def _cluster_data_object(self, x, y=None):
"""
Sends a data objected encoded as a numpy array or a list and cluster it
"""
self._send_array(x, y)
cluster, distance = SubCMediansWrapper_c.clusterize_SubCMedianspoint_with_model(
self._p_subcmedians_c, self._data_object)
return [int(y), cluster], distance
def _transform_array(self, x):
"""
Apply the transform function to objects x in order to compute the distance to each candidate center in the model.
"""
self._send_array(x)
cluster, distance = SubCMediansWrapper_c.clusterize_SubCMedianspoint_with_model(
self._p_subcmedians_c, self._data_object)
SubCMediansWrapper_c.get_distances_to_core_point(
cluster, self._p_subcmedians_c, self._data_object,
self._distances_to_cluster_getter)
return array(self._distances_to_cluster_getter)
def score(self, X):
"""
Compute the mean intra-cluster distance
"""
X_ = self._check_X_matrix_validity(X)
scores = []
for i, x in enumerate(X_):
self._send_array(x)
cluster, distance = SubCMediansWrapper_c.clusterize_SubCMedianspoint_with_model(
self._p_subcmedians_c, self._data_object)
scores.append(distance)
return np.asarray(scores).mean()
def predict(self, X):
"""
sklearn-like predict function, receives a dataset and compute the cluster membership of its data objects
"""
X_ = self._check_X_matrix_validity(X)
Y_ = array([])
for i, x in enumerate(X_):
self._send_array(x)
cluster, distance = SubCMediansWrapper_c.clusterize_SubCMedianspoint_with_model(
self._p_subcmedians_c, self._data_object)
Y_ = append(Y_, cluster)
return Y_