def __init__(self,
inputs,
targets,
nRBF,
sigma=0,
usekmeans=0,
normalise=0):
self.nin = np.shape(inputs)[1]
self.nout = np.shape(targets)[1]
self.ndata = np.shape(inputs)[0]
self.nRBF = nRBF
self.usekmeans = usekmeans
self.normalise = normalise
if usekmeans:
self.kmeansnet = kmeansnet.kmeans(self.nRBF, inputs)
self.hidden = np.zeros((self.ndata, self.nRBF + 1))
if sigma == 0:
# Set width of Gaussians
d = (inputs.max(axis=0) - inputs.min(axis=0)).max()
self.sigma = d / np.sqrt(2 * nRBF)
else:
self.sigma = sigma
self.perceptron = pcn.pcn(self.hidden[:, :-1], targets)
# Initialise network
self.weights1 = np.zeros((self.nin, self.nRBF))
def __init__(self,inputs,targets,nRBF,sigma=0,usekmeans=0,normalise=0):
self.nin = np.shape(inputs)[1]
self.nout = np.shape(targets)[1]
self.ndata = np.shape(inputs)[0]
self.nRBF = nRBF
self.usekmeans = usekmeans
self.normalise = normalise
if usekmeans:
self.kmeansnet = kmeansnet.kmeans(self.nRBF, inputs)
self.hidden = np.zeros((self.ndata, self.nRBF+1))
if sigma==0:
# Set width of Gaussians
d = (inputs.max(axis=0)-inputs.min(axis=0)).max()
self.sigma = d/np.sqrt(2*nRBF)
else:
self.sigma = sigma
self.perceptron = pcn.pcn(self.hidden[:, :-1], targets)
# Initialise network
self.weights1 = np.zeros((self.nin, self.nRBF))
target = spam[:, 57]
order = range(np.shape(spam)[0])
np.random.shuffle(order)
spam = spam[order, :]
target = target[order]
train = spam[0:3449, 0:56]
traint = target[0:3449]
valid = spam[3450:4029, 0:56]
validt = target[3450:4029]
test = spam[4030:4600, 0:56]
testt = target[4030:4600]
net = kmeansnet.kmeans(3, train)
net.kmeanstrain(train)
cluster = net.kmeansfwd(test)
net = som.som(6, 6, train)
net.somtrain(train, 400)
best = np.zeros(np.shape(train)[0], dtype=int)
for i in range(np.shape(train)[0]):
best[i], activation = net.somfwd(train[i, :])
pl.plot(net.map[0, :], net.map[1, :], 'k.', ms=15)
where = pl.find(traint == 0)
pl.plot(net.map[0, best[where]], net.map[1, best[where]], 'rs', ms=30)
where = pl.find(traint == 1)
pl.plot(net.map[0, best[where]], net.map[1, best[where]], 'gv', ms=30)
random.shuffle(order)
data = data[order,:]
target = target[order,:]
train = data[::2,0:p]
traint = target[::2]
valid = data[1::4,0:p]
validt = target[1::4]
test = data[3::4,0:p]
testt = target[3::4]
#print train.max(axis=0), train.min(axis=0)
# Train the network
import kmeansnet
net = kmeansnet.kmeans(6,train)
net.kmeanstrain(train)
cluster = net.kmeansfwd(test)
kprediction = 1.*cluster
actual = data[3::4,p]
correct = 0.
for i in range(len(actual)):
if kprediction[i] == actual[i]:
correct += 1.
print 'K-means percentage correct =', correct/len(actual)
import som
net = som.som(7,7,train)
net.somtrain(train,400)
np.random.shuffle(order)
iris = iris[order, :]
target = target[order]
train = iris[::2, 0:4]
traint = target[::2]
valid = iris[1::4, 0:4]
validt = target[1::4]
test = iris[3::4, 0:4]
testt = target[3::4]
# print train.max(axis=0), train.min(axis=0)
import kmeansnet
# import kmeans as kmeansnet
net = kmeansnet.kmeans(3, train)
net.kmeanstrain(train)
cluster = net.kmeansfwd(test)
print(1. * cluster)
print(iris[3::4, 4])
import som
net = som.som(6, 6, train)
net.somtrain(train, 400)
best = np.zeros(np.shape(train)[0], dtype=int)
for i in range(np.shape(train)[0]):
best[i], activation = net.somfwd(train[i, :])
pl.plot(net.map[0, :], net.map[1, :], 'k.', ms=15)
random.shuffle(order)
data = data[order, :]
target = target[order, :]
train = data[::2, 0:p]
traint = target[::2]
valid = data[1::4, 0:p]
validt = target[1::4]
test = data[3::4, 0:p]
testt = target[3::4]
#print train.max(axis=0), train.min(axis=0)
# Train the network
import kmeansnet
net = kmeansnet.kmeans(6, train)
net.kmeanstrain(train)
cluster = net.kmeansfwd(test)
kprediction = 1. * cluster
actual = data[3::4, p]
correct = 0.
for i in range(len(actual)):
if kprediction[i] == actual[i]:
correct += 1.
print 'K-means percentage correct =', correct / len(actual)
import som
net = som.som(7, 7, train)
net.somtrain(train, 400)