@Author: Massimiliano Natale
"""
from knn import KNN
from resultHelper import ResultHelper
"""
Trigger the classification.
Create the output file and the chart to visualize the result.
"""
if __name__ == "__main__":
knn = KNN("data/classification/trainingData.csv",
"data/classification/testData.csv")
#K=10, n=2
classificationData = knn.buildClassificationData(
lambda x: knn.classifyWithDistanceWeight(x[:-1], knn.
_trainingData[:, :-1], 10, 2))
# Save partial result to a file and draw the charts
resultHelper = ResultHelper("part2.output.txt")
resultHelper.write(classificationData)
resultHelper.draw(
"KNN classification [weighted-distance] with K=10 and N=2")
#K=20, n=2
classificationData = knn.buildClassificationData(
lambda x: knn.classifyWithDistanceWeight(x[:-1], knn.
_trainingData[:, :-1], 20, 2))
# Save partial result to a file and draw the charts
"""
Classification related to part 1.
KNN classification with K=1 and euclidean distance. Votes are not distance weighted.
@Author: Massimiliano Natale
"""
from knn import KNN
from resultHelper import ResultHelper
"""
Trigger the classification.
Create the output file and the chart to visualize the result.
"""
if __name__ == "__main__":
knn = KNN("data/classification/trainingData.csv",
"data/classification/testData.csv")
classificationData = knn.buildClassificationData(
lambda x: knn.classify(x[:-1], knn._trainingData[:, :-1], 1))
# Save partial result to a file and draw the charts
resultHelper = ResultHelper("part1.output.txt")
resultHelper.write(classificationData)
resultHelper.draw("KNN classification [not-weighted-distance] with K=1")
