From 296b0b3b609af1ba46fe38614f93fef4c4f8728a Mon Sep 17 00:00:00 2001
From: Ziqi Meng <140048727+TabErica@users.noreply.github.com>
Date: Thu, 13 Nov 2025 16:07:54 +0100
Subject: [PATCH 1/4] part 2

---
 numpy_questions.py   | 10 ++++++++--
 sklearn_questions.py |  9 ++++++++-
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..db815601 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,7 +41,8 @@ def max_index(X):
     j = 0
 
     # TODO
-
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
     return i, j
 
 
@@ -64,4 +65,9 @@ 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 2.0    
+    n = np.arange(1, n_terms + 1)
+    term = term = (4 * n ** 2) / (4 * n ** 2 - 1)
+    product = np.prod(term)    
+    return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..cec95690 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -45,6 +45,8 @@ def fit(self, X, y):
         self.n_features_in_ = X.shape[1]
 
         # XXX fix
+        self.X_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
@@ -60,6 +62,11 @@ def predict(self, X):
         )
 
         # XXX fix
+        for x_test in X:
+            distances = np.sum((self.X_ - x_test)**2, axis=1)
+            closest_idx = np.argmin(distances)
+            y_pred.append(self.y_[closest_idx])
+        
         return y_pred
 
     def score(self, X, y):
@@ -71,4 +78,4 @@ def score(self, X, y):
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)

From a290d76c65ad42a0dbae0b277e9053ad8dcc031c Mon Sep 17 00:00:00 2001
From: Ziqi Meng <140048727+TabErica@users.noreply.github.com>
Date: Thu, 13 Nov 2025 16:14:01 +0100
Subject: [PATCH 2/4] 1

---
 sklearn_questions.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sklearn_questions.py b/sklearn_questions.py
index cec95690..17438cea 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -66,7 +66,6 @@ def predict(self, X):
             distances = np.sum((self.X_ - x_test)**2, axis=1)
             closest_idx = np.argmin(distances)
             y_pred.append(self.y_[closest_idx])
-        
         return y_pred
 
     def score(self, X, y):
@@ -76,6 +75,5 @@ def score(self, X, y):
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
         # XXX fix
         return np.mean(y_pred == y)

From 59a78aa5c25f4dfc8dc624547f8c3d1590bd743f Mon Sep 17 00:00:00 2001
From: Ziqi Meng <140048727+TabErica@users.noreply.github.com>
Date: Thu, 13 Nov 2025 16:21:25 +0100
Subject: [PATCH 3/4] 2

---
 numpy_questions.py   | 5 +++--
 sklearn_questions.py | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index db815601..a0e76487 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -66,8 +66,9 @@ 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.
     if n_terms == 0:
-        return 2.0    
+        return 2.0
     n = np.arange(1, n_terms + 1)
     term = term = (4 * n ** 2) / (4 * n ** 2 - 1)
-    product = np.prod(term)    
+    product = np.prod(term)
+
     return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 17438cea..e6faf523 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass

From d1d1f32e05d039e68d4fcf055789968d669924af Mon Sep 17 00:00:00 2001
From: Ziqi Meng <140048727+TabErica@users.noreply.github.com>
Date: Thu, 13 Nov 2025 16:39:50 +0100
Subject: [PATCH 4/4] 3

---
 numpy_questions.py   | 7 ++++++-
 sklearn_questions.py | 4 ++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index a0e76487..58427bc2 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,6 +41,11 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Input must be 2D")
+
     flat_index = np.argmax(X)
     i, j = np.unravel_index(flat_index, X.shape)
     return i, j
@@ -66,7 +71,7 @@ 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.
     if n_terms == 0:
-        return 2.0
+        return 1.0
     n = np.arange(1, n_terms + 1)
     term = term = (4 * n ** 2) / (4 * n ** 2 - 1)
     product = np.prod(term)
diff --git a/sklearn_questions.py b/sklearn_questions.py
index e6faf523..86f3f5aa 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -62,10 +62,10 @@ def predict(self, X):
         )
 
         # XXX fix
-        for x_test in X:
+        for i, x_test in enumerate(X):
             distances = np.sum((self.X_ - x_test)**2, axis=1)
             closest_idx = np.argmin(distances)
-            y_pred.append(self.y_[closest_idx])
+            y_pred[i] = self.y_[closest_idx]
         return y_pred
 
     def score(self, X, y):