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)
class testactivate(unittest.TestCase):
def setUp(self):
weights_in = -2 + np.random.rand(NN.ni, NN.nh) * 4
weights_out = -2 + np.random.rand(NN.nh, NN.no) * 4
self.net1 = NN()
self.net1.wi = weights_in.copy()
self.net1.wo = weights_out.copy()
self.net2 = NN()
self.net2.wi = weights_in.copy()
self.net2.wo = weights_out.copy()
inputs = -2 + np.random.rand(4) * 8
for i in range(NN.ni):
self.net1.ai[i] = np.tanh(inputs[i])
for j in range(NN.nh):
self.net1.ah[j] = sigmoid(sum([self.net1.ai[i] * self.net1.wi[i][j] for i in range(NN.ni)]))
for k in range(NN.no):
self.net1.ao[k] = sum([self.net1.ah[j] * self.net1.wo[j][k] for j in range(NN.nh)])
self.net1.ao = np.where(self.net1.ao > 0.5, 1.0, 0.0)
self.net2.activate(inputs)
def testactivationfunctions(self):
npt.assert_array_equal(self.net1.ai,
self.net2.ai)
npt.assert_array_equal(self.net1.ah,
self.net2.ah)
npt.assert_array_equal(self.net1.ao,
self.net2.ao)
def testOutput(self):
self.assertEqual(bincount(self.net2.ao.astype('int'))[0],
2)
self.assertEqual(bincount(self.net2.ao.astype('int'))[1],
1)
def testDataAssignedCorrectly(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
all_aos = []
for i, x in enumerate(self.trn_d['input']):
nn.activate(x)
out = self.net.activate(x)
all_aos.append(nn.ao)
if not (out - self.trn_d['target'][i]).any():
correct += 1
else:
wrong += 1
for i in range(len(array(NN.pat)[:, 0])):
npt.assert_array_equal(self.trn_d['input'][i],
array(NN.pat)[:, 0][i])
npt.assert_array_equal(self.trn_d['input'][i],
array(nn.pat)[:, 0][i])
npt.assert_array_equal(self.trn_d['target'][i],
array(NN.pat)[:, 1][i])
npt.assert_array_equal(self.trn_d['target'][i],
array(nn.pat)[:, 1][i])
class testactivate(unittest.TestCase):
def setUp(self):
weights_in = -2 + np.random.rand(NN.ni, NN.nh) * 4
weights_out = -2 + np.random.rand(NN.nh, NN.no) * 4
self.net1 = NN()
self.net1.wi = weights_in.copy()
self.net1.wo = weights_out.copy()
self.net2 = NN()
self.net2.wi = weights_in.copy()
self.net2.wo = weights_out.copy()
inputs = -2 + np.random.rand(4) * 8
for i in range(NN.ni):
self.net1.ai[i] = np.tanh(inputs[i])
for j in range(NN.nh):
self.net1.ah[j] = sigmoid(
sum([
self.net1.ai[i] * self.net1.wi[i][j] for i in range(NN.ni)
]))
for k in range(NN.no):
self.net1.ao[k] = sum(
[self.net1.ah[j] * self.net1.wo[j][k] for j in range(NN.nh)])
self.net1.ao = np.where(self.net1.ao > 0.5, 1.0, 0.0)
self.net2.activate(inputs)
def testactivationfunctions(self):
npt.assert_array_equal(self.net1.ai, self.net2.ai)
npt.assert_array_equal(self.net1.ah, self.net2.ah)
npt.assert_array_equal(self.net1.ao, self.net2.ao)
def testOutput(self):
self.assertEqual(bincount(self.net2.ao.astype('int'))[0], 2)
self.assertEqual(bincount(self.net2.ao.astype('int'))[1], 1)
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)
class testUpdate(unittest.TestCase):
def setUp(self):
self.nn = NN()
self.in_to_hidden = self.nn.wi
self.hidAstroL = AstrocyteLayer(self.nn.ah, self.in_to_hidden)
def testUpdateNeuronCounters(self):
self.hidAstroL.neur_activs = linspace(-1, 1, NN.nh)
