import numpy as np

class AIlib:
    def sigmoid(x):
        return 1/(1 + np.exp(-x))

    def sigmoid_der(x):
        return AIlib.sigmoid(x) * (1 - AIlib.sigmoid(x))

    def correctFunc(inp:np.array): # generates the correct answer for the AI 
        return np.array( [inp[2], inp[1], inp[0]] ) # basically invert the rgb values

    def calcCost( predicted:np.array, correct:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad
        return (predicted - correct)**2

    def calcCost_derv( predicted:np.array, correct:np.array ):
        return (predicted - correct)*2

    def genRandomMatrix( x:int, y:int, min: float=0.0, max: float=1.0 ): # generate a matrix with x, y dimensions with random values from min-max in it
        # apply ranger with * and -
        mat = np.random.rand(x, y) - 0.25
        return mat

    def think( inp:np.array, weights:list, bias:list, layerIndex: int=0, layers: list=[] ): # recursive thinking, hehe
        maxLayer = len(weights) - 1
        weightedLayer = np.dot( inp, weights[layerIndex] ) # dot multiply the input and the weights
        layer = AIlib.sigmoid( np.add(weightedLayer, bias[layerIndex]) ) # add the biases
        layers[layerIndex] = layer # save it to the layer buffer

        if( layerIndex < maxLayer ):
            return AIlib.think( layer, weights, bias, layerIndex + 1, layers )
        else:
            out = np.squeeze(np.asarray(layer))
            print("-Result-")
            print(out)
            print("\n")
            return out, layers

    def gradient( prop, cost:float, inp:np.array, predicted:np.array, correct:np.array ):
        # Calculate the gradient
        return gradient


    def mutateProp( prop:list, lr:float, gradient ):
        newProp = [None] * len(prop)

        for i in range(len(prop)):
            newProp[i] = prop[i] - (lr*gradient)

        return newProp

    def learn( inp:np.array, obj, theta:float ):
        # Calculate the derivative for:
        # Cost in respect to weights
        # Cost in respect to biases

        predicted = AIlib.think( inp, obj.weights, obj.bias ) # Think the first result
        correct = AIlib.correctFunc( inp )
        cost = AIlib.calcCost( predicted, correct ) # Calculate the cost of the thought result

        inp2 = np.asarray( inp + theta ) # make the new input with `theta` as diff
        res2 = AIlib.think( inp2, obj.weights, obj.bias ) # Think the second result
        cost2 = AIlib.calcCost( inp2, res2 ) # Calculate the cost

        print("Cost: ", cost1)