kmeans.py
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from sklearn.cluster import KMeans
import pickle
from abstract_clustering import AbstractClustering
class kmeans():
def __init__(self):
self.kmeans_model = None
def predict(self, features):
"""
@param features:
@return:
"""
return self.kmeans_model.predict(features)
def load(self, model_path: str):
"""
@param model_path:
@return:
"""
with open(model_path, "rb") as f:
self.kmeans_model = pickle.load(f)
def save(self, model_path: str):
"""
@param model_path:
@return:
"""
with open(model_path, "wb") as f:
pickle.dump(self.kmeans_model, f)
def fit(self, features, k: int):
"""
@param features:
@param k:
@return:
"""
self.kmeans_model = KMeans(n_clusters=k, n_init=10, random_state=0).fit(features)