Augustin Hourquet assignment PR

numpy_questions.py

@@ -39,9 +39,12 @@ def max_index(X):
     """
     i = 0
     j = 0
-
-    # TODO
-
+    if not isinstance(X, np.ndarray):
+        raise ValueError("The X input must be an Array!")
+    if X.ndim != 2:
+        raise ValueError("The X input must be 2-Dimensional!")
+    indexes = np.argmax(X)
+    i, j = np.unravel_index(indexes, X.shape)
     return i, j
 
 
@@ -64,4 +67,11 @@ 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.
+    wp = 1.0
+    if n_terms == 0:
+        return wp
+    else:
+        for n in range(1, n_terms + 1):
+            wp = wp * (4*(n**2))/(4*(n**2) - 1)
+        pi = wp * 2
+        return pi

sklearn_questions.py

@@ -23,34 +23,59 @@
 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.multiclass import check_classification_targets
+from sklearn.utils.validation import check_array
+from sklearn.metrics import pairwise_distances_argmin_min
 
 
-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 classifier.
+
+        This function stores training data X and the labels y.
+
+        Parameters
+        ----------
+        X : Training data (n_samples, n_features).
+
+        y : Target labels (n_samples).
 
-        And describe parameters
+        Returns
+        -------
+        self : returns the fitted classifier.
+
+        Raises
+        ------
+        ValueError
+            If X and y have different numbers of samples
         """
         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_ = np.asarray(X)
+        self.y_ = np.asarray(y)
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Return the predicted class for a data set in an numpy array.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples)
+            The predicted classes for the n_samples.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +84,26 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        indexes, _ = pairwise_distances_argmin_min(X, self.X_)
+        y_pred = self.y_[indexes]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the score of the OneNearestNeighbor on a data set.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
+        y : ndarray of shape (n_samples)
+            The true classes of the samples.
+
+        Returns
+        -------
+        score : float
+            The percentage of samples accurately predicted.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        score = np.mean(y_pred == y)
+        return score