Dmitrii I Dmitrii-I

SSH Server with the Python Twisted Library

Installing the library

Assuming you have Python installed on your system:

pip install twisted
pip install pyOpenSSL
pip install service_identity

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

Topology and Data by Gunnar Carlsson
Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

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

Analysis: 3-point shot percentage after other team makes or misses

This analysis shows how pbprdf was used to analyze NBA players' 3-point shot percentage after a player on the other team either made or missed a shot over the 2017-2018 regular season.

Overview

We're interested in finding 3-point shots that were made immediately after a 3-point shot taken by the other team. The way we do this is by running a SPARQL query that calculates each player's season 3-point shot percentage, the 3P% after a player on the other team misses, and the 3P% after a player on the other team makes. We want to calculate the percentage of 3-point shots that they make or miss when we include only shots that they took immediately after a player on the other team took a 3-pointer (within two lines on the play-by-play).

Here's the data that we're using to do this analysis. Each play has a set of triples that inc

	#include <stdio.h>
	#include <string.h>
	#include <stdlib.h>
	#include <unistd.h>
	#include <fcntl.h>

	#include <netinet/tcp.h>
	#include <sys/socket.h>
	#include <sys/types.h>
	#include <netinet/in.h>

	/*****************************************************************************
	* QuantCup 1: Price-Time Matching Engine
	*
	* Submitted by: voyager
	*
	* Design Overview:
	* In this implementation, the limit order book is represented using
	* a flat linear array (pricePoints), indexed by the numeric price value.
	* Each entry in this array corresponds to a specific price point and holds
	* an instance of struct pricePoint. This data structure maintains a list

	#include <Rcpp.h>
	using namespace Rcpp;

	// [[Rcpp::export]]
	double sdSample(NumericVector x) {
	int n = x.size();
	NumericVector sampled(n);

	for (int i = 0; i < n; ++i) {
	sampled[i] = x[rand() % n];

	"""
	A deep neural network with or w/o dropout in one file.

	License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
	"""

	import numpy, theano, sys, math
	from theano import tensor as T
	from theano import shared
	from theano.tensor.shared_randomstreams import RandomStreams

	This note explains how to build Postgres from source and setup to debug it using LLDB on a Mac. I used this technique to research this article:
	http://patshaughnessy.net/2014/10/13/following-a-select-statement-through-postgres-internals

	1. Shut down existing postgres if necessary - you don’t want to mess up your existing DB or work :)

	$ ps aux \| grep postgres
	pat 456 0.0 0.0 2503812 828 ?? Ss Sun10AM 0:11.59 postgres: stats collector process
	pat 455 0.0 0.0 2649692 2536 ?? Ss Sun10AM 0:05.00 postgres: autovacuum launcher process
	pat 454 0.0 0.0 2640476 304 ?? Ss Sun10AM 0:00.74 postgres: wal writer process
	pat 453 0.0 0.0 2640476 336 ?? Ss Sun10AM 0:00.76 postgres: writer process