More classes in Microdown

Author: SergeStinckwich

src/Math-KDTree/PMIndexedKDTree.class.st

@@ -1,13 +1,13 @@
 "
-IndexedKDTree returns the indices of the nearest neighbours instead of the nearest neighbours itself. additionally it returns the squared distances between the vector and its neighbours, if more than one neighbour is searched.
+`PMIndexedKDTree` returns the indices of the nearest neighbours instead of the nearest neighbours itself. additionally it returns the squared distances between the vector and its neighbours, if more than one neighbour is searched.
 "
 Class {
 	#name : #PMIndexedKDTree,
 	#superclass : #PMKDTree,
 	#instVars : [
 		'index'
 	],
-	#category : 'Math-KDTree'
+	#category : #'Math-KDTree'
 }
 
 { #category : #'instance creation' }

src/Math-KDTree/PMKDTree.class.st

@@ -1,5 +1,5 @@
 "
-KDTree is a space partitioning data structure to store points of that space with ""KDTree withAll: aCollectionOfVectors"", where a vector can be any collection of numbers that understands ""at:"". you can then find nearest neighbours of aVector with ""nnSearch: aVector i: numberOfNearestNeighbours"".   
+`PMKDTree` is a space partitioning data structure to store points of that space with ""KDTree withAll: aCollectionOfVectors"", where a vector can be any collection of numbers that understands ""at:"". you can then find nearest neighbours of aVector with ""nnSearch: aVector i: numberOfNearestNeighbours"".   
 "
 Class {
 	#name : #PMKDTree,
@@ -10,7 +10,7 @@ Class {
 		'dim',
 		'value'
 	],
-	#category : 'Math-KDTree'
+	#category : #'Math-KDTree'
 }
 
 { #category : #'instance creation' }

src/Math-KDTree/PMNNStore.class.st

@@ -1,7 +1,7 @@
 "
-NNStore stores neighbours together with distances and uses the distances for sorting. 
+`PMNNStore` stores neighbours together with distances and uses the distances for sorting. 
 
-can easily be subclassed if one needs an Array sorted depending on some outside data. see StupidNN for a simple example and comment on ""withAll:"" or just use ""newFrom:"".
+can easily be subclassed if one needs an Array sorted depending on some outside data. see `PMStupidNN` for a simple example and comment on ""withAll:"" or just use ""newFrom:"".
 "
 Class {
 	#name : #PMNNStore,
@@ -11,7 +11,7 @@ Class {
 		'maxDistance',
 		'lastUsedIndex'
 	],
-	#category : 'Math-KDTree'
+	#category : #'Math-KDTree'
 }
 
 { #category : #'instance creation' }

src/Math-KDTree/PMStupidNN.class.st

@@ -1,11 +1,11 @@
 "
-StupidNN is a naive nearest neighbour search. KDTree is much faster though, if you have to do several searches. StupidNN exists only for the KDTreeTests (and as an example, how simple it is to subclass NNStore) .
+`PMStupidNN` is a naive nearest neighbour search. `PMKDTree` is much faster though, if you have to do several searches. `PMStupidNN` exists only for the `PMKDTreeTest`s (and as an example, how simple it is to subclass `PMNNStore`).
 "
 Class {
 	#name : #PMStupidNN,
 	#superclass : #PMNNStore,
 	#type : #variable,
-	#category : 'Math-KDTree'
+	#category : #'Math-KDTree'
 }
 
 { #category : #evaluating }