jakelevi1996

Notes on bash

This is just a random collection of commands which I have found useful in Bash. This Gist is expected to grow over time (until I have mastered the whole of Bash). Another useful resource is this list of Unix commands on Wikipedia. Hyperlinked bash commands in general lead to relevant Man (manual) pages.

Contents

• Notes on bash
  • Contents
  • Get the current date and time and generate timestamped filenames with date
• Display date and time in bash history using HISTTIMEFORMAT

jakelevi1996

Notes on VSCode

Contents

• Notes on VSCode
  • Contents
  • Installed extensions
  • settings.json
  • keybindings.json
• ./.vscode/settings.json (workspace settings, including pytestArgs and python.analysis.extraPaths)

botforge

from matplotlib import animation
import matplotlib.pyplot as plt
import gym 

"""
Ensure you have imagemagick installed with 
sudo apt-get install imagemagick

Open file in CLI with:
xgd-open <filelname>

jakelevi1996

Speed tests

Shown below is some code to perform generic speed tests and save a log-log graph of the results (in this example, eigenvalue decomposition for SPD matrices is being tested). The resulting graph is shown below the code:

import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from time import perf_counter

jakelevi1996

Gist "To do" list

Here is my to-do list for Gists/things I intend to write Gists about:

• Generic Bayesian linear regression: as basis functions, define some classes with __call__ methods (EG Gaussian, polynomials) which take appropriate parameters to their __init__ method (EG location, scale, polynomial order); a given Bayesian linear regression solution should accept a list of basis functions, whose outputs for each data point can be calculated with the built-in map function, or numpy.vectorise, and compare the output with the MAP and ML solutions, EG:

import numpy as np

class Sigmoid:
    def __init__(self, loc): self.loc = loc
 def __call__(self, x): return 1.0 / (1.0 + np.exp(self.loc - x))

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

evertrol

Makefile cheat sheet

Mostly geared towards GNU make

I've used ->| to indicate a tab character, as it's clearer to read than ⇥

• Set a target, its dependencies and the commands to execute in order

target: [dependencies]
->| 

justincbagley

How to convert markdown to PDF:

This post reviews several methods for converting a Markdown (.md) formatted file to PDF, from UNIX or Linux machines.

Using Pandoc:

$ pandoc How_I_got_svg-resizer_working_on_Mac_OSX.md -s -o test1.pdf

jovianlin

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

get_available_gpus()

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)