upd regressor

neighbours/regressor.py

@@ -5,7 +5,26 @@ from .exceptions import *
 
 
 class KNNRegressor:
+    """K-nearest neighbors regressor
+
+    Nadaraya-Watson kNN regressor based on random projection forest.
+
+    Supports different (including custom) smoothing kernels and distance metrics.
+
+    Attributes:
+        features: number of features in each sample
+        forest: an instance of RPTForest
+        targets: an array of target values corresponding to loaded train points
+    """
+
     def __init__(self, features, trees_count, rpt_m):
+        """Initializes new regressor
+
+        :param features: number of features in each sample
+        :param trees_count: number of trees in the forest
+        :param rpt_m: maximum number of samples in one leaf of an RP tree
+        """
+
         self.features = features
         self.forest = RPTForest(features, trees_count, rpt_m)
         self.targets = None
@@ -36,16 +55,25 @@ class KNNRegressor:
         self.forest.load(points)
 
     def predict(self, point: np.ndarray, distance, kernel, h):
+        """Predict target value for given point
+
+        :param point: target point as np.ndarray
+        :param distance: distance metric function (e.g., from neighbours.distance)
+        :param kernel: smoothing kernel function (e.g., from neighbours.kernel)
+        :param h: bandwidth
+        :return: predicted value or numpy.nan if unable to obtain a prediction
+        """
+
         nearest_point_indexes = self.forest.get_neighbours(point)
 
         # Nadaraya-Watson estimator
 
         numerator = float(0)
-        denominator = float(0.0000001)
+        denominator = float(0)
 
         for point_ix in nearest_point_indexes:
             weight = kernel(distance(point, self.forest.get_point(point_ix)) / h)
             numerator += weight * self.targets[point_ix]
             denominator += weight
 
-        return numerator / denominator
+        return np.nan if denominator == 0 else numerator / denominator