Add KNNClassifier, exceptions

neighbours/__init__.py

@@ -1 +1 @@
-from .rp_neighbours import *
+from .knn_classifier import *

neighbours/exceptions.py

@@ -0,0 +1,6 @@
+class InvalidDimensionError(Exception):
+    pass
+
+
+class InvalidType(Exception):
+    pass

neighbours/knn_classifier.py

@@ -0,0 +1,33 @@
+import numpy as np
+
+from .rp_neighbours import *
+from .exceptions import *
+
+
+class KNNClassifier:
+    def __init__(self, features, trees_count, rpt_m):
+        self.features = features
+        self.forest = RPTForest(features, trees_count, rpt_m)
+        self.classes = None
+
+    def load(self, points, classes):
+        if not isinstance(points, np.ndarray):
+            raise InvalidType("points should be represented as np.ndarray")
+
+        if not isinstance(classes, np.ndarray) and not isinstance(classes, list):
+            raise InvalidType("classes should be represented as np.ndarray or list")
+
+        self.classes = classes
+
+        if points.ndim != 2:
+            raise InvalidDimensionError("points array should be two-dimensional")
+
+        if points.shape[1] != self.features:
+            raise InvalidDimensionError(
+                "invalid number of features in sample (expected {}, got {})".format(self.features, points.shape[1])
+            )
+
+        self.forest.load(points)
+
+    def predict(self, point: np.ndarray):
+        pass

neighbours/rp_neighbours.py

@@ -85,20 +85,20 @@ class RPTForest:
     def get_point(self, ix):
         """Returns stored point by index
 
-        :param ix: point index
+        :param ix: target point index
         :return: np.ndarray representing the point
         """
 
         return self.points[ix]
 
-    def load(self, points: list) -> None:
+    def load(self, points: np.ndarray) -> None:
         """Loads a list of points and builds the corresponding forest
 
-        :param points: list of points
+        :param points: numpy.ndarray of points
         """
 
         self.trees.clear()
-        self.points = np.array(points)
+        self.points = points
 
         ixs = list(range(len(self.points)))
 
@@ -110,7 +110,7 @@ class RPTForest:
 
         :param root: root of a random projection tree to search
         :param point: target point
-        :return: set of points located in the same region of space
+        :return: set of indexes of points located in the same region of space
         """
 
         while isinstance(root, SplittingNode):