Charles Odend'hal

numpy_questions.py

@@ -37,12 +37,19 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if type(X) is not np.ndarray:
+        raise ValueError("input is not np array")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("input is not of dimension 2")
 
-    return i, j
+    max = np.argmax(X)
+    n_columns = X.shape[1]
+
+    row = max // n_columns
+    column = max % n_columns
+
+    return (row, column)
 
 
 def wallis_product(n_terms):
@@ -62,6 +69,9 @@ 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
+    else:
+        n = 4 * np.arange(1, n_terms + 1) ** 2
+        pi = 2 * np.prod(n / (n - 1))
+        return pi

sklearn_questions.py

@@ -22,53 +22,95 @@
 import numpy as np
 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_X_y, check_array
 from sklearn.utils.validation import check_is_fitted
 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.
+
+        Parameters
+        -----------
+        self : instance of the class (OneNearestNeighbor)
+
+        X : array of shape(n_samples, n_features)
+        matrix of the features; independent and explanatory variables
+
+        y : array of shape(n_samples,)
+        matrix of the explained variable
+
+        Returns
+        -------
+        self : object
+        fitted estimator
 
-        And describe parameters
         """
         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_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class.
+
+        Parameters
+        ----------
+        X : array of shape(n_samples, n_features)
+        matrix of the features; independent and explanatory variables
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : array of shape(n_samples,)
+        predictions for y
         """
         check_is_fitted(self)
+
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
 
-        # XXX fix
-        return y_pred
+        y_pred = []
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"""X has {X.shape[1]} features but expects
+                  {self.n_features_in_} features as input""")
+
+        for value in X:
+            distance = np.linalg.norm(self.X_train_ - value, axis=1)
+            index = np.argmin(distance)
+            y_pred.append(self.y_train_[index])
+
+        return np.array(y_pred)
 
     def score(self, X, y):
-        """Write docstring.
+        """Compute accuracy score.
+
+        Parameters
+        ----------
+        X : array of shape(n_samples, n_features)
+        matrix of the features; independent and explanatory variables
+
+        y : array of shape(n_samples,)
+        explained variable
+
+        Returns
+        -------
+        accuracy score : type float
 
-        And describe parameters
         """
+        check_is_fitted(self)
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)