vitasiku’s gists

vitasiku / gym.py

Created September 29, 2021 20:32 — forked from Alir3z4/gym.py

	import os
	import pickle
	import warnings

	import numpy as np
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from tensorflow.keras.callbacks import EarlyStopping
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.layers import Dropout

vitasiku / celery.sh

Created June 10, 2021 21:08 — forked from amatellanes/celery.sh

Celery handy commands

	/* Useful celery config.

	app = Celery('tasks',
	broker='redis://localhost:6379',
	backend='redis://localhost:6379')

	app.conf.update(
	CELERY_TASK_RESULT_EXPIRES=3600,
	CELERY_QUEUES=(
	Queue('default', routing_key='tasks.#'),

vitasiku / start-celery-for-dev.py

Created June 10, 2021 10:04 — forked from chenjianjx/start-celery-for-dev.py

A python script which starts celery worker and auto reload it when any code change happens.

	'''
	A python script which starts celery worker and auto reload it when any code change happens.
	I did this because Celery worker's "--autoreload" option seems not working for a lot of people.
	'''

	import time
	from watchdog.observers import Observer ##pip install watchdog
	from watchdog.events import PatternMatchingEventHandler
	import psutil ##pip install psutil
	import os

vitasiku / README-python-framework-benchmark.md

Created February 7, 2021 14:38 — forked from nhymxu/README-python-framework-benchmark.md

Flask vs Falcon vs FastAPI benchmark

gunicorn run:app --workers=9
gunicorn run:app --workers=9 --worker-class=meinheld.gmeinheld.MeinheldWorker

Macbook Pro 2015 Python 3.7

Framework	Server	Req/s	Max latency	+/- Stdev

vitasiku / py-args-for-bash.sh

Created December 22, 2020 07:56 — forked from benkehoe/py-args-for-bash.sh

Python argument parsing for bash scripts

	#!/bin/sh

	# This is free and unencumbered software released into the public domain.
	#
	# Anyone is free to copy, modify, publish, use, compile, sell, or
	# distribute this software, either in source code form or as a compiled
	# binary, for any purpose, commercial or non-commercial, and by any
	# means.
	#
	# In jurisdictions that recognize copyright laws, the author or authors

vitasiku / MonteCarloKMeansClustering.py

Created February 26, 2019 14:58 — forked from StuartGordonReid/MonteCarloKMeansClustering.py

Monte Carlo K-Means Clustering

vitasiku / AdditionaToClustering.py

Created February 26, 2019 14:57 — forked from StuartGordonReid/AdditionaToClustering.py

	class Clustering:

	def k_means_clustering(self, n, s=1.0):
	"""
	This method performs the K-means clustering algorithm on the data for n iterations. This involves updating the
	centroids using the mean-shift heuristic n-times and reassigning the patterns to their closest centroids.
	:param n: number of iterations to complete
	:param s: the scaling factor to use when updating the centroids
	pick on which has a better solution (according to some measure of cluster quality)
	"""

vitasiku / KMeansClustering.py

Created February 26, 2019 14:56 — forked from StuartGordonReid/KMeansClustering.py

Implementation of the K-Means Clustering Algorithm

	class Clustering:
	"""
	An instance of the Clustering is a solution i.e. a particular partitioning of the (heterogeneous) data set into
	homogeneous subsets. For Centroid based clustering algorithms this involves looking at each pattern and assigning
	it to it's nearest centroid. This is done by calculating the distance between each pattern and every centroid and
	selecting the one with the smallest distance. Here we use are using fractional distance with the default parameters.

	:param d: dimensionality of the input patterns
	:param k: the pre-specified number of clusters & centroids
	:param z: the patterns in the data set

vitasiku / ClusteringObjectiveFunctions.py

Created February 26, 2019 14:56 — forked from StuartGordonReid/ClusteringObjectiveFunctions.py

Clustering Objective Functions

	class ClusteringQuality:
	"""
	Instances of this class implement the two measures of clustering quality discussed in the article, namely the davies
	bouldin index and the silhouette index. It also implements a number of useful helper methods.
	:param solution: the clustering solution of type Clustering
	:param minimum: the minimum distance allowable
	"""

	def __init__(self, solution, minimum):
	"""

vitasiku / setup.py

Created January 21, 2018 22:41

Flask app's cx_freeze's setup.py file.

	from cx_Freeze import setup, Executable

	include_files = [ 'app/templates/',
	'app/static/',]

	# Note: without 'jinja2.ext' in this list, we won't get the templates working.

	include = [ 'jinja2', 'jinja2.ext',]
	flaskapp = Executable(script="run.py",
	base="Win32GUI",

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