From 2eec7b2fb9b88e41c32c945694b2679034a2dd1a Mon Sep 17 00:00:00 2001 From: Francesco Azzarello Date: Tue, 10 Nov 2026 14:50:36 +0100 Subject: [PATCH 1/4] Implement max_index and wallis_product --- numpy_questions.py | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..7c0b8888 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,10 +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("The input is not a numpy array") - # TODO + if X.ndim != 2: + raise ValueError("The shape is not 2D") + + flat_index = np.argmax(X) + i, j = np.unravel_index(flat_index, X.shape) return i, j @@ -64,4 +68,15 @@ 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 1. + + k_array = np.arange(1, n_terms+1) + fractions_array = (4*(k_array**2))/(4*(k_array**2)-1) + pi = 2 * np.prod(fractions_array) + return pi + + + + From effe91d722c097a6eeefa36e98ba33c60acc642f Mon Sep 17 00:00:00 2001 From: Francesco Azzarello Date: Tue, 10 Nov 2026 14:58:07 +0100 Subject: [PATCH 2/4] Fix flake8 style issues --- numpy_questions.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 7c0b8888..844d61b6 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -71,12 +71,7 @@ def wallis_product(n_terms): if n_terms == 0: return 1. - k_array = np.arange(1, n_terms+1) fractions_array = (4*(k_array**2))/(4*(k_array**2)-1) pi = 2 * np.prod(fractions_array) return pi - - - - From 98bdf5a459a12b492a198a1c56181f88ea6216b7 Mon Sep 17 00:00:00 2001 From: Francesco Azzarello Date: Tue, 10 Nov 2026 15:29:43 +0100 Subject: [PATCH 3/4] Implement OneNearestNeighbor classifier --- sklearn_questions.py | 70 ++++++++++++++++++++++++++++++++------------ 1 file changed, 52 insertions(+), 18 deletions(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..26cfd382 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -23,52 +23,86 @@ from sklearn.base import BaseEstimator from sklearn.base import ClassifierMixin from sklearn.utils.validation import check_X_y -from sklearn.utils.validation import check_array from sklearn.utils.validation import check_is_fitted from sklearn.utils.multiclass import check_classification_targets +from sklearn.utils.validation import validate_data -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 one nearest neighbor classifier. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The training input samples. + y : ndarray of shape (n_samples,) + The target labels. + + Returns + ------- + self : OneNearestNeighbor + The fitted classifier. """ - X, y = check_X_y(X, y) + X, y = validate_data(self, X, y) + self.n_features_in_ = X.shape[1] 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 the class labels for the input samples. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The input samples to classify. - And describe parameters + Returns + ------- + y_pred : ndarray of shape (n_samples,) + The predicted class label for each sample. """ check_is_fitted(self) - X = check_array(X) + X = validate_data(self, X, reset=False) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype ) - # XXX fix + for i in range(len(X)): + distances = np.linalg.norm(self.X_train_ - X[i], axis=1) + nearest = np.argmin(distances) + y_pred[i] = self.y_train_[nearest] + return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Compute the accuracy of the classifier on the given data. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + The input samples. + y : ndarray of shape (n_samples,) + The true class labels. + + 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() + accuracy = (y_pred == y).sum() / len(y) + return accuracy From c2c24cffeba855d4baff7f3321dec24a5b5c2f32 Mon Sep 17 00:00:00 2001 From: Francesco Azzarello Date: Tue, 10 Nov 2026 16:03:39 +0100 Subject: [PATCH 4/4] Resolve merge conflict and remove duplicate import --- sklearn_questions.py | 1 - 1 file changed, 1 deletion(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index 3b3c49fd..5b491f70 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -26,7 +26,6 @@ from sklearn.utils.validation import check_is_fitted from sklearn.utils.validation import validate_data from sklearn.utils.multiclass import check_classification_targets -from sklearn.utils.validation import validate_data class OneNearestNeighbor(ClassifierMixin, BaseEstimator):