Deep Learning Probability

deeplprob.py

try: #If running in colab 
    import google.colab
    IN_COLAB = True
    %tensorflow_version 2.x
except:
    IN_COLAB = False
 
import tensorflow as tf
if (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed
    print('Please install tensorflow 2.0 to run this notebook')
print('Tensorflow version: ',tf.__version__, ' running in colab?: ', IN_COLAB)
 
#load required libraries:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('default')
 
# Assume a die with only one face with a joker sign
# Calculate probability to observe in 6 throws 1- or 2-times the J-sign
6*(1/6)*(5/6)**5, 15*(1/6)**2*(5/6)**4
 
from scipy.stats import binom
# Define the numbers of possible successes (0 to 6)
njoker = np.asarray(np.linspace(0,6,7), dtype='int') 
# Calculate probability to get the different number of possible successes
pjoker_sign = binom.pmf(k=njoker, n=6, p=1/6)    
plt.stem(njoker, pjoker_sign)
plt.xlabel('Number of Joker signs')
plt.ylabel('Probability')