def testPercentErrorIsSame(self): NN.pat = zip(self.trn_d['input'], self.trn_d['target']) pyb_ws = self.net.params.copy() nn = NN() nn.wi = pyb_ws[:nn.wi.size].reshape(NN.nh, NN.ni).T nn.wo = pyb_ws[nn.wi.size:].reshape(NN.no, NN.nh).T correct = 0 wrong = 0 argmax_cor = 0 argmax_wng = 0 all_aos = [] for i, x in enumerate(self.trn_d['input']): nn.activate(x) out = self.net.activate(x) # print 'ga bp trg', nn.ao, out, self.trn_d['target'][i], '++++' if not (out - self.trn_d['target'][i]).any() else '-' all_aos.append(nn.ao.copy()) if not (out - self.trn_d['target'][i]).any(): correct += 1 else: wrong += 1 if argmax(out) == argmax(self.trn_d['target'][i]): argmax_cor += 1 else: argmax_wng += 1 print 'actual', wrong, 'wrong', correct, 'correct', float(wrong) / (wrong + correct) * 100 print 'using argmax', argmax_wng, 'wrong', argmax_cor, 'correct', float(argmax_wng) / (argmax_wng + argmax_cor) * 100 argmax_perc_err = float(argmax_wng) / (argmax_wng + argmax_cor) * 100 res = nn.sumErrors() nn_perc_err = 100 - res[1] pb_nn_perc_err = percentError(self.trainer.testOnClassData(), self.trn_d['class']) self.assertAlmostEqual(nn_perc_err, pb_nn_perc_err) self.assertAlmostEqual(nn_perc_err, pb_nn_perc_err, argmax_perc_err)
def testPercentErrorIsSame(self):
NN.pat = zip(self.trn_d['input'], self.trn_d['target'])
pyb_ws = self.net.params.copy()
nn = NN()
nn.wi = pyb_ws[:nn.wi.size].reshape(NN.nh, NN.ni).T
nn.wo = pyb_ws[nn.wi.size:].reshape(NN.no, NN.nh).T
correct = 0
wrong = 0
argmax_cor = 0
argmax_wng = 0
all_aos = []
for i, x in enumerate(self.trn_d['input']):
nn.activate(x)
out = self.net.activate(x)
# print 'ga bp trg', nn.ao, out, self.trn_d['target'][i], '++++' if not (out - self.trn_d['target'][i]).any() else '-'
all_aos.append(nn.ao.copy())
if not (out - self.trn_d['target'][i]).any():
correct += 1
else:
wrong += 1
if argmax(out) == argmax(self.trn_d['target'][i]):
argmax_cor += 1
else:
argmax_wng += 1
print 'actual', wrong, 'wrong', correct, 'correct', float(wrong) / (
wrong + correct) * 100
print 'using argmax', argmax_wng, 'wrong', argmax_cor, 'correct', float(
argmax_wng) / (argmax_wng + argmax_cor) * 100
argmax_perc_err = float(argmax_wng) / (argmax_wng + argmax_cor) * 100
res = nn.sumErrors()
nn_perc_err = 100 - res[1]
pb_nn_perc_err = percentError(self.trainer.testOnClassData(),
self.trn_d['class'])
self.assertAlmostEqual(nn_perc_err, pb_nn_perc_err)
self.assertAlmostEqual(nn_perc_err, pb_nn_perc_err, argmax_perc_err)
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
count = 0
for i, t in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
count += 1 if list(o) == list(t) else -1
print float(count) / len(tester.test_pat)
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
count = 0
for i, t in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
count += 1 if list(o) == list(t) else -1
print float(count) / len(tester.test_pat)
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
right = 0
for i, t, c, l in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
right += 1 if list(o) == list(t) else 0
print right, len(tester.test_pat)
print 100 * (float(right) / len(tester.test_pat))
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
right = 0
for i, t, c, l in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
right += 1 if list(o) == list(t) else 0
print right, len(tester.test_pat)
print 100 * (float(right) / len(tester.test_pat))
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
return tester.sumErrors(test_data=True)
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
return tester.sumErrors(test_data=True)
