Yonas Grossard-Amin Assignment done ?

numpy_questions.py

@@ -41,7 +41,18 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError
+    if len(X.shape) != 2:
+        raise ValueError
 
+    max = X[0][0]
+    for n in range(X.shape[0]):
+        for m in range(X.shape[1]):
+            if X[n][m] > max:
+                max = X[n][m]
+                i = n
+                j = m
     return i, j
 
 
@@ -64,4 +75,9 @@ 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.
+    tmp = 1.0
+    if n_terms == 0:
+        return tmp
+    for i in range(1, n_terms + 1):
+        tmp *= (4*(i**2))/((4*(i**2)) - 1)
+    return tmp*2

sklearn_questions.py

@@ -23,53 +23,86 @@
 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 validate_data
 from sklearn.utils.multiclass import check_classification_targets
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the 1NN model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training Data
+        y : array-like of shape (n_samples,)
+            Training Data Labels
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            Fitted classifier
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(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.
+        """Predict labels for the given samples.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to classify
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted Class Labels
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
         # XXX fix
+        distances = np.linalg.norm(X[:, np.newaxis, :] -
+                                   self.X_[np.newaxis, :, :], axis=2)
+        pred_index = np.argmin(distances, axis=1)
+        y_pred = self.y_[pred_index]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Calculate the accuracy of the predictions.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples
+        y : array-like of shape (n_samples,)
+            True Class Labels
+
+        Returns
+        -------
+        score : float
+            Accuracy
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.sum(y_pred == y) / len(y)