culurciello

# E. Culurciello, February 2024

# generated with Gemini:
# generate a python script that can recursively parse a directory of files with specific files extensions and copy all the files into a text string. Must be able to take as argument the directory string, and must have a variable to specify multiple file extensions. It should save the text file as prompt.txt 
# run as: python github2llm.py my_dir/ "swift,json,xcodeproj,plist"

import os
import sys

def parse_directory(directory, extensions):

culurciello

#!/bin/bash
# Script: my-pi-temp.sh
# Purpose: Display the ARM CPU and GPU  temperature of Raspberry Pi
# -------------------------------------------------------
cpu=$(</sys/class/thermal/thermal_zone0/temp)
echo "--- Measure Raspberry Pi CPU and GPU temperature ---"
echo "$(date) @ $(hostname)"
echo "-------------------------------------------"
echo "GPU $(vcgencmd measure_temp)"
echo "CPU temp=$((cpu/1000))'C"

culurciello

<?xml version="1.0"?>
<robot name="visual">
<!-- E. Culurciello, March 2021 -->
<!-- basic 2-fingers gripper -->
  <link name="base">
  <inertial>
    <mass value="1"/>
    <inertia ixx="100" ixy="0" ixz="0" iyy="100" iyz="0" izz="100" />
    <origin/>
  </inertial>

culurciello

http://pytorch.org/

https://discuss.pytorch.org/

https://github.com/pytorch/


https://github.com/bfortuner/pytorch-cheatsheet/blob/master/pytorch-cheatsheet.ipynb

culurciello

elab@gpu5 ~/pytorch-examples/imagenet [master*]$ python3 main.py -a alexnet /media/SuperSSD/test-dataset-train-val/
=> creating model 'alexnet'
Traceback (most recent call last):
  File "main.py", line 286, in <module>
    main()
  File "main.py", line 129, in main
    train(train_loader, model, criterion, optimizer, epoch)
  File "main.py", line 165, in train
    output = model(input_var)
  File "/usr/local/lib/python3.4/dist-packages/torch/nn/modules/module.py", line 210, in __call__

culurciello

-- E. Culurciello, November 2016
-- test to see if table insert/remove is slower than table addressing

iters = 1e5
tablesize = 1e4
tab1={}
item = torch.randn(iters,1024)

sys.tic()

culurciello

-- Xception model
-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
-- E. Culurciello, October 2016

require 'nn'
local nClasses = 1000

function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
   local block = nn.Sequential()
   block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))

culurciello

#! /usr/bin/env python3
# E. Culurciello, example of reinforcement learning in Python
#
# http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Game: 5 rooms connected through doors. One room is the goal-room. 
# the goal of the game is to get to the goal-room

import numpy as np

# this is how rooms are connected:

culurciello

-- Eugenio Culurciello
-- August 2016
-- a test to learn to code PredNet-like nets in Torch7

require 'nn'
require 'nngraph'

torch.setdefaulttensortype('torch.FloatTensor')
nngraph.setDebug(true)

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local cv = require 'cv'
require 'cv.highgui'
require 'cv.imgproc'
require 'cv.imgcodecs'
require 'image'

-- local image = cv.imread{arg[1] or 'demo/lena.jpg', cv.IMREAD_GRAYSCALE}
imgT = image.lena()
imgT = image.lena()
imgTg = imgT[2] -- convert to grayscale and remove the first dimension