In [131]:

    

class Node(object):
    def __init__(self):
        self.value = 0.0
        self.connections = []
    def connect(self, function, target, strength=0.1):
        self.connections.append((function, target, strength))
    def apply_connections(self):
        for f,t,s in self.connections:
            t += f(self.value) * s
    def __iadd__(self, amount):
        self.value += amount
        if self.value > 1.0: 
            self.value = 1.0
        if self.value < -1.0: 
            self.value = -1.0
        if self.value < -0.1: 
            self.value = -0.1
        return self
            
        
        
nouns = ['DOG', 'CAT']
verbs = ['CHASE']

def f_thresh(x):
    if x > 0.5:
        return 1.0
    else:
        return 0.0
    
def f_linear(x):
    if x<0: return 0
    if x>1: return 1
    return x

def f_sigmoid(x):
    v = np.arctan((x-0.5)*10)/np.pi+0.5
    if v < 0.1: v = 0
    return v

nodes = {}
for noun in nouns:
    base = Node()
    n = Node()
    nodes[noun] = base
    nodes['N_'+noun] = n
    base.connect(f_sigmoid, n)
    n.connect(f_sigmoid,base, strength=-0.2)
for verb in verbs:
    base = Node()
    v = Node()
    nodes[verb] = base
    nodes['V_'+verb] = v
    base.connect(f_sigmoid, v)
    v.connect(f_sigmoid, base, strength=-0.2)
    
    
    for noun in nouns:
        vp = Node()
        nodes['VP_'+verb+'_'+noun] = vp
        def vp_func(x, n=nodes[noun]):
            return f_sigmoid(x * n.value)
        v.connect(vp_func, vp)
        vp.connect(f_sigmoid, vp)
        vp.connect(f_sigmoid, v, -0.2)
        vp.connect(f_sigmoid, n, -0.2)
        vp.connect(f_sigmoid, nodes[noun], -0.2)


    

In [140]:

    

T = 100

stims = 'DOG CHASE CAT'.split()

keys = nodes.keys()
keys.sort()

for n in nodes.values():
    n.value = 0

actv = []
for stim in stims:
    for t in range(T):
        nodes[stim] += 0.1
        for n in nodes.values():
            n.apply_connections()
        a = [nodes[k].value for k in keys]
        actv.append(a)


    

In [141]:

    

figure(figsize=(10,6))
lines=plot(actv, linewidth=4)
legend(lines, keys, loc='best')
ylim(-0.1, 1.1)
show()


    

In [116]:

    

print keys


    

    




['CAT', 'CHASE', 'DOG', 'N_CAT', 'N_DOG', 'VP_CHASE_CAT', 'VP_CHASE_DOG', 'V_CHASE']

In [15]:

    

print nodes


    

    




{'DOG': None, 'V_CHASE': <__main__.Node object at 0x000000000A0BE748>, 'CAT': <__main__.Node object at 0x000000000A448F60>, 'N_CAT': <__main__.Node object at 0x000000000A448D30>, 'N_DOG': <__main__.Node object at 0x000000000A448B00>, 'CHASE': <__main__.Node object at 0x000000000A40DDA0>}

In [121]:

    

x = np.linspace(-1, 1, 100)
plot(x, np.arctan(x*10)*2/np.pi)


    

    
Out[121]:





[<matplotlib.lines.Line2D at 0xc46a588>]






    

In [ ]: