liseTEYSSIER - assignment

numpy_questions.py

@@ -37,10 +37,20 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Shape of Array is not 2D")
+
     i = 0
     j = 0
-
-    # TODO
+    cur_max = X[0, 0]
+    for a in range(X.shape[0]):
+        for b in range(X.shape[1]):
+            if X[a, b] > cur_max:
+                cur_max = X[a, b]
+                i = a
+                j = b
 
     return i, j
 
@@ -62,6 +72,15 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
+    product = 1
+    if n_terms == 0:
+        return 1
+
+    for x in range(1, n_terms + 1):
+        curr_factor = 4 * (x**2) / (4 * (x**2) - 1)
+        product = product * curr_factor
+
     # XXX : The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+
+    return product * 2

sklearn_questions.py

@@ -25,51 +25,100 @@
 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
+from sklearn.utils.validation import validate_data
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the fitted classifier based on training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training data.
+        y : ndarray of shape (n_samples,)
+            The target labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted classifier.
         """
+        # Check that X and y have compatible shapes and valid values.
         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
+        # Store the data
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict labels using the nearest training sample.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted labels for input sample.
         """
         check_is_fitted(self)
         X = check_array(X)
+
+        # Check that the data has the same features:
+        # Reads n_features_in_ back from the estimator
+        # And compares it with the number of columns in the new X
+        X = validate_data(self, X, reset=False)
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
+        for i, sample in enumerate(X):
+            # Subtract this sample from every training observation
+            differences = self.X_ - sample
+
+            # Find one distance per training observation
+            distances = np.sqrt(np.sum(differences ** 2, axis=1))
+
+            # Select the nearest observation and copy its label
+            min_index = np.argmin(distances)
+            y_pred[i] = self.y_[min_index]
 
-        # XXX fix
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Measure accuracy of predictions.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The samples to evaluate.
+        y : ndarray of shape (n_samples,)
+            The correct labels.
+
+        Returns
+        -------
+        accuracy : float
+            The proportion of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        # Calculate how accurate the predictions are
+        # In proportion to the observed classes
+        accuracy = sum(y_pred == y) / len(y)
+        return accuracy