TEJODONKENG MENKEM Luc

numpy_questions.py

@@ -37,10 +37,16 @@ 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) or X.ndim != 2:
+        raise ValueError("Input array should be a 2-dimensional numpy array")
 
-    # TODO
+    i, j = 0, 0
+    max_val = X[0][0]
+    for row in range(X.shape[0]):
+        for col in range(X.shape[1]):
+            if X[row][col] > max_val:
+                i, j = row, col
+                max_val = X[row][col]
 
     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.
+    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

sklearn_questions.py

@@ -35,22 +35,46 @@ 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)
         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 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()