def betas_fused_corr(net_s, net_a, orth):
(ns, gs, ts) = load_network(net_s)
(na, ga, ta) = load_network(net_a)
#we want to enumerate the constraints, fused_L2 can do that
constraints = fl.orth_to_constraints(['B_subtilis','B_anthracis'], [gs, ga], [ts, ta], orth, 0)
coeffs_s = []
coeffs_a = []
for con in constraints:
if con.c1.sub == 1:
continue
beta_s = ns[con.c1.r, con.c1.c]
beta_a = na[con.c2.r, con.c2.c]
coeffs_s.append(beta_s)
coeffs_a.append(beta_a)
return np.corrcoef(coeffs_s, coeffs_a)[0,1]
def betas_fused_visualize(net_s, net_a, orth):
from matplotlib import pyplot as plt
(ns, gs, ts) = load_network(net_s)
(na, ga, ta) = load_network(net_a)
#we want to enumerate the constraints, fused_L2 can do that
constraints = fl.orth_to_constraints(['B_subtilis','B_anthracis'], [gs, ga], [ts, ta], orth, 0)
coeffs_s = []
coeffs_a = []
for con in constraints:
if con.c1.sub == 1:
continue
beta_s = ns[con.c1.r, con.c1.c]
beta_a = na[con.c2.r, con.c2.c]
coeffs_s.append(beta_s)
coeffs_a.append(beta_a)
print np.corrcoef(coeffs_s, coeffs_a)
plt.scatter(coeffs_s, coeffs_a)
plt.xlabel('B subtilis')
plt.ylabel('B anthracis')
plt.show()
cs = np.array(coeffs_s)
ca = np.array(coeffs_a)