def fit_from_centroids():
data_test = [178.75, 5.97390157]
data_height = [170., 186., 182., 177.]
data_size = [40, 42, 45, 47, 50]
data_injuries = [3, 4, 1, 0]
centers = [[175., 4.08], [44.8, 3.54], [2., 1.58]]
fcm = FCM(n_clusters=3, max_iter=1)
fcm.cluster_centers_ = np.array(centers)
fcm.fit(fcm.cluster_centers_, [0, 1, 2])
membership_height = fcm.predict(np.array([data_test]))
membership_height = fcm.predict(
np.array([[np.average(data_height),
np.std(data_height)]]))
membership_size = fcm.predict(
np.array([[np.average(data_size),
np.std(data_size)]]))
membership_injuries = fcm.predict(
np.array([[np.average(data_injuries),
np.std(data_injuries)]]))
def load_mlmodel_into_fcm(model):
"""
:param model: MLModel
:param data: matrix input as np array
:return: FCM model
"""
num_of_cols = len(model.cluster_set.all())
centers = []
for clus_model in model.cluster_set.all():
logger.debug("load_mlmodel_into_fcm> getting clus %s" %
clus_model.name)
p = clus_model.center.split(',')
centers.append(p)
centers_np = np.array(centers, dtype='f')
# logger.debug("Centers: ")
# logger.debug(centers_np)
fcm = FCM(n_clusters=num_of_cols, max_iter=1, logger=logger)
fcm.cluster_centers_ = centers_np
data = centers_np
logger.debug("load_mlmodel_into_fcm> will fit the data with %d clusters" %
num_of_cols)
fcm.fit(data, range(num_of_cols))
logger.info("generated the FCM model from the MLModel")
return fcm