KONIECZNY_Szymon Part 2

numpy_questions.py

@@ -37,10 +37,13 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if type(X) is not np.ndarray:
+        raise ValueError("Input is not a numpy array")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError(f"Input is not 2D: {X.ndim}")
+
+    i, j = np.unravel_index(np.argmax(X), X.shape)
 
     return i, j
 
@@ -62,6 +65,6 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
-    # XXX : The n_terms is an int that corresponds to the number of
-    # terms in the product. For example 10000.
-    return 0.
+    x = np.arange(1, n_terms+1)
+
+    return 1 if n_terms == 0 else 2 * np.prod((4 * x**2) / (4 * x**2 - 1))

sklearn_questions.py

@@ -23,52 +23,71 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
+from sklearn.utils.validation import validate_data
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor model from the training dataset.
 
-        And describe parameters
+        Parameters:
+            X: array-like of shape (n_samples, n_features)
+            y: array-like of shape (n_samples,)
+
+        Returns
+            self: the fitted instance of OneNearestNeighbor
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_train_ = X
+        self.y_train_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class labels for the provided data.
+
+        Parameters:
+            X: array-like of shape (n_samples, n_features)
 
-        And describe parameters
+        Returns
+            y_pred: array-like of shape (n_samples,)
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X=X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_train_ - x, axis=1)
+            closest_idx = np.argmin(distances)
+            y_pred[i] = self.y_train_[closest_idx]
+
         return y_pred
 
-    def score(self, X, y):
-        """Write docstring.
+    def score(self, X, y, sample_weight=None):
+        """Return the mean accuracy on the given test data and labels.
+
+        Parameters:
+            X: array-like of shape (n_samples, n_features)
+            y: array-like of shape (n_samples,)
+            sample_weight: not used
 
-        And describe parameters
+        Returns
+            Mean accuracy of self.predit(X)
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)