|
| 1 | + |
| 2 | +""" |
| 3 | +Reference: https://en.wikipedia.org/wiki/Gaussian_function |
| 4 | +
|
| 5 | +python/black : True |
| 6 | +python : 3.7.3 |
| 7 | +
|
| 8 | +""" |
| 9 | +from numpy import pi, sqrt, exp |
| 10 | + |
| 11 | + |
| 12 | + |
| 13 | +def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int: |
| 14 | + """ |
| 15 | + >>> gaussian(1) |
| 16 | + 0.24197072451914337 |
| 17 | + |
| 18 | + >>> gaussian(24) |
| 19 | + 3.342714441794458e-126 |
| 20 | +
|
| 21 | + Supports NumPy Arrays |
| 22 | + Use numpy.meshgrid with this to generate gaussian blur on images. |
| 23 | + >>> import numpy as np |
| 24 | + >>> x = np.arange(15) |
| 25 | + >>> gaussian(x) |
| 26 | + array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03, |
| 27 | + 1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12, |
| 28 | + 5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27, |
| 29 | + 2.14638374e-32, 7.99882776e-38, 1.09660656e-43]) |
| 30 | + |
| 31 | + >>> gaussian(15) |
| 32 | + 5.530709549844416e-50 |
| 33 | +
|
| 34 | + >>> gaussian([1,2, 'string']) |
| 35 | + Traceback (most recent call last): |
| 36 | + ... |
| 37 | + TypeError: unsupported operand type(s) for -: 'list' and 'float' |
| 38 | +
|
| 39 | + >>> gaussian('hello world') |
| 40 | + Traceback (most recent call last): |
| 41 | + ... |
| 42 | + TypeError: unsupported operand type(s) for -: 'str' and 'float' |
| 43 | +
|
| 44 | + >>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL |
| 45 | + Traceback (most recent call last): |
| 46 | + ... |
| 47 | + OverflowError: (34, 'Result too large') |
| 48 | +
|
| 49 | + >>> gaussian(10**-326) |
| 50 | + 0.3989422804014327 |
| 51 | +
|
| 52 | + >>> gaussian(2523, mu=234234, sigma=3425) |
| 53 | + 0.0 |
| 54 | + """ |
| 55 | + return 1 / sqrt(2 * pi * sigma ** 2) * exp(-(x - mu) ** 2 / 2 * sigma ** 2) |
| 56 | + |
| 57 | + |
| 58 | +if __name__ == "__main__": |
| 59 | + import doctest |
| 60 | + |
| 61 | + doctest.testmod() |
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