self.hidAstroL.updateNeuronCounters()
npt.assert_array_equal(self.hidAstroL.neur_counters,
array([-1, -1, -1, 1, 1, 1]))
def testUpdateAstrocyteActivations(self):
result = map(self.hidAstroL._checkIfAstroActive,
map(int, linspace(-3, 3, 8)))
trgt = array([-1, -1, -1, 0, 0, 1, 1, 1])
npt.assert_array_equal(result, trgt)
def testPerformAstrocyteActions(self):
self.hidAstroL.neur_in_ws[:] = ones(24).reshape(NN.ni, NN.nh)
"""Assuming we are at the fourth iter of minor iters and that astro
parameters are reset after each input pattern, neur counters can only
be in [4, -4, 2, -2, 0]"""
self.hidAstroL.neur_counters[:] = array([4, -4, 2, -2, 0, 0])
self.hidAstroL.remaining_active_durs[:] = array([3, -3, 2, -2, 0, 0])
self.hidAstroL.astro_statuses[:] = array([1, -1, 1, -1, 0, 0])
self.hidAstroL.performAstroActions()
target_weights = empty([NN.ni, NN.nh])
target_weights[:, 0] = 1.25
target_weights[:, 1] = .5
target_weights[:, 2] = 1.25
target_weights[:, 3] = .5
target_weights[:, 4] = 1.
target_weights[:, 5] = 1.
npt.assert_array_equal(self.in_to_hidden, target_weights)
target_activations = array([1, -1, 1, -1, 0, 0])
npt.assert_array_equal(target_activations,
self.hidAstroL.astro_statuses)
target_remaining_active_durs = array([2, -2, 1, -1, 0, 0])
npt.assert_array_equal(target_remaining_active_durs,
self.hidAstroL.remaining_active_durs)
def testObjectActivationsAreUpdated(self):
self.nn.activate(rand(4) * 4)
a = self.hidAstroL.neur_in_ws
npt.assert_array_equal(self.in_to_hidden, a)
def testObjectWeightsAreUpdated(self):
# don't understand this test don't think i need it
# it is a bit random lol
target = empty([NN.ni, NN.nh])
for j in range(len(self.nn.ah)):
self.hidAstroL.neur_in_ws[:, j] = 10**j
target[:, j] = 10**j
npt.assert_array_equal(self.in_to_hidden, self.hidAstroL.neur_in_ws)
npt.assert_array_equal(self.in_to_hidden, target)
class testUpdate(unittest.TestCase):
def setUp(self):
self.nn = NN()
self.in_to_hidden = self.nn.wi
self.hidAstroL = AstrocyteLayer(self.nn.ah, self.in_to_hidden)
def testUpdateNeuronCounters(self):
self.hidAstroL.neur_activs = linspace(-1, 1, NN.nh)
self.hidAstroL.updateNeuronCounters()
npt.assert_array_equal(self.hidAstroL.neur_counters,
array([-1, -1, -1, 1, 1, 1]))
def testUpdateAstrocyteActivations(self):
result = map(self.hidAstroL._checkIfAstroActive,
map(int, linspace(-3, 3, 8)))
trgt = array([-1, -1, -1, 0, 0, 1, 1, 1])
npt.assert_array_equal(result, trgt)
def testPerformAstrocyteActions(self):
self.hidAstroL.neur_in_ws[:] = ones(24).reshape(NN.ni, NN.nh)
"""Assuming we are at the fourth iter of minor iters and that astro
parameters are reset after each input pattern, neur counters can only
be in [4, -4, 2, -2, 0]"""
self.hidAstroL.neur_counters[:] = array([4, -4, 2, -2, 0, 0])
self.hidAstroL.remaining_active_durs[:] = array([3, -3, 2, -2, 0, 0])
self.hidAstroL.astro_statuses[:] = array([1, -1, 1, -1, 0, 0])
self.hidAstroL.performAstroActions()
target_weights = empty([NN.ni, NN.nh])
target_weights[:,0] = 1.25
target_weights[:,1] = .5
target_weights[:,2] = 1.25
target_weights[:,3] = .5
target_weights[:,4] = 1.
