def mutual_information(state, col_1, col_2, N=1000):
view_1 = state.Zv[col_1]
view_2 = state.Zv[col_2]
if view_1 != view_2:
print("mutual_information: not in same view: MI = 0.0")
return 0.0
log_crp = su.get_cluster_crps(state, view_1)
K = len(log_crp)
clusters_col_1 = su.create_cluster_set(state, col_1)
clusters_col_2 = su.create_cluster_set(state, col_2)
MI = 0
Px = numpy.zeros(K)
Py = numpy.zeros(K)
Pxy = numpy.zeros(K)
for i in range(N):
c = utils.log_pflip(log_crp)
x = clusters_col_1[c].predictive_draw()
y = clusters_col_2[c].predictive_draw()
for k in range(K):
Px[k] = clusters_col_1[k].predictive_logp(x)
Py[k] = clusters_col_2[k].predictive_logp(y)
Pxy[k] = Px[k] + Py[k] + log_crp[k]
Px[k] += log_crp[k]
Py[k] += log_crp[k]
PX = logsumexp(Px)
PY = logsumexp(Py)
PXY = logsumexp(Pxy)
MI += (PXY - PX - PY)
MI /= float(N)
if MI < 0.0:
print("mutual_information: MI < 0 (%f)" % MI)
MI = 0.0
return MI
def mutual_information(state, col_1, col_2, N=1000):
view_1 = state.Zv[col_1]
view_2 = state.Zv[col_2]
if view_1 != view_2:
print("mutual_information: not in same view: MI = 0.0")
return 0.0
log_crp = su.get_cluster_crps(state, view_1)
K = len(log_crp)
clusters_col_1 = su.create_cluster_set(state, col_1)
clusters_col_2 = su.create_cluster_set(state, col_2)
MI = 0
Px = numpy.zeros(K)
Py = numpy.zeros(K)
Pxy = numpy.zeros(K)
for i in range(N):
c = utils.log_pflip(log_crp)
x = clusters_col_1[c].predictive_draw()
y = clusters_col_2[c].predictive_draw()
for k in range(K):
Px[k] = clusters_col_1[k].predictive_logp(x)
Py[k] = clusters_col_2[k].predictive_logp(y)
Pxy[k] = Px[k]+Py[k]+log_crp[k]
Px[k] += log_crp[k]
Py[k] += log_crp[k]
PX = logsumexp(Px)
PY = logsumexp(Py)
PXY = logsumexp(Pxy)
MI += (PXY-PX-PY)
MI /= float(N)
if MI < 0.0:
print("mutual_information: MI < 0 (%f)" % MI)
MI = 0.0
return MI
def test_predictive_draw(state, N=None):
import pylab
if state.n_cols != 2:
print("state must have exactly 2 columns")
return
if N is None:
N = state.n_rows
view_1 = state.Zv[0]
view_2 = state.Zv[1]
if view_1 != view_2:
print("Columns not in same view")
return
log_crp = su.get_cluster_crps(state, 0)
K = len(log_crp)
X = numpy.zeros(N)
Y = numpy.zeros(N)
clusters_col_1 = su.create_cluster_set(state, 0)
clusters_col_2 = su.create_cluster_set(state, 1)
for i in range(N):
c = utils.log_pflip(log_crp)
x = clusters_col_1[c].predictive_draw()
y = clusters_col_2[c].predictive_draw()
X[i] = x
Y[i] = y
pylab.scatter(X, Y, color='red', label='inferred')
pylab.scatter(state.dims[0].X,
state.dims[1].X,
color='blue',
label='actual')
pylab.show()
def test_predictive_draw(state, N=None):
import pylab
if state.n_cols != 2:
print("state must have exactly 2 columns")
return
if N is None:
N = state.n_rows
view_1 = state.Zv[0]
view_2 = state.Zv[1]
if view_1 != view_2:
print("Columns not in same view")
return
log_crp = su.get_cluster_crps(state, 0)
K = len(log_crp)
X = numpy.zeros(N)
Y = numpy.zeros(N)
clusters_col_1 = su.create_cluster_set(state, 0)
clusters_col_2 = su.create_cluster_set(state, 1)
for i in range(N):
c = utils.log_pflip(log_crp)
x = clusters_col_1[c].predictive_draw()
y = clusters_col_2[c].predictive_draw()
X[i] = x
Y[i] = y
pylab.scatter(X,Y, color='red', label='inferred')
pylab.scatter(state.dims[0].X, state.dims[1].X, color='blue', label='actual')
pylab.show()