yakzan

Coding Rules and Instructions

Test-Driven Development (TDD) with pytest: Always write a failing test before writing implementation code (Red-Green-Refactor). Use pytest and pytest-fixtures for test setup, execution, and teardown.
KISS (Keep It Simple, Stupid): Favor the simplest solution that meets the requirements.
DRY (Don't Repeat Yourself): Avoid code duplication. Extract reusable logic into functions or classes.
Standard Libraries and Tools: Utilize standard Python libraries (like datetime for date/time, requests for HTTP requests, and logging) and external libraries, including BeautifulSoup4 for HTML parsing, to avoid reinventing the wheel. Favor well-maintained and widely-used libraries.
YAGNI (You Ain't Gonna Need It): Don't implement features or functionality unless they are currently required.
SOLID Principles & Extensibility: Adhere to SOLID principles, promoting maintainability, testability, and future extension. Consider potential future requi

After reading Why I'm Frequently Absent from Open Source by James Long and listening the corresponding The Changelog episode, I dwelt on the idea and believe that open source maintainers...

... should never be ashamed if they don't have time for a project.
... should be honest with themselves and open with their users so that everybody can be on the same page
... are people and they have at one time or another responsibilities or hardships that they need to attend to which reasonably take them away from a project
... may also reasonbly decide that they don't like the direction of a project or that they would like to explore other things and may leave a project permanently.

Along this line of thinking I've created a set of descriptions for different levels at which a project might be maintained. A maintainer can use these to announce to their users the current ability that they have to dedicate to a pr

	{
	"customModes": [
	{
	"slug": "lean-prompt-code",
	"name": "Code (@GosuCoder Lean Prompt)",
	"roleDefinition": "You are Roo, a highly skilled software engineer with extensive knowledge in many programming languages, frameworks, design patterns, and best practices.",
	"groups": [
	"read",
	"edit",
	"command",

	<Prompt>
	<Context>
	You're tasked with coding a project and need to follow specific guidelines to ensure quality and consistency across various programming languages and frameworks.
	</Context>

	<Progress>
	Document all tasks. Create a folder in the project root named .cline and keep a log of tasks in the following format.

	GOAL: Detail the goal of the task
	IMPLMENTATION: Describe how it was implemented.


	<TEMPLATE>
	<INSTRUCTIONS>
	Use the <CODEBASE> code as reference, and convert the high-level <TASK> into a set of very detailed step-by-step instructions that an AI coding agent can complete.
	Only includes steps an AI coding agent can take. Do not include testing or any other work a human would do to confirm the task has been completed.
	ALWAYS have the agent run a build when it is complete. Be specific and decisive about what the agent should do.
	Do not include any additional meta instructions to the user. Use markdown formatting.
	</INSTRUCTIONS>
	<TASK>

	#Resume Phrase Matcher code


	#importing all required libraries

	import PyPDF2
	import os
	from os import listdir
	from os.path import isfile, join
	from io import StringIO

	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...

	http://www.oreilly.com/data/free/files/2014-data-science-salary-survey.pdf
	http://www.oreilly.com/data/free/files/2015-data-science-salary-survey.pdf
	http://www.oreilly.com/data/free/files/Data_Analytics_in_Sports.pdf
	http://www.oreilly.com/data/free/files/advancing-procurement-analytics.pdf
	http://www.oreilly.com/data/free/files/ai-and-medicine.pdf
	http://www.oreilly.com/data/free/files/analyzing-data-in-the-internet-of-things.pdf
	http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf
	http://www.oreilly.com/data/free/files/architecting-data-lakes.pdf
	http://www.oreilly.com/data/free/files/being-a-data-skeptic.pdf
	http://www.oreilly.com/data/free/files/big-data-analytics-emerging-architecture.pdf

	#!/bin/bash

	# install CUDA Toolkit v9.0
	# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
	CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
	wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
	sudo dpkg -i ${CUDA_REPO_PKG}
	sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
	sudo apt-get update
	sudo apt-get -y install cuda-9-0

	import numpy as np
	from scipy.spatial.distance import cdist
	from .dtw import dtw
	from .lb import envelope, lb_keogh_cdist, lb_keogh_from_bounds
	from .window import window_ts

	def dtw_nearest_neighbours(query, db, bound_reach, step_size, subseq=False):
	assert query.ndim == 1
	assert db.ndim == 2
	assert query.flags.contiguous