refining MultiLineFitterTest

src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitter.py

@@ -0,0 +1,59 @@
+import numpy as np
+from skspatial.objects import Line
+
+# implementation of "Robust Multiple Structures Estimation with J-linkage"
+class MultiLineFitter:
+    
+    @staticmethod
+    def fitLines(points, lines, consensusThreshold):
+        preferenceMatrix = MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold)
+        _, preferenceMatrix4Clusters = MultiLineFitter.createClusters(preferenceMatrix)
+        lineIndexes = MultiLineFitter._getLineIndexes(preferenceMatrix4Clusters)
+        return [lines[lineIndex] for lineIndex in lineIndexes]
+
+    @staticmethod
+    def createClusters(preferenceMatrix):
+        keepClustering = True
+        numClusters = preferenceMatrix.shape[0]
+        clusters = [[i] for i in range(numClusters)]
+        while keepClustering:
+            maxDistance = 0
+            bestClusterIndexCombination = None
+            keepClustering = False
+            numClusters = preferenceMatrix.shape[0]
+            for clusterIndexA in range(numClusters):
+                preferenceSetA = preferenceMatrix[clusterIndexA]
+                for clusterIndexB in range(clusterIndexA):
+                    preferenceSetB = preferenceMatrix[clusterIndexB]
+                    distance = MultiLineFitter._intersectionOverUnion(preferenceSetA, preferenceSetB);
+                    if distance > maxDistance:
+                        keepClustering = True
+                        maxDistance = distance
+                        bestClusterIndexCombination = (clusterIndexA, clusterIndexB)
+
+            if keepClustering:
+                (clusterIndexA, clusterIndexB) = bestClusterIndexCombination
+                clusters[clusterIndexA] += clusters[clusterIndexB]
+                clusters.pop(clusterIndexB)
+                preferenceMatrix[clusterIndexA] = np.logical_and(preferenceMatrix[clusterIndexA], preferenceMatrix[clusterIndexB])
+                preferenceMatrix = np.delete(preferenceMatrix, clusterIndexB, axis = 0)
+
+        return clusters, preferenceMatrix
+
+    @staticmethod
+    def _createPreferenceMatrix(points, lines, consensusThreshold):
+        preferenceMatrix = np.zeros([len(points), len(lines)], dtype = int)
+        for pointIndex, point in enumerate(points):
+            for lineIndex, line in enumerate(lines):
+                preferenceMatrix[pointIndex, lineIndex] = 1 if line.distance_point(point) <= consensusThreshold else 0
+        return preferenceMatrix
+
+    @staticmethod
+    def _intersectionOverUnion(setA, setB):
+        intersection = np.count_nonzero(np.logical_and(setA, setB))
+        union = np.count_nonzero(np.logical_or(setA, setB))
+        return 1. * intersection / union
+
+    @staticmethod
+    def _getLineIndexes(preferenceMatrix):
+        return [list(lines).index(1) for lines in preferenceMatrix]