TEJODONKENG MENKEM Luc

numpy_questions.py

@@ -41,10 +41,17 @@ 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 line in range(X.shape[0]):
+        for c in range(X.shape[1]):
+            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.
 
@@ -64,4 +71,11 @@ 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.
+    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()