Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic links in README
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (0.8%) to scientific vocabulary
Keywords
multi-instance-learning
Last synced: 6 months ago
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JSON representation
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Repository
Repo for master thesis
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
multi-instance-learning
Created about 4 years ago
· Last pushed almost 4 years ago
Metadata Files
Citation
Owner
- Name: Ruben Vijverman
- Login: blubber-rubber
- Kind: user
- Repositories: 1
- Profile: https://github.com/blubber-rubber
Citation (CitationKNN.py)
from Utils import Owa_weights
from Utils.BagEncodings import *
from Utils.Distances import *
def citationKNN(X, test, k=5, dist=None, internal_dist=distance.euclidean, weight=None, encoding=None):
if k >= len(X):
return int(sum(bag.iloc[0, -1] for bag in X) / len(X) >= 0.5)
distances = [math.inf] * k
neighbour_classes = [None] * k
for bag in X:
if weight is not None: # distances where weight should be passed on
test_distance = dist(bag.iloc[:, 1:-1], test.iloc[:, 1:-1], internal_dist=internal_dist, weight=weight)
elif encoding is not None: # distances where encoding should be passed on
test_distance = dist(bag.iloc[:, :-1], test.iloc[:, :-1], internal_dist=internal_dist, encoding=encoding)
else: # distances that do not use owa or encoding
test_distance = dist(bag.iloc[:, 1:-1], test.iloc[:, 1:-1], internal_dist=internal_dist)
index = k - 1
while test_distance < distances[index] and index >= 0:
index -= 1
if index + 1 < k:
distances.insert(index + 1, test_distance)
neighbour_classes.insert(index + 1, bag.iloc[0, -1])
distances = distances[:k]
neighbour_classes = neighbour_classes[:k]
return int(sum(neighbour_classes) / k >= 0.5)