def get_top_perfs(top_file):
perfs = []
f = h5py.File(top_file, 'r')
for net_key in f.keys():
net_grp = f[net_key]
net = EchoStateNetwork()
net.from_hdf5(net_grp)
net.compile()
W = net.W
Win = net.Win
input_node = Win.argmax()
lperf = float(net_grp['performance']['standard'][()])
(evals, evecs) = np.linalg.eig(W)
pdata = PerformanceData()
pdata.file_name = top_file
pdata.net = net
pdata.net_key = net_key
pdata.input_node = input_node
pdata.logit_perf = lperf
pdata.W = W
pdata.Win = Win.squeeze()
pdata.eigen_values = evals
perfs.append(pdata)
f.close()
perfs.sort(key=operator.attrgetter('logit_perf'), reverse=True)
return perfs
def create_fullyconnected_net(num_nodes, rescale_frac=0.999, noise_std=0.0, random_initial_state=False,
symmetric=False, feedforward=False, connection_rate=1.0):
net = EchoStateNetwork()
net.noise_std = noise_std
for k in range(num_nodes):
n = k
istate = 0.0
if random_initial_state:
istate = np.abs(np.random.randn())
net.create_node(n, initial_state=istate)
W = np.random.randn(num_nodes, num_nodes)
if symmetric:
for k1 in range(num_nodes):
for k2 in range(k1):
W[k2, k1] = W[k1, k2]
if feedforward:
for k1 in range(num_nodes):
for k2 in range(k1+1):
W[k1, k2] = 0.0
for k1 in range(num_nodes):
for k2 in range(num_nodes):
rn = np.random.rand()
if rn < connection_rate:
net.connect_nodes(k1, k2, W[k1, k2])
net.rescale_weights(rescale_frac)
return net
def run_morse_nets(stim_file, net_file, input_gain=1.0, noise_std=0.0, num_trials=1, exp_desc='default', net_keys=None, fixed_seed=None):
stimset = MorseStimSet()
stimset.from_hdf5(stim_file)
f = h5py.File(net_file, 'a')
burn_time = 100
post_stim_time = 30
pre_stim_time = 30
if net_keys is None:
net_keys = f.keys()
for net_key in net_keys:
if fixed_seed is not None:
#use a fixed random seed so each network sees the same set of random stimuli
print 'Using fixed random seed..'
np.random.seed(fixed_seed)
print 'Running network %s' % net_key
net_grp = f[net_key]
#read network weights from hdf5
net = EchoStateNetwork()
net.from_hdf5(net_grp)
net.noise_std = noise_std
#set the input gain
net.connect_input(0, 0, input_gain)
#rebuild network and run
net.compile()
net_sims = run_sim(net, stimset.all_stims,
burn_time=burn_time,
pre_stim_time=pre_stim_time, post_stim_time=post_stim_time,
num_trials=num_trials)
#save experiment to network
exp_grp = net_grp.create_group(exp_desc)
exp_grp['noise_std'] = noise_std
exp_grp['num_stims'] = len(net_sims)
exp_grp['input_gain'] = input_gain
resp_grp = exp_grp.create_group('responses')
for stim_key,sim in net_sims.iteritems():
stim_grp = resp_grp.create_group(stim_key)
sim.to_hdf5(stim_grp)
f.close()
def create_inputless_net():
net = EchoStateNetwork()
net.create_node(1, initial_state=np.abs(np.random.randn()))
net.create_node(2, initial_state=np.abs(np.random.randn()))
net.create_node(3, initial_state=np.abs(np.random.randn()))
net.connect_nodes(1, 2, 1.0)
#net.connect_nodes(1, 3, 0.25)
net.connect_nodes(2, 3, 1.1)
net.connect_nodes(3, 1, 0.75)
net.compile()
return net
