network_hypers = {'c': np.zeros(K, dtype=np.int), 'p': 0.5, 'kappa': 10.0, 'v': 10*3.0}
bkgd_hypers = {"alpha": 1., "beta": 10.}
model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt, dt_max=dt_max,
weight_hypers={"parallel_resampling": False},
network_hypers=network_hypers)
model.generate(T=T)
# Gibbs sample and then generate new data
N_samples = 10000
samples = []
lps = []
for itr in progprint_xrange(N_samples, perline=50):
# Resample the model
model.resample_model()
samples.append(model.copy_sample())
lps.append(model.log_likelihood())
# Geweke step
model.data_list.pop()
model.generate(T=T)
# Compute sample statistics for second half of samples
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
lps = np.array(lps)
offset = 0
def demo(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1
K = 10
T = 1000
dt = 1.0
B = 3
# Create a true model
p = 0.8 * np.eye(C)
v = 10.0 * np.eye(C) + 20.0 * (1 - np.eye(C))
# m = 0.5 * np.ones(C)
c = (0.0 * (np.arange(K) < 10) + 1.0 * (np.arange(K) >= 10)).astype(np.int)
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(C=C,
K=K,
dt=dt,
B=B,
c=c,
p=p,
v=v)
# Plot the true network
plt.ion()
plot_network(true_model.weight_model.A,
true_model.weight_model.W,
vmax=0.5)
plt.pause(0.001)
# Sample from the true model
S, R = true_model.generate(T=T)
# Make a new model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
test_model.add_data(S)
# Plot the true and inferred firing rate
kplt = 0
plt.figure()
plt.plot(np.arange(T), R[:, kplt], '-k', lw=2)
plt.ion()
ln = plt.plot(np.arange(T), test_model.compute_rate(ks=kplt), '-r')[0]
plt.show()
# Gradient descent
N_steps = 10000
lls = []
for itr in xrange(N_steps):
W, ll, grad = test_model.gradient_descent_step(stepsz=0.001)
lls.append(ll)
# Update plot
if itr % 5 == 0:
ln.set_data(np.arange(T), test_model.compute_rate(ks=kplt))
plt.title("Iteration %d" % itr)
plt.pause(0.001)
plt.ioff()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print "ll true: ", true_model.log_likelihood()
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
print "ll test: ", test_model.log_likelihood()
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K, K)), test_model.W, vmax=0.5)
plt.show()
def demo(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
raise NotImplementedError("This example needs to be updated.")
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1
K = 10
T = 1000
dt = 1.0
B = 3
# Create a true model
p = 0.8 * np.eye(C)
v = 10.0 * np.eye(C) + 20.0 * (1-np.eye(C))
# m = 0.5 * np.ones(C)
c = (0.0 * (np.arange(K) < 10) + 1.0 * (np.arange(K) >= 10)).astype(np.int)
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(C=C, K=K, dt=dt, B=B, c=c, p=p, v=v)
# Plot the true network
plt.ion()
plot_network(true_model.weight_model.A,
true_model.weight_model.W,
vmax=0.5)
plt.pause(0.001)
# Sample from the true model
S,R = true_model.generate(T=T)
# Make a new model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
test_model.add_data(S)
# Plot the true and inferred firing rate
kplt = 0
plt.figure()
plt.plot(np.arange(T), R[:,kplt], '-k', lw=2)
plt.ion()
ln = plt.plot(np.arange(T), test_model.compute_rate(ks=kplt), '-r')[0]
plt.show()
# Gradient descent
N_steps = 10000
lls = []
for itr in xrange(N_steps):
W,ll,grad = test_model.gradient_descent_step(stepsz=0.001)
lls.append(ll)
# Update plot
if itr % 5 == 0:
ln.set_data(np.arange(T), test_model.compute_rate(ks=kplt))
plt.title("Iteration %d" % itr)
plt.pause(0.001)
plt.ioff()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print "ll true: ", true_model.log_likelihood()
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
print "ll test: ", test_model.log_likelihood()
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K,K)), test_model.W, vmax=0.5)
plt.show()
K=K,
dt=dt,
dt_max=dt_max,
weight_hypers={"parallel_resampling": False},
network_hypers=network_hypers)
model.generate(T=T)
# Gibbs sample and then generate new data
N_samples = 10000
samples = []
lps = []
for itr in progprint_xrange(N_samples, perline=50):
# Resample the model
model.resample_model()
samples.append(model.copy_sample())
lps.append(model.log_likelihood())
# Geweke step
model.data_list.pop()
model.generate(T=T)
# Compute sample statistics for second half of samples
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
lps = np.array(lps)
offset = 0
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
