Completed assignment: NumPy + OneNearestNeighbor implementation with one issue on the sklearn

numpy_questions.py

@@ -40,7 +40,14 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if isinstance(X, np.ndarray) and X.ndim == 2:
+        index = np.argmax(X)
+        n_rows, n_cols = X.shape
+
+        i = index // n_cols
+        j = index % n_cols
+    else:
+        raise ValueError("X must be a 2D numpy array")
 
     return i, j
 
@@ -64,4 +71,12 @@ def wallis_product(n_terms):
     """
     # XXX : The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+
+    if n_terms == 0:
+        return 1.0
+
+    result = 1.0
+    for i in range(1, n_terms + 1):
+        result = result* (4 * i * i) / (4 * i * i - 1)
+
+    return 2 * result

sklearn_questions.py

@@ -35,40 +35,78 @@ def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor classifier.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
-        return self
+        self.X_ = X
+        self.y_ = y
 
+        return self
+
     def predict(self, X):
-        """Write docstring.
+        """Classifies the samples in X.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
+
+
+        for i, x in enumerate(X):
+            neighbour = np.argmin(np.sqrt(((self.X_ - x) ** 2).sum(axis=1)))
+            y_pred[i] = self.y_[neighbour]
 
-        # XXX fix
         return y_pred
 
+
+
     def score(self, X, y):
-        """Write docstring.
+        """Computes the accuracy of the classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
 
-        And describe parameters
+        y : ndarray of shape (n_samples,)
+            True labels.
+
+        Returns
+        -------
+        score : float
+            The accuracy of predictions w.r.t. the true labels.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.sum(y_pred == y)/len(y)
+