def bench1(mode = 'diag'):
    #===========================================
    # GMM of 20 comp, 20 dimension, 1e4 frames
    #===========================================
    d       = 15
    k       = 30
    nframes = 1e5
    niter   = 10
    mode    = 'diag'

    print "============================================================="
    print "(%d dim, %d components) GMM with %d iterations, for %d frames" \
            % (d, k, niter, nframes)

    #+++++++++++++++++++++++++++++++++++++++++++
    # Create an artificial GMM model, samples it
    #+++++++++++++++++++++++++++++++++++++++++++
    print "Generating the mixture"
    # Generate a model with k components, d dimensions
    w, mu, va   = GM.gen_param(d, k, mode, spread = 3)
    # gm          = GM(d, k, mode)
    # gm.set_param(w, mu, va)
    gm          = GM.fromvalues(w, mu, va)

    # Sample nframes frames  from the model
    data    = gm.sample(nframes)

    #++++++++++++++++++++++++
    # Learn the model with EM
    #++++++++++++++++++++++++

    # Init the model
    print "Init a model for learning, with kmean for initialization"
    lgm = GM(d, k, mode)
    gmm = GMM(lgm, 'kmean')
    
    gmm.init(data)
    # Keep the initialized model for drawing
    gm0 = copy.copy(lgm)

    # The actual EM, with likelihood computation
    like    = N.zeros(niter)

    print "computing..."
    for i in range(niter):
        print "iteration %d" % i
        g, tgd  = gmm.sufficient_statistics(data)
        like[i] = N.sum(N.log(N.sum(tgd, 1)))
        gmm.update_em(data, g)
Beispiel #2
0
def bench1(mode='diag'):
    #===========================================
    # GMM of 20 comp, 20 dimension, 1e4 frames
    #===========================================
    d = 15
    k = 30
    nframes = 1e5
    niter = 10
    mode = 'diag'

    print "============================================================="
    print "(%d dim, %d components) GMM with %d iterations, for %d frames" \
            % (d, k, niter, nframes)

    #+++++++++++++++++++++++++++++++++++++++++++
    # Create an artificial GMM model, samples it
    #+++++++++++++++++++++++++++++++++++++++++++
    print "Generating the mixture"
    # Generate a model with k components, d dimensions
    w, mu, va = GM.gen_param(d, k, mode, spread=3)
    # gm          = GM(d, k, mode)
    # gm.set_param(w, mu, va)
    gm = GM.fromvalues(w, mu, va)

    # Sample nframes frames  from the model
    data = gm.sample(nframes)

    #++++++++++++++++++++++++
    # Learn the model with EM
    #++++++++++++++++++++++++

    # Init the model
    print "Init a model for learning, with kmean for initialization"
    lgm = GM(d, k, mode)
    gmm = GMM(lgm, 'kmean')

    gmm.init(data)
    # Keep the initialized model for drawing
    gm0 = copy.copy(lgm)

    # The actual EM, with likelihood computation
    like = N.zeros(niter)

    print "computing..."
    for i in range(niter):
        print "iteration %d" % i
        g, tgd = gmm.sufficient_statistics(data)
        like[i] = N.sum(N.log(N.sum(tgd, 1)))
        gmm.update_em(data, g)
Beispiel #3
0
# Create an artificial GM model, samples it
#+++++++++++++++++++++++++++++++++++++++++++
w, mu, va = GM.gen_param(d, k, mode, spread=1.5)
gm = GM.fromvalues(w, mu, va)

# Sample nframes frames  from the model
data = gm.sample(nframes)

#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++

# Init the model
lgm = GM(d, k, mode)
gmm = GMM(lgm, 'kmean')
gmm.init(data)

# Keep a copy for drawing later
gm0 = copy.copy(lgm)

# The actual EM, with likelihood computation. The threshold
# is compared to the (linearly appromixated) derivative of the likelihood
em = EM()
like = em.train(data, gmm, maxiter=30, thresh=1e-8)

#+++++++++++++++
# Draw the model
#+++++++++++++++
import pylab as P

P.subplot(2, 1, 1)
Beispiel #4
0
# Create an artificial GM model, samples it
#+++++++++++++++++++++++++++++++++++++++++++
w, mu, va   = GM.gen_param(d, k, mode, spread = 1.5)
gm          = GM.fromvalues(w, mu, va)

# Sample nframes frames  from the model
data    = gm.sample(nframes)

#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++

# Init the model
lgm = GM(d, k, mode)
gmm = GMM(lgm, 'kmean')
gmm.init(data)

# Keep a copy for drawing later
gm0 = copy.copy(lgm)

# The actual EM, with likelihood computation. The threshold
# is compared to the (linearly appromixated) derivative of the likelihood
em      = EM()
like    = em.train(data, gmm, maxiter = 30, thresh = 1e-8)

#+++++++++++++++
# Draw the model
#+++++++++++++++
import pylab as P
P.subplot(2, 1, 1)