Python NonstationaryLogistic.gibbs_improve_perm Examples

Example #1

File: test_gibbs.py Project: othercriteria/StochasticBlockmodel

net.new_node_covariate_int('r')[:] = 1
net.new_node_covariate_int('c')[:] = 1
data_model = FixedMargins(data_model, 'r', 'c')

coverage_levels = np.append(0.0, np.cumsum(params['coverage_increments']))
traces = { 'wall_time': [],
           'nll': [] }

for rep in range(params['num_reps']):
    net.generate(data_model, arbitrary_init = params['arb_init'])

    wall_time_trace = [net.gen_info['wall_time']]
    nll_trace = [data_model.nll(net)]

    for coverage_inc in params['coverage_increments']:
        data_model.gibbs_improve_perm(net, net.as_dense(), coverage_inc)

        wall_time_trace.append(net.gen_info['wall_time'])
        nll_trace.append(data_model.nll(net))

    traces['wall_time'].append(wall_time_trace)
    traces['nll'].append(nll_trace)

plt.figure()
plt.title('Computation time')
plt.xlabel('Coverage level')
plt.ylabel('Wall time (msec)')
for rep in range(params['num_reps']):
    plt.plot(coverage_levels, traces['wall_time'][rep], '-')

plt.figure()

Example #2

File: test_gibbs.py Project: wang-xinhong/StochasticBlockmodel

net.new_node_covariate_int('r')[:] = 1
net.new_node_covariate_int('c')[:] = 1
data_model = FixedMargins(data_model, 'r', 'c')

coverage_levels = np.append(0.0, np.cumsum(params['coverage_increments']))
traces = { 'wall_time': [],
           'nll': [] }

for rep in range(params['num_reps']):
    net.generate(data_model, arbitrary_init = params['arb_init'])

    wall_time_trace = [net.gen_info['wall_time']]
    nll_trace = [data_model.nll(net)]

    for coverage_inc in params['coverage_increments']:
        data_model.gibbs_improve_perm(net, net.adjacency_matrix(), coverage_inc)

        wall_time_trace.append(net.gen_info['wall_time'])
        nll_trace.append(data_model.nll(net))

    traces['wall_time'].append(wall_time_trace)
    traces['nll'].append(nll_trace)

plt.figure()
plt.title('Computation time')
plt.xlabel('Coverage level')
plt.ylabel('Wall time (msec)')
for rep in range(params['num_reps']):
    plt.plot(coverage_levels, traces['wall_time'][rep], '-')

plt.figure()

Example #3

File: test_gibbs.py Project: ZugenLiu/StochasticBlockmodel

# Specify data model as generation permuation networks
net.new_node_covariate_int('r')[:] = 1
net.new_node_covariate_int('c')[:] = 1
data_model = FixedMargins(data_model, 'r', 'c')

coverage_levels = np.append(0.0, np.cumsum(params['coverage_increments']))
traces = {'wall_time': [], 'nll': []}

for rep in range(params['num_reps']):
    net.generate(data_model, arbitrary_init=params['arb_init'])

    wall_time_trace = [net.gen_info['wall_time']]
    nll_trace = [data_model.nll(net)]

    for coverage_inc in params['coverage_increments']:
        data_model.gibbs_improve_perm(net, net.as_dense(), coverage_inc)

        wall_time_trace.append(net.gen_info['wall_time'])
        nll_trace.append(data_model.nll(net))

    traces['wall_time'].append(wall_time_trace)
    traces['nll'].append(nll_trace)

plt.figure()
plt.title('Computation time')
plt.xlabel('Coverage level')
plt.ylabel('Wall time (msec)')
for rep in range(params['num_reps']):
    plt.plot(coverage_levels, traces['wall_time'][rep], '-')

plt.figure()