From 05afc43e5473588a2a1667026689f13ce67bdf7e Mon Sep 17 00:00:00 2001
From: quillotm <mathias.quillot@univ-avignon.fr>
Date: Tue, 24 Aug 2021 09:08:10 +0200
Subject: [PATCH] Adding new implementation of kmeans to test. Simple
 implementation. Need to add to take into account n_init parameter and to add
 random seed.

---
 volia/clustering_modules/kmeans_multidistance.py | 54 ++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 volia/clustering_modules/kmeans_multidistance.py

diff --git a/volia/clustering_modules/kmeans_multidistance.py b/volia/clustering_modules/kmeans_multidistance.py
new file mode 100644
index 0000000..f1c7944
--- /dev/null
+++ b/volia/clustering_modules/kmeans_multidistance.py
@@ -0,0 +1,54 @@
+
+import pickle
+from abstract_clustering import AbstractClustering
+from KMeans_Multidistance.KMeans_Class import KMeans
+
+class kmeansMultidistance():
+    def __init__(self, distance="cosine"):
+        self.kmeans_model = None
+        self.centroids = None
+        self.distance = distance
+
+    def predict(self, features):
+        """
+
+        @param features:
+        @return:
+        """
+        return self.kmeans_model.assign_clusters(data=features, centroids=self.centroids, distance=self.kmeans_model.distance)
+
+    def load(self, model_path: str):
+        """
+
+        @param model_path:
+        @return:
+        """
+        with open(model_path, "rb") as f:
+            data = pickle.load(f)
+            self.kmeans_model = data["kmeans_model"]
+            self.centroids = data["centroids"]
+            self.distance = self.kmeans_model.distance
+
+    def save(self, model_path: str):
+        """
+
+        @param model_path:
+        @return:
+        """
+        with open(model_path, "wb") as f:
+            pickle.dump({
+                "kmeans_model": self.kmeans_model,
+                "centroids": self.centroids
+            }, f)
+
+    def fit(self, features, k: int, tol: float, ninit: int, maxiter: int=300, debug: bool=False):
+        """
+
+        @param features:
+        @param k:
+        @return:
+        """
+        model = KMeans(k=5, maxiter=maxiter, distance=self.distance, record_heterogeneity=[], verbose=True, seed=123)
+        centroids, _ = model.fit(features)
+        self.centroids = centroids
+        self.kmeans_model = model
\ No newline at end of file
-- 
1.8.2.3