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- 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
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