target_weights[:,5] = 1.
npt.assert_array_equal(self.in_to_hidden, target_weights)
target_activations = array([1, -1, 1, -1, 0, 0])
npt.assert_array_equal(target_activations, self.hidAstroL.astro_statuses)
target_remaining_active_durs = array([2, -2, 1, -1, 0, 0])
npt.assert_array_equal(target_remaining_active_durs,
self.hidAstroL.remaining_active_durs)
def testObjectActivationsAreUpdated(self):
self.nn.activate(rand(4)*4)
a = self.hidAstroL.neur_in_ws
npt.assert_array_equal(self.in_to_hidden, a)
def testObjectWeightsAreUpdated(self):
# don't understand this test don't think i need it
# it is a bit random lol
target = empty([NN.ni, NN.nh])
for j in range(len(self.nn.ah)):
self.hidAstroL.neur_in_ws[:,j] = 10 ** j
target[:,j] = 10 ** j
npt.assert_array_equal(self.in_to_hidden, self.hidAstroL.neur_in_ws)
npt.assert_array_equal(self.in_to_hidden, target)
def testWeightsAndActivationsEquivalent(self): pyb_ws = self.net.params 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 for i, x in enumerate(self.trn_d['input']): nn.activate(x) out = self.net.activate(x) npt.assert_array_equal(nn.ai, self.net['in'].outputbuffer[0]) # self.assertItemsEqual(list(nn.ah), list(self.net['hidden0'].outputbuffer[0])) for j, pb_ah in enumerate(self.net['hidden0'].outputbuffer[0]): self.assertAlmostEqual(nn.ah[j], pb_ah) for k, pb_ao in enumerate(out): self.assertAlmostEqual(nn.ao[k], pb_ao)
def testWeightsAndActivationsEquivalent(self):
pyb_ws = self.net.params
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
for i, x in enumerate(self.trn_d['input']):
nn.activate(x)
out = self.net.activate(x)
npt.assert_array_equal(nn.ai, self.net['in'].outputbuffer[0])
# self.assertItemsEqual(list(nn.ah), list(self.net['hidden0'].outputbuffer[0]))
for j, pb_ah in enumerate(self.net['hidden0'].outputbuffer[0]):
self.assertAlmostEqual(nn.ah[j], pb_ah)
for k, pb_ao in enumerate(out):
self.assertAlmostEqual(nn.ao[k], pb_ao)
class testactivate(unittest.TestCase): def setUp(self): self.nn = NN() self.inputs = [1, 2, 3, 4] self.nn.activate(self.inputs) def testInputActivation(self): targets = self.nn.activate_input(self.inputs) npt.assert_array_equal(self.nn.in_activations, targets) def testHiddenActivation(self): # targets = NN.activate_hidden([1, 2, 3, 4]) # npt.assert_array_equal(self.nn.in_activations, # targets) pass def testShapesUnchanged(self): pass
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 testDataAssignedCorrectly(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 all_aos = [] for i, x in enumerate(self.trn_d['input']): nn.activate(x) out = self.net.activate(x) all_aos.append(nn.ao) if not (out - self.trn_d['target'][i]).any(): correct += 1 else: wrong += 1 for i in range(len(array(NN.pat)[:,0])): npt.assert_array_equal(self.trn_d['input'][i], array(NN.pat)[:,0][i]) npt.assert_array_equal(self.trn_d['input'][i], array(nn.pat)[:,0][i]) npt.assert_array_equal(self.trn_d['target'][i], array(NN.pat)[:,1][i]) npt.assert_array_equal(self.trn_d['target'][i], array(nn.pat)[:,1][i])
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
