def test_build_layer_inputs_and_outputs(self):
listCLayer = [random.randint(2, 4) for _ in xrange(4)]
net = build_net(listCLayer)
listDblInput = randlist(-1.0, 1.0, net.cInputs)
listIn, listOut = nn.build_layer_inputs_and_outputs(net, listDblInput)
self.assertEqual(len(listIn), len(listOut))
listZip = zip(listIn, listOut, listCLayer, listCLayer[1:], net.listLayer)
for listDblIn, listDblOut, cSizeIn, cSizeOut, layer in listZip:
self.assertEqual(cSizeIn, len(listDblIn))
self.assertEqual(cSizeOut, len(listDblOut))
self.assertEqual(listDblOut, nn.feed_forward_layer(layer, listDblIn))
def test_build_layer_inputs_and_outputs(self):
listCLayer = [random.randint(2,4) for _ in xrange(4)]
net = build_net(listCLayer)
listDblInput = randlist(-1.0,1.0,net.cInputs)
listIn,listOut = nn.build_layer_inputs_and_outputs(net, listDblInput)
self.assertEqual(len(listIn),len(listOut))
listZip = zip(listIn,listOut,listCLayer,listCLayer[1:],net.listLayer)
for listDblIn,listDblOut,cSizeIn,cSizeOut,layer in listZip:
self.assertEqual(cSizeIn, len(listDblIn))
self.assertEqual(cSizeOut, len(listDblOut))
self.assertEqual(listDblOut,nn.feed_forward_layer(layer,listDblIn))
def test_feed_forward_layer(self):
listPcpt = []
cInput = random.randint(5, 10)
cPcpt = random.randint(5, 10)
listDblTarget = randlist(-0.75, 0.75, cPcpt)
listDblInput = randlist(-1.0, 1.0, cInput)
for ix, dblTarget in enumerate(listDblTarget):
listDblProduct = randlist_for_sum(dblTarget, cInput + 1, 0.5)
listDblW = []
for dblProduct, dblInput in zip(listDblProduct, listDblInput):
listDblW.append(dblProduct / dblInput)
listPcpt.append(nn.Perceptron(listDblW, listDblProduct[-1], ix))
layer = nn.NeuralNetLayer(cInput, listPcpt)
listDblOutput = nn.feed_forward_layer(layer, listDblInput)
listDblLogit = [logit(dbl) for dbl in listDblOutput]
for dblTarget, dblLogit in zip(listDblTarget, listDblLogit):
self.assertAlmostEqual(dblTarget, dblLogit, 4)
def test_feed_forward_layer(self):
listPcpt = []
cInput = random.randint(5,10)
cPcpt = random.randint(5,10)
listDblTarget = randlist(-0.75, 0.75, cPcpt)
listDblInput = randlist(-1.0,1.0,cInput)
for ix,dblTarget in enumerate(listDblTarget):
listDblProduct = randlist_for_sum(dblTarget, cInput+1, 0.5)
listDblW = []
for dblProduct,dblInput in zip(listDblProduct,listDblInput):
listDblW.append(dblProduct/dblInput)
listPcpt.append(nn.Perceptron(listDblW, listDblProduct[-1] ,ix))
layer = nn.NeuralNetLayer(cInput, listPcpt)
listDblOutput = nn.feed_forward_layer(layer, listDblInput)
listDblLogit = [logit(dbl) for dbl in listDblOutput]
for dblTarget,dblLogit in zip(listDblTarget,listDblLogit):
self.assertAlmostEqual(dblTarget,dblLogit, 4)
