consumtions = Consumption(products, resources) index = 0 for j in range(1, len(products), 2): consumtions[j][1 + index] = 1 consumtions[j + 1][1 + index] = 1 index = index + 1 del index # Erträge revenues = Revenue(products) for j in range(1, len(products), 2): revenues[j] = 100 revenues[j + 1] = 1000 # Buchungsperioden times = Time(100) # Normalverteilung from scipy.stats import norm # Wahrscheinlichkeiten probs = Prob(products, times) probs[1][81:101] = np.linspace(0.3, 0, 20) probs[2][81:101] = np.linspace(0.3, 0, 20) probs[3][61:101] = np.linspace(0.3, 0, 40) probs[4][61:101] = np.linspace(0.3, 0, 40) probs[5][41:61] = np.linspace(0.3, 0, 20) probs[6][41:61] = np.linspace(0.3, 0, 20) probs[7][21:81] = np.linspace(0.3, 0, 60) probs[8][21:81] = np.linspace(0.3, 0, 60) probs[9][1:41] = np.linspace(0.3, 0, 40)
consumtions = Consumption(products, resources) index = 0 for j in range(1, len(products), 2): consumtions[j][1 + index] = 1 consumtions[j + 1][1 + index] = 1 index = index + 1 del index # Erträge revenues = Revenue(products) for j in range(1, len(products), 2): revenues[j] = 100 revenues[j + 1] = 1000 # Buchungsperioden times = Time(30) # Normalverteilung from scipy.stats import norm # Wahrscheinlichkeiten probs = Prob(products, times) probs[1][1:16] = 0.2 probs[2][1:16] = 0.2 probs[3][1:] = 0.2 probs[4][1:] = 0.2 # Gegenwahrscheinlichkeiten against_probs = Against_Prob(probs) probs[0] = against_probs