exercises done

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.
 """
+
 import numpy as np
 
 
@@ -40,7 +41,21 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Not a numpy array")
+
+    if X.ndim != 2:
+        raise ValueError("Not a 2D array")
+
+    n = X.shape[0]
+    m = X.shape[1]
+    max = 0
+    for k in range(n):
+        for p in range(m):
+            if X[k][p] > max:
+                max = X[k][p]
+                i = k
+                j = p
 
     return i, j
 
@@ -64,4 +79,19 @@ 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.
+    product = 1.0
+
+    if n_terms == 0:
+        return 1.0
+
+    n = 1
+    while n <= n_terms:
+        numerator = 4 * n * n
+        denominator = (2 * n - 1) * (2 * n + 1)
+        product = product * (numerator / denominator)
+        n += 1
+
+    pi = product * 2
+
+    # approximation of order n_terms
+    return pi

sklearn_questions.py

@@ -19,6 +19,7 @@
 for the methods you code and for the class. The docstring will be checked using
 `pydocstyle` that you can also call at the root of the repo.
 """
+
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
@@ -29,7 +30,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -44,7 +45,9 @@ def fit(self, X, 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):
@@ -54,13 +57,16 @@ def predict(self, X):
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
+
+        distances = np.sqrt(
+            np.sum((
+                X[:, np.newaxis, :] - self.X_train_[np.newaxis, :, :]
+                ) ** 2, axis=2
+            )
         )
+        nearest_indices = np.argmin(distances, axis=1)
 
-        # XXX fix
-        return y_pred
+        return self.y_train_[nearest_indices]
 
     def score(self, X, y):
         """Write docstring.
@@ -70,5 +76,5 @@ def score(self, X, y):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        accuracy = np.mean(y == y_pred)
+        return accuracy