M README.md → README.md

@@ -1,5 +1,5 @@
-# A Delsarte Bound calculator in MATLAB
-
-The `delsarte(n, d)` function returns an upper bound for $A_2(n, d)$: the maximum number of binary code-words of length $n$ and having Hamming distance $d$ between each other.
-
+# A Delsarte Bound calculator in MATLAB
+
+The `delsarte(n, d)` function returns an upper bound for $A_2(n, d)$: the maximum number of binary code-words of length $n$ and having Hamming distance $d$ between each other.
+
 The obtained bound can often be improved (see [this table](https://www.win.tue.nl/~aeb/codes/binary-1.html)), with further observations and only for special cases, but this function can compute a valid bound for any value of $n$ and $d$.

M comb.m → comb.m

@@ -1,3 +1,3 @@
-function r = comb(n, k)
-    r = gamma(n + 1) / (gamma(k + 1) * gamma(n - k + 1)); 
+function r = comb(n, k)
+    r = gamma(n + 1) / (gamma(k + 1) * gamma(n - k + 1)); 
 end

M delsarte.m → delsarte.m

@@ -1,43 +1,43 @@
-function r = delsarte(n, d)
-    % https://www.win.tue.nl/~aeb/codes/binary-1.html
-
-    if d > n
-        r = 1;
-        return
-    end
-
-    if 1.5 * d > n
-        r = 2;
-        return
-    end
-    
-    m = n - d + 1;
-
-    % populate the objective function
-    c = -ones(1, m);
-    
-    A = zeros(n, m);
-    b = zeros(1, n);
-    for t = 1:n
-        % populate the A matrix
-        for j = 1:m
-            i = n - m + j;
-            A(t, j) = - k(t, n, i);
-        end
-        % populate the b vector
-        b(1, t) = k(t, n, 0);
-    end
-
-    % populate non-negativity constraints
-    lb = zeros(1, m);
-    
-    % fill other parameters
-    Aeq = [];
-    beq = [];
-
-    % find an optimal solution
-    s = sym(linprog(c, A, b, Aeq, beq, lb));
-
-    % add the value of x_0 and round the result
-    r = fix(sum(s) + 1);
+function r = delsarte(n, d)
+    % https://www.win.tue.nl/~aeb/codes/binary-1.html
+
+    if d > n
+        r = 1;
+        return
+    end
+
+    if 1.5 * d > n
+        r = 2;
+        return
+    end
+    
+    m = n - d + 1;
+
+    % populate the objective function
+    c = -ones(1, m);
+    
+    A = zeros(n, m);
+    b = zeros(1, n);
+    for t = 1:n
+        % populate the A matrix
+        for j = 1:m
+            i = n - m + j;
+            A(t, j) = - k(t, n, i);
+        end
+        % populate the b vector
+        b(1, t) = k(t, n, 0);
+    end
+
+    % populate non-negativity constraints
+    lb = zeros(1, m);
+    
+    % fill other parameters
+    Aeq = [];
+    beq = [];
+
+    % find an optimal solution
+    s = sym(linprog(c, A, b, Aeq, beq, lb));
+
+    % add the value of x_0 and round the result
+    r = fix(sum(s) + 1);
 end

M k.m → k.m

@@ -1,6 +1,6 @@
-function r = k(t, n, i)
-    r = 0;
-    for j = 0:min(i, t)
-        r = r + ((-1)^j * comb(i, j) * comb(n - i, t - j));
-    end
+function r = k(t, n, i)
+    r = 0;
+    for j = 0:min(i, t)
+        r = r + ((-1)^j * comb(i, j) * comb(n - i, t - j));
+    end
 end