MLP

- added backprop
- fixed data for multiclass
- fixed confusion matrix

src/learning/ml.py

@@ -22,6 +22,9 @@ class MLAlgorithm(ABC):
         self._validset = valid
         self._testset = test
 
+    def with_bias(self, x:np.ndarray) -> np.ndarray:
+        return np.hstack([x, np.ones(shape=(x.shape[0], 1))])
+
     def learn(self, epochs:int, early_stop:float=0.0000001, max_patience:int=10, verbose:bool=False) -> tuple[int, list, list]:
         learn = []
         valid = []
@@ -89,8 +92,14 @@ class MLAlgorithm(ABC):
         and self._target_type != TargetType.MultiClassification:
             return None
 
-        h0 = np.where(self._h0(self._testset.x) > 0.5, 1, 0)
-        return ConfusionMatrix(self._testset.y, h0)
+        h0 = self._h0(self._testset.x)
+        y = self._testset.y
+        if h0.ndim == 1:
+            h0 = np.where(h0 > 0.5, 1, 0)
+        else:
+            h0 = np.argmax(h0, axis=1)
+            y = np.argmax(y, axis=1)
+        return ConfusionMatrix(y, h0)
 
     def test_r_squared(self) -> float:
         if self._target_type != TargetType.Regression: