From 613006b2a930e9fbca7e91d1cec46925ba9914d1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=B8rgen=20Galdal?= <jorgen.4@live.no>
Date: Sun, 18 Aug 2024 13:51:26 +0000
Subject: [PATCH] cluttering -> clustering

---
 algorithm/src/mip_matching/match_meetings.py | 26 ++++++++++----------
 algorithm/src/mip_matching/utils.py          |  2 +-
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/algorithm/src/mip_matching/match_meetings.py b/algorithm/src/mip_matching/match_meetings.py
index 8af9371d..27f6d6bb 100644
--- a/algorithm/src/mip_matching/match_meetings.py
+++ b/algorithm/src/mip_matching/match_meetings.py
@@ -15,11 +15,11 @@
 APPLICANT_BUFFER_LENGTH = timedelta(minutes=15)
 
 # Et mål på hvor viktig det er at intervjuer er i nærheten av hverandre
-CLUTTERING_WEIGHT = 0.001
+CLUSTERING_WEIGHT = 0.001
 
 # Når på dagen man helst vil ha intervjuene rundt
-CLUTTERING_TIME_BASELINE = time(12, 00)
-MAX_SCALE_CLUTTERING_TIME = timedelta(seconds=43200)  # TODO: Rename variable
+CLUSTERING_TIME_BASELINE = time(12, 00)
+MAX_SCALE_CLUSTERING_TIME = timedelta(seconds=43200)  # TODO: Rename variable
 
 
 class MeetingMatch(TypedDict):
@@ -71,25 +71,25 @@ def match_meetings(applicants: set[Applicant], committees: set[Committee]) -> Me
                 model += m[(applicant, *interview_a)] + \
                     m[(applicant, *interview_b)] <= 1  # type: ignore
 
-    # Legger til sekundærmål om at man ønsker å sentrere intervjuer rundt CLUTTERING_TIME_BASELINE
-    cluttering_objectives = []
+    # Legger til sekundærmål om at man ønsker å sentrere intervjuer rundt CLUSTERING_TIME_BASELINE
+    clustering_objectives = []
 
     for name, variable in m.items():
         applicant, committee, interval = name
-        if interval.start.time() < CLUTTERING_TIME_BASELINE:
-            relative_distance_from_baseline = subtract_time(CLUTTERING_TIME_BASELINE,
-                                                            interval.end.time()) / MAX_SCALE_CLUTTERING_TIME
+        if interval.start.time() < CLUSTERING_TIME_BASELINE:
+            relative_distance_from_baseline = subtract_time(CLUSTERING_TIME_BASELINE,
+                                                            interval.end.time()) / MAX_SCALE_CLUSTERING_TIME
         else:
             relative_distance_from_baseline = subtract_time(interval.start.time(),
-                                                            CLUTTERING_TIME_BASELINE) / MAX_SCALE_CLUTTERING_TIME
+                                                            CLUSTERING_TIME_BASELINE) / MAX_SCALE_CLUSTERING_TIME
 
-        cluttering_objectives.append(
-            CLUTTERING_WEIGHT * relative_distance_from_baseline * variable)  # type: ignore
+        clustering_objectives.append(
+            CLUSTERING_WEIGHT * relative_distance_from_baseline * variable)  # type: ignore
 
         # Setter mål til å være maksimering av antall møter
-        # med sekundærmål om å samle intervjuene rundt CLUTTERING_TIME_BASELINE
+        # med sekundærmål om å samle intervjuene rundt CLUSTERING_TIME_BASELINE
     model.objective = mip.maximize(
-        mip.xsum(m.values()) + mip.xsum(cluttering_objectives))
+        mip.xsum(m.values()) + mip.xsum(clustering_objectives))
 
     # Kjør optimeringen
     solver_status = model.optimize()
diff --git a/algorithm/src/mip_matching/utils.py b/algorithm/src/mip_matching/utils.py
index 8f9dd758..b248ae3e 100644
--- a/algorithm/src/mip_matching/utils.py
+++ b/algorithm/src/mip_matching/utils.py
@@ -17,7 +17,7 @@ def group_by_committee(meetings: list[tuple[Applicant, Committee, TimeInterval]]
     return result
 
 
-def measure_cluttering(meetings: list[tuple[Applicant, Committee, TimeInterval]]) -> int:
+def measure_clustering(meetings: list[tuple[Applicant, Committee, TimeInterval]]) -> int:
     grouped_meetings = group_by_committee(meetings)
 
     holes = 0