initial import

learn.py

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+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import SGDClassifier
+
+class Classifier(object):
+        def __init__(self, datagrabber):
+                self.grabber = datagrabber
+                self.reload()
+                
+        def reload(self):
+                Xs, Ys = self.grabber()
+
+                self.vect = CountVectorizer(analyzer='word',ngram_range=(1,3))
+                self.train_vec = self.vect.fit_transform(Xs)
+                
+                self.clf = SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, n_iter=500, random_state=42)
+                self.text_clf = self.clf.fit(self.train_vec, Ys)
+
+        def scan(self, name):
+                v = self.vect.transform([name])
+                return self.text_clf.decision_function(v)[0]
+
+        def add(self, name, state):
+                # implement add using partial_fit
+                # this would mean switching to hashing vectorizer, which means we can't reverse the model
+                # so for now we're just going to reload completely
+                self.reload()