for L in LS:
for I in IS:
# data = data_stream(path='C:\\Users\\karnk\\git\\data_stream\\dataset', type=type, pattern=pattern, len=L,
# interval=I)
data = data_stream(
path=
'D:\\git_project\\data stream\\lightcurve_benchmark\\{}'
.format(data_type),
type=type,
pattern=pattern,
len=L,
interval=I)
data.load_data_fromfile()
# binning process
cheb = adwin(max_window=60, k=k)
for i, file_name in enumerate(data.get_files_test()):
change_points = []
# file_name = data.get_file_name(i)
instances = data.get_dataset_test(i)
for index, instance in enumerate(instances):
is_change = cheb.add_element(instance)
if is_change:
change_points.append(index)
# change_points = [1,200,300]
data.set_change_points(change_points)
# print(change_points)
# result_name = '{}_W{}_L{}'.format(type,L,I)
IS = [5,10,30,60]
process_var = 1
process_mean = 0
for type in types:
for pattern in patterns:
for L in LS:
for I in IS:
# data = data_stream(path='C:\\Users\\karnk\\git\\data_stream\\dataset', type=type, pattern=pattern, len=L,
# interval=I)
data = data_stream(path='D:\\git_project\\data stream\\lightcurve_benchmark\\pca', type=type,
pattern=pattern, len=L,
interval=I)
data.load_data_fromfile()
# cheb
cheb = adwin(max_window=500,k=3)
#Kalman
for i,file_name in enumerate(data.get_files_test()):
change_points = []
# file_name = data.get_file_name(i)
measurements = data.get_dataset_test(i)
xs = np.zeros((len(measurements), 2))
ps = np.zeros((len(measurements), 2))
x = x_start
x_var = x_var_start
for index, measurement in enumerate(measurements):
x, x_var = kalman.update(x, x_var, measurement, model_variance)
xs[i] = x,x_var