本文整理汇总了Python中Network.Network.new_node_covariate_int方法的典型用法代码示例。如果您正苦于以下问题：Python Network.new_node_covariate_int方法的具体用法？Python Network.new_node_covariate_int怎么用？Python Network.new_node_covariate_int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.new_node_covariate_int方法的4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: FixedMargins
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import new_node_covariate_int [as 别名]
if (i_1 + 1) % m == 0:
if (i_1 - i_2 + 1 - 2 * m) % N == 0:
return params['cov_mult'] * np.sqrt(3)
else:
if (i_1 - i_2 + 1 - m) % N == 0:
return params['cov_mult'] * np.sqrt(3)
return 0
else:
print 'Unrecognized covariate structure.'
import sys; sys.exit()
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# 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.adjacency_matrix(), coverage_inc)示例2: range
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import new_node_covariate_int [as 别名]
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
data_base_model.beta[name] = np.random.normal(0, params['beta_sd'])
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize data (block)model from base model
class_probs = np.random.dirichlet(np.repeat(params['class_conc'], params['K']))
z = np.where(np.random.multinomial(1, class_probs, params['N']) == 1)[1]
net.new_node_covariate_int('z_true')[:] = z
data_model = Blockmodel(data_base_model, params['K'], 'z_true')
Theta = np.random.normal(params['Theta_mean'], params['Theta_sd'],
(params['K'],params['K']))
Theta += params['Theta_diag'] * np.identity(params['K'])
Theta -= np.mean(Theta)
data_model.Theta = Theta
net.generate(data_model)
if params['plot_network']:
net.show_heatmap('z_true')
# Initialize fitting model
fit_base_model = StationaryLogistic()
for c in covariates:
fit_base_model.beta[c] = None示例3: Network
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import new_node_covariate_int [as 别名]
#!/usr/bin/env python
from Models import Stationary, Blockmodel, FixedMargins
from Network import Network
net = Network(100)
net.new_node_covariate_int('r')[:] = 20
net.new_node_covariate_int('c')[:] = 20
net.new_node_covariate_int('z')[:] = ([0] * 50) + ([1] * 50)
base_model = Blockmodel(Stationary(),2)
base_model.Theta[0,0] = 3.0
base_model.Theta[0,1] = -1.0
base_model.Theta[1,0] = -2.0
base_model.Theta[1,1] = 0.0
model = FixedMargins(base_model)
net.generate(base_model)
net.show_heatmap('z')
net.generate(model)
net.show_heatmap('z')
示例4: alpha_norm
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import new_node_covariate_int [as 别名]
('rr', 3.0),
('lr', -2.0)]:
data_model.beta[name] = block_theta
alpha_norm(net, alpha_sd)
data_model.match_kappa(net, ('row_sum', 2))
net.generate(data_model)
net.show_heatmap()
net.offset_extremes()
fit_base_model = NonstationaryLogistic()
fit_base_model.beta['x'] = None
fit_model = Blockmodel(fit_base_model, 2)
#fit_model.base_model.fit = fit_model.base_model.fit_conditional
# Initialize block assignments
net.new_node_covariate_int('z')
if from_truth:
net.node_covariates['z'][:] = net.node_covariates['value'][:]
else:
net.node_covariates['z'][:] = np.random.random(N) < 0.5
# Calculate NLL at initialized block assignments
fit_model.fit_sem(net, cycles = 1, sweeps = 0,
use_best = False, store_all = True)
baseline_nll = fit_model.sem_trace[0][0]
nll_trace = []
z_trace = np.empty((steps,N))
disagreement_trace = []
theta_trace = []