def calcOutput(self, x, params):
        #params - cik - cak
        #prvi sloj - tip 1
        outs = []
        cnt = 0
        for i in range(self.sizes[1]):
            curr = 0
            for j in range(self.sizes[0]):
                curr += abs(x[j] - params[cnt]) / abs(params[cnt + 1])
                cnt += 2
            outs.append(1.0 / (1.0 + curr))
        #print("outs == ", outs)
        #prvih onoliko koliko ima u proslom sloju su wi, onda bias i tako za svaki neuron
        for layer_sz in range(2, len(self.sizes)):
            outs_new = []
            for nn in range(0, self.sizes[layer_sz]):
                net = 0
                for i in range(0, self.sizes[layer_sz - 1]):
                    net += params[cnt] * outs[i]
                    cnt += 1
                net += params[cnt]
                cnt += 1
                #print("net == ", net)
                outs_new.append(1.0 / (1.0 + math.exp(-net)))
            #kopirat iz outs_new u out
            outs = outs_new[:]
        return outs