def make_NN2(ann, sw, dataset, groups, without):
ann2 = create_ann(2, sw, 3)
inputs_train = []
outputs_train = []
protCodes = []
for s in groups[without]:
protCodes.append(s)
for p in protCodes:
sec = dataset[p]['sec']
prim = dataset[p]['prim']
#prim = prim[-2:]
prim = inOutFunctions.merge_sequences(prim)
ins, outs = inOutFunctions.convert_inputNN(sec, prim, sw)
pred = ann.predict(np.array(ins, np.float32))
ins, outs = inOutFunctions.convert_inputNN2(sec, pred, sw)
for q in ins:
inputs_train.append(q)
for q in outs:
outputs_train.append(q)
ann2 = train_ann(ann2, inputs_train, outputs_train)
return ann2
def test_NN2(ann, ann2, sw, dataset, groups, without):
sum = 0
q3 = 0
qh = 0
qhp = 0
qe = 0
qep = 0
qc = 0
qcp = 0
sovh, zh = 0, 0.01
sove, ze = 0, 0.01
sovc, zc = 0, 0.01
z = 126/7
protCodes = []
for s in groups[without]:
protCodes.append(s)
for p in protCodes:
sec = dataset[p]['sec']
prim = dataset[p]['prim']
#prim = prim[-2:]
prim = inOutFunctions.merge_sequences(prim)
ins, outs = inOutFunctions.convert_inputNN(sec, prim, sw)
pred = ann.predict(np.array(ins, np.float32))
ins, outs = inOutFunctions.convert_inputNN2(sec, pred, sw)
pred = ann2.predict(np.array(ins, np.float32))
predx = []
for p in pred:
predx.append(winner(p))
pred = inOutFunctions.display_result(predx, codes.alphabeth)
sum += measurePrediction.compare(pred, sec)
q3 += measurePrediction.calcQ3(pred, sec)
qh += measurePrediction.calcQ(pred, sec, 'H')
qhp += measurePrediction.calcQpred(pred, sec, 'H')
qe += measurePrediction.calcQ(pred, sec, 'E')
qep += measurePrediction.calcQpred(pred, sec, 'E')
qc += measurePrediction.calcQ(pred, sec, 'C')
qcp += measurePrediction.calcQpred(pred, sec, 'C')
_sovh = measurePrediction.calcSOV(pred, sec, 'H')
_sove = measurePrediction.calcSOV(pred, sec, 'E')
_sovc = measurePrediction.calcSOV(pred, sec, 'C')
if _sovh != None:
sovh += _sovh
zh += 1
if _sove != None:
sove += _sove
ze += 1
if _sovc != None:
sovc += _sovc
zc += 1
return (q3/z, qh/z, qhp/z, qe/z, qep/z, qc/z, qcp/z, sovh/zh, sove/ze, sovc/zc)