ScalaPerceptron

A simple core Scala implementation of a perceptron neural network. There are only three classes:

• Perceptron.scala, which implements the perceptron learning algorithm, and uses
• BinaryThresholdNeuron.scala, which is a neuron that outputs either 0 or 1, and is a wrapper around
• LinearNeuron.scala, which implements the neural activation formula.

This generic perceptron implementation can be used for any finite number of input connections where the corresponding input values are of type Double. There is no hard-coded or generated input data in src/main; the intent is to implement and isolate only the perceptron theoretical concepts, formulas, and algorithms using functional and object-oriented design, and with minimal code.

Note that a perceptron can only learn to differentiate between classes that are linearly separable.

To use the library,

var decisionUnit = new BinaryThresholdNeuron(Seq.fill(3)(1.0), 0)
val perceptron = new Perceptron

val inputsGood = Seq(1.1,2.2,3.3)
perceptron.train(decisionUnit, inputsGood, 1) match {
  case Success(u) => decisionUnit = u
}
val inputsBad = Seq(-1.1,-2.2,-3.3)
perceptron.train(decisionUnit, inputsBad, 0) match {
  case Success(u) => decisionUnit = u
}
// more training ...

// test perceptron's learning
val inputs = Seq(3.3,2.2,1.1)
decisionUnit.output(inputs) match {
  case Success(v) => println(s"$inputs is classified as $v") // either 0 or 1
}

The unit test class PerceptronTest.scala shows a working example.