From caff88196d7b46b062b8230c7f301a7e71df16a6 Mon Sep 17 00:00:00 2001 From: Ben Henri Bazon Poppe Date: Tue, 10 Nov 2026 15:33:53 +0100 Subject: [PATCH 1/2] Implement max_index and wallis_product --- numpy_questions.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..743bf9bf 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,12 +37,14 @@ 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("X must be a numpy array.") + if X.ndim != 2: + raise ValueError("X must be a 2D array.") - # TODO + i, j = np.unravel_index(np.argmax(X), X.shape) - return i, j + return int(i), int(j) def wallis_product(n_terms): @@ -62,6 +64,11 @@ 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. + + n = np.arange(1, n_terms + 1) + four_n_sq = 4 * n ** 2 + pi_approx = 2 * np.prod(four_n_sq / (four_n_sq - 1)) + + return float(pi_approx) From 45c5d14ffa0cf5e64e3172b5936fc0c48bff7996 Mon Sep 17 00:00:00 2001 From: Ben Henri Bazon Poppe Date: Tue, 10 Nov 2026 16:11:32 +0100 Subject: [PATCH 2/2] Implement OneNearestNeighbor --- sklearn_questions.py | 70 ++++++++++++++++++++++++++++++++++---------- 1 file changed, 55 insertions(+), 15 deletions(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..f08b91aa 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -28,47 +28,87 @@ from sklearn.utils.multiclass import check_classification_targets -class OneNearestNeighbor(BaseEstimator, ClassifierMixin): - "OneNearestNeighbor classifier." +class OneNearestNeighbor(ClassifierMixin, BaseEstimator): + """OneNearestNeighbor classifier. + + Predict the label of each sample as the label of its closest + training sample, measured with the Euclidean distance. + """ def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Write docstring. - - And describe parameters + """Fit the one nearest neighbor classifier. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The training samples. + y : ndarray of shape (n_samples,) + The labels of the training samples. + + Returns + ------- + self : OneNearestNeighbor + The 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 + self.X_train_ = X + self.y_train_ = y return self def predict(self, X): - """Write docstring. - - And describe parameters + """Predict the labels of the samples in X. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The samples to classify. + + Returns + ------- + y_pred : ndarray of shape (n_samples,) + The predicted label of each sample, i.e. the label of its + nearest training 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 in enumerate(X): + distances = np.sqrt(np.sum((self.X_train_ - x) ** 2, axis=1)) + y_pred[i] = self.y_train_[np.argmin(distances)] return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Return the accuracy of the classifier on X and y. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The test samples. + y : ndarray of shape (n_samples,) + The true labels of the test samples. + + Returns + ------- + score : float + The 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)