from mdp.nodes import RBMNode import mdp from numpy import * import time import read_spro X = read_spro.load_mfcc_file() rbm = RBMNode(10, X.shape[1]) x2 = X.dot(X.T) print x2.shape mdp_pca = mdp.pca(x2) print X.shape
from read_spro import load_mfcc_file import rbm from numpy import * X = load_mfcc_file() bias = ones((X.shape[0], 1)) #X = hstack((bias, X)) ndata, nfeatures = X.shape nhid = 20 W = zeros(nfeatures) hids = X rbm.trainW(X, hids, W, 10, 0.001) #obs = mydata # (Ndata*Nfeatures array) #obs = addColumnOfOnesForBias(obs) #for layer = 1:5 #WB = zeros(1, N_nodes_in_this_hidden_layer) #out = zeros(Ndata, N_nodes_in_this_hidden_layer) #for datapoint = 1:Ndata #out[datapoint, :] = bolzmannprobs(obs[datapoint,:], other args) #out[datapoint, :] = drawSamplesFrom(out[datapoint, :]) #do a training step with that input and output #obs = addColumnOfOnesForBias(out)
kernel = stats.kde.gaussian_kde(val.T)
Z = reshape(kernel(positions.T).T, X.T.shape)
p.imshow( rot90(Z) , cmap=p.cm.YlGnBu, extent=[0, 1, 0, 1])
p.plot(dat[0,:], dat[1,:], 'r.')
p.axis([0.0, 1.0, 0.0, 1.0])
if __name__ == "__main__":
print gmtime()
seed(12345)
import read_spro
features = read_spro.load_mfcc_file()
rbm = RBM(features.shape[1],8)
rbm.dat = features
rbm.Ndat = features.shape[0]
rbm.learn(10)
print gmtime()
kkk=0
p.figure(1)
p.plot(xrange(size(rbm.E)),rbm.E, 'b+')
p.figure(2)
