def demo(seed=None):
"""
Fit a weakly sparse
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
N = 27 # Number of neurons
T = 60000 # Number of time bins
dt = 1.0 # Time bin width
dt_max = 10.0 # Max time of synaptic influence
B = 2 # Number of basis functions for the weights
# Bias hyperparameters
bias_hypers = {"mu_0": -2.0, "sigma_0": 0.25}
p = 0.1 # Probability of connection for each pair of clusters
mu = -2 * np.ones((B,)) # Mean weight for each pair of clusters
sigma = 0.1 * np.eye(B) # Covariance of weight for each pair of clusters
# Define the true network model for the GLM
true_network = FactorizedNetworkDistribution(
N,
BernoulliAdjacencyDistribution, {"p": p},
FixedGaussianWeightDistribution, {"B": B, "mu": mu, "sigma": sigma})
true_model = NegativeBinomialPopulation(N=N, dt=dt, dt_max=dt_max, B=B,
bias_hypers=bias_hypers,
network=true_network)
###########################################################
# Create a test spike-and-slab model
###########################################################
# Create another copy of the model with the true network model
test_network = FactorizedNetworkDistribution(
N,
BernoulliAdjacencyDistribution, {"p": p},
FixedGaussianWeightDistribution, {"B": B, "mu": mu, "sigma": sigma})
observation_hypers = {"xi": 10., "alpha_xi": 1.0, "beta_xi": 1.0}
test_model = NegativeBinomialPopulation(N=N, dt=dt, dt_max=dt_max, B=B,
bias_hypers=bias_hypers,
network=test_network,
observation_hypers=observation_hypers)
# Sample some synthetic data from the true model
S = true_model.generate(T=T, keep=True, verbose=False)
# Add training data in chunks
chunksz = 1024
for offset in xrange(0, T, chunksz):
test_model.add_data(S[offset:min(offset+chunksz,T)])
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 3
samples = []
lps = []
for itr in xrange(N_samples):
lps.append(test_model.log_probability())
samples.append(test_model.copy_sample())
print ""
print "Gibbs iteration ", itr
print "LP: ", lps[-1]
test_model.collapsed_resample_model()
with open("gibbs_profile.txt", "w") as f:
show_line_stats(f)
dt = 1.0 # Time bin width
dt_max = 10.0 # Max time of synaptic influence
B = 2 # Number of basis functions for the weights
# Bias hyperparameters
bias_hypers = {"mu_0": -1.0, "sigma_0": 0.25}
p = 0.5 # Probability of connection for each pair of clusters
mu = np.zeros((B, )) # Mean weight for each pair of clusters
sigma = 1.0 * np.eye(B) # Covariance of weight for each pair of clusters
# Define the true network model for the GLM
true_network = FactorizedNetworkDistribution(N, BernoulliAdjacencyDistribution,
{"p": p},
FixedGaussianWeightDistribution, {
"B": B,
"mu": mu,
"sigma": sigma
})
true_model = Population(N=N,
dt=dt,
dt_max=dt_max,
B=B,
bias_hypers=bias_hypers,
network=true_network)
# Create another copy of the model with the true network model
test_network = FactorizedNetworkDistribution(N, BernoulliAdjacencyDistribution,
{"p": p},
FixedGaussianWeightDistribution, {
def demo(seed=None):
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
# Create an eigenmodel with N nodes and D-dimensional feature vectors
N = 30 # Number of nodes
D = 2 # Dimensionality of the feature space
true_model = \
FactorizedNetworkDistribution(N,
CompleteAdjacencyDistribution, {},
LatentDistanceGaussianWeightDistribution, dict(dim=D, b=-12.))
# Set the true locations to be on a grid
# w = 4
# s = 0.8
# x = s * (np.arange(N) % w)
# y = s * (np.arange(N) // w)
# L = np.hstack((x[:,None], y[:,None]))
# true_model.adjacency.L = L
# Set the true locations to be on a circle
r = 1.5 + np.arange(N) // (N/2.)
th = np.linspace(0, 4 * np.pi, N, endpoint=False)
x = r * np.cos(th)
y = r * np.sin(th)
L = np.hstack((x[:,None], y[:,None]))
true_model.weights.L = L
# Sample a graph from the eigenmodel
A,W = true_model.rvs()
# Make a figure to plot the true and inferred network
plt.ion()
fig = plt.figure()
ax_true = fig.add_subplot(1,2,1, aspect="equal")
ax_test = fig.add_subplot(1,2,2, aspect="equal")
true_model.weights.plot(A, W, ax=ax_true)
test_model = \
FactorizedNetworkDistribution(N,
CompleteAdjacencyDistribution, {},
LatentDistanceGaussianWeightDistribution, dict(dim=D, b=-12.))
