Python爬虫实例

Python 学习爬虫初步知识，包含爬虫的基础架构，URL管理器，迭代，正则表达式的匹配等等，是初学者学习爬虫知识的良好基础结构。供大家学习交流。

基于原型的目标函数聚类方法，数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则(A prototype based objective function clustering method, the data point to a certain distance from the prototype is used as the objective function of the optimization, and the adjustment rule of the iterative operation is obtained by the method of finding the extremum of the function.)

说明：  python code for cmapss segmentation

使用人工神经网络实现手写图片的识别。。。。。。。(Recognition of handwritten pictures based on Neural Network)

python语言实现最大值法合成月EVI数据(Python Language Realizes the Maximum Method to Synthesize Monthly EVI Data)

简单的NAO机器人挥手动作，choregraphe上可以使用模拟

说明：  输入一个奇数，然后判断最少几个 9 除于该数的结果为整数。(Enter an odd number and then determine the minimum of 9 and the result of the number is an integer.)

说明：  裂纹是典型的线结构，在许多计算机视觉应用中都很有趣。在实际应用中，路面裂缝等许多裂缝连续性差、对比度低，给利用低层特征进行基于图像的裂缝检测带来了很大的挑战。在本文中，我们提出了深度裂纹——一个端到端可训练的深度卷积神经网络，通过学习用于裂纹表示的高级特征来自动检测裂纹。该方法将在层次卷积阶段学习到的多尺度深度卷积特征融合在一起，以获取线路结构。更详细的表示在大比例尺的feature maps中进行，更全面的表示在小比例尺的feature maps中进行。我们在SegNet的编码器解码器架构上构建深度裂纹网，并对在相同尺度下在编码器网络和解码器网络中生成的卷积特征进行配对融合。(Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack---an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale.)

进行电离层电子密度反演是与标准数据的对比分析(The inversion of ionospheric electron density is a comparison with standard data)

说明：  本程序用于实现模拟退火算法计算最短路径问题,读取txt数据,最后计算出任意两点之间的距离(This program is used to realize simulated annealing algorithm to calculate the shortest path problem, read TXT data, and finally calculate the distance between any two points.)