def compErrors2(x, y, samples, ks, trueMI, p, z=None):
nsamples = len(samples)
nks = len(ks)
res1 = np.zeros((nks, nsamples))
ni = 0
for n in samples:
ki = 0
for k in ks:
aa = KnnEstimator(k=k, p=p)
if z is not None:
res1[ki, ni] = trueMI - aa._cmi1(x[:n, :], y[:n, :], z[:n, :])
else:
res1[ki, ni] = trueMI - aa._cmi1(x[:n, :], y[:n, :], z)
ki += +1
ni += 1
return res1
def testCMI2(seed, k=3, tests=10):
np.random.seed(seed)
samples = [100, 25000]
ic = np.array([[1.0, -0.2, 0], [-0.2, 1.0, 0.6], [0, 0.6, 1.0]])
c = np.linalg.inv(ic)
c[0, 0] = 10 * c[0, 0]
n = np.max(samples)
nsamples = len(samples)
res1 = np.zeros((2, nsamples))
res2 = np.zeros((2, nsamples))
res3 = np.zeros((2, nsamples))
knn = KnnEstimator(k=k)
for tt in range(0, tests):
data = np.random.multivariate_normal([0, 0, 0], c, n)
jj = 0
for ss in samples:
x, y, z = 0, 2, 1
cmixy_zT = mvnCMI(c, [x], [y], [z])
res1[0, jj] += (cmixy_zT - knn._cmi1(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
res1[1, jj] += (cmixy_zT - knn._cmi2(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
x, y, z = 1, 2, 0
cmixy_zT = mvnCMI(c, [x], [y], [z])
res2[0, jj] += (cmixy_zT - knn._cmi1(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
res2[1, jj] += (cmixy_zT - knn._cmi2(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
x, y, z = 0, 1, 2
cmixy_zT = mvnCMI(c, [x], [y], [z])
res3[0, jj] += (cmixy_zT - knn._cmi1(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
res3[1, jj] += (cmixy_zT - knn._cmi2(
data[:ss, [x]], data[:ss, [y]], data[:ss, [z]])) / tests
jj += 1
plt.figure()
plt.xlim(0.9, len(samples) + 0.1)
x = list(range(1, len(samples) + 1, 1))
for ii in range(0, 2):
lab = "MI_" + str(ii)
plt.plot(x, res1[ii, :], label=lab, marker='o')
plt.xticks(x, samples)
#plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.legend(loc='best')
plt.show()
plt.figure()
plt.xlim(0.9, len(samples) + 0.1)
x = list(range(1, len(samples) + 1, 1))
for ii in range(0, 2):
lab = "MI_" + str(ii)
plt.plot(x, res2[ii, :], label=lab, marker='o')
plt.xticks(x, samples)
#plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.legend(loc='best')
plt.show()
plt.figure()
plt.xlim(0.9, len(samples) + 0.1)
x = list(range(1, len(samples) + 1, 1))
for ii in range(0, 2):
lab = "MI_" + str(ii)
plt.plot(x, res3[ii, :], label=lab, marker='o')
plt.xticks(x, samples)
#plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.legend(loc='best')
plt.show()