diff --git a/Assignment_2/Image2Graph.m b/Assignment_2/Image2Graph.m
index 5d81366..7d4e27e 100644
--- a/Assignment_2/Image2Graph.m
+++ b/Assignment_2/Image2Graph.m
@@ -9,25 +9,11 @@ function myAffinityMat = Image2Graph (imIn)
     % Initializes helper variables
     imageWidth = size(imIn, 2);
     imageHeight = size(imIn, 1);
-
-    % Initializes output matrix
-%     myAffinityMat = zeros(imageWidth * imageHeight, imageWidth * ...
-%         imageHeight);
-    
-%     for row = 1:imageHeight
-%         for column = 1:imageWidth
-%             % Calculates distance matrix for the current element
-%             diff = imIn - imIn(row, column, :);
-%             dist = sum(diff .^ 2, 3) .^ 0.5;
-%             
-%             % Calculates affinity matrix
-%             myAffinityMat((row - 1) * imageWidth + column, :) = ...
-%                 reshape((1 ./ exp(dist)).', 1, []);
-%         end
-%     end
+    imageChannels = size(imIn, 3);
     
-    som = reshape(imIn, 1, [], 3);
-    diff = repmat(som, imageWidth * imageHeight, 1, 1) - permute(som, [2 1 3]);
+    singleLineTransform = reshape(imIn, 1, [], imageChannels);
+    diff = repmat(singleLineTransform, imageWidth * imageHeight, 1, 1) - ...
+        permute(singleLineTransform, [2 1 3]);
     dist = sum(diff .^ 2, 3) .^ 0.5;
     myAffinityMat = 1 ./ exp(dist);
 end
diff --git a/Assignment_2/myNCuts.m b/Assignment_2/myNCuts.m
new file mode 100644
index 0000000..9e2a6cf
--- /dev/null
+++ b/Assignment_2/myNCuts.m
@@ -0,0 +1,19 @@
+function clusterIdx = myNCuts (anAffinityMat, k)
+%Implementation of the N-cuts algorithm
+%   Usage clusterIdx = myNCuts (anAffinityMat, k), where:
+%       Inputs
+%       - anAffinityMat is a rectagular, symmetrical affinity matrix
+%           representation of an image
+%       - k is the desired number of clusters
+%       Output
+%       - clusterIdx is a vector storing the cluster Id of each node
+
+    if ~issymmetric(anAffinityMat)
+        error('The affinity matrix provided is not symmetric.');
+    end
+
+    D = diag(sum(anAffinityMat, 2));
+    L = D - anAffinityMat;
+    [eigenvectorsMatrix, ~] = eigs(double(L), double(D), k, 'sm');
+    clusterIdx = kmeans(eigenvectorsMatrix, k);
+end
diff --git a/Assignment_2/mySpectralClustering.m b/Assignment_2/mySpectralClustering.m
index 2e5834e..de999f5 100644
--- a/Assignment_2/mySpectralClustering.m
+++ b/Assignment_2/mySpectralClustering.m
@@ -8,8 +8,11 @@ function clusterIdx = mySpectralClustering (anAffinityMat, k)
 %       Output
 %       - clusterIdx is a vector storing the cluster Id of each node
 
+    if ~issymmetric(anAffinityMat)
+        error('The affinity matrix provided is not symmetric.');
+    end
+
     L = diag(sum(anAffinityMat, 2)) - anAffinityMat;
-    [eigenvectorsMatrix, ~] = eigs(L, k, 'sm');
+    [eigenvectorsMatrix, ~] = eigs(double(L), k, 'sm');
     clusterIdx = kmeans(eigenvectorsMatrix, k);
 end
-
diff --git a/Assignment_2/tester.m b/Assignment_2/tester.m
new file mode 100644
index 0000000..24e9bfa
--- /dev/null
+++ b/Assignment_2/tester.m
@@ -0,0 +1,35 @@
+image = imageT;
+k = 3;
+
+graph = Image2Graph(image);
+%clusters = mySpectralClustering(graph, k);
+clusters = myNCuts(graph, k);
+
+clusters = reshape(clusters, size(image, 1), []);
+
+redChannel = image(:, :, 1);
+greenChannel = image(:, :, 2);
+blueChannel = image(:, :, 3);
+
+segImR = clusters;
+segImG = clusters;
+segImB = clusters;
+
+for cluster = 1:k
+    meanR = mean(redChannel(clusters == cluster));
+    meanG = mean(greenChannel(clusters == cluster));
+    meanB = mean(blueChannel(clusters == cluster));
+    
+    segImR(clusters == cluster) = meanR;
+    segImG(clusters == cluster) = meanG;
+    segImB(clusters == cluster) = meanB;
+end
+
+segIm = zeros(size(image, 1), size(image, 2), 3);
+segIm(:, :, 1) = segImR;
+segIm(:, :, 2) = segImG;
+segIm(:, :, 3) = segImB;
+imshow(segIm)
+
+clearvars segImR segImG segImB meanR meanG meanB graph redChannel ...
+    greenChannel blueChannel clusters k image cluster segIm
\ No newline at end of file