import baxcat.cc_state
import numpy
from baxcat.utils import cc_legacy_utils as lu
n_rows = 100
view_weights = numpy.ones(2)/2
cluster_weights = [numpy.ones(3)/3.0, numpy.ones(2)/2.0]
cctypes = ['normal']*5
distargs = [None]*5
separation = [.7, .9]
T, Zv, Zc, dims = tu.gen_data_table(
n_rows,
view_weights,
cluster_weights,
cctypes,
distargs,
separation,
return_dims=True)
state = cc_state.cc_state(T, cctypes, distargs)
state.transition(N=10)
M_c, X_L, X_D = lu.get_legacy_metadata(state)
Tcc = T[0]
for i in range(1,len(T)):
Tcc = numpy.vstack( (Tcc, T[i]) )
Tcc = numpy.transpose(Tcc)
n_transitions = 300
n_data_sets = 10 # the number of samples (chains)
n_kernels = 2
total_itr = n_kernels*n_data_sets
itr = 0
cctypes = ['normal']*n_cols
distargs = [None]*n_cols
Ts, Zv, Zc = tu.gen_data_table(n_rows,
numpy.array([.5,.5]),
[numpy.array([1./2]*2),
numpy.array([1./5]*5)],
cctypes,
distargs,
[1.0]*n_cols)
for kernel in range(n_kernels):
# for a set number of chains
ARI_view = numpy.zeros((n_data_sets, n_transitions))
ARI_cols = numpy.zeros((n_data_sets, n_transitions))
for r in range(n_data_sets):
S = cc_state.cc_state(Ts, cctypes, ct_kernel=kernel, distargs=distargs)
for c in range(n_transitions):
S.transition(N=1)
