Normalize weights not to overflow (#63)

scripts/train.py

@@ -166,15 +166,14 @@ def fit(X_train: npt.NDArray[np.bool_],
   for t in range(iters):
     print('=== %s ===' % (t))
     if chunk_size is None:
-      res: npt.NDArray[np.float64] = w.dot(Y_train[:, None] ^ X_train) / w.sum()
+      res: npt.NDArray[np.float64] = w.dot(Y_train[:, None] ^ X_train)
     else:
       res = np.zeros(M_train)
       for i in range(0, N_train, chunk_size):
         Y_train_chunk = Y_train[i:i + chunk_size]
         X_train_chunk = X_train[i:i + chunk_size]
         w_chunk = w[i:i + chunk_size]
         res += w_chunk.dot(Y_train_chunk[:, None] ^ X_train_chunk)
-      res = res / w.sum()
     err = 0.5 - np.abs(res - 0.5)
     m_best = int(err.argmin())
     pol_best = res[m_best] < 0.5
@@ -188,6 +187,7 @@ def fit(X_train: npt.NDArray[np.bool_],
     if not pol_best:
       miss = ~(miss)
     w = w * np.exp(alpha * miss)
+    w = w / w.sum()
     with open(weights_filename, 'a') as f:
       feature = features[m_best] if m_best < len(features) else 'BIAS'
       f.write('%s\t%.3f\n' % (feature, alpha if pol_best else -alpha))