JULIEN KANAAN assignment

numpy_questions.py

@@ -37,12 +37,20 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    # Check if input is a numpy array
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array")
 
-    # TODO
+    # Check if array is 2D
+    if X.ndim != 2:
+        raise ValueError("Input must be a 2D array")
 
-    return i, j
+    # Find the index of the maximum value
+    # np.argmax returns the flattened index, so we use unravel_index
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
+
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -62,6 +70,15 @@ 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.
+    # Wallis product: pi/2 = (2/1 * 2/3) * (4/3 * 4/5) * (6/5 * 6/7) * ...
+    # Or: pi/2 = product of (4n^2) / (4n^2 - 1) for n from 1 to n_terms
+    if n_terms == 0:
+        return 1.0
+
+    product = 1.0
+
+    for n in range(1, n_terms + 1):
+        product *= (4 * n**2) / (4 * n**2 - 1)
+
+    # Multiply by 2 to get pi
+    return 2 * product

sklearn_questions.py

@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -44,7 +44,10 @@ def fit(self, X, y):
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        # Store training data
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
@@ -59,7 +62,15 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        # For each test sample, find the nearest training sample
+        for i, x in enumerate(X):
+            # Calculate Euclidean distances to all training samples
+            distances = np.sqrt(np.sum((self.X_ - x) ** 2, axis=1))
+            # Find index of nearest neighbor
+            nearest_idx = np.argmin(distances)
+            # Assign the label of the nearest neighbor
+            y_pred[i] = self.y_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
@@ -70,5 +81,7 @@ def score(self, X, y):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        # Calculate accuracy: proportion of correct predictions
+        accuracy = np.mean(y_pred == y)
+
+        return accuracy