guodongxiaren

# 保存到 ~/.gdbinit
python
import sys 
sys.path.insert(0, '/usr/share/gcc-4.8.2/python') # 这个路径以实际情况为准
from libstdcxx.v6.printers import register_libstdcxx_printers
register_libstdcxx_printers (None)
end

#                                                                                                        
#   STL GDB evaluators/views/utilities - 1.03

nikola-j

def my_atan2(y, x):
    pi = torch.from_numpy(np.array([np.pi])).to(y.device, y.dtype)
    ans = torch.atan(y / (x + 1e-6))
    ans += ((y > 0) & (x < 0)) * pi
    ans -= ((y < 0) & (x < 0)) * pi
    ans *= (1 - ((y > 0) & (x == 0)) * 1.0)
    ans += ((y > 0) & (x == 0)) * (pi / 2)
    ans *= (1 - ((y < 0) & (x == 0)) * 1.0)
    ans += ((y < 0) & (x == 0)) * (-pi / 2)
    return ans

pcyin

def log_sum_exp(inputs, keepdim=False, mask=None):
    """Numerically stable logsumexp on the last dim of `inputs`.
       reference: https://github.com/pytorch/pytorch/issues/2591
    Args:
        inputs: A Variable with any shape.
        keepdim: A boolean.
        mask: A mask variable of type float. It has the same shape as `inputs`.
              **ATTENTION** invalid entries are masked to **ONE**, not ZERO
    Returns:
        Equivalent of log(sum(exp(inputs), keepdim=keepdim)).

mbinna

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

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.

UnaNancyOwen

#include <vector>
#include <cmath>
#include <opencv2/opencv.hpp>
#include <opencv2/plot.hpp>

int main( int argc, char* argv[] )
{
    // Initialize Data
    std::vector<double> sine;
    for( int t = 0; t < 360; t++ ){

morgangiraud

# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Select one driver (the last one is a decent choice)
sudo apt install nvidia-370

gnilchee

#!/usr/bin/env python3
import sys, os, socket
from socketserver import ThreadingMixIn
from http.server import SimpleHTTPRequestHandler, HTTPServer

HOST = socket.gethostname()

class ThreadingSimpleServer(ThreadingMixIn, HTTPServer):
    pass

cdjhlee

#!/usr/bin/bash

##############################################
#from http://zeromq.org/intro:get-the-software
##############################################

#get zeromq
wget http://download.zeromq.org/zeromq-4.0.5.tar.gz

#unpack tarball package

jstadler

# bhattacharyya test
import numpy
import math

h1 = [ 1, 2, 3, 4, 5, 6, 7, 8 ];
h2 = [ 6, 5, 4, 3, 2, 1, 0, 0 ];
h3 = [ 8, 7, 6, 5, 4, 3, 2, 1 ];
h4 = [ 1, 2, 3, 4, 4, 3, 2, 1 ];
h5 = [ 8, 8, 8, 8, 8, 8, 8, 8 ];