i] = sum;
+                sum = 0.0;
+            }
+
+            return output;
+        }
+
+        static void Main()
+        {
+            // Create sawtooth functions for x and y
+            var x = CreateSawtooth(200);
+            var y = CreateSawtooth(200);
+
+            // Normalization is not strictly necessary, but good practice
+            Normalize(x);
+            Normalize(y);
+
+            // Full convolution, output will be the size of x + y - 1
+            var fullLinearOutput = ConvolveLinear(x, y, x.Length + y.Length - 1);
+            // Simple boundaries
+            var simpleLinearOutput = ConvolveLinear(x, y, x.Length);
+            // Cyclic convolution
+            var cyclicOutput = ConvolveCyclic(x, y);
+
+            // Output convolutions to different files for plotting
+            File.WriteAllText("full_linear.dat", String.Join(Environment.NewLine, fullLinearOutput));
+            File.WriteAllText("simple_linear.dat", String.Join(Environment.NewLine, simpleLinearOutput));
+            File.WriteAllText("cyclic.dat", String.Join(Environment.NewLine, cyclicOutput));
+        }
+    }
+}