From ea5d5f4fde902b04b79b134969c4d6acf4e282e9 Mon Sep 17 00:00:00 2001 From: Abdelilah Nossair Date: Tue, 10 Nov 2026 17:32:40 +0100 Subject: [PATCH 1/2] Assigments done --- numpy_questions.py | 19 ++++++++++--- sklearn_questions.py | 65 +++++++++++++++++++++++++++++++++----------- 2 files changed, 64 insertions(+), 20 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..cc9e86e5 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,11 +37,15 @@ 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 of the iput is not 2D") + index = np.argmax(X) # Get the flattened index of the maimum in the array + i = index // X.shape[1] # Get the row number of the maximum + j = index % X.shape[1] # Get the column number of the max return i, j @@ -64,4 +68,11 @@ 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_2 = 1 + if n_terms == 0: + return pi_2 + + for i in range(1, n_terms+1): + pi_2 = pi_2 * (4*i**2)/(4*i**2-1) + + return pi_2*2 diff --git a/sklearn_questions.py b/sklearn_questions.py index 948c2e54..22c7e7bf 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -25,51 +25,84 @@ 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.validation import validate_data +# from sklearn.utils.validation import validate_data from sklearn.utils.multiclass import check_classification_targets class OneNearestNeighbor(ClassifierMixin, BaseEstimator): - "OneNearestNeighbor classifier." + """OneNearestNeighbor classifier.""" def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Write docstring. - - And describe parameters + """Store training observations and their labels. + + Parameters + ---------- + X : np.ndarray + 2D array of training features of shape (n_samples, n_features) + y : np.ndarray + 1D array of training labels of shape (n_samples,) + + Returns + ------- + self : OneNearestNeighbor object + Fitted model storing training data """ - X, y = check_X_y(X, y) + X, y = check_X_y(X, y) # Raise ValueError() 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 label of the nearest training point. - And describe parameters + Parameters + ---------- + X : np.ndarray + 2D array of obs to classify of shape (n_samples, n_features) + + Returns + ------- + y_pred : ndarray of shape (n_samples,) + Predicted label for each observation. """ - check_is_fitted(self) + check_is_fitted(self) # Raise NotFittedError X = check_array(X) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype ) - # XXX fix + for i, x in enumerate(X): + dists = np.linalg.norm(self.X_train_ - x, axis=1) + nearest_index = np.argmin(dists) + y_pred[i] = self.y_train_[nearest_index] + return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Compute the mean accuracy of the classification. + + Parameters + ---------- + X : np.ndarray + 2D array of samples to evaluate of shape (n_samples, n_features) + y : np.ndarray + 1D array of true labels of shape (n_samples,) + + Returns + ------- + accuracy : float + Proportion of correctly classified samples over all 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) From 865480e82fab3211cb855e8acec0ada75ef84025 Mon Sep 17 00:00:00 2001 From: Abdelilah Nossair Date: Tue, 10 Nov 2026 17:43:38 +0100 Subject: [PATCH 2/2] Replace check_array with validate_data --- sklearn_questions.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index 22c7e7bf..254088ab 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -23,9 +23,9 @@ 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_array from sklearn.utils.validation import check_is_fitted -# from sklearn.utils.validation import validate_data +from sklearn.utils.validation import validate_data from sklearn.utils.multiclass import check_classification_targets @@ -50,7 +50,7 @@ def fit(self, X, y): self : OneNearestNeighbor object Fitted model storing training data """ - X, y = check_X_y(X, y) # Raise ValueError() + X, y = validate_data(self, X, y) check_classification_targets(y) self.classes_ = np.unique(y) self.n_features_in_ = X.shape[1] @@ -74,7 +74,7 @@ def predict(self, X): Predicted label for each observation. """ check_is_fitted(self) # Raise NotFittedError - X = check_array(X) + X = X = validate_data(self, X, reset=False) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype