def append_dim(self, X_f, cctype, distargs=None, ct_kernel=0, m=1):
"""
Add a new data column to X.
Inputs:
-- X_f: a numpy array of data
-- cctype: type of the data
Keyword args:
-- distargs: for multinomial data
-- ct_kernel: must be 0 or 2. MH kernel cannot be used to append
-- m: for ct_kernel=2. Number of auxiliary parameters
"""
col = self.n_cols
n_grid = self.n_grid
if _is_uncollapsed[cctype]:
dim = cc_dim_uc(X_f, _cctype_class[cctype], col, n_grid=n_grid, distargs=distargs)
else:
dim = cc_dim(X_f, _cctype_class[cctype], col, n_grid=n_grid, distargs=distargs)
self.n_cols += 1
self.dims.append(dim)
self.Zv = numpy.append(self.Zv, -1)
if _is_uncollapsed[cctype]:
column_transition_kernel_collapsed(m=m, append=True)
else:
column_transition_kernel_uncollapsed(m=m, append=True)
self.__check_partitions()
def gen_dims_from_structure(T, Zv, Zc, cc_types, distargs):
n_cols = len(Zv)
dims = []
for c in range(n_cols):
v = Zv[c]
cc_type = cc_types[c]
cc_type_class = _cctype_class[cc_type]
if _is_uncollapsed[cc_type]:
dim_c = cc_dim_uc.cc_dim_uc(T[c], cc_type_class, c, Z=Zc[v], distargs=distargs[c])
else:
dim_c = cc_dim.cc_dim(T[c], cc_type_class, c, Z=Zc[v], distargs=distargs[c])
dims.append(dim_c)
return dims
def __init__(self, X, cctypes, distargs, n_grid=30, Zv=None, Zrcv=None, hypers=None, seed=None):
"""
cc_state constructor
input arguments:
-- X: a list of numpy data columns.
-- cctypes: a list of strings where each entry is the data type for
each column.
-- distargs: a list of distargs appropriate for each type in cctype.
For details on distrags see the documentation for each data type.
optional arguments:
-- n_grid: number of bins for hyperparameter grids. Default = 30.
-- Zv: The assignment of columns to views. If not specified, a
partition is generated randomly
-- Zrcv: The assignment of rows to clusters for each view
-- ct_kernel: which column transition kenerl to use. Default = 0 (Gibbs)
-- seed: seed the random number generator. Default = system time.
example:
>>> import numpy
>>> n_rows = 100
>>> X = [numpy.random.normal(n_rows), numpy.random.normal(n_rows)]
>>> State = cc_state(X, ['normal', 'normal'], [None, None])
"""
if seed is not None:
random.seed(seed)
numpy.random.seed(seed)
self.n_rows = len(X[0])
self.n_cols = len(X)
self.n_grid = n_grid
# construct the dims
self.dims = []
for col in range(self.n_cols):
Y = X[col]
cctype = cctypes[col]
if _is_uncollapsed[cctype]:
dim = cc_dim_uc(Y, _cctype_class[cctype], col, n_grid=n_grid, distargs=distargs[col])
else:
dim = cc_dim(Y, _cctype_class[cctype], col, n_grid=n_grid, distargs=distargs[col])
self.dims.append(dim)
# set the hyperparameters in the dims
if hypers is not None:
for d in range(self.n_cols):
self.dims[d].set_hypers(hypers[d])
# initialize CRP alpha
self.alpha_grid = utils.log_linspace(1.0/self.n_cols, self.n_cols, self.n_grid)
self.alpha = random.choice(self.alpha_grid)
assert len(self.dims) == self.n_cols
if Zrcv is not None:
assert Zv is not None
assert len(Zv) == self.n_cols
assert len(Zrcv) == max(Zv)+1
assert len(Zrcv[0]) == self.n_rows
# construct the view partition
if Zv is None:
Zv, Nv, V = utils.crp_gen(self.n_cols, self.alpha)
else:
Nv = utils.bincount(Zv)
V = len(Nv)
# construct views
self.views = []
for view in range(V):
indices = [i for i in range(self.n_cols) if Zv[i] == view]
dims_view = []
for index in indices:
dims_view.append(self.dims[index])
if Zrcv is None:
self.views.append(cc_view(dims_view, n_grid=n_grid))
else:
self.views.append(cc_view(dims_view, Z=numpy.array(Zrcv[view]), n_grid=n_grid))
self.Zv = numpy.array(Zv)
self.Nv = Nv
self.V = V
