bpn = BackPropogationNetwork( (1,4,1), lFuncs, 0.4)
samples = np.zeros(20, dtype=[('x', float, 1), ('y', float, 1)])
samples['x'] = np.linspace(0,1,20)
samples['y'] = np.sin(samples['x']*np.pi)
input = np.array([samples['x']]).T
target = np.array([samples['y']]).T
max = 100000
#lnErr = 1e-5
lnErr = 1e-2
for i in range(max+1):
err = 0.0;
for ii in range(len(input)):
err += bpn.trainEpoch(npA(input[ii]),npA(target[ii]))
if i%2500 == 0:
#if i%20 == 0:
print "iteration {0}\tError : {1:0.6f}".format(i,err)
#bpn.NN_cout("Train_"+str(i))
if err <= lnErr:
print "Min error reached at {0}".format(i)
break
#bpn.setWeights()
bpn.printWeights()
plt.figure(figsize=(10,5))
# Draw real function
x,y = samples['x'],samples['y']
input = np.array([ [ll[0],ll[1]] for ll in zip(samples['x'],samples['x2']) ])
input2 = np.array([ [ll[0],ll[1]] for ll in zip(samples2['x'],samples2['x2']) ])
target = np.array([samples['y']]).T
target2 = np.array([samples2['y']]).T
max = 100000
#lnErr = 1e-5
#lnErr = 0.2
lnErr = 5.0
for i in range(max+1):
err = 0.0;
for ii in range(len(input)):
err += bpn.trainEpoch(npA(input[ii]),npA(target[ii]))
err += bpn.trainEpoch(npA(input2[ii]),npA(target2[ii]))
if i%2500 == 0:
#if i%20 == 0:
print "iteration {0}\tError : {1:0.6f}".format(i,err)
#bpn.NN_cout("Train_"+str(i))
if err <= lnErr:
print "Min error reached at {0}".format(i)
break
#bpn.setWeights()
bpn.printWeights()
for i in range(len(input)):
output = bpn.run(npA(input[i]))
print "in :"+str(input[i])+", ou : "+str(output)
