Perceptron: Difference between revisions

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 ######OOOOOOOOOOOOOO
 ######OOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
+####OOOOOOOOOOOOOOOO
+####OOOOOOOOOOOOOOOO
+Output from perceptron after 5 training runs:
+##############OOOOOO
+#############OOOOOOO
+#############OOOOOOO
+############OOOOOOOO
+############OOOOOOOO
+###########OOOOOOOOO
+###########OOOOOOOOO
+##########OOOOOOOOOO
+##########OOOOOOOOOO
+#########OOOOOOOOOOO
+#########OOOOOOOOOOO
+########OOOOOOOOOOOO
+########OOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
+####OOOOOOOOOOOOOOOO</pre>
+=={{header|XLISP}}==
+Like the Pascal example, this is a text-based program using a 20x20 grid. It is slightly more general, however, because it allows the function that is to be learnt and the perceptron's bias and learning constant to be passed as arguments to the <tt>trainer</tt> and <tt>perceptron</tt> objects.
+<lang scheme>(define-class perceptron
+    (instance-variables weights bias learning-constant) )
+(define-method (perceptron 'initialize b lc)
+    (defun random-weights (n)
+        (if (> n 0)
+            (cons (- (/ (random 20000) 10000) 1) (random-weights (- n 1))) ) )
+    (setq weights (random-weights 3))
+    (setq bias b)
+    (setq learning-constant lc)
+    self )
+(define-method (perceptron 'value x y)
+    (if (> (+ (* x (car weights)) (* y (cadr weights)) (* bias (caddr weights))) 0)
+    -1 ) )
+(define-method (perceptron 'print-grid)
+    (print-row self 10) )
+(define-method (perceptron 'learn source runs)
+    (defun learn-row (row)
+        (defun learn-cell (cell)
+            (define inputs `(,cell ,row ,bias))
+            (define error (- (source 'value cell row) (self 'value cell row)))
+            (defun reweight (ins ws)
+                (if (car ins)
+                    (cons (+ (car ws) (* error (car ins) learning-constant)) (reweight (cdr ins) (cdr ws))) ) )
+            (setq weights (reweight inputs weights))
+            (if (< cell 10)
+                (learn-cell (+ cell 1)) ) )
+        (learn-cell -9)
+        (if (> row -9)
+            (learn-row (- row 1)) ) )
+    (do ((i 1 (+ i 1))) ((> i runs))
+        (learn-row 10) ) )
+(define-class trainer
+    (instance-variables fn) )
+(define-method (trainer 'initialize function)
+    (setq fn function)
+    self )
+(define-method (trainer 'print-grid)
+    (print-row self 10) )
+(define-method (trainer 'value x y)
+    (if (apply fn `(,x ,y))
+        -1 ) )
+(defun print-row (obj row)
+    (defun print-cell (cell)
+        (if (= (obj 'value cell row) 1)
+            (display "#")
+            (display "O") )
+        (if (< cell 10)
+            (print-cell (+ cell 1))
+            (newline) ) )
+    (print-cell -9)
+    (if (> row -9)
+        (print-row obj (- row 1))
+        (newline) ) )
+(define ptron (perceptron 'new 1 0.01))
+(define training (trainer 'new
+    (lambda (x y) (> y (+ (* x 2) 1))) ) )
+(newline)
+(display "Target output for y = 2x + 1:")
+(newline)
+(training 'print-grid)
+(display "Output from untrained perceptron:")
+(newline)
+(ptron 'print-grid)
+(display "Output from perceptron after 1 training run:")
+(newline)
+(ptron 'learn training 1)
+(ptron 'print-grid)
+(display "Output from perceptron after 5 training runs:")
+(newline)
+(ptron 'learn training 4)
+(ptron 'print-grid)</lang>
+{{out}}
+<pre>Target output for y = 2x + 1:
+##############OOOOOO
+#############OOOOOOO
+#############OOOOOOO
+############OOOOOOOO
+############OOOOOOOO
+###########OOOOOOOOO
+###########OOOOOOOOO
+##########OOOOOOOOOO
+##########OOOOOOOOOO
+#########OOOOOOOOOOO
+#########OOOOOOOOOOO
+########OOOOOOOOOOOO
+########OOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
+####OOOOOOOOOOOOOOOO
+Output from untrained perceptron:
+######OOOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+########OOOOOOOOOOOO
+########OOOOOOOOOOOO
+########OOOOOOOOOOOO
+#########OOOOOOOOOOO
+#########OOOOOOOOOOO
+#########OOOOOOOOOOO
+##########OOOOOOOOOO
+##########OOOOOOOOOO
+##########OOOOOOOOOO
+###########OOOOOOOOO
+###########OOOOOOOOO
+###########OOOOOOOOO
+############OOOOOOOO
+############OOOOOOOO
+############OOOOOOOO
+Output from perceptron after 1 training run:
+###############OOOOO
+###############OOOOO
+##############OOOOOO
+##############OOOOOO
+#############OOOOOOO
+############OOOOOOOO
+############OOOOOOOO
+###########OOOOOOOOO
+##########OOOOOOOOOO
+##########OOOOOOOOOO
+#########OOOOOOOOOOO
+#########OOOOOOOOOOO
+########OOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+#######OOOOOOOOOOOOO
+######OOOOOOOOOOOOOO
+#####OOOOOOOOOOOOOOO
 #####OOOOOOOOOOOOOOO
 ####OOOOOOOOOOOOOOOO