It doesn't work

[keithbriggs]

kbriggs:~/Downloads/NPEET> python3 test.py
For a uniform distribution with width alpha, the differential entropy is log_2 alpha, setting alpha = 2
and using k=1, 2, 3, 4, 5
Traceback (most recent call last):
File "./test.py", line 16, in
print("result:", [ee.entropy([[2 * random.random()] for i in range(1000)], k=j + 1) for j in range(5)])
File "./test.py", line 16, in
print("result:", [ee.entropy([[2 * random.random()] for i in range(1000)], k=j + 1) for j in range(5)])
File "/home/kbriggs/Downloads/NPEET/entropy_estimators.py", line 28, in entropy
return (const + d * np.mean(map(log, nn))) / log(base)
File "/usr/local/lib/python3.4/dist-packages/numpy/core/fromnumeric.py", line 2909, in mean
out=out, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/numpy/core/_methods.py", line 82, in _mean
ret = ret / rcount
TypeError: unsupported operand type(s) for /: 'map' and 'int'

---

kbriggs:~/Downloads/NPEET> python2 test.py
For a uniform distribution with width alpha, the differential entropy is log_2 alpha, setting alpha = 2
and using k=1, 2, 3, 4, 5
('result:', [0.95063690299507952, 0.98051458362141108, 1.0803462913574611, 1.0316551234094444, 1.0289725544677049])

Gaussian random variables

Conditional Mutual Information
covariance matrix
[[4 3 1]
[3 4 1]
[1 1 2]]
('true CMI(x:y|x)', 0.5148736716970265)
('samples used', [10, 25, 50, 100, 200])
('estimated CMI', [0.24721094773861269, 0.39550091844389834, 0.46211431227905897, 0.48994541664326197, 0.49993287186420526])
('95% conf int. (a, b) means (mean - a, mean + b)is interval\n', [(0.32947891495120885, 0.47883105656907937), (0.42410443410041138, 0.40553319741348437), (0.29607520550148525, 0.29646667472554578), (0.17646000212101254, 0.19139043703562886), (0.1623733550388789, 0.19292824321772967)])
Mutual Information
('true MI(x:y)', 0.5963225389711981)
('samples used', [10, 25, 50, 100, 200])
('estimated MI', [0.32218586252030301, 0.54386805987295483, 0.59630897787131887, 0.60762939695898355, 0.60418593716673841])
('95% conf int.\n', [(0.42363251380820954, 0.46980791508516928), (0.46583034399247247, 0.50990786157500079), (0.35170125121037665, 0.33635610406503746), (0.23654160340493391, 0.30032100823502828), (0.2007355329953654, 0.17193438029361319)])

IF you permute the indices of x, e.g., MI(X:Y) = 0
('samples used', [10, 25, 50, 100, 200])
('estimated MI', [0.032435448506589186, -0.027013576228861892, -0.0048799193000058135, 0.0023174460892350754, -0.0002141277047037321])
('95% conf int.\n', [(0.28988781354141774, 0.41434201574331025), (0.24203605944116111, 0.29849816049646066), (0.18081726377075832, 0.18040335534919902), (0.15879645329878422, 0.22733498191676946), (0.13263900209136867, 0.13325413690339941)])

Test of the discrete entropy estimators

For z = y xor x, w/x, y uniform random binary, we should get H(x)=H(y)=H(z) = 1, H(x:y) etc = 0, H(x:y|z) = 1
Traceback (most recent call last):
File "./test.py", line 116, in
print("H(x), H(y), H(z)", ee.entropyd(x), ee.entropyd(y), ee.entropyd(z))
File "/home/kbriggs/Downloads/NPEET/entropy_estimators.py", line 114, in entropyd
return entropyfromprobs(hist(sx), base=base)
File "/home/kbriggs/Downloads/NPEET/entropy_estimators.py", line 149, in hist
sx = discretize(sx)
File "/home/kbriggs/Downloads/NPEET/entropy_estimators.py", line 280, in discretize
return [discretize_one(x) for x in xs]
File "/home/kbriggs/Downloads/NPEET/entropy_estimators.py", line 275, in discretize_one
if len(x) > 1:
TypeError: object of type 'int' has no len()

[zhengzh]

add this line in discretize_one function works for me
if not isinstance(x, list): return x