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