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+# Neighbours
+
+Weighted kNN classifier implementation based on random projection forest.
+
+### Classifier description
+
+This project implements weighted kNN classifier with kernel smoothing.
+
+Built-in smoothing kernels:
+
+* rectangular
+* gaussian
+* epanechnikov
+
+Built-in distance metrics:
+
+* euclidean
+* manhattan
+
+Custom distance metrics and smoothing kernels are supported.
+
+### Neighbors search algorithm
+
+Random projection forest is used to search for neighbours.
+
+RP trees are built by recursive binary splits of space by selected hyperplanes.
+
+On each step algorithm chooses two random objects from train set and calculates a hyperplane symmetrically separating these two objects in feature space.
+Such hyperplanes become splitting nodes of an RP tree.
+
+Each resulting subset is split again if it contains more than `m` (hyperparameter) objects. 
+
+Hyperplane in each splitting node is represented with an equation of the form
+w · x + b = 0, where w is a vector normal to the hyperplane and b is an offset.
+
+For simplicity, we assume what points may be located either "to the right"
+or "to the left" of the hyperplane.
+
+We also define points belonging to a hyperplane as located "to the left."
+
+So for any point x we can clearly determine its location:
+
+```
+w · x + b > 0 point x is located "to the right"
+w · x + b ≤ 0 point x is located "to the left"
+```
+
+Since nodes are organized into a tree, we can perform search by evaluating the expression and proceeding to the corresponding child node.
+