def __init__(self, matrix, dimensionality):
concept_axes, axis_weights, feature_axes = matrix.svd(k=dimensionality)
self.predict = divisi2.reconstruct(concept_axes,
axis_weights,
feature_axes)
self.concept_sim = divisi2.reconstruct_activation(concept_axes,
axis_weights,
post_normalize=True)
self.feature_sim = divisi2.reconstruct_activation(feature_axes,
axis_weights,
post_normalize=True)
def __init__(self, matrix_path=data_path+'feature_matrix_zh.smat'): # AnalogySpace A = divisi2.load(matrix_path) self.A = A.normalize_all() self.concept_axes, axis_weights, self.feature_axes = self.A.svd(k=100) self.sim = divisi2.reconstruct_similarity(\ self.concept_axes, axis_weights, post_normalize=False) self.predict = divisi2.reconstruct(\ self.concept_axes, axis_weights, self.feature_axes) # Fast spreading activation assoc = divisi2.load(data_path+'assoc_matrix_zh.smat') self.assoc = assoc.normalize_all() U, S, _ = self.assoc.svd(k=100) self.spread = divisi2.reconstruct_activation(U, S)
def make_common_sense(model_dir, lang='en', config=None):
"""
Make a LuminosoModel whose initial matrix contains common sense
for some language.
"""
if config is None:
config = _default_config()
config['reader'] = 'simplenlp.'+lang
if os.access(model_dir, os.F_OK):
raise StudyExists("The model directory %r already exists." % model_dir)
LOG.info("Making common sense matrix")
assoc = divisi2.network.conceptnet_assoc(lang)
(mat_U, diag_S, _) = assoc.normalize_all().svd(k=100)
rmat = divisi2.reconstruct_activation(
mat_U, diag_S, post_normalize=True
)
model = LuminosoModel.make(model_dir, rmat.left, config)
model.config['iteration'] = 1000
return model
