本文整理汇总了Python中Network.Network.as_dense方法的典型用法代码示例。如果您正苦于以下问题：Python Network.as_dense方法的具体用法？Python Network.as_dense怎么用？Python Network.as_dense使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Network.Network的用法示例。
在下文中一共展示了Network.as_dense方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: f_x
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import as_dense [as 别名]
def f_x(i_1, i_2):
return np.random.normal(0, 1.0)
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Instantiate network according to data model
data_model.match_kappa(net, ('row_sum', target_degree))
net.generate(data_model)
#net.show_heatmap(order_by_row = 'alpha_out')
#net.show_heatmap(order_by_col = 'alpha_in')
# Display network
plt.figure(figsize = (11, 3.2))
plt.subplot(141)
plt.title('Network')
graph = nx.DiGraph()
A = net.as_dense()
for i in range(N):
graph.add_node(i)
for i in range(N):
for j in range(N):
if A[i,j]:
graph.add_edge(i,j)
pos = nx.nx_pydot.graphviz_layout(graph, prog = 'neato')
nx.draw(graph, pos, node_size = 60, with_labels = False)
def grid_fit(fit_model, f_nll, profile = False, pre_offset = False):
# Initialize grid
theta_star_1 = data_model.beta[covariates[0]]
theta_star_2 = data_model.beta[covariates[1]]
x = np.linspace(theta_star_1 - 2.0, theta_star_1 + 2.0, G)
y = np.linspace(theta_star_2 - 2.0, theta_star_2 + 2.0, G)示例2: FixedMargins
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import as_dense [as 别名]
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()