I wrote a blog post about this here.
I tweeted about it here and again
Inspired, of course, by the various wonderful comparisons in the sklearn docs, like this one for classifiers.
| # We want to get unique items in a sequence, but to keep the order in which they appear. | |
| # There are quite a few solutions here > http://www.peterbe.com/plog/uniqifiers-benchmark | |
| # Good, up to date summary of methods > https://stackoverflow.com/a/17016257/3381305 | |
| # Some test data: text... | |
| tdat = 100 * ['c', 'a', 'c', 'b', 'e', 'd', 'f', 'g', 'h', 'i', 'j', 'j'] | |
| tarr = np.array(tdat) | |
| tser = pd.Series(tdat) | |
| # ... and numbers. | |
| narr = np.random.randint(0, 10, size=1200) |
Answering this question on Cross Validated.
| # Properties of the scaled standard deviational hyperellipsoid. | |
| # | |
| # Author: Matt Hall, [email protected] | |
| # Copyright: 2022, Matt Hall | |
| # Licence: Apache 2.0, https://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # These small functions implement n-dimensional lookup of the beta-distribution | |
| # approximation to this problem. They answer the questions, "What proportion | |
| # of a multivariate Gaussian distribution is contained by `r` standard | |
| # deviations?" and "How many standard deviations contain a proportion `p` of |
| """ | |
| Given a 4D array of shape (n, h, w, c) representing n images of shape (h, w, c), | |
| make a single image consisting of a regular grid of smaller images. | |
| License: MIT No attribution | |
| """ | |
| import numpy as np | |
| def reshape(arr, rows, cols, pixels=False): | |
| """Reshapes a 4D array into a grid of images. |
| def has_illegal_chars(string: str, illegal: str = ',;"!+=') -> bool: | |
| """ | |
| Detect the presence of illegal characters in a string. | |
| By default, illegal characters are: `,;"!+=` | |
| Args: | |
| string: A string of text of any length. | |
| illegal: A sequence of characters that are not allowed. | |
| Returns: |