def get_optimal_lag_exper(p_src_index, src_neighbor_indices,
normalized_cells_response_curve):
from statsmodels.tsa.api import VAR
#get the src neighbors
number_of_points = len(src_neighbor_indices)
optimal_lag_vector = dict()
for p_dst_index in src_neighbor_indices:
src_dst_data = None
try:
src_dst_data = normalized_cells_response_curve[
[p_src_index, p_dst_index], :]
src_dst_data = np.transpose(src_dst_data)
model = VAR(src_dst_data)
maxlags = None
lag_order_results = model.select_order(maxlags=maxlags)
lags = [
lag_order_results.aic, lag_order_results.bic,
lag_order_results.fpe, lag_order_results.hqic
]
min_i = np.argmin(lags)
model = model.fit(maxlags=lags[min_i], ic=None)
p_value_whiteness = model.test_whiteness(nlags=lags[min_i]).pvalue
if p_value_whiteness == float('nan') or p_value_whiteness < 0.05:
raise ValueError('found autocorrelation in residuals.')
#i = models[min_i].k_ar + 1
#while i < 12 * (models[min_i].nobs/100.)**(1./4):
# result_auto_co = model._estimate_var(i, trend='c')
# if result_auto_co.test_whiteness(nlags=i).pvalue > 0.05:
# break
# i += 1
# print 'error order:' + str(models[min_i].k_ar)
# print 'found correlation ' + str(i)
optimal_lag_vector[p_dst_index] = lags[min_i]
except:
print('src index: ' + str(p_src_index) + ' dst index: ' +
str(p_dst_index))
if src_dst_data is not None:
print(src_dst_data)
raise
return optimal_lag_vector
def get_optimal_lag(p_src_index, neighbor_indices,
normalized_cells_response_curve):
#get the src neighbors
number_of_points = len(neighbor_indices)
src_neighbor_indices = neighbor_indices[p_src_index]
optimal_lag_vector = np.zeros((number_of_points))
for p_dst_index in src_neighbor_indices:
#find the common neighbours
dst_neighbor_indices = neighbor_indices[p_dst_index]
disjoint_neighbours = get_disjoint_neighbours(p_src_index, p_dst_index,
neighbor_indices)
src_dst_data = normalized_cells_response_curve[
[p_src_index, p_dst_index], :]
src_dst_data = np.transpose(src_dst_data)
model = VAR(src_dst_data)
maxlags = None
lag_order_results = model.select_order(maxlags=maxlags)
lags = [
lag_order_results.aic, lag_order_results.bic,
lag_order_results.fpe, lag_order_results.hqic
]
min_i = np.argmin(lags)
model = model.fit(maxlags=lags[min_i], ic=None)
if model.test_whiteness(nlags=lags[min_i]).pvalue < 0.05:
raise ValueError('found autocorrelation in residuals.')
#i = models[min_i].k_ar + 1
#while i < 12 * (models[min_i].nobs/100.)**(1./4):
# result_auto_co = model._estimate_var(i, trend='c')
# if result_auto_co.test_whiteness(nlags=i).pvalue > 0.05:
# break
# i += 1
# print 'error order:' + str(models[min_i].k_ar)
# print 'found correlation ' + str(i)
optimal_lag_vector[p_dst_index] = lags[min_i]
break
return optimal_lag_vector
