def init_prb_state_sparse(tract1, tract2, nearest = 10):
'''
distribution based on the convert of distance
'''
dm12 = bundles_distances_mam(tract1, tract2)
#print dm12
cs_idxs = [dm12[i].argsort()[:nearest] for i in np.arange(len(tract1))] #chosen indices
ncs_idxs = [dm12[i].argsort()[nearest:] for i in np.arange(len(tract1))] #not chosen indices
for i in np.arange(size1):
dm12[i][ncs_idxs[i]] = 0
'''
test sparse optimzation
'''
#print dm12
from common_functions import normalize_sum_row_1
prb = normalize_sum_row_1(dm12)
#print prb
return np.array(prb,dtype='float'), cs_idxs
def init_prb_state_2(size1, size2):
'''
random distribution
'''
prb_1 = np.random.rand(size1, size2)
from common_functions import normalize_sum_row_1
prb = normalize_sum_row_1(prb_1)
return np.array(prb,dtype='float')
def init_prb_state_1(tract1, tract2):
'''
distribution based on the convert of distance
'''
dm12 = bundles_distances_mam(tract1, tract2)
from common_functions import normalize_sum_row_1
prb = normalize_sum_row_1(dm12)
return np.array(prb,dtype='float')
def init_prb_state_sparse(tract1, tract2, nearest = 10):
'''
distribution based on the convert of distance
'''
from dipy.tracking.distances import bundles_distances_mam
dm12 = bundles_distances_mam(tract1, tract2)
#print dm12
cs_idxs = [dm12[i].argsort()[:nearest] for i in np.arange(len(tract1))] #chosen indices
ncs_idxs = [dm12[i].argsort()[nearest:] for i in np.arange(len(tract1))] #not chosen indices
size1 = len(tract1)
for i in np.arange(size1):
cs_idxs[i].sort()
ncs_idxs[i].sort()
dm12[i][ncs_idxs[i]] = 0
'''
test sparse optimzation
'''
#print cs_idxs
#print dm12
prb = np.zeros((size1,nearest))
for i in np.arange(size1):
prb[i] = dm12[i][cs_idxs[i]]
from common_functions import normalize_sum_row_1
prb = normalize_sum_row_1(prb)
#print prb
#stop
return np.array(prb,dtype='float'),np.array(cs_idxs, dtype = 'float')
