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)