Add shortest path algorithm intentded to be used in ABCD algorithm in Druid

src/AI-Algorithms-Graph/AIShortestPathWithMaxAndMinNodes.class.st

@@ -0,0 +1,114 @@
+"
+Algorithm for shortest path in graphs with ""max"" vertices as well as ""min"" vertices, used in the implementation of ABCD.
+The graph can have negative cycles, as long as they contain at least one max node.
+If a path doesn't exist it returns Float infinity.
+
+Possible results are {1, 0, -1}, which map to {True, Reduced, False} in the paper's algorithm.
+True and Reduced means the check is redundant (with Reduced meaning a cycle was reduced)
+C is a table used for memoization, that maps [v-a<=c] into {True, False, Reduced}
+active detects cycles: if active[v] != null, then active[v] is the distance of v from b, where b is the check’s index variable.
+active maintains the distance for each vertex v that is on the path on the current DFS stack
+isMaxNodePredicate is used to know if a given node is a max node (i.e. is in V_phi)
+"
+Class {
+	#name : 'AIShortestPathWithMaxAndMinNodes',
+	#superclass : 'AIGraphAlgorithm',
+	#instVars : [
+		'start',
+		'isMaxNodePredicate',
+		'active',
+		'C'
+	],
+	#category : 'AI-Algorithms-Graph-Shortest path',
+	#package : 'AI-Algorithms-Graph',
+	#tag : 'Shortest path'
+}
+
+{ #category : 'running' }
+AIShortestPathWithMaxAndMinNodes >> distanceTo: aModel isLessThan: c [
+
+	| memo isMaxNode v |
+	"start == a"
+	v := self findNode: aModel.
+
+	memo := C at: { v. start } ifAbsentPut: [ Dictionary new ].
+
+	memo keysAndValuesDo: [ :key :val |
+		(key <= c and: val = 1) ifTrue: [ ^ 1 ] ].
+
+	memo keysAndValuesDo: [ :key :val |
+		(key >= c and: val = -1) ifTrue: [ ^ -1 ] ].
+
+	memo keysAndValuesDo: [ :key :val |
+		(key <= c and: val = 0) ifTrue: [ ^ 0 ] ].
+
+	(v = start and: c >= 0) ifTrue: [ ^ 1 ].
+
+	v incomingEdges ifEmpty: [ ^ -1 ].
+
+	active at: v ifPresent: [ :d |
+		^ c > d
+			  ifTrue: [ -1 ]
+			  ifFalse: [ 0 ] ].
+
+	active at: v put: c.
+	isMaxNode := isMaxNodePredicate value: v model.
+	v incomingEdges do: [ :e |
+		| u res prev |
+		u := e from.
+		res := self distanceTo: u model isLessThan: c - e weight.
+		prev := memo at: c ifAbsent: [isMaxNode
+													ifTrue: [ 1 ]
+													ifFalse: [ -1 ]].
+		memo at: c put: (isMaxNode
+			ifTrue: [ prev min: res ]
+			ifFalse: [ prev max: res ])
+	].
+	active removeKey: v.
+
+	^ memo at: c
+]
+
+{ #category : 'configuration' }
+AIShortestPathWithMaxAndMinNodes >> edgeClass [
+
+	^ AIWeightedEdge
+]
+
+{ #category : 'initialization' }
+AIShortestPathWithMaxAndMinNodes >> initialize [
+
+	super initialize.
+	active := Dictionary new. "Deberia venir de arriba?"
+]
+
+{ #category : 'initialization' }
+AIShortestPathWithMaxAndMinNodes >> isMaxNodePredicate: aBlock [
+
+	isMaxNodePredicate := aBlock
+]
+
+{ #category : 'initialization' }
+AIShortestPathWithMaxAndMinNodes >> memoDictionary: aDictionary [
+
+	C := aDictionary
+]
+
+{ #category : 'configuration' }
+AIShortestPathWithMaxAndMinNodes >> nodeClass [
+
+	^ AIWeightedHitsNode
+]
+
+{ #category : 'running' }
+AIShortestPathWithMaxAndMinNodes >> run [
+
+
+]
+
+{ #category : 'initialization' }
+AIShortestPathWithMaxAndMinNodes >> start: aModel [
+
+	start := (self findNode: aModel).
+
+]