regexident · May 31, 2018 14:38 · May 31, 2018
diff --git a/Perceptron.swift b/Perceptron.swift
@@ -0,0 +1,101 @@
+//: Playground - noun: a place where people can play
+
+import Cocoa
+
+struct Vector {
+	let x: Float
+	let y: Float
+	let z: Float
+
+	static func *(lhs: Vector, rhs: Float) -> Vector {
+		return Vector(x: lhs.x * rhs, y: lhs.y * rhs, z: lhs.z * rhs)
+	}
+
+	static func +(lhs: Vector, rhs: Vector) -> Vector {
+		return Vector(x: lhs.x + rhs.x, y: lhs.y + rhs.y, z: lhs.z + rhs.z)
+	}
+
+	static func -(lhs: Vector, rhs: Vector) -> Vector {
+			return Vector(x: lhs.x - rhs.x, y: lhs.y - rhs.y, z: lhs.z - rhs.z)
+		}
+
+	func dot(_ other: Vector) -> Float {
+		return (self.x * other.x) + (self.y * other.y) + (self.z * other.z)
+	}
+}
+
+struct Perceptron {
+
+	var weights: Vector = Vector(x: 1.0, y: 1.0, z: 1.0)
+	var bias: Float = 1.0
+
+	func activation(_ example: Vector) -> Float {
+		return weights.dot(example) - bias
+	}
+
+	func predict(_ example: Vector) -> Float {
+		let activation = self.activation(example)
+		return (activation >= 0) ? 1.0 : 0.0
+	}
+
+	mutating func error(example: Vector, expected: Float) -> Float {
+		let predicted: Float = self.predict(example)
+		return (expected - predicted)
+	}
+
+	mutating func learn(example: Vector, expected: Float, learningRate: Float) {
+		let error: Float = self.error(example: example, expected: expected)
+		let scaleFactor: Float = error * learningRate
+		self.weights = weights + (example * scaleFactor)
+		self.bias = bias + (bias * scaleFactor)
+	}
+}
+
+let magenta = Vector(x: 1.0, y: 0.0, z: 1.0)
+let red = Vector(x: 1.0, y: 0.0, z: 0.0)
+let green = Vector(x: 0.0, y: 1.0, z: 0.0)
+let blue = Vector(x: 0.0, y: 0.0, z: 1.0)
+let yellow = Vector(x: 1.0, y: 1.0, z: 0.0)
+let cyan = Vector(x: 0.0, y: 1.0, z: 1.0)
+
+var perceptron = Perceptron()
+
+let examples: [(Vector, Bool)] = [
+	(magenta, true),
+	(red, false),
+	(magenta, true),
+	(green, false),
+	(magenta, true),
+	(blue, false),
+	(magenta, true),
+	(yellow, false),
+	(magenta, true),
+	(cyan, false),
+]
+
+let maxLearningRate: Float = 0.1
+
+print()
+print("Before training:")
+dump(perceptron)
+
+let maxEpochs = 10_000
+for epoch in 0..<maxEpochs {
+	let learningRate = maxLearningRate * (Float(maxEpochs - epoch) / Float(maxEpochs))	
+	for (example, isPositive) in examples {
+		let expected: Float = (isPositive) ? 1.0 : 0.0
+		perceptron.learn(example: example, expected: expected, learningRate: learningRate)
+	}
+}
+
+print()
+print("After training:")
+dump(perceptron)
+
+print()
+print("Magenta is magenta:", perceptron.predict(magenta))
+print("Red is magenta:", perceptron.predict(red))
+print("Green is magenta:", perceptron.predict(green))
+print("Blue is magenta:", perceptron.predict(blue))
+print("Yellow is magenta:", perceptron.predict(yellow))
+print("Cyan is magenta:", perceptron.predict(cyan))