Python for simple random simulations-toss and toss coins

Or in the last mention of the charm of the book, see the simulation of this chapter, there is a Python simulation script, but the book is not complete, they simply wrote down.

Process: Under different equilibrium parameters P (uniform at 0.5), simulate 60 experiments, each coin 8 times, count the number of positive face up, and plotted.

Import Randomimport Matplotlib.pyplot as pltrepeats, tosses =, 8# p is the balance parameter, tosses is the number of coins thrown in each repetition test # Returns the current equilibrium parameter p,     Number of positive times in 8 experiments def heads (tosses, p): h = 0 for x in range (0, tosses): if Random.random () < P:h + = 1 return HX = [] # store balance parameter py = [] # Store each p, 60 repetitions of the H value p = 0 # Initialize p, starting from 0 while p < 1.01:hh = [] for T in RA        Nge (0, repeats): H = Heads (tosses,p) # Add a small jitter value to prevent overlapping of points H + = (Random.random ()/4) *random.choice ([ -1,1]) Hh.append (h) #print (p, ' t ', Heads (tosses,p)) y.append (HH) x.append (p) p + = 0.05# arrows Callout p=0.5, that is, the point pl of the coin balance T.plot (x, y, ' g^ ') plt.annotate (' Balence ', xy= (0.5, 5), xytext= (0.15, 8), arrowprops=dict (facecolor= ' black ', Shrin k=0.05),) plt.show () "# An interesting attempt ... # start not to make the ideal picture, think an x, corresponding to multiple Y can not draw (in fact, forget the point of jitter) ... # Here the Loop plot is also possible. For I in range (6 0): Q = [] for n in range (len (y)): yn = y[n][i] q.append (yn) plt.plot (x,q, ' ro ') '

The output graph is as follows:

It can be seen that, in the case of p=0.5, where the coin is evenly spaced, the number of positive occurrences of the coin is mostly near 4.

Python for simple random simulations-toss and toss coins