Celia Morais assignment

numpy_questions.py

@@ -37,10 +37,13 @@ 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 should be a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Input should be a 2D numpy array.")
 
-    # TODO
+    index = np.argmax(X)
+    i, j = np.unravel_index(index, X.shape)
 
     return i, j
 
@@ -62,6 +65,9 @@ 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.
+    if n_terms == 0:
+        return 1
+    pi = 1.0
+    for i in range(1, n_terms + 1):
+        pi = pi * (4 * i ** 2) / (4 * i ** 2 - 1)
+    return pi * 2

sklearn_questions.py

@@ -23,34 +23,49 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_array
 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.
+        """Fit the OneNearestNeighbor model to the given training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+        y : ndarray of shape (n_samples,)
 
-        And describe parameters
+        Returns
+        -------
+        self : object
+            The fitted model
         """
         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_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the labels for the given training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +74,27 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i in range(len(X)):
+            distances = np.linalg.norm(self.X_ - X[i, :], axis=1)
+            nearest_index = distances.argmin()
+            y_pred[i] = self.y_[nearest_index]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the number of correct label predictions on the given data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+        y : ndarray of shape (n_samples,)
 
-        And describe parameters
+        Returns
+        -------
+        score : float
+            The number of correct predictions.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
+        y_pred = (y_pred == y)
 
-        # XXX fix
-        return y_pred.sum()
+        return y_pred.sum()/len(y_pred)