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* The black formatter is no longer beta
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* pre-commit autoupdate
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62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
"""
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Reference: https://en.wikipedia.org/wiki/Gaussian_function
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"""
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from numpy import exp, pi, sqrt
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def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
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"""
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>>> gaussian(1)
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0.24197072451914337
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>>> gaussian(24)
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3.342714441794458e-126
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>>> gaussian(1, 4, 2)
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0.06475879783294587
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>>> gaussian(1, 5, 3)
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0.05467002489199788
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Supports NumPy Arrays
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Use numpy.meshgrid with this to generate gaussian blur on images.
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>>> import numpy as np
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>>> x = np.arange(15)
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>>> gaussian(x)
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array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03,
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1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
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5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
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2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
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>>> gaussian(15)
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5.530709549844416e-50
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>>> gaussian([1,2, 'string'])
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Traceback (most recent call last):
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...
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TypeError: unsupported operand type(s) for -: 'list' and 'float'
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>>> gaussian('hello world')
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Traceback (most recent call last):
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...
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TypeError: unsupported operand type(s) for -: 'str' and 'float'
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>>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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OverflowError: (34, 'Result too large')
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>>> gaussian(10**-326)
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0.3989422804014327
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>>> gaussian(2523, mu=234234, sigma=3425)
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0.0
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"""
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return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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