range_lambda = np.array([0.001, 0.5, 1.0, 2.0, 4.0])
optimize_t = [True, True, True, True, True]
num_sample_iter = 5
# -----------------------------------------------------------------------------#
# initialize distributions
# generate random robot species with fixed diversity
if not load_data:
rk = 0
print 'Computing Species-Trait matrix'
while rk != desired_rank:
species_traits, rk, s = generate_Q(num_species, num_traits)
else:
species_traits = pickle.load(open(load_prefix+"species_traits.p", "rb"))
print species_traits
random_transition = random_transition_matrix(num_nodes, max_rate/2) # Divide max_rate by 2 for the random matrix to give some slack.
print 'Getting high enough starting error...'
sys.stdout.flush()
ratio_init = 0.0
while ratio_init < 0.25 and not load_data:
# sample initial
print ratio_init
deploy_robots_init = np.random.randint(0, 100, size=(num_nodes, num_species))
num_iter = 1
QQ = np.zeros((num_traits*num_nodes+num_species, num_species*num_nodes))
QQ_rks = np.zeros((num_species,num_iter))
Q_rks = np.zeros((num_species,num_iter))
print "num rows: ", QQ.shape[0]
print "num cols: ", QQ.shape[1]
for it in range(num_iter):
print it
for ui in range(1,num_species+1):
rk = 0
sys.stdout.flush()
while rk!=ui:
Q, rk, s = generate_Q(num_species, ui)
#print Q
# generate QQ
QQ = np.zeros((ui*num_nodes+num_species, num_species*num_nodes))
QQ[0:ui*num_nodes,:] = sp.linalg.block_diag(*([Q.T]*num_nodes))
QQ[ui*num_nodes:,:] = np.concatenate([np.identity(num_species)]*num_nodes,axis=1)
rk_QQ = np.linalg.matrix_rank(QQ)
ind = ui-1
Q_rks[ind,it]= rk
QQ_rks[ind,it] = rk_QQ
coef = QQ_rks / Q_rks
ind = 0
