PR Sila BOUKHOBZA

numpy_questions.py

@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+# Modification
 import numpy as np
 
 
@@ -37,11 +38,13 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Shape is not 2D")
 
+    idx_flat = np.argmax(X)
+    i, j = np.unravel_index(idx_flat, X.shape)
     return i, j
 
 
@@ -64,4 +67,9 @@ 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.
-    return 0.
+    res = 1
+    if n_terms == 0:
+        return res
+    for i in range(1, n_terms+1):
+        res *= (4 * i**2) / (4 * i**2 - 1)
+    return 2*res

sklearn_questions.py

@@ -28,29 +28,34 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-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.
+        """
+        Fits the model to data X and y.
 
-        And describe parameters
+        X : training features
+        y : training targets
         """
         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.
+        """
+        Predicts data X according to fit.
 
-        And describe parameters
+        X : estimators
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +64,20 @@ 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)
+            min_dist = np.argmin(distances)
+            y_pred[i] = self.y_train_[min_dist]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """
+        Compute the accuracy score for evaluation.
 
-        And describe parameters
+        X : estimators
+        y : target
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)