Xu assignment

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "."
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

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
 
 
@@ -37,12 +38,24 @@ 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):
+        raise ValueError("Input must be a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Input must be a 2D array.")
 
-    # TODO
+    i_max = 0
+    j_max = 0
+    max_value = -np.inf
+    n_samples = len(X)
+    n_features = len(X[0])
+    for i in range(n_samples):
+        for j in range(n_features):
+            if X[i, j] > max_value:
+                max_value = X[i, j]
+                i_max = i
+                j_max = j
 
-    return i, j
+    return i_max, j_max
 
 
 def wallis_product(n_terms):
@@ -57,11 +70,17 @@ def wallis_product(n_terms):
         Number of steps in the Wallis product. Note that `n_terms=0` will
         consider the product to be `1`.
 
+
+
     Returns
     -------
     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.
+    if n_terms == 0:
+        return 1
+    product = 1
+    for n in range(n_terms):
+        quatre_n_2 = 4 * ((n + 1) ** 2)
+        product *= quatre_n_2 / (quatre_n_2 - 1)
+    return product * 2

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
@@ -28,7 +29,7 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
     "OneNearestNeighbor classifier."
 
     def __init__(self):  # noqa: D107
@@ -43,32 +44,51 @@ def fit(self, X, y):
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
-
-        # XXX fix
+        self.X_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
         """Write docstring.
 
+
         And describe parameters
         """
         check_is_fitted(self)
+
         X = check_array(X)
+
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but {self.__class__.__name__} "
+                f"is expecting {self.n_features_in_} features as input."
+            )
+
         y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
+            shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype
         )
-
-        # XXX fix
+        for i, x_i in enumerate(X):
+            min_d = np.inf
+            j_min = np.inf
+            for j, x_j in enumerate(self.X_):
+                d = np.linalg.norm(x_i - x_j)
+                if d < min_d:
+                    min_d = d
+                    j_min = j
+            y_pred[i] = self.y_[j_min]
         return y_pred
 
     def score(self, X, y):
         """Write docstring.
 
         And describe parameters
         """
+        n = len(y)
+        accuracy = 0
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
+        for i in range(n):
+            if y_pred[i] == y[i]:
+                accuracy += 1
 
-        # XXX fix
-        return y_pred.sum()
+        return accuracy / n