def kneedle_novel(points, args):
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
knees = kneedle.auto_knees(points_reduced, p=kneedle.PeakDetection.All)
#x = points_reduced[:, 0]
#y = points_reduced[:, 1]
#plt.plot(x, y)
#plt.plot(x[knees], y[knees], 'r+')
#plt.show()
knees = pp.filter_worst_knees(points_reduced, knees)
knees = pp.filter_corner_knees(points_reduced, knees, t=args.c)
knees = pp.filter_clusters(points_reduced, knees, clustering.average_linkage, args.t, args.k)
knees = rdp.mapping(knees, reduced, removed)
return knees
def main(args):
# get the expected file from the input file
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
expected_file = os.path.join(os.path.normpath(dirname), f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
points = np.genfromtxt(args.i, delimiter=',')
## Knee detection code ##
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
knees = np.arange(1, len(reduced))
t_k = pp.filter_worst_knees(points_reduced, knees)
t_k = pp.filter_corner_knees(points_reduced, t_k, t=args.c)
filtered_knees = pp.filter_clusters(points_reduced, t_k, clustering.average_linkage, args.t, args.k)
##########################################################################################
# add even points
if args.a:
knees = pp.add_points_even(points, reduced, filtered_knees, removed)
else:
knees = rdp.mapping(filtered_knees, reduced, removed)
rmspe_k = evaluation.rmspe(points, knees, expected, evaluation.Strategy.knees)
rmspe_e = evaluation.rmspe(points, knees, expected, evaluation.Strategy.expected)
cm = evaluation.cm(points, knees, expected, t = 0.01)
mcc = evaluation.mcc(cm)
logger.info(f'RMSE(knees) RMSE(exp) MCC')
logger.info(f'-------------------------------------------')
logger.info(f'{rmspe_k:10.2E} {rmspe_e:10.2E} {mcc:10.2E}')
# store outpout
if args.o:
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
output = os.path.join(os.path.normpath(dirname), f'{filename}_output.csv')
dataset = points[knees]
with open(output, 'w') as f:
writer = csv.writer(f)
writer.writerows(dataset)
# display result
if args.g:
x = points[:, 0]
y = points[:, 1]
plt.plot(x, y)
plt.plot(x[knees], y[knees], 'r+')
plt.show()