data.append(block)
# Construct the mean shift object from it, including a composite kernel...
kde = []
for ds in data:
ms = MeanShift()
ms.set_data(ds, 'df')
if len(kde)==0:
ms.set_kernel('composite(2:gaussian,2:fisher(32.0))')
ms.set_spatial('kd_tree')
ms.set_scale(numpy.array([10.0,5.0,1.0,1.0]))
ms.merge_range = 0.05
else:
ms.copy_all(kde[0])
kde.append(ms)
# Visualise the data set...
for ind, ds in enumerate(data):
img = numpy.zeros((size, size, 3), dtype=numpy.float32)
for sample in ds:
s_x = (size-1) * sample[1] / scale
s_y = (size-1) * sample[0] / scale
e_x = (size-1) * (sample[1] + angle_len * sample[3]) / scale
e_y = (size-1) * (sample[0] + angle_len * sample[2]) / scale
# Create two distributions...
spatial_scale = 8.0
scale = numpy.array([spatial_scale, spatial_scale, spatial_scale, 1.0, 1.0, 1.0, 1.0])
mult_a = MeanShift()
mult_a.set_data(data_a, 'df', None, '...V')
mult_a.set_kernel('composite(3:gaussian,4:mirror_fisher(512.0))')
mult_a.set_spatial('kd_tree')
mult_a.set_scale(scale)
mult_b = MeanShift()
mult_b.set_data(data_b, 'df', None, '...V')
mult_b.copy_all(mult_a)
mult_b.set_scale(scale)
# A function for converting a distribution into a ply file...
def to_ply(fn, samples):
# Open and header...
f = open(fn, 'w')
f.write('ply\n')
f.write('format ascii 1.0\n');
f.write('element vertex %i\n' % (samples.shape[0]*5))
f.write('property float x\n')
f.write('property float y\n')
f.write('property float z\n')
ms.quality = 0.5
ms.set_data(numpy.array([1, 0, 0], dtype=numpy.float32), 'f')
ms.set_kernel('fisher(%.1f%s)' % (2**power, code))
ms.set_spatial('kd_tree')
return ms
options = map(ms_by_conc, xrange(8)) + [
ms_by_conc(8, 'c'), ms_by_conc(8, 'a')
] + map(ms_by_conc, xrange(9, 16))
# Create it and do the bandwidth estimation...
ms = MeanShift()
ms.set_data(data, 'df')
p = ProgBar()
best = ms.scale_loo_nll_array(options, p.callback)
del p
print 'Selected kernel =', ms.get_kernel()
print 'LOO score =', best
# Visualise the best option...
visualise('bandwidth_fisher.png', ms)
# Also visualise correct vs approximate, for sanity checking...
for option in [ms_by_conc(8, 'c'), ms_by_conc(8, 'a')]: #options:
ms.copy_all(option)
visualise('bandwidth_fisher_%s.png' % option.get_kernel(), ms)
mult_a.set_data(data_a, 'd', None, 'A')
mult_a.set_kernel('fisher(128.0)')
mult_b = MeanShift()
mult_b.set_data(data_b, 'd', None, 'A')
mult_b.set_kernel('fisher(512.0)')
# Do multiplication...
data_ab = numpy.empty((128,1), dtype=numpy.float32)
MeanShift.mult((mult_a, mult_b), data_ab)
mult_ab = MeanShift()
mult_ab.set_data(data_ab, 'df', None, 'A')
mult_ab.copy_all(mult_b)
# Visualise all angles...
img = numpy.zeros((64, 1024,3), dtype=numpy.float32)
for i in xrange(img.shape[1]):
ang = 2.0 * numpy.pi * i / float(img.shape[1])
img[:,i,0] = mult_a.prob(numpy.array([ang]))
img[:,i,1] = mult_ab.prob(numpy.array([ang]))
img[:,i,2] = mult_b.prob(numpy.array([ang]))
img *= 255.0 / img.max()
(-1, 1)), numpy.sin(direction).reshape((-1, 1))),
axis=1)
data.append(block)
# Construct the mean shift object from it, including a composite kernel...
kde = []
for ds in data:
ms = MeanShift()
ms.set_data(ds, 'df')
if len(kde) == 0:
ms.set_kernel('composite(2:gaussian,2:fisher(32.0))')
ms.set_spatial('kd_tree')
ms.set_scale(numpy.array([10.0, 5.0, 1.0, 1.0]))
ms.merge_range = 0.05
else:
ms.copy_all(kde[0])
kde.append(ms)
# Visualise the data set...
for ind, ds in enumerate(data):
img = numpy.zeros((size, size, 3), dtype=numpy.float32)
for sample in ds:
s_x = (size - 1) * sample[1] / scale
s_y = (size - 1) * sample[0] / scale
e_x = (size - 1) * (sample[1] + angle_len * sample[3]) / scale
e_y = (size - 1) * (sample[0] + angle_len * sample[2]) / scale
for i in xrange(angle_step):
t = float(i) / (angle_step - 1)
MeanShift.mult([mult_a, mult_b], draws)
prod_a_b = MeanShift()
prod_a_b.set_data(draws, 'df')
prod_a_b.set_kernel('mirror_fisher(512.0)')
visualise('mirror_fisher_prod_a_b.png', prod_a_b)
print 'Prepared and visualised product of a and b'
## Multiply b and c distributions and visualise...
draws = numpy.empty((count, 2))
MeanShift.mult((mult_b, mult_c), draws)
prod_b_c = MeanShift()
prod_b_c.set_data(draws, 'df')
prod_b_c.copy_all(prod_a_b)
visualise('mirror_fisher_prod_b_c.png', prod_b_c)
print 'Prepared and visualised product of b and c'
## Multiply c and a distributions and visualise...
## This doesn't work - equal sampling of initial state, where one is ultimatly much more probable but forming two islands that could require millions of sampling steps to transfer between, so it ends up with the islands having equal probability when they really shouldn't...
draws = numpy.empty((count, 2))
MeanShift.mult((mult_c, mult_a), draws)
prod_c_a = MeanShift()
prod_c_a.set_data(draws, 'df')
prod_c_a.copy_all(prod_a_b)
visualise('mirror_fisher_prod_c_a_wrong.png', prod_c_a)
print 'Prepared and visualised product of c and a'
ms.set_spatial('kd_tree')
return ms
options = map(ms_by_conc, xrange(8)) + [ms_by_conc(8,'c'), ms_by_conc(8,'a')] + map(ms_by_conc, xrange(9,16))
# Create it and do the bandwidth estimation...
ms = MeanShift()
ms.set_data(data, 'df')
p = ProgBar()
best = ms.scale_loo_nll_array(options, p.callback)
del p
print 'Selected kernel =', ms.get_kernel()
print 'LOO score =', best
# Visualise the best option...
visualise('bandwidth_fisher.png', ms)
# Also visualise correct vs approximate, for sanity checking...
for option in [ms_by_conc(8,'c'), ms_by_conc(8,'a')]: #options:
ms.copy_all(option)
visualise('bandwidth_fisher_%s.png' % option.get_kernel(), ms)
