softmax.py

f1 = lambda x: 1./np.exp(0.1*(x + 80.))
f2 = lambda x: -.26*(x+58)

def weight(i, fs):
    return lambda x: np.exp(fs[i](x)) / np.sum([np.exp(f(x)) for f in fs])

def softmax(fs):
    return lambda x: np.sum([f(x)*weight(i,fs)(x) for i,f in enumerate(fs)])
  
softmax([f1,f2])(np.linspace(-80,0))