QA Official

Selective Sorting in Single Linked List (Headed Node) 2019-04-28
//选择排序(有头节点) template<typename T> void chainWithHeader<T>::selectionSort() {chainNode<T>*pd,*pf,*pe,*pa,*pmax;pf = headerNode;pd = NULL;bool sorted = false; //已经排好序之后退出循环while (pf->next->next!=pd&&!sorted){pmax = pf;pa= pf->next;sorted = true;T temp;while (pa->next!= pd){if (pmax->next->

Some Precautions for Use of OpenCV&amp;#x27;s reshape Function 2019-04-28
1) In matlab, reshape comes in columns, while in opencv, reshape of mat comes in rows. 2)reshape requires the transformation object matrix A to be continuous and can be judged by A.iscontinous ().If it is not continuous, it will not be able to devote all one's efforts to reshape, and it will report an error.Generally, the cropped Mat images are no longer continuous, such as cv::Mat B = A(rect);Crop_img, cv:: Matb = a (range (begin _ row, end _ row), etc.

Start learning speech recognition technology 2019-04-28
As a front-end ordinary employee working in telecom, inside decided to devote himself to the study of speech recognition in the next day because he was more interested in technical research (in fact, it was also an escape from insufficient marketing ability) and solved the problems encountered in his working life. Continue to keep learning progress updated on a daily basis. The text here is used to urge you to hurry up.

Use and Difference of DES and md5 2019-04-28
1.DES algorithm has three entry parameters: Key, Data and Mode.The Key is 7 bytes and 56 bits, which is the working key of DES algorithm.Data is 8 bytes and 64 bits, which is data to be encrypted or decrypted;Mode is DES's working mode, there are two kinds: encryption or decryption.Des is reversible and can be decrypted using a seven-bit key. <span style="font-family:Comic Sans MS;"><span style="font-size:12px;">package com.sica.des;  import com.

[ Scientific Research Notes ] Considerations on Artificial Intelligence and Algorithm Projects 2019-04-28
Preface [ scientific research notes ] series is my essays and thoughts on the road of scientific research. the content is not limited. it is an open series of articles, which also gives me some free space. This article is some thoughts on artificial intelligence technology and algorithm project management based on my own experience.You are welcome to criticize and correct me and put forward valuable opinions. The subsection of the article is arranged as follows:

[Sword Refers to offer Face Test] Jump Step Upgrade, Abnormal Jump Step 2019-04-28
A frog can jump up to 1 step or 2 steps at a time ... It can also jump up to N steps.Find out how many jumping methods the frog can use to jump up an n-step.Thinking: We have finished the face test that frogs can only jump on 1 or 2 steps at a time. See [Sword Finger offer Face Test] The Frog Jumped StepSo what about the upgraded version of it, the abnormal jumping step?

how to quickly understand a large program 2019-04-28
In the process of software development, we often encounter the following situations: many students studying development or working programmers often bother to get a large program and cannot read it quickly in a short time.When they got a large-scale program, they began to read and analyze sentence by sentence. Day and night, they were short of To study diligently, but the results were still not ideal, and they often entered the following states:(1) It took a long time and did not make much progress, which was far from the expected date at that time.

program brick removal solution 2019-04-28
Come on, let's move bricks happily!!!2333333! As an old bird flying earlier, people have been asking: "I can't write here, what should I do?""I don't know how to start writing at all!""Seeing this demand makes me feel helpless!" Especially when I saw my new colleague's face looking stupid, it reminded me of the days in Kubility.Now, don't worry, I will teach you my strongest faking solution. No, it's brick removal solution!

pytorch is used for classification (two-classification, multi-classification) 2019-04-28
import numpy as np import sklearn import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F import numpy as np import matplotlib.pyplot as plt import torch.optim from sklearn import datasets n_data = torch.ones(100,2) x0 = torch.normal(2*n_data,1) y0 = torch.zeros(100) x1 = torch.normal(-2*n_data,1) y1 = torch.ones(100) x =,x1),0).type(torch.FloatTensor) y =,y1),0).type(torch.LongTensor) # plt.scatter(, # class SoftMax(nn.Module): def __init__(self,n_feature,n_hidden,n_out): super(SoftMax,self).__init__() self.hidden = nn.Linear(n_feature,n_hidden) self.out =

segnet-training 2019-04-28
1.SegNet 简介 SegNet是Cambridge提出旨在解决自动驾驶或者智能机器人的图像语义分割深度网络,开放源码,基于caffe框架。SegNet基