main.jl

using adaptive_systeme

data = [[1 0 0],[1 0 1], [1 1 0], [1 1 1]]

result_OR = [0; 1; 1; 1]
result_AND = [0; 0; 0; 1]
result_XOR = [0; 1; 1; 0]

p = Perzeptron(.5, .1, result_OR, 1000, lineare)
training(p, data)

println(calculate(p, data[1, 1:end]'))
println(calculate(p, data[2, 1:end]'))
println(calculate(p, data[3, 1:end]'))
println(calculate(p, data[4, 1:end]'))

perzeptron.jl

module adaptive_systeme 

using Match

export Perzeptron, training, calculate, lineare

type Perzeptron
	theta
	my
	d
	iter
	sign
	w
	Perzeptron(theta, my, d, iter, sign) = new(theta, my, d, iter, sign, [[-theta],[1],[1]]) 
end

calculate(p::Perzeptron, x) = p.sign((p.w'*x)[1])

delta(p::Perzeptron, x, d) = p.my*(d-calculate(p,x))

function training(p::Perzeptron, data)
	count = 0
	while count != p.iter 
		count += 1
		for i = 1:length(data[1:end,1])
			xi = data[i, 1:end]'
			p.w += delta(p, xi, p.d[i]) * xi
		end
	end
	p.w
end

function lineare(x::Number)
	c = 2
	@match x begin
		x > c => 1
		x < -c => 0
		_ => 1/(2*c)*(x+c)
	end
end

end