From 0f86c6aebf995da5a1bd734bf6ecfefd137d3a1a Mon Sep 17 00:00:00 2001 From: NelsonTEJO Date: Thu, 13 Nov 2025 17:59:32 +0100 Subject: [PATCH 1/2] Modification des deux fichiers avec les fonctions valides --- numpy_questions.py | 23 ++++++++++++++++- sklearn_questions.py | 59 ++++++++++++++++++++++++++++++++++++++------ 2 files changed, 73 insertions(+), 9 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..a84ea0c3 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -41,6 +41,18 @@ def max_index(X): j = 0 # TODO + if not isinstance(X, np.ndarray) or X.ndim!=2: + raise ValueError("Input array should be a 2-dimensional numpy array") + + i,j=0,0 + max=X[0][0] + + for l in range(X.shape[0]): + for c in range(X.shape[1]): + if X[l][c]>max: + i,j=l,c + max=X[l][c] + continue return i, j @@ -64,4 +76,13 @@ 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. + + res=1 + if n_terms==0: + return 1 + + for k in range(1,n_terms+1): + res*=(4*k**2)/(4*k**2 - 1) + return 2*res + + diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..c638bda3 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -35,9 +35,20 @@ def __init__(self): # noqa: D107 pass def fit(self, X, y): - """Write docstring. - - And describe parameters + """ + Train the OneNearestNeighbor predictor. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Train set. + y : array-like of shape (n_samples,) + Target variable. + + Returns + ------- + self : object + """ X, y = check_X_y(X, y) check_classification_targets(y) @@ -45,12 +56,25 @@ def fit(self, X, 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 of each sample of X. - And describe parameters + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + test set. + + Returns + ------- + y_pred : ndarray of shape (n_samples,) + predictions """ check_is_fitted(self) X = check_array(X) @@ -60,15 +84,34 @@ def predict(self, X): ) # XXX fix + for i, x in enumerate(X): + + distances = np.sqrt(np.sum((self.X_train_ - x) ** 2, axis=1)) + + idx_min = np.argmin(distances) + + y_pred[i] = self.y_train_[idx_min] + return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """ + Evaluate the performance of the model + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + test set. + y : array-like of shape (n_samples,) + Target variable. + + Returns + ------- + accuracy: percentage of good predictions """ 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() From c48bcae88fd73d6069ac398fa4079d3964064f53 Mon Sep 17 00:00:00 2001 From: NelsonTEJO Date: Thu, 13 Nov 2025 18:14:43 +0100 Subject: [PATCH 2/2] corrige les erreurs --- numpy_questions.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index a84ea0c3..d3f53a87 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -43,20 +43,15 @@ def max_index(X): # TODO if not isinstance(X, np.ndarray) or X.ndim!=2: raise ValueError("Input array should be a 2-dimensional numpy array") - i,j=0,0 max=X[0][0] - - for l in range(X.shape[0]): + for line in range(X.shape[0]): for c in range(X.shape[1]): - if X[l][c]>max: - i,j=l,c - max=X[l][c] + if X[line][c]>max: + i,j=line,c + max=X[line][c] continue - return i, j - - def wallis_product(n_terms): """Implement the Wallis product to compute an approximation of pi. @@ -76,11 +71,9 @@ 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. - res=1 if n_terms==0: return 1 - for k in range(1,n_terms+1): res*=(4*k**2)/(4*k**2 - 1) return 2*res