Karin Yamakawa Github assignment (lab4) - numpy questions completed

numpy_questions.py

@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+
 import numpy as np
 
 
@@ -37,12 +38,17 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    i = -1
+    j = -1
+
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input should be a numpy array.")
+    if len(X.shape) != 2:
+        raise ValueError("Input should be a 2D numpy array.")
 
-    # TODO
+    (i, j) = np.unravel_index(X.argmax(), X.shape)
 
-    return i, j
+    return (i, j)
 
 
 def wallis_product(n_terms):
@@ -64,4 +70,13 @@ 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.
+
+    if n_terms == 0:
+        return 1.0
+
+    pi_half = 1
+
+    for n in range(1, n_terms + 1):
+        pi_half = pi_half * (4 * n ** 2) / (4 * n ** 2 - 1)
+
+    return 2 * pi_half

sklearn_questions.py

@@ -20,55 +20,89 @@
 `pydocstyle` that you can also call at the root of the repo.
 """
 import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.base import ClassifierMixin
+from sklearn.base import ClassifierMixin, BaseEstimator
 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
 
 
-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 one nearest neighbor classifier from the training dataset.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
+
+        y : ndarray of shape (n_samples)
+            The target 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
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class labels for new data points X_i.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples)
+            The predicted class labels.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = check_array(X, reset=False)
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i in range(len(X)):
+            distances = np.linalg.norm(self.X_ - X[i, :], axis=1)
+            y_pred[i] = self.y_[np.argmin(distances)]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the classifier accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
 
-        And describe parameters
+        y : ndarray of shape (n_samples)
+            The target values.
+
+        Returns
+        -------
+        score : float
+            The accuracy of the classifier;
+            number of samples corectly classified.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        accurate_points = 0
+        for i in range(len(y)):
+            if y_pred[i] == y[i]:
+                accurate_points += 1
+
+        return accurate_points / len(y)