# Hemming Recurrent network (newhem)ΒΆ

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

"""

import numpy as np
import neurolab as nl

target = [[-1, 1, -1, -1, 1, -1, -1, 1, -1],
[1, 1, 1, 1, -1, 1, 1, -1, 1],
[1, -1, 1, 1, 1, 1, 1, -1, 1],
[1, 1, 1, 1, -1, -1, 1, -1, -1],
[-1, -1, -1, -1, 1, -1, -1, -1, -1]]

input = [[-1, -1, 1, 1, 1, 1, 1, -1, 1],
[-1, -1, 1, -1, 1, -1, -1, -1, -1],
[-1, -1, -1, -1, 1, -1, -1, 1, -1]]

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

output = net.sim(target)
print "Test on train samples (must be [0, 1, 2, 3, 4])"
print np.argmax(output, axis=0)

output = net.sim([input[0]])
print "Outputs on recurent cycle:"
print np.array(net.layers[1].outs)

output = net.sim(input)
print "Outputs on test sample:"
print output
```
```Test on train samples (must be [0, 1, 2, 3, 4])
[0 1 2 3 4]
Outputs on recurent cycle:
[[ 0.       0.24     0.48     0.       0.     ]
[ 0.       0.144    0.432    0.       0.     ]
[ 0.       0.0576   0.4032   0.       0.     ]
[ 0.       0.       0.39168  0.       0.     ]]
Outputs on test sample:
[[ 0.          0.          0.39168     0.          0.        ]
[ 0.          0.          0.          0.          0.39168   ]
[ 0.07516193  0.          0.          0.          0.07516193]]```

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Hopfield Recurrent network (newhop)

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