reorganise directories

upd chapter_01/binary.py

chapter_12/README.md

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+# K-Nearest Neighbours
+
+Useful for classification, regression and feature extraction. By examining a data point against its K nearest neighbours we can:
+
+-   categorize into a group
+-   predict responses
+-   convert the item into a list of features
+
+A good starting point for machine learning.

chapter_12/loaves.py

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+import logging
+from dataclasses import dataclass
+
+import numpy as np
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Point:
+    identifier: str
+    weather: int
+    is_weekend: bool
+    is_game_on: bool
+    sold: int = 0
+    distance: int = 0
+
+    @property
+    def array(self):
+        return np.array([self.weather, int(self.is_weekend), int(self.is_game_on)])
+
+
+def knn(point: Point, neighbours):
+    for neighbour in neighbours:
+        neighbour.distance = np.linalg.norm(point.array - neighbour.array)
+        logger.debug(f"{neighbour.identifier}: {neighbour.distance}")
+
+    return sorted(neighbours, key=lambda x: x.distance)[:K]
+
+
+neighbours = [
+    Point("A", 5, True, False, 300),
+    Point("B", 3, True, True, 225),
+    Point("C", 1, True, False, 75),
+    Point("D", 4, False, True, 200),
+    Point("E", 4, False, False, 150),
+    Point("F", 2, False, False, 50),
+]
+
+point = Point("T", 4, True, False)
+K = 4
+
+k_nearest = knn(point, neighbours)
+
+total = 0
+for n in k_nearest:
+    total += n.sold
+average = total / K
+
+logger.debug(average)
+print(f"Number of loaves to make: {int(round(average, 0))}")