test_model.weights.plot(A, W, ax=ax_test, L_true=true_model.weights.L)
plt.pause(0.001)
# Fit with Gibbs sampling
N_samples = 1000
lps = [test_model.log_probability((A,W))]
for smpl in xrange(N_samples):
print "Iteration ", smpl
test_model.resample((A,W))
lps.append(test_model.log_probability((A,W)))
print "LP: ", lps[-1]
print ""
# Update the test plot
if smpl % 1 == 0:
ax_test.cla()
test_model.weights.plot(A, W, ax=ax_test,
L_true=true_model.weights.L)
plt.pause(0.001)
plt.ioff()
NIWGaussianWeightDistribution, \
SBMGaussianWeightDistribution
from graphistician.networks import FactorizedNetworkDistribution
seed = 1234
# seed = np.random.randint(2**32)
# Create an latent distance model with N nodes and D-dimensional locations
N = 30 # Number of nodes
N_test = 1 # Number of nodes to hold out for testing
B = 1 # Dimensionality of the weights
D = 2 # Dimensionality of the feature space
true_model = FactorizedNetworkDistribution(
N+N_test,
LatentDistanceAdjacencyDistribution, {},
NIWGaussianWeightDistribution, {})
# Sample a graph from the eigenmodel
Afull, Wfull = true_model.rvs()
Atrain= Afull[:N,:N]
Wtrain= Wfull[:N,:N,:]
Atest_row = Afull[N:,:].ravel()
Wtest_row = Wfull[N:,:,:].reshape((N+N_test,1))
Atest_col = Afull[:,N:].ravel()
Wtest_col = Wfull[:,N:,:].reshape((N+N_test,1))
# Make a figure to plot the true and inferred network
adj_models = [
LatentDistanceAdjacencyDistribution,
SBMAdjacencyDistribution,
NIWGaussianWeightDistribution, \
SBMGaussianWeightDistribution
from graphistician.networks import FactorizedNetworkDistribution
seed = 1234
# seed = np.random.randint(2**32)
# Create an latent distance model with N nodes and D-dimensional locations
N = 30 # Number of nodes
N_test = 1 # Number of nodes to hold out for testing
B = 1 # Dimensionality of the weights
D = 2 # Dimensionality of the feature space
true_model = FactorizedNetworkDistribution(
N + N_test, LatentDistanceAdjacencyDistribution, {},
NIWGaussianWeightDistribution, {})
# Sample a graph from the eigenmodel
Afull, Wfull = true_model.rvs()
Atrain = Afull[:N, :N]
Wtrain = Wfull[:N, :N, :]
Atest_row = Afull[N:, :].ravel()
Wtest_row = Wfull[N:, :, :].reshape((N + N_test, 1))
Atest_col = Afull[:, N:].ravel()
Wtest_col = Wfull[:, N:, :].reshape((N + N_test, 1))
# Make a figure to plot the true and inferred network
adj_models = [
LatentDistanceAdjacencyDistribution,
SBMAdjacencyDistribution,
def demo(seed=None):
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
# Create an eigenmodel with N nodes and D-dimensional feature vectors
N = 30 # Number of nodes
D = 2 # Dimensionality of the feature space
true_model = \
FactorizedNetworkDistribution(N,
CompleteAdjacencyDistribution, {},
LatentDistanceGaussianWeightDistribution, dict(dim=D, b=-12.))
# Set the true locations to be on a grid
# w = 4
# s = 0.8
# x = s * (np.arange(N) % w)
# y = s * (np.arange(N) // w)
# L = np.hstack((x[:,None], y[:,None]))
# true_model.adjacency.L = L
# Set the true locations to be on a circle
r = 1.5 + np.arange(N) // (N / 2.)
th = np.linspace(0, 4 * np.pi, N, endpoint=False)
x = r * np.cos(th)
y = r * np.sin(th)
L = np.hstack((x[:, None], y[:, None]))
true_model.weights.L = L
# Sample a graph from the eigenmodel
A, W = true_model.rvs()
# Make a figure to plot the true and inferred network
plt.ion()
fig = plt.figure()
ax_true = fig.add_subplot(1, 2, 1, aspect="equal")
ax_test = fig.add_subplot(1, 2, 2, aspect="equal")
true_model.weights.plot(A, W, ax=ax_true)
test_model = \
FactorizedNetworkDistribution(N,
CompleteAdjacencyDistribution, {},
LatentDistanceGaussianWeightDistribution, dict(dim=D, b=-12.))
test_model.weights.plot(A, W, ax=ax_test, L_true=true_model.weights.L)
plt.pause(0.001)
# Fit with Gibbs sampling
N_samples = 1000
lps = [test_model.log_probability((A, W))]
for smpl in xrange(N_samples):
print "Iteration ", smpl
test_model.resample((A, W))
lps.append(test_model.log_probability((A, W)))
print "LP: ", lps[-1]
print ""
# Update the test plot
if smpl % 1 == 0:
ax_test.cla()
test_model.weights.plot(A,
W,
ax=ax_test,
L_true=true_model.weights.L)
plt.pause(0.001)
plt.ioff()