def make(model_dir, orig_dmat, config):
"""
Make a new LuminosoModel in the (nonexistent) directory `dir`,
with initial half-association matrix `orig_dmat`. (A half-association
matrix is a matrix that gives an association matrix when it is
multiplied by its transpose.)
"""
# Adjust the size of the matrix to match the config, if necessary.
if os.access(model_dir, os.F_OK):
raise StudyExists("The model directory %r already exists." % model_dir)
rows = config['num_concepts']
cols = config['num_axes']
if orig_dmat.shape != (rows, cols):
dmat = divisi2.DenseMatrix((rows, cols))
rows_to_copy = orig_dmat.shape[0]
if rows < rows_to_copy:
raise ValueError("num_concepts is too small to fit the "
"existing concepts.")
cols_to_copy = min(cols, orig_dmat.shape[1])
dmat[:rows_to_copy, :cols_to_copy] = \
orig_dmat[:rows_to_copy, :cols_to_copy]
dmat.row_labels = orig_dmat.row_labels
else:
dmat = orig_dmat
# Make sure that the matrix has a PrioritySet for its row labels.
_prioritize_labels(dmat, rows)
rmat = divisi2.reconstruct_symmetric(dmat)
# Make the model directory and populate its initial files.
os.mkdir(model_dir)
rmat_file = model_dir + os.sep + LuminosoModel.ASSOC_FILENAME
config_file = model_dir + os.sep + LuminosoModel.CONFIG_FILENAME
save_pickle(rmat, rmat_file)
save_config_file(config, config_file)
# Now load the model from that directory and return it.
model = LuminosoModel(model_dir)
return model
def save_assoc(self):
"Save the association matrix to a file."
save_pickle(self.assoc,
self.filename_in_dir(LuminosoModel.ASSOC_FILENAME))
