perceptrón simple

binary_entry_generator.m

@@ -0,0 +1,10 @@
+function entry = binary_entry_generator(n)
+    for i = [0:n-1]
+        x = 2^i; 
+        % 2x*r = 2^n
+        % r = 2^n/2x
+        % x = 2^i
+        % r = 2^(n-1-i)
+        entry(i+1,:) = repmat([ones(1,x),-1*ones(1,x)],1,2^(n-i-1));
+    end
+end

simple_perceptron.m

@@ -0,0 +1,37 @@
+1;
+% entry n * 1
+% weights 1 * n
+function output = get_output(entry,weights,activation_func)
+    output = activation_func(weights * entry);
+end
+
+% entry n * 1
+% weights 1 * n
+function weights = update_weights(learning_factor, output, expected_output, weights, entry)
+    weights = weights + learning_factor*(expected_output - output) * entry';
+end
+
+function [weights, output] = simple_perceptron_learn(entries, expected_output, activation_func=@sign, learning_factor=.5,
+                                                            max_iterations=1000, tolerance=1e-5)
+    n = length(entries(:,1));
+    % agrego umbral
+    entries = [-1*ones(1,2^n);entries];
+    weights = rand(1,n+1) .- 0.5;
+
+    for iteration = 1:max_iterations
+        for index = randperm(2^n);
+                entry = entries(:,index);
+                output(index) = get_output(entry,weights,activation_func);
+                weights = update_weights(learning_factor, output(index), expected_output(index), weights, entry);
+        end
+        if (sum(abs(expected_output-output)) <= tolerance)
+                'solution found'
+                return;
+        end
+    end
+    'max iterations reached'
+end
+
+function x = id(x)
+end
+