def stdRunClassify(path):
"""
Standard run for classify pre given files based on their respective
folders.
Returns a tuple with the test and data sets.
"""
folders = files.getDir(path)
data = stdClassify(path, folders)
return makeSets(data)
def stdRunClassify(path):
"""
Standard run for classify pre given files based on their respective
folders.
Returns a tuple with the test and data sets.
"""
folders = files.getDir(path)
data = stdClassify(path, folders)
return makeSets(data)
def runOnClassified(data):
for i in range(len(data)):
aux = stdShannonRun(data[i][1], graph=False, reader=False)
beats = pul.findBeats(aux, 4, 6)
t1, t2 = pul.getT(aux, beats, flt.distinguish(aux), 0.01)
t11 = pul.getT11(beats[0], flt.distinguish(aux))
t12 = pul.getT12(beats[0], flt.distinguish(aux))
data[i][1] = [[t11], [t12], [t1], [t2]]
return data
if __name__ == '__main__':
import os
path = sys.argv[1]
folders = files.getDir(path)
cl = stdClassify(path, folders)
cl = runOnClassified(cl)
test, train = makeSets(cl, perc=80)
print("test", test)
print("train", train)
pass
knn = KNN(train, 1)
formated = formatting(test)
result = []
for item in formated:
classification = knn.classify(item, 5)
result.append([item[-1], classification])
print("result ", result)
print(len(result))
matrix = confusionMatrix(result)
return makeSets(data)
def runOnClassified(data):
for i in range(len(data)):
aux = stdShannonRun(data[i][1], graph=False, reader=False)
beats = pul.findBeats(aux, 4, 6)
t1, t2 = pul.getT(aux, beats, flt.distinguish(aux), 0.01)
t11 = pul.getT11(beats[0], flt.distinguish(aux))
t12 = pul.getT12(beats[0], flt.distinguish(aux))
data[i][1] = [[t11],[t12],[t1],[t2]]
return data
if __name__ == '__main__':
import os
path = sys.argv[1]
folders = files.getDir(path)
cl = stdClassify(path, folders)
cl = runOnClassified(cl)
test, train = makeSets(cl, perc=80)
print("test",test)
print("train",train)
pass
knn = KNN(train, 1)
formated = formatting(test)
result = []
for item in formated:
classification = knn.classify(item,5)
result.append([item[-1], classification])
print("result ",result)
print(len(result))
matrix = confusionMatrix(result)
