Ninajankovic assignment

numpy_questions.py

@@ -37,12 +37,19 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if not isinstance(X, np.ndarray):
+        raise ValueError('The input is not an array.')
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError('The input is not 2D array')
+    overall_max = np.max(X)
 
-    return i, j
+    for i in range(np.shape(X)[0]):
+        for j in range(np.shape(X)[1]):
+            if X[i][j] == overall_max:
+                return i, j
+
+    return 0, 0
 
 
 def wallis_product(n_terms):
@@ -62,6 +69,11 @@ 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.
+    if n_terms == 0:
+        return 1
+    else:
+        prod = 1
+        for i in range(1, n_terms + 1):
+            prod *= (4 * i ** 2) / (4 * i**2 - 1)
+
+        return prod * 2

sklearn_questions.py

@@ -28,47 +28,96 @@
 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.
-
-        And describe parameters
+        """
+        Fit the classifier with training data.
+
+        Parameters:
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+        y : array-like 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
+        self.X_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """
+        Predict labels for new samples.
+
+        For each input point, finds the closest training sample (using
+        Euclidean distance) and returns its label.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels for each input sample.
         """
         check_is_fitted(self)
         X = check_array(X)
+
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"is expecting {self.n_features_in_} features as input."
+            )
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x_test in enumerate(X):
+            distances = np.sqrt(np.sum((self.X_train_ - x_test) ** 2, axis=1))
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """
+        Compute the accuracy of the classifier.
+
+        Accuracy is the fraction of correctly predicted samples
+        compared to the true labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+        y : array-like of shape (n_samples,)
+            True labels for the test data.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of predictions.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)