From 866ed38dc5976bcded3d76151a4764845ac7484d Mon Sep 17 00:00:00 2001 From: ithierda Date: Thu, 13 Nov 2025 16:07:20 +0100 Subject: [PATCH 1/2] answered the questions in numpy and sklearn --- numpy_questions.py | 19 +++++++++++++++---- sklearn_questions.py | 35 ++++++++++++++++++++++++----------- 2 files changed, 39 insertions(+), 15 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..455b8cd1 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -40,7 +40,13 @@ def max_index(X): i = 0 j = 0 - # TODO + if not isinstance(X, np.ndarray): + raise ValueError("Input must be a numpy array") + if X.ndim != 2: + raise ValueError("Input array must be 2-dimensional") + + flat_index = np.argmax(X) + i, j = np.unravel_index(flat_index, X.shape) return i, j @@ -62,6 +68,11 @@ 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 1.0 + + product = 1.0 + for n in range(1, n_terms+1): + product *= (4 * n**2) / (4 * n**2 - 1) + return 2 * product diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..8b2430ff 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -28,29 +28,37 @@ from sklearn.utils.multiclass import check_classification_targets -class OneNearestNeighbor(BaseEstimator, ClassifierMixin): +class OneNearestNeighbor(ClassifierMixin, BaseEstimator): "OneNearestNeighbor classifier." def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Write docstring. + """Fit the 1-NN classifier from the training data - And describe parameters + X : ndarray of shape (n_samples, n_features) + y : ndarray of shape (n_samples,) + + Returns the fitted estimator """ 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. + """Predict class labels for the provided data - And describe parameters + X : ndarray of shape (n_samples, n_features) + + Return : + y_pred : ndarray of shape (n_samples,) -> the predicted class labels """ check_is_fitted(self) X = check_array(X) @@ -59,16 +67,21 @@ def predict(self, X): dtype=self.classes_.dtype ) - # XXX fix + for i, x in enumerate(X): + distances = np.linalg.norm(self.X_train_ - x, axis=1) + nearest_index = np.argmin(distances) + y_pred[i] = self.y_train_[nearest_index] return y_pred def score(self, X, y): - """Write docstring. + """Return the mean accuracy on the given test data and labels + + X : ndarray of shape (n_samples, n_features) + y : ndarray of shape (n_samples,) - And describe parameters + Return the score """ 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 0c8214ccf765869e51715138ff6c20a8c39f60ff Mon Sep 17 00:00:00 2001 From: ithierda Date: Thu, 13 Nov 2025 16:22:38 +0100 Subject: [PATCH 2/2] Changing the pydocstyle --- numpy_questions.py | 3 +-- sklearn_questions.py | 12 ++++++------ 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 455b8cd1..3c9611e6 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -66,9 +66,8 @@ def wallis_product(n_terms): Returns ------- pi : float - The approximation of order `n_terms` of pi using the Wallis product. + The approximation of order `n_terms` of pi using the Wallis product. """ - if n_terms == 0: return 1.0 diff --git a/sklearn_questions.py b/sklearn_questions.py index 8b2430ff..450758de 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -29,18 +29,18 @@ class OneNearestNeighbor(ClassifierMixin, BaseEstimator): - "OneNearestNeighbor classifier." + """OneNearestNeighbor classifier.""" def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Fit the 1-NN classifier from the training data + """Fit the 1-NN classifier from the training data. X : ndarray of shape (n_samples, n_features) y : ndarray of shape (n_samples,) - Returns the fitted estimator + Returns the fitted estimator. """ X, y = check_X_y(X, y) check_classification_targets(y) @@ -53,12 +53,12 @@ def fit(self, X, y): return self def predict(self, X): - """Predict class labels for the provided data + """Predict class labels for the provided data. X : ndarray of shape (n_samples, n_features) Return : - y_pred : ndarray of shape (n_samples,) -> the predicted class labels + y_pred : ndarray of shape (n_samples,) -> the predicted class labels. """ check_is_fitted(self) X = check_array(X) @@ -74,7 +74,7 @@ def predict(self, X): return y_pred def score(self, X, y): - """Return the mean accuracy on the given test data and labels + """Return the mean accuracy on the given test data and labels. X : ndarray of shape (n_samples, n_features) y : ndarray of shape (n_samples,)