Exemple #1
0
    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))
Exemple #3
0
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)
Exemple #5
0
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)
Exemple #6
0
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)