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refactoring

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This commit is contained in:
frankknoll
2023-11-17 07:45:20 +01:00
parent fe7c2b1c88
commit fcd8614420
2 changed files with 18 additions and 14 deletions
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22
src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitter.py
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@@ -1,18 +1,26 @@
import numpy as np
from skspatial.objects import Line
# implementation of "Robust Multiple Structures Estimation with J-linkage"
# implementation of "Robust Multiple Structures Estimation with J-linkage" adapted from https://github.com/fkluger/vp-linkage
class MultiLineFitter:
@staticmethod
def fitLines(points, lines, consensusThreshold):
preferenceMatrix = MultiLineFitter._createPreferenceMatrix(points, lines, consensusThreshold)
_, preferenceMatrix4Clusters = MultiLineFitter.createClusters(preferenceMatrix)
_, preferenceMatrix4Clusters = MultiLineFitter._createClusters(preferenceMatrix)
lineIndexes = MultiLineFitter._getLineIndexes(preferenceMatrix4Clusters)
return [lines[lineIndex] for lineIndex in lineIndexes]
@staticmethod
def createClusters(preferenceMatrix):
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 _createClusters(preferenceMatrix):
keepClustering = True
numClusters = preferenceMatrix.shape[0]
clusters = [[i] for i in range(numClusters)]
@@ -40,14 +48,6 @@ class MultiLineFitter:
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))

10
src/SymptomsCausedByVaccines/MultiLineFitting/MultiLineFitterTest.py
View File

@@ -55,7 +55,7 @@ class MultiLineFitterTest(unittest.TestCase):
])
# When
clusters, _ = MultiLineFitter.createClusters(preferenceMatrix)
clusters, _ = MultiLineFitter._createClusters(preferenceMatrix)
# Then
np.testing.assert_array_equal(
@@ -77,7 +77,7 @@ class MultiLineFitterTest(unittest.TestCase):
])
# When
clusters, _ = MultiLineFitter.createClusters(preferenceMatrix)
clusters, _ = MultiLineFitter._createClusters(preferenceMatrix)
# Then
np.testing.assert_array_equal(
@@ -107,9 +107,13 @@ class MultiLineFitterTest(unittest.TestCase):
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])
line3 = Line.from_points([0, 0], [0, 1])
# When
fittedLines = MultiLineFitter.fitLines(points, lines = [line1, line2], consensusThreshold = 0.001)
fittedLines = MultiLineFitter.fitLines(points, lines = [line1, line2, line3], consensusThreshold = 0.001)
# Then
np.testing.assert_array_equal(fittedLines, [line1, line2])
#FK-TODO: erzeuge LinesFactory.createLines(points = [(1, 0), (2, 0), (3, 0), (1, 1), (2, 2), (3, 3)])
# Diese Funktion soll alle Linien erzeugen, die jeweils zwei verschiedene Punkte aus points verbinden.