Standard learning algorithm for a Perceptron that servers as a linear classifier

Perceptron.groovy

// Perceptron
// see https://towardsdatascience.com/perceptron-learning-algorithm-d5db0deab975
 
def weights = [0, 0, 0] // b, xCoeff, yCoeff such that classifier will become b + xCoeff * x + yCoeff * y == 0
def data    = [
[1, 0, 0, false], // bias (start with 1), x, y, classification value
[1, 1, 0, true],  // here: training the "or" function. Expected:  [-1, 1, 1]
[1, 1, 1, true],
[1, 0, 1, true]
]
 
def weightedSum = { sample ->
def sum = 0
weights.size().times { i -> sum += weights[i]*sample[i] }
return sum
}
def classifiesCorrectly  = { sample -> weightedSum(sample) >= 0 == sample.last() }
def sampleThatDoesNotFit = { _      -> data.find { sample -> ! classifiesCorrectly(sample) } }
 
def checkAndAdapt = { sample ->
def weight = weightedSum(sample)
println "sample: $sample weight: $weight"
if (weight < 0 && sample.last() ) {
weights.size().times { i -> weights[i] += sample[i]}
}
if (weight >= 0 && !sample.last() ) {
weights.size().times { i -> weights[i] -= sample[i]}
}
println "adapted weights $weights"
}
 
while(todo = sampleThatDoesNotFit() ) {
checkAndAdapt(todo)   
}
println weights