Cyprien Kutek - assignment

numpy_questions.py

@@ -37,15 +37,30 @@ 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("X must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array")
     i = 0
     j = 0
-
-    # TODO
-
+    n_samples, n_features = np.shape(X)
+    maxi = 0
+    for k in range(n_samples):
+        for l in range(n_features):
+            if X[k][l] > maxi:
+                maxi = X[k][l]
+                i = k
+                j = l
     return i, j
 
 
 def wallis_product(n_terms):
+    if n_terms == 0:
+        return 1
+    pi = 1
+    for k in range(1, n_terms+1):
+        pi *= (4*(k**2))/((4*(k**2))-1)
+    return 2*pi
     """Implement the Wallis product to compute an approximation of pi.
 
     See:

sklearn_questions.py

@@ -35,22 +35,40 @@ def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the classifier on the training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training input samples.
+        y : ndarray of shape (n_samples,)
+            The target labels.
+
+        Returns
+        -------
+        self : object
+            The fitted estimator.
         """
         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 labels for the given samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted class for each sample.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +77,28 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_train_ - x, axis=1)
+            nearest = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the accuracy of the classifier on the given data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples.
+        y : ndarray of shape (n_samples,)
+            The true labels.
+
+        Returns
+        -------
+        score : float
+            The fraction of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)