X-Git-Url: https://git.yukkurigames.com/?p=mlpccg-meta.git;a=blobdiff_plain;f=mlpccg%2FClustering.py;h=a3abadea9b4244513056312b1c83dc754a5366c7;hp=e1910fdb5a75bd71a22e9e2a0982423bb74c898a;hb=858c55746fe4f039a9908646e75cc7df07298c3e;hpb=0f08a0d1ebcd8d825a7df448221a7eb23600bba5

diff --git a/mlpccg/Clustering.py b/mlpccg/Clustering.py
index e1910fd..a3abade 100644
--- a/mlpccg/Clustering.py
+++ b/mlpccg/Clustering.py
@@ -8,25 +8,21 @@ from mlpccg.DeckList import DeckList
 
 class Clustering:
     def __init__(self, records):
-        self.records = records
         self.decks = []
         self.features = []
-        self.features_alt = []
         self.placements = {}
         self.clusters = defaultdict(list)
         self.all_ids = CARDDB._by_id.keys()
+        self.labels = []
+        self.records = records
+        self.vectorizer = DictVectorizer()
 
-        for record in self.records.all():
-            if record['decklist_url']:
-                decklist = DeckList(name=record['decklist_name'], url=record['decklist_url'])
-                self.features += [self.extract_features(decklist)]
-                self.features_alt += [self.extract_features_alt(decklist)]
-                self.decks += [decklist]
-                self.placements[decklist] = int(record['placement'])
+        for record in self.records:
+            self.features += [self.extract_features_alt(record['decklist'])]
+            self.decks += [record['decklist']]
+            self.placements[record['decklist']] = int(record['placement'])
 
-        self.vectorizer = DictVectorizer()
         X = self.vectorizer.fit_transform(self.features).toarray()
-
         self.af = AffinityPropagation().fit(X)
         self.labels = self.af.labels_
 
@@ -43,8 +39,6 @@ class Clustering:
         return features
 
     def extract_features(self, deck):
-        return self.extract_features_alt(deck)
-
         features = {
             'Blue': 0, 'Yellow': 0, 'Purple': 0, 'White': 0, 'Orange': 0, 'Pink': 0,
             'Friend': 0, 'Event': 0, 'Resource': 0, 'Troublemaker': 0, 'Mane': 0, 'Problem': 0,
@@ -67,7 +61,7 @@ class Clustering:
         return features
 
     def predict(self, decklist):
-        X = self.vectorizer.fit_transform([self.extract_features(decklist)]).toarray()
+        X = self.vectorizer.fit_transform([self.extract_features_alt(decklist)]).toarray()
         return self.af.predict(X)[0]
 
     def ranking(self):