def show(self, x, gd, density=None, axes=None):
"""
Function to plot a GMM, still in progress
Currently, works only in 1D and 2D
Parameters
----------
x: array of shape(n_samples, dim)
the data under study
gd: GridDescriptor instance
density: array os shape(prod(gd.n_bins))
density of the model one the discrete grid implied by gd
by default, this is recomputed
"""
import matplotlib.pylab as mp
# recompute the density if necessary
if density==None:
density = self.mixture_likelihood(gd,x)
import pylab
if axes is None:
axes = pylab.figure()
if gd.dim==1:
from nipy.neurospin.utils.emp_null import smoothed_histogram_from_samples
h, c = smoothed_histogram_from_samples(x, normalized=True)
#step = 3.5*np.std(x)/np.exp(np.log(np.size(x))/3)
#bins = max(10,(x.max()-x.min())/step)
#xmin = 1.1*x.min() - 0.1*x.max()
#xmax = 1.1*x.max() - 0.1*x.min()
#h,c = np.histogram(x, bins, [xmin, xmax], normed=True)
offset = (c.max()-c.min())/(2*c.size)
grid = gd.make_grid()
h /= h.sum()
h /= (2*offset)
mp.plot(c[:-1]+offset, h)
mp.plot(grid, density)
if gd.dim==2:
mp.figure()
xm = gd.lim[0]
xM = gd.lim[1]
ym = gd.lim[2]
yM = gd.lim[3]
gd0 = gd.n_bins[0]
Pdens= np.reshape(density, (gd0, np.size(density)/gd0))
axes.imshow(Pdens.T, None, None, None, 'nearest',
1.0, None, None,'lower',[xm, xM, ym, yM])
axes.plot(x[:, 0], x[:, 1], '.k')
axes.axis([xm, xM, ym, yM])
return axes
def test_smooth_histo(): """ test smoothed histogram geenration """ n=100 x = np.random.randn(n) h, c = smoothed_histogram_from_samples(x, normalized=True) thh = 1./np.sqrt(2*np.pi) hm = h.max() #print hm, thh assert np.absolute(hm-thh)<0.15