def cal_k_tran(data, input_k, input_bin):
x_start = 10
x_var_start = 10000
model_mean = 0
model_variance = 0.01
data.clear_change_points()
cheb = chebyshev_base(min_size=60, max_window=60, k=input_k)
for i, file_name in enumerate(data.get_files_test()):
change_points = []
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
x, x_var = kalman.predict(x, x_var, model_mean, model_variance)
# ps[i] = x, x_var
is_change = cheb.add_element(x)
if is_change:
change_points.append(index)
data.set_change_points(change_points)
return data
def get_changepoint_kalmanBinCheb(self,
instances,
inpput_x=10,
input_var=10000,
model_mean=0,
model_variance=0.01):
x_start = inpput_x
x_var_start = input_var
model_mean = model_mean
model_variance = model_variance
cheb = chebyshev_base(min_size=self.min_winsize,
max_window=self.max_winsize,
k=self.K)
bin = binning(bin_period=self.Bin_size)
change_points = []
measurements = instances
# 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
x, x_var = kalman.predict(x, x_var, model_mean, model_variance)
# ps[i] = x, x_var
is_change = False
can_add_bin = bin.can_add_bin()
if not can_add_bin:
mean_bin = mean(bin.get_bin())
is_change = cheb.add_element(mean_bin)
bin.reset_bin()
bin.add_instance(x)
if is_change:
for j in range(index - self.bin_period + 1, index + 1):
change_points.append(j)
return change_points
def get_changepoint_kalmanCheb(self,
measurements,
inpput_x=10,
input_var=10000,
model_mean=0,
model_variance=0.01):
x_start = inpput_x
x_var_start = input_var
model_mean = model_mean
model_variance = model_variance
cheb = chebyshev_base(min_size=self.min_winsize,
max_window=self.max_winsize,
k=self.K)
change_points = []
# measurements = instances
# 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
x, x_var = kalman.predict(x, x_var, model_mean, model_variance)
# ps[i] = x, x_var
is_change = False
is_change = cheb.add_element(x)
if is_change:
change_points.append(index)
std_low_list = cheb.get_std_low_list()
std_high_list = cheb.get_std_high_list()
mean_list = cheb.get_mean_list()
return change_points, std_low_list, std_high_list, mean_list
def cal_k_tran(data, input_k, input_bin):
x_start = 10
x_var_start = 10000
model_mean = 0
model_variance = 0.01
data.clear_change_points()
cheb = chebyshev_base(min_size=60, max_window=60, k=input_k)
bin = binning(bin_period=input_bin)
for i, file_name in enumerate(data.get_files_test()):
change_points = []
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
x, x_var = kalman.predict(x, x_var, model_mean, model_variance)
ps[i] = x, x_var
is_change = False
can_add_bin = bin.can_add_bin()
if not can_add_bin:
mean_bin = mean(bin.get_bin())
is_change = cheb.add_element(mean_bin)
bin.reset_bin()
bin.add_instance(x)
if is_change:
for j in range(index - bin_period + 1, index + 1):
change_points.append(j)
data.set_change_points(change_points)
return data
#Kalman
for i in range(data.get_file_lenght()):
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):
pre_x = x
x, x_var = kalman.update(x, x_var, measurement,
model_variance)
xs[index] = x, x_var
x, x_var = kalman.predict(x, x_var, model_mean,
model_variance)
ps[index] = x, x_var
if (index == 31300):
print("a")
is_change = kalman_detection.add_element(
pre_x, measurement)
if is_change:
change_points.append(index)
data.set_change_points(change_points)
# print(change_points)
result_name = '{}_W{}_L{}'.format(type, L, I)
data.save_result_to_csv(dataset_index=0,
threshold_after=100,
L=L,