refining MultiLineFitterTest

src/HowBadIsMyBatch.ipynb

@@ -669,8 +669,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "symptomX = 'Immunosuppression'\n",
-    "symptomY = 'Infection' # 'Immunoglobulin therapy'"
+    "symptomX = 'HIV test' # 'Immunosuppression'\n",
+    "symptomY = 'Immunoglobulin therapy' # 'Infection' # 'Immunoglobulin therapy'"
    ]
   },
   {
@@ -723,7 +723,7 @@
     "    s = 100,\n",
     "    label = \"Dots\")\n",
     "for cluster, line in zip(clusters, lines):\n",
-    "    if(len(cluster) > 2):\n",
+    "    if len(cluster) >= 3:\n",
     "        coords = line.transform_points(cluster)\n",
     "        magnitude = line.direction.norm()\n",
     "        line.plot_2d(ax, t_1 = min(coords) / magnitude, t_2 = max(coords) / magnitude)\n",

src/SymptomsCausedByVaccines/MultiLineFitting/CharacteristicFunctions.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+class CharacteristicFunctions:
+
+    @staticmethod
+    def apply(characteristicFunction, elements):
+        return np.array(elements)[CharacteristicFunctions._getIndexes(characteristicFunction)]
+
+    @staticmethod
+    def _getIndexes(characteristicFunction):
+        return [index for (index, value) in enumerate(characteristicFunction) if value == 1]

src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitter.py

@@ -1,6 +1,7 @@
 import numpy as np
 from SymptomsCausedByVaccines.MultiLineFitting.LinesFactory import LinesFactory
 from SymptomsCausedByVaccines.MultiLineFitting.Utils import getPairs
+from SymptomsCausedByVaccines.MultiLineFitting.CharacteristicFunctions import CharacteristicFunctions
 
 # implementation of "Robust Multiple Structures Estimation with J-linkage" adapted from https://github.com/fkluger/vp-linkage
 class MultiLineFitter:
@@ -12,10 +13,22 @@ class MultiLineFitter:
     @staticmethod
     def fitLines(points, lines, consensusThreshold):
         preferenceMatrix = MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold)
-        clusters, preferenceMatrix4Clusters = MultiLineFitter._createClusters(preferenceMatrix)
+        _, preferenceMatrix4Clusters = MultiLineFitter._createClusters(preferenceMatrix)
+        fittedLines = MultiLineFitter._getLines(lines, preferenceMatrix4Clusters)
         return (
-            MultiLineFitter._getClusterPoints(points, clusters),
-            MultiLineFitter._getLines(lines, preferenceMatrix4Clusters))
+            MultiLineFitter._getFittedPointsList(points, fittedLines, consensusThreshold),
+            fittedLines)
+
+    @staticmethod
+    def _getFittedPointsList(points, lines, consensusThreshold):
+        return MultiLineFitter._getPointsList(
+            points,
+            MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold))
+
+    @staticmethod
+    def _getPointsList(points, preferenceMatrix):
+        characteristicFunctionsOfConsensusSets = np.transpose(preferenceMatrix)
+        return [CharacteristicFunctions.apply(characteristicFunctionOfConsensusSet, points) for characteristicFunctionOfConsensusSet in characteristicFunctionsOfConsensusSets]
 
     @staticmethod
     def _createPreferenceMatrix(points, lines, consensusThreshold):

src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitterTest.py

@@ -111,10 +111,10 @@ class MultiLineFitterTest(unittest.TestCase):
 
     def test_fitLines(self):
         # Given
-        points = [(1, 0), (2, 0), (3, 0), (1, 1), (2, 2), (3, 3)]
+        points = [(0, 0), (1, 0), (2, 0), (1, 1), (2, 2)]
         line1 = Line.from_points([0, 0], [1, 0])
         line2 = Line.from_points([0, 0], [1, 1])
-        line3 = Line.from_points([0, 0], [0, 1])
+        line3 = Line.from_points([-10, 0], [-10, 1])
 
         # When
         clusters, fittedLines = MultiLineFitter.fitLines(points, lines = [line1, line2, line3], consensusThreshold = 0.001)
@@ -129,13 +129,13 @@ class MultiLineFitterTest(unittest.TestCase):
         np.testing.assert_array_equal(
             clusters,
             [
-                [(1, 0), (2, 0), (3, 0)],
-                [(1, 1), (2, 2), (3, 3)]
+                [(0, 0), (1, 0), (2, 0)],
+                [(0, 0), (1, 1), (2, 2)]
             ])
 
     def test_fitPointsByLines(self):
         # Given
-        points = [(1, 0), (2, 0), (3, 0), (1, 1), (2, 2), (3, 3)]
+        points = [(0, 0), (1, 0), (2, 0), (1, 1), (2, 2)]
 
         # When
         clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.001)
@@ -147,6 +147,6 @@ class MultiLineFitterTest(unittest.TestCase):
         np.testing.assert_array_equal(
             clusters,
             [
-                [(1, 0), (2, 0), (3, 0)],
-                [(1, 1), (2, 2), (3, 3)]
+                [(0, 0), (1, 0), (2, 0)],
+                [(0, 0), (1, 1), (2, 2)]
             ])