The goal is to simulate the actual number of occurrence given theoretical probabilities.

For example, a 6-faces biased dice with probability of landing (1,2,3,4,5,6) being (0.1,0.2,0.15,0.25,0.1,0.2). Roll the dice for 1000 times and output simulated number of getting each face.

I know numpy.random.choices offer the function to generate each rolling, but I need kind of summary of number of landing of each face. What is the optimal scripts with Python for above?

CodePudding user response：

Numpy can be used to do that easily and very efficently:

faces = np.arange(0, 6)
faceProbs = [0.1, 0.2, 0.15, 0.25, 0.1, 0.2]        # Define the face probabilities
v = np.random.choice(faces, p=faceProbs, size=1000) # Roll the dice for 1000 times
counts = np.bincount(v, minlength=6)                # Count the existing occurrences
prob = counts / len(v)                              # Compute the probability