def __init__(self, data, target, count=5, sigma=0, normalise=False):
self.dm, self.dn = shape(data)
self.data = data
self.target = target
self.count = count
self.normalise = normalise
self.hidden = np.zeros((self.dm, self.count+1))
if sigma == 0:
d = (data.max(axis=0) - data.min(axis=0)).max()
self.sigma = d / np.sqrt(2*self.count)
else:
self.sigma = sigma
self.pcn = Perceptron(self.hidden[:, :-1], target, itercount=3000)
self.weight = np.zeros((self.dn, self.count))
class RDFNetwork(object):
'''Radial Basis Function Network'''
def __init__(self, data, target, count=5, sigma=0, normalise=False):
self.dm, self.dn = shape(data)
self.data = data
self.target = target
self.count = count
self.normalise = normalise
self.hidden = np.zeros((self.dm, self.count+1))
if sigma == 0:
d = (data.max(axis=0) - data.min(axis=0)).max()
self.sigma = d / np.sqrt(2*self.count)
else:
self.sigma = sigma
self.pcn = Perceptron(self.hidden[:, :-1], target, itercount=3000)
self.weight = np.zeros((self.dn, self.count))
def train(self, data=None, target=None):
if data is not None:
self.dm, self.dn = shpae(data)
self.data = np.hstack((data, -np.ones((dm, 1))))
if target is not None:
self.target = target
indices = range(self.dm)
np.random.shuffle(indices)
for i in range(self.count):
self.weight[:, i] = self.data[indices[i], :]
for i in range(self.count):
self.hidden[:, i] = np.exp(-np.sum(self.data - np.ones((1, self.dn)) * self.weight[:, i]**2, axis=1) / (2*self.sigma**2))
if self.normalise:
pass
self.pcn.train(self.hidden[:, :-1], self.target)
def fwd(self, data=None):
if data is not None:
self.dm, self.dn = shape(data)
hidden = np.zeros((self.dm, self.count+1))
for i in range(self.count):
hidden[:, i] = np.exp(-np.sum(data - np.ones((1, self.dn)) * self.weight[:, i]**2, axis=1) / (2*self.sigma**2))
if self.normalise:
pass
output = self.pcn.fwd(hidden[:, :-1])
return output
def score(self, input_data, target):
output = self.fwd(input_data)
m = input_data.shape[0]
s = np.sum([(output[i]==target[i]).all() for i in range(m)])
return float(s) / float(m) * 100.0
