avorobey

/* Adapted from https://github.com/v7unix/v7unix/blob/master/v7/usr/src/cmd/ed.c with slight fixes. */
/*
 * Editor
 */

#include <signal.h>
#include <sgtty.h>
#include <setjmp.h>
#include <stdlib.h>
#include <string.h>

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random

gocarlos

// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle

#include <Eigen/Dense>

Matrix<double, 3, 3> A;               // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B;         // Fixed rows, dynamic cols.
Matrix<double, Dynamic, Dynamic> C;   // Full dynamic. Same as MatrixXd.
Matrix<double, 3, 3, RowMajor> E;     // Row major; default is column-major.
Matrix3f P, Q, R;                     // 3x3 float matrix.

teeks99

I wasn't able to get the basic quickstart example for Boost.Python working without some modifications, so I wanted to document it for others.

Here are the various issues I encountered with the default example:

• The Bjam file (in the root of boost or in the quickstart, i'm not sure) defaults to threading=single, but something else is defaulting to threading=multi, so they are incompatible (shows up as saying the boost_python-vcXXX-mt-gd-1_XX.lib is not found when it just generated boost_python-vcXXX-gd-1_XX.lib). Manually specifying threading=multi for the bjam commands seems to fix things.
• I wanted to get this going with boost 1.57.0, but there was a change to the boost::build directory structure in 1.56+ that hasn't been reflected in the quickstarts boost-build.jam file, so I reverted back to 1.55.0.
• Visual Studio 2010 (msvc-10.0) has a bug that keeps it

Chaser324

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

turnersr

class RunningStat(object):
    """ 
    Based on ideas presented in
    1. Numerically Stable, Single-Pass, Parallel Statistics Algorithms - http://www.janinebennett.org/index_files/ParallelStatisticsAlgorithms.pdf
    2. Accurately computing running variance - http://www.johndcook.com/standard_deviation.html 
    """ 
    
    def __init__(self):
        self.m_n = 0
        self.m_oldM = 0

markjlorenz

Reverse Port Tunneling with EC2

Reverse port tunneling is used to give a user outside of a networks firewall accesst to a computer inside the firewall where direct SSH connections aren't allowed. It works by the in-firewall computer SSH'ing to a middleman computer that then forwards incomming SSH connections on a given port to the firewalled computer.

Setup the middleman

• Get an ubuntu EC2 instance
• Download it's security keys (both in-firewall and out-firewall computers will need the private key)
• Setup the security group to allow connections on port 10002
• SSH into the middleman and add: GatewayPorts yes to /etc/ssh/sshd_config

kwmsmith

#cython:boundscheck=False
#cython:wraparound=False

import numpy as np
from cython.parallel cimport prange
from libc.math cimport sqrt

cdef inline double dotp(int i, int j, int N, double[:, ::1] X) nogil:
    cdef:
        int k

fabianp

int covariance(int n, int m, double data[], int strides[2], char mode, double matrix[])
/* This algorithm is described in:
 * B.P. Welford:
 * "Note on a method for calculating corrected sums of squares and products."
 * Technometrics 4(3): 419-420 (1962).
 * Also see:
 * Peter M. Neely:
 * "Comparison of several algorithms for computation of means, standard
 * deviations and correlation coefficients."
 * Communications of the ACM 9(7): 496-499 (1966).