From 9cc2c95f54f75d6ded8187ec3759ba8fb17aa202 Mon Sep 17 00:00:00 2001 From: gauthierbyn Date: Tue, 10 Nov 2026 16:37:12 +0100 Subject: [PATCH 1/3] Gauthier Boyen final pull request --- numpy_questions.py | 25 +++++++++++++++--- sklearn_questions.py | 62 +++++++++++++++++++++++++++++++------------- 2 files changed, 65 insertions(+), 22 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..cff7824c 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -18,7 +18,7 @@ import numpy as np -def max_index(X): +def max_index(X): """Return the index of the maximum in a numpy array. Parameters @@ -37,10 +37,19 @@ def max_index(X): If the input is not a numpy array or if the shape is not 2D. """ + # TODO + + if not isinstance(X, np.ndarray): + raise ValueError("The input should be a numpy array.") + if X.ndim != 2: + raise ValueError("The input should be a 2D numpy array.") i = 0 j = 0 - - # TODO + for row in range(X.shape[0]): + for col in range(X.shape[1]): + if X[row, col] > X[i, j]: + i = row + j = col return i, j @@ -64,4 +73,12 @@ 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: + raise ValueError("The number of terms should be a positive integer.") + if n_terms == 0: + return 1 + pi_approx = 1 + for k in range(1, n_terms + 1): + product = (4 * k ** 2) / (4 * k ** 2 - 1) + pi_approx = pi_approx * product + return 2*pi_approx diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..f33f9ced 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -23,52 +23,78 @@ 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. + """Fit the model using X as training data and y as target values. - And describe parameters + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) (Training data.) + y : ndarray of shape (n_samples,) (Target values.) + + Returns + ------- + self : object (Returns the instance itself.) """ - X, y = check_X_y(X, y) + X, y = validate_data(self, 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 the class labels for the provided data. - And describe parameters + Parameters + ---------- + X : ndarray of shape (n_queries, n_features) + Test samples. + + Returns + ------- + y_pred : ndarray of shape (n_queries,) + Class labels for each data 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, x_i in enumerate(X): + distances = np.linalg.norm(self.X_ - x_i, axis=1) + nearest_idx = np.argmin(distances) + y_pred[i] = self.y_[nearest_idx] return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Return the mean accuracy on the given test data and labels. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Test samples. + y : ndarray of shape (n_samples,) + True labels for X. + + Returns + ------- + score : float + Mean accuracy of self.predict(X) with respect to 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) From eb54ff98224a737bb7b7601f258d2e05ccdf9510 Mon Sep 17 00:00:00 2001 From: gauthierbyn Date: Tue, 10 Nov 2026 16:45:59 +0100 Subject: [PATCH 2/3] Final push Gauthier Boyen --- numpy_questions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/numpy_questions.py b/numpy_questions.py index cff7824c..45d98801 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -18,7 +18,7 @@ import numpy as np -def max_index(X): +def max_index(X): """Return the index of the maximum in a numpy array. Parameters From b23ed08012050d3b2bd3a77e9c5659e515cf4445 Mon Sep 17 00:00:00 2001 From: gauthierbyn Date: Tue, 10 Nov 2026 16:50:36 +0100 Subject: [PATCH 3/3] Final version Gauthier Boyen --- sklearn_questions.py | 1 - 1 file changed, 1 deletion(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index 4375c9e5..c37001f7 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):