| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from sklearn import manifold | |
| cities = "Athens Barcelona Brussels Calais Cherbourg Cologne Copenhagen Geneva Gibraltar Hamburg HookOfHolland Lisbon Lyons Madrid Marseilles Milan Munich Paris Rome Stockholm Vienna".split(" ") | |
| d = np.array([ | |
| # from eurodist dataset: https://rstudio-pubs-static.s3.amazonaws.com/221886_5c57ad0f5ff546e8af6386162f29fabc.html | |
| [ 0, 3313, 2963, 3175, 3339, 2762, 3276, 2610, 4485, 2977, 3030, 4532, 2753, 3949, 2865, 2282, 2179, 3000, 817, 3927, 1991], | |
| [3313, 0, 1318, 1326, 1294, 1498, 2218, 803, 1172, 2018, 1490, 1305, 645, 636, 521, 1014, 1365, 1033, 1460, 2868, 1802], | |
| [2963, 1318, 0, 204, 583, 206, 966, 677, 2256, 597, 172, 2084, 690, 1558, 1011, 925, 747, 285, 1511, 1616, 1175], |
| name: filter | |
| channels: | |
| - conda-forge | |
| - defaults | |
| dependencies: | |
| - numpy | |
| - scipy | |
| - matplotlib | |
| - sympy | |
| - dynamicisttoolkit |
| %Find Ybus Admittance Matrix(any number of Bus) | |
| %I am used 3 Bus and , 2 bus ans 3 bus are not connected | |
| clc | |
| clear | |
| % |FromBus|ToBus|Impedance|LineCharging| | |
| d = [ 1 2 .4j -20j ; | |
| 1 3 .25j -50j; | |
| 2 3 0j 0j ; ]; | |
| fb = d(:,1); tb = d(:,2); | |
| y = 1./d(:,3); |
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).
Matrix multiplication is a mathematical operation that defines the product of
| # Having problems with the nvidia drivers | |
| # Arch wiki page on XPS 15 | |
| # https://wiki.archlinux.org/index.php/Dell_XPS_15_9560 | |
| # Install ARCH Linux with encrypted file-system and UEFI on Dell XPS 15 | |
| # The official installation guide (https://wiki.archlinux.org/index.php/Installation_Guide) contains a more verbose description. | |
| # Download the archiso image from https://www.archlinux.org/ | |
| # Copy to a usb-drive | |
| dd if=archlinux.img of=/dev/sdX bs=16M && sync # on linux |
this guide may be outdated for MacOS versions beyond 10.14 Mojave (and implied Xcode versions)
Table of Contents
| #!/bin/bash | |
| # clean dist directory and copy all files from src | |
| rm -rf dist && mkdir dist | |
| cp -a src/. dist/ | |
| ### images | |
| # declare sizes | |
| declare -a sizes=('710' '1000' '1420') |
Reference - https://www.eriksmistad.no/getting-started-with-google-test-on-ubuntu/
sudo apt-get install libgtest-dev
sudo apt-get install cmake # install cmake
cd /usr/src/gtest
sudo cmake CMakeLists.txt
cd to navigate to where you want to put your project
The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.
I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).
So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht