Korouhanba Khuman Laikhuram Part B

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 not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Input array is not 2D")
 
-    # TODO
+    flat_idx = np.argmax(X)
+    i, j = np.unravel_index(flat_idx, X.shape)
 
     return i, j
 
@@ -62,6 +65,15 @@ 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
+    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)
+
+    return 2 * product
+
+    # The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+    # return 0.

sklearn_questions.py

@@ -28,29 +28,50 @@
 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 OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training input samples.
+        y : ndarray of shape (n_samples,)
+            Target labels associated with each training sample.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
         """
         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_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for given samples.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Samples for which to predict labels.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class label for each sample.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +81,37 @@ def predict(self, X):
         )
 
         # XXX fix
+        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"
+            )
+
+        diff = X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]
+        distances = np.sum(diff ** 2, axis=2)
+
+        nearest_idx = np.argmin(distances, axis=1)
+        y_pred[:] = self.y_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute accuracy of the classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+        y : ndarray of shape (n_samples,)
+            True target labels.
+
+        Returns
+        -------
+        score : float
+            Accuracy of predictions: fraction of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)