insaneyilin

/*
# Released under MIT License

Copyright (c) 2017 insaneyilin.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

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# 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

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def tile_along_axis(x, dim, n_tile):
    init_dim = x.size(dim)
    repeat_idx = [1] * x.dim()
    repeat_idx[dim] = n_tile
    x = x.repeat(*(repeat_idx))
    order_index = torch.tensor(
        torch.cat([init_dim * torch.arange(n_tile, device=x.device) + i for i in range(init_dim)]),
        dtype=torch.long, device=x.device)
    return torch.index_select(x, dim, order_index)

insaneyilin

#!/bin/bash
while true
do
    x=`xdotool getmouselocation | grep -oP '(?<=x:)\d+'`
    y=`xdotool getmouselocation | grep -oP '(?<=y:)\d+'`
    xdotool mousemove $x $y
    xdotool click 1
    sleep 1
done

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vector <string> Names {"Karl", "Martin", "Paul", "Jennie"};
vector <int> Score{45, 5, 14, 24};

std::vector<int> indices(Names.size());
 std::iota(indices.begin(), indices.end(), 0);
 std::sort(indices.begin(), indices.end(),
           [&](int A, int B) -> bool {
                return Score[A] < Score[B];
            });

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def masked_log_softmax(input, mask, dim=1):
    masked_input = input * mask.float()
    max_input = torch.max(masked_input, dim=dim, keepdim=True)[0]
    exps = torch.exp(masked_input - max_input)
    masked_exps = exps * mask.float()
    masked_sums = masked_exps.sum(dim, keepdim=True)
    zeros = (masked_sums == 0)
    masked_sums += zeros.float()
    masked_exps += 1e-6  # avoid zero input of log.
    return torch.log(masked_exps / masked_sums)

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// reference: https://ddorobot.tistory.com/entry/OpenCV-Draw-DotDash-Line

#include <iostream>
#include <vector>
#include <string>
#include <algorithm>

#include "opencv2/opencv.hpp"

void DrawDashedLine(cv::Mat& img, cv::Point pt1, cv::Point pt2,

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# reference: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

import copy
import numpy as np
from scipy.linalg import block_diag
from filterpy.kalman import IMMEstimator
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise

def make_cv_filter(dt):

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#pragma once

#include <cmath>

// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
template <typename T>
class WelfordMeanStddevCalculator {
 public:
  WelfordMeanStddevCalculator() = default;
  virtual ~WelfordMeanStddevCalculator() = default;

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#define MODULE_NAME   "MY_LIB"
// __VA_ARGS__ 对应宏定义中参数列表的最后一个参数为省略号（也就是三个点）
// 加上##的作用在于，当可变参数的个数为0时，这里的##起到把前面多余的","去掉的作用,否则会编译出错
#define error_print(fmt, ...)  printf("[ERROR]["MODULE_NAME"](%s|%d)" fmt, __func__, __LINE__, ##__VA_ARGS__)

// 单个 # 用来把参数转换成字符串

// ## 是个粘合剂，将前后两部分粘合起来，也就是有“字符化”的意思。但是“##”不能随意粘合任意字符，必须是合法的C语言标示符。在单一的宏定义中，最多可以出现一次“#”或“##”预处理操作符。