Hopfield Recurrent network (newhop)ΒΆ

Use neurolab.net.newhop(), neurolab.tool.simhop()

```# -*- coding: utf-8 -*-
"""
Example of use Hopfield Recurrent network
=========================================

"""

import numpy as np
import neurolab as nl

# N E R O
target =  [[1,0,0,0,1,
1,1,0,0,1,
1,0,1,0,1,
1,0,0,1,1,
1,0,0,0,1],
[1,1,1,1,1,
1,0,0,0,0,
1,1,1,1,1,
1,0,0,0,0,
1,1,1,1,1],
[1,1,1,1,0,
1,0,0,0,1,
1,1,1,1,0,
1,0,0,1,0,
1,0,0,0,1],
[0,1,1,1,0,
1,0,0,0,1,
1,0,0,0,1,
1,0,0,0,1,
0,1,1,1,0]]

chars = ['N', 'E', 'R', 'O']
target = np.asfarray(target)
target[target == 0] = -1

# Create and train network
net = nl.net.newhop(target)

output = net.sim(target)
print("Test on train samples:")
for i in range(len(target)):
print(chars[i], (output[i] == target[i]).all())

print("\nTest on defaced N:")
test =np.asfarray([0,0,0,0,0,
1,1,0,0,1,
1,1,0,0,1,
1,0,1,1,1,
0,0,0,1,1])
test[test==0] = -1
out = net.sim([test])
print ((out[0] == target[0]).all(), 'Sim. steps',len(net.layers[0].outs))
```
```Test on train samples:
N True Sim. steps 1
E True Sim. steps 1
R True Sim. steps 1
O True Sim. steps 1

Test on defaced N:
True Sim. steps 2
```

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