def running_agglo_2(xTr,
patClassIdTr,
xVal,
patClassIdVal,
teta=0.26,
sigma=0.5,
simil='short'):
accelClassifier = AccelBatchGFMM(gamma=1,
teta=teta,
bthres=sigma,
simil=simil,
sing='max',
isDraw=False,
oper='min',
isNorm=False)
accelClassifier.fit(xTr, xTr, patClassIdTr)
result = accelClassifier.predict(xVal, xVal, patClassIdVal)
err = 1
if result != None:
err = result.summis / len(patClassIdVal)
return err
teta=teta,
bthres=simil_thres,
simil='short',
sing='max',
isDraw=False,
oper='min',
isNorm=False)
accelClassifier.fit(Xtr, Xtr, patClassIdTr)
training_time_short_si_save = np.append(
training_time_short_si_save,
accelClassifier.elapsed_training_time)
numhyperbox_short_si_save = np.append(numhyperbox_short_si_save,
len(accelClassifier.classId))
result = accelClassifier.predict(Xtest, Xtest, patClassIdTest)
if result != None:
numTestSample = Xtest.shape[0]
err = result.summis / numTestSample
testing_error_short_si_save = np.append(
testing_error_short_si_save, err)
for simil_thres in np.arange(0.02, 1, 0.02):
accelClassifier = AccelBatchGFMM(gamma=1,
teta=teta,
bthres=simil_thres,
simil='long',
sing='max',
isDraw=False,
oper='min',
isNorm=False)
training_time_online_gfmm_save = np.append(training_time_online_gfmm_save, olnClassifier.elapsed_training_time + (end_t - start_t))
numhyperbox_online_gfmm_save = np.append(numhyperbox_online_gfmm_save, len(olnClassifier.classId))
result = olnClassifier.predict(testingData, testingData, testingLabel)
if result != None:
err = np.round(result.summis / numTestSample * 100, 3)
testing_error_online_gfmm_save = np.append(testing_error_online_gfmm_save, err)
# agglo-2
accelClassifier = AccelBatchGFMM(gamma = 1, teta = tetaAGGLO2, bthres = 0, simil = 'long', sing = 'min', isDraw = False, oper = 'min', isNorm = False)
accelClassifier.fit(trainingData, trainingData, trainingLabel)
numhyperbox_before_prun_accel_agglo_save = np.append(numhyperbox_before_prun_accel_agglo_save, len(accelClassifier.classId))
result = accelClassifier.predict(testingData, testingData, testingLabel)
if result != None:
err = np.round(result.summis / numTestSample * 100, 3)
testing_error_before_prun_accel_agglo_save = np.append(testing_error_before_prun_accel_agglo_save, err)
start_t = time.perf_counter()
accelClassifier.pruning_val(validationData, validationData, validationLabel)
end_t = time.perf_counter()
training_time_accel_agglo_save = np.append(training_time_accel_agglo_save, accelClassifier.elapsed_training_time + (end_t - start_t))
numhyperbox_accel_agglo_save = np.append(numhyperbox_accel_agglo_save, len(accelClassifier.classId))
result = accelClassifier.predict(testingData, testingData, testingLabel)
if result != None: