From 59c827cb5ccafb029611e30037c5170ef01a849c Mon Sep 17 00:00:00 2001 From: Arnaud Cazanave Date: Tue, 10 Nov 2026 17:16:51 +0100 Subject: [PATCH 1/4] Numpy questions --- numpy_questions.py | 34 ++++++++++++++++++++++++++++++---- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..346a9a70 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,12 +37,22 @@ def max_index(X): If the input is not a numpy array or if the shape is not 2D. """ - i = 0 - j = 0 # TODO - return i, j + if type(X) is not np.ndarray: + + raise ValueError("X must be a numpy array") + + if len(X.shape) != 2: + + raise ValueError("X must be a 2 dimensional array") + + index_array = np.where(X == np.max(X)) + + result = tuple (x[0].item() for x in index_array) + + return result def wallis_product(n_terms): @@ -64,4 +74,20 @@ 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. + + pi = 1 + n = 1 + + if n_terms == 0: + + return pi + + while n <= n_terms : + + pi = pi * (4*n**2 / (4*n**2-1)) + + n += 1 + + return 2*pi + + From 2fb880aae7ee515485f2ccc3e64e17d8b8ffe168 Mon Sep 17 00:00:00 2001 From: Arnaud Cazanave Date: Tue, 10 Nov 2026 17:45:01 +0100 Subject: [PATCH 2/4] flake8 adjustements --- numpy_questions.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 346a9a70..122d8cc1 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -50,7 +50,7 @@ def max_index(X): index_array = np.where(X == np.max(X)) - result = tuple (x[0].item() for x in index_array) + result = tuple(x[0].item() for x in index_array) return result @@ -74,7 +74,7 @@ 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. - + pi = 1 n = 1 @@ -82,12 +82,10 @@ def wallis_product(n_terms): return pi - while n <= n_terms : - + while n <= n_terms: + pi = pi * (4*n**2 / (4*n**2-1)) n += 1 return 2*pi - - From f1fa33892f87b5a6f5ed34f1956a3732aad5af4c Mon Sep 17 00:00:00 2001 From: Arnaud Cazanave Date: Tue, 10 Nov 2026 17:59:41 +0100 Subject: [PATCH 3/4] pydocstyle adjustements --- numpy_questions.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 122d8cc1..ed4821f1 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,9 +37,6 @@ def max_index(X): If the input is not a numpy array or if the shape is not 2D. """ - - # TODO - if type(X) is not np.ndarray: raise ValueError("X must be a numpy array") From ece2bc3fe155ed5a2e3fbd2959b89cfbc8d5ae07 Mon Sep 17 00:00:00 2001 From: Arnaud Cazanave Date: Tue, 10 Nov 2026 19:14:00 +0100 Subject: [PATCH 4/4] sklearn_questions --- sklearn_questions.py | 72 +++++++++++++++++++++++++++++++++----------- 1 file changed, 55 insertions(+), 17 deletions(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..62881130 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -19,56 +19,94 @@ 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 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): + """One-nearest-neighbor classifier.""" def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Write docstring. - - And describe parameters + """Fit the classifier to the training data. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Training feature matrix. + y : ndarray of shape (n_samples,) + Training class labels. + + Returns + ------- + self : OneNearestNeighbor + Fitted classifier. """ - 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_train_ = X + self.y_train_ = y + return self def predict(self, X): - """Write docstring. + """Predict class labels using the nearest neighbor. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Observations to classify. - And describe parameters + Returns + ------- + y_pred : ndarray of shape (n_samples,) + Predicted class labels. """ 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], + shape=len(X), + fill_value=self.classes_[0], dtype=self.classes_.dtype ) - # XXX fix + for i, x in enumerate(X): + distances = np.linalg.norm(self.X_train_ - x, axis=1) + index = np.argmin(distances) + y_pred[i] = self.y_train_[index] + return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Compute the classification accuracy. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Observations to classify. + y : ndarray of shape (n_samples,) + True class labels. + + Returns + ------- + score : float + Proportion of correctly classified observations. """ X, y = check_X_y(X, y) y_pred = self.predict(X) - # XXX fix + y_pred = (y_pred == y) / len(y) + return y_pred.sum()