Move separate perceptron code into methods

Main.py

@@ -3,30 +3,69 @@ from random import Random
 from Dataset import Dataset
 from Graph import *
 
-rand = Random()
-data = Dataset(open("input.txt").read().split('\n'), open("target.txt").read().split('\n'))
-learn_rate = 0.01
 graph = Graph(640, 480, "Perceptron")
 graph.drawLine(Point(-1000, -1000 + 20), Point(1000, 1000 + 20))
 
+rand = Random()
+data = Dataset(open("input.txt").read().split('\n'), open("target.txt").read().split('\n'))
+learn_rate = 0.01
+
 p = Perceptron()
 i = data.inputs
-p.add_input("1", 1, rand.uniform(-1, 1))
-p.add_input("2", 1, rand.uniform(-1, 1))
-p.add_input("bias", 1, rand.uniform(-1, 1))
 
-# train
-for ind in range(0, 500):
-    i = data.inputs
-    for key in data.inputs:
-        p.input(i[key].value1, i[key].value2)
-        print("error: %f" % p.guess(i[key].target, learn_rate))
 
-for i in range(0, 1000):
-    x = input("Arg1: ")
-    y = input("Arg2: ")
-    p.input(x, y)
-    if p.activation() == 1:
-        print("TRUE")
-    else:
-        print("FALSE")
+def line_perceptron():
+    p.add_input("1", 1, rand.uniform(-1, 1))
+    p.add_input("2", 1, rand.uniform(-1, 1))
+
+    def answer(p_x, p_y):
+        if p_y > calc_y(p_x):
+            return 1
+        else:
+            return -1
+
+
+    def calc_y(p_x):
+        return p_x + 2
+
+    for ind in range(0, 1000):
+        x = rand.randint(-640 / 2, 640 / 2)
+        y = rand.randint(-480 / 2, 480 / 2)
+        print(x, y)
+        p.input(x, y)
+        print("error: %f" % p.guess(answer(x, y), learn_rate))
+
+    for i in range(0, 1000):
+        x = int(input("x: "))
+        y = int(input("y: "))
+        p.input(x, y)
+        if p.activation() == 1:
+            graph.win.plot(x, y, "green")
+            print("TRUE")
+        else:
+            graph.win.plot(x, y, "red")
+            print("FALSE")
+
+
+def and_perceptron():
+    p.add_input("1", 1, rand.uniform(-1, 1))
+    p.add_input("2", 1, rand.uniform(-1, 1))
+    p.add_input("bias", 1, rand.uniform(-1, 1))
+
+    # train
+    for ind in range(0, 500):
+        i = data.inputs
+        for key in data.inputs:
+            p.input(i[key].value1, i[key].value2)
+            print("error: %f" % p.guess(i[key].target, learn_rate))
+
+    for i in range(0, 1000):
+        x = input("Arg1: ")
+        y = input("Arg2: ")
+        p.input(x, y)
+        if p.activation() == 1:
+            print("TRUE")
+        else:
+            print("FALSE")
+
+line_perceptron()