implement max_index and Wallis product

numpy_questions.py

@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+
 import numpy as np
 
 
@@ -37,10 +38,21 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
+    if not isinstance(X, np.ndarray):
+        raise ValueError("X must be a 2D array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array")
+
+    n_rows, n_cols = X.shape
     i = 0
     j = 0
-
-    # TODO
+    max_val = X[0, 0]
+    for row in range(n_rows):
+        for col in range(n_cols):
+            if X[row, col] > max_val:
+                max_val = X[row, col]
+                i = row
+                j = col
 
     return i, j
 
@@ -62,6 +74,20 @@ 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 not isinstance(n_terms, (int, np.integer)):
+        raise ValueError("n_terms must be an integer")
+    if n_terms < 0:
+        raise ValueError("n_terms must be non-negative")
+    if n_terms == 0:
+        return 1.0
+
+    product = 1.0
+
+    # Wallis product
+
+    for n in range(1, n_terms + 1):
+        numerator = 4.0 * n * n
+        denominator = numerator - 1
+        product *= numerator / denominator  # this approximates pi/2
+
+    return 2.0 * product

sklearn_questions.py

@@ -19,6 +19,7 @@
 for the methods you code and for the class. The docstring will be checked using
 `pydocstyle` that you can also call at the root of the repo.
 """
+
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
@@ -29,46 +30,80 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the 1-NN classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        Training data
+        y: array-like of shape (n_samples,)
+        Target labels.
+
+        Returns
+        ----------
+        self : OneNearestNeighbor
+            Fitted classifier.
         """
         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
+        # Store training data
+        self.X_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for samples in X.
+
+        Parameters
+        ----------
+        X : array-like 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
+            shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        # compute distances to all training samples
+        diff = X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]
+        distances = np.linalg.norm(diff, axis=2)
+        nearest_idx = np.argmin(distances, axis=1)
+
+        # fill y_pred with nearest-neighbor labels
+        y_pred[:] = self.y_[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute mean accuracy of the classifier.
+
+        Parameters
+        ----------
+        X: array-like of shape (n_samples, n_features)
+            Test samples.
+        y : array-like of shape (n_samples, )
+            True labels for X
+
+        Returns
+        ----------
+        score : float
+            Mean accuracy of predictions on X compared to true labels y
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)