Kenneth C. Arnold kcarnold

Example based on https://github.com/UKPLab/sentence-transformers/blob/master/examples/applications/clustering/fast_clustering.py

# Install the needed libraries
!pip install -q sentence-transformers

# Import libraries we'll need
from sentence_transformers import SentenceTransformer, util
import os
import csv

This is a simple demo of affinity propagation. Drag the slider to adjust the 'preference' or self-affinity level.

References:

Frey, B. and D. Dueck. Clustering by Passing Messages Between Data Points. Science, 2007.
http://scikit-learn.org/stable/modules/clustering.html#affinity-propagation
I looked at implementations in Julia and Python while writing this, but it was simpler to just follow the paper.

	# Sebastian Raschka 09/24/2022
	# Fixed to avoid problems with spaces and other special characters in filenames, Ken Arnold 10/25/2022
	#
	# Create a new conda environment and packages
	# conda create -n whisper python=3.9
	# conda activate whisper
	# conda install mlxtend -c conda-forge

	# Install ffmpeg
	# macOS & homebrew

	import sched
	import time
	import asyncio
	import datetime

	start_time = time.time()

	def tick(a='default'):
	print("From tick", time.time() - start_time, a)

	print(
	', '.join(
	sorted(
	subprocess.check_output('pbpaste').decode('utf-8').split('\n'),
	key=lambda name: name.split()[-1])
	)
	)

	class VecPile:
	"""An attribute-accesed collection that asserts that all of its elements have the same length.

	Useful for keeping several collections together, such as vectors with labels, or several different representations of the same data."""

	def __init__(self, **kw):
	for k, v in kw.items():
	setattr(self, k, v)

	@staticmethod

	on run {input, parameters}
	set PDFs to input
	set msubject to "to print: "
	repeat with aFile in PDFs
	tell application "Finder"
	set filename to ((name of aFile) as string)
	end tell
	set msubject to msubject & filename & ","
	end repeat
	tell application "Microsoft Outlook"

	# https://black.readthedocs.io/en/stable/
	import json
	import black

	import re
	magic_re = re.compile(r'^%', flags=re.MULTILINE)
	ipymagic = '##IpyMagic##'

	filename = 'notebook.ipynb'

	import numpy as np


	class IncrementalMoments(object):
	'''Knuth's incremental mean and variance computation, applied elementwise.'''
	def __init__(self, shape, initial_items=[]):
	self.n = 0
	self.mean = np.zeros(shape)
	self.m2 = np.zeros(shape)
	self.update_seq(initial_items)