Backpropogation functions

#https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

static log2(x:float)->float:
    return log(x) / log(2)

class Neuron:
    var bias:float
    var weights:float
    var output:float    #holds result for backpropogation
    var error:float     #holds error for backpropogation
    func _init(_bias)->void:
        bias = _bias

# 1) get total net input for nodes
static func calculate_total_net_input(inst:Object, inputs:Array)->float:
    var total: = 0.0
    for i in inputs.size():
        total += inputs[i] * inst.weights[i]
    return total + bias

total_net_input = w1*i1 + w2*i2 + b*1


# 2) squash it with logistic function
static func squash(total_net_input:float)->float:
    return 1 / (1 + exp(-total_net_input))

h1_out = squash(total_net_input)

static func calculate_output(inst:Object, inputs:Array)->float:
    inst.output = squash(calculate_total_net_input(inst, inputs))
    return inst.output

# 3) do the same for each output node


# 4) calculate error
static func calculate_error(inst:Object, target_output:float, training_rate:float = 0.5)->float:
    inst.error = training_rate * pow((target_output - inst.output), 2)
    return inst.error

# 5) sum all errors together
static func calculate_total_error(instances:Array)->float:
    var total: = 0.0
    for inst in instances:
        total += inst.error
    return total

static func cross_entropy(p:Array, q:Array)->float:
    var total: = 0.0
    for i in p.size():
        total += p[i] * log2(q[i])