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)