part_indices = []
for f in xrange(detector.num_features):
II = feature.peak_local_max(np.fabs(w[...,f]), threshold_rel=0, min_distance=1, footprint=morphology.disk(3), labels=(1+(w[...,f]>0)), exclude_border=False)
part_indices.append(np.hstack([II, f*np.ones((II.shape[0], 1))]))
indices = np.concatenate(part_indices)
if 0:
w_p = w.copy()
w_p[w < 0] = 0.0
w_n = w.copy()
w_n[w > 0] = 0.0
indices_p = get_key_points(w_p, 4)
indices_n = get_key_points(w_n, 4)
indices = np.concatenate([indices_p, indices_n])
#alphas, _, coefs = linear_model.lars_path(X, y, method='lar', verbose=True)
if 0:
loop = 0
while True:
I = array_argmax(w)
if w[I] == 0.0:
break
#for f in xrange(F):
#grid.set_image(w[...,f], f, 0, vmin=-mm, vmax=mm, cmap=cm.RdBu_r)
fn = os.path.join(plot_dir, 'plotb{}.png'.format(loop))
#grid.save(fn, scale=6)
plt.savefig(fn)
os.chmod(fn, 0644)
#np.linalg.solve(Sigma, 0.0 * np.ones(F))
#plt.plot(new_Z)
#end = time.time()
#print end - start
# Set indices
if indices is None:
from train_superimposed import get_key_points
indices = get_key_points(w)
indices_mask = np.zeros(w.shape, dtype=bool)
for i, index in enumerate(indices):
indices_mask[tuple(index)] = True
#new_w[i] = w[tuple(index)]
print 'supp shape', supp.shape
print 'indices_mask shape', indices_mask.shape
print 'indices shape', indices.shape
print 'w shape', w.shape
supp *= indices_mask
w[~indices_mask] = 0
#for f in xrange(F):
#grid.set_image(w[...,f], f, 0, vmin=-mm, vmax=mm, cmap=cm.RdBu_r)
fn = os.path.join(plot_dir, 'plotb{}.png'.format(loop))
#grid.save(fn, scale=6)
plt.savefig(fn)
os.chmod(fn, 0644)
#np.linalg.solve(Sigma, 0.0 * np.ones(F))
#plt.plot(new_Z)
#end = time.time()
#print end - start
# Set indices
if indices is None:
from train_superimposed import get_key_points
indices = get_key_points(w)
indices_mask = np.zeros(w.shape, dtype=bool)
for i, index in enumerate(indices):
indices_mask[tuple(index)] = True
#new_w[i] = w[tuple(index)]
print 'supp shape', supp.shape
print 'indices_mask shape', indices_mask.shape
print 'indices shape', indices.shape
print 'w shape', w.shape
supp *= indices_mask
w[~indices_mask] = 0
