本文整理汇总了Python中Network.Network.new_node_covariate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方法的具体用法？Python Network.new_node_covariate怎么用？Python Network.new_node_covariate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 new_node_covariate [as 别名]
(0, 1, 'left_to_right')]:
covariates.append(name)
def f_x(i_1, i_2):
return ((net.node_covariates['value'][i_1] == v_1) and
(net.node_covariates['value'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Degree heterogeneity covariates
if params['degree_covs']:
r = np.array(net.network.asfptype().sum(1),dtype=np.int).flatten()
c = np.array(net.network.asfptype().sum(0),dtype=np.int).flatten()
degree = r + c
med_degree = np.median(degree)
net.new_node_covariate('low_degree').from_pairs(net.names,
degree < med_degree)
for v_1, v_2, name in [(0, 0, 'high_to_high'),
(1, 1, 'low_to_low'),
(0, 1, 'high_to_low')]:
covariates.append(name)
def f_x(i_1, i_2):
return ((net.node_covariates['low_degree'][i_1] == v_1) and
(net.node_covariates['low_degree'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize fitting model
fit_model = StationaryLogistic()
n_fit_model = NonstationaryLogistic()
for c in covariates:示例2: Network
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import new_node_covariate [as 别名]
from Models import alpha_zero, alpha_norm
from Experiment import minimum_disagreement
# Parameters
N = 20
theta = 3.0
alpha_sd = 2.0
from_truth = True
steps = 100
# Set random seed for reproducible outputs
np.random.seed(137)
net = Network(N)
net.new_node_covariate('value').from_pairs(net.names, [0]*(N/2) + [1]*(N/2))
for v_1, v_2, name in [(0, 0, 'll'),
(1, 1, 'rr'),
(0, 1, 'lr')]:
def f_x(i_1, i_2):
return ((net.node_covariates['value'][i_1] == v_1) and
(net.node_covariates['value'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate('x').from_binary_function_ind(f_x)
data_model = NonstationaryLogistic()
data_model.beta['x'] = theta