refining MultiLineFitterTest

src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitter.py

@@ -2,8 +2,15 @@ import numpy as np
 from skspatial.objects import Line
 
 # implementation of "Robust Multiple Structures Estimation with J-linkage"
-class ClustersFactory:
+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
@@ -18,7 +25,7 @@ class ClustersFactory:
                 preferenceSetA = preferenceMatrix[clusterIndexA]
                 for clusterIndexB in range(clusterIndexA):
                     preferenceSetB = preferenceMatrix[clusterIndexB]
-                    distance = ClustersFactory._intersectionOverUnion(preferenceSetA, preferenceSetB);
+                    distance = MultiLineFitter._intersectionOverUnion(preferenceSetA, preferenceSetB);
                     if distance > maxDistance:
                         keepClustering = True
                         maxDistance = distance

src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitterTest.py

@@ -1,10 +1,10 @@
 import unittest
 import numpy as np
 from skspatial.objects import Line
-from SymptomsCausedByVaccines.MultiLineFitting.ClustersFactory import ClustersFactory
+from SymptomsCausedByVaccines.MultiLineFitting.MultiLineFitter import MultiLineFitter
 
 
-class ClustersFactoryTest(unittest.TestCase):
+class MultiLineFitterTest(unittest.TestCase):
 
     def test_createPreferenceMatrix(self):
         # Given
@@ -13,7 +13,7 @@ class ClustersFactoryTest(unittest.TestCase):
         consensusThreshold = 4.0
 
         # When
-        preferenceMatrix = ClustersFactory._createPreferenceMatrix(points, lines, consensusThreshold)
+        preferenceMatrix = MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold)
 
         # Then
         np.testing.assert_array_equal(
@@ -31,7 +31,7 @@ class ClustersFactoryTest(unittest.TestCase):
         consensusThreshold = 0.001
 
         # When
-        preferenceMatrix = ClustersFactory._createPreferenceMatrix(points, lines, consensusThreshold)
+        preferenceMatrix = MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold)
 
         # Then
         np.testing.assert_array_equal(
@@ -55,7 +55,7 @@ class ClustersFactoryTest(unittest.TestCase):
                 ])
 
         # When
-        clusters, _ = ClustersFactory.createClusters(preferenceMatrix)
+        clusters, _ = MultiLineFitter.createClusters(preferenceMatrix)
 
         # Then
         np.testing.assert_array_equal(
@@ -77,7 +77,7 @@ class ClustersFactoryTest(unittest.TestCase):
                 ])
 
         # When
-        clusters, _ = ClustersFactory.createClusters(preferenceMatrix)
+        clusters, _ = MultiLineFitter.createClusters(preferenceMatrix)
 
         # Then
         np.testing.assert_array_equal(
@@ -97,7 +97,19 @@ class ClustersFactoryTest(unittest.TestCase):
                 ])
 
         # When
-        lineIndexes = ClustersFactory._getLineIndexes(preferenceMatrix)
+        lineIndexes = MultiLineFitter._getLineIndexes(preferenceMatrix)
 
         # Then
         np.testing.assert_array_equal(lineIndexes, [2, 1])
+
+    def test_fitLines(self):
+        # Given
+        points = [(1, 0), (2, 0), (3, 0), (1, 1), (2, 2), (3, 3)]
+        line1 = Line.from_points([0, 0], [1, 0])
+        line2 = Line.from_points([0, 0], [1, 1])
+
+        # When
+        fittedLines = MultiLineFitter.fitLines(points, lines = [line1, line2], consensusThreshold = 0.001)
+
+        # Then
+        np.testing.assert_array_equal(fittedLines, [line1, line2])