def test_win_base():
signal = data_month['sku_num_sum'].values
n, dim = 500, 3 # number of samples, dimension
n_bkps, sigma = 3, 5 # number of change points, noise standart deviation
signal, bkps = rpt.pw_constant(n, dim, n_bkps, noise_std=sigma)
# change point detection
model = "l2" # "l1", "rbf", "linear", "normal", "ar"
algo = rpt.Window(width=40, model=model).fit(signal)
my_bkps = algo.predict(n_bkps=3)
# show results
rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
plt.show()
# change point detection
model = "l2" # "l1", "rbf", "linear", "normal", "ar"
algo = rpt.Window(width=40, model=model).fit(signal)
my_bkps = algo.predict(n_bkps=3)
# show results
rpt.show.display(signal, my_bkps, figsize=(10, 6))
plt.show()
pass
def test_1():
# generate signal
n_samples, dim, sigma = 1000, 3, 4
n_bkps = 4 # number of breakpoints
signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)
# detection
# algo = rpt.Pelt(model="rbf").fit(signal[:,1])
rpt_data = data_month['sku_num_sum'].values
# rpt_data = data['sku_num_sum'].values
algo = rpt.Pelt(model="rbf").fit(rpt_data)
# algo = rpt.Binseg(model="rbf").fit(rpt_data)
res_kps = algo.predict(pen=3)
# display
# rpt.display(signal, bkps, res_kps)
rpt.display(rpt_data, res_kps)
plt.show()
import openpyxl
import numpy as np
import matplotlib.pyplot as plt
import ruptures as rpt
# generate signal
n_samples, dim, sigma = 1000, 3, 4
n_bkps = 3 # number of breakpoints
signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)
# open data points
print(signal[0] [1])
workbook = openpyxl.load_workbook('TemperingTestdaten_var4.xlsx')
print(type(workbook))
worksheet = workbook.get_sheet_by_name('Tabelle9')
data = np.zeros((3300,1))
for i in range(0,3300):
data[i]= float(worksheet.cell(row=i+3, column=3).value)
#for i in range(0,3300):
# data [i][1] = float(worksheet.cell(row=i+3, column=3).value)