def granger_partial_r(i):
'''
Depracated
'''
n = i.n_nodes
cc = granger_r(i)
ccc_mean = np.zeros((n, n))
ccc_max = np.zeros((n, n))
ccc_min = np.zeros((n, n))
ccc = np.zeros((n, n, n))
granger_a = load_r_file('gc1a.R', "granger_a")
for ii in range(n):
xii, _ = i.get(ii)
for jj in range(n):
xjj, _ = i.get(jj)
for kk in range(n):
xkk, _ = i.get(kk)
d = np.vstack((xii.T, xjj.T, xkk.T)).T
temp = granger_a.granger_part(d, 1)[0]
if math.isnan(temp) or math.isinf(temp):
temp = 0.0
ccc[ii][jj][kk] = temp
ccc_mean[ii][jj] = np.mean(ccc[ii][jj][:])
ccc_max[ii][jj] = np.max(ccc[ii][jj][:])
ccc_min[ii][jj] = np.min(ccc[ii][jj][:])
return ccc_max
def granger_r(x1, x2):
d = np.vstack((np.matrix(x1), np.matrix(x2))).T
granger_a = load_r_file('gc1a.R', "granger_a")
return granger_a.granger(d, 1)[0]
