好用的Floyd算法、dijkstra算法例子

模式识别实验验程序 极大似然估计和Fisher线形判别分析(Experimental test procedures for pattern recognition maximum likelihood estimation and Fisher linear discriminant analysis)

a function for image thresholding

切换乒乓效应Switching ping-pong effect(Switching ping-pong effect)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

自适应滤波器原理一书第五章的程序,希望对大家有所帮组,()

说明：  此代码通过模拟退火算法优化BP神经网络进行汇率预测(This code optimizes BP neural network to predict exchange rate by simulated annealing algorithm)

采用改进的K最近邻算法对混沌时间序列进行预测(The improved K-nearest neighbor algorithm to predict chaotic time series)

skeletal action recognition

水下航行器协调控制技术中航行器跟踪避障控制(Underwater vehicle coordinated control technology in the aircraft track obstacle avoidance control )

此代码是输入图像得到原图像的灰度直方图，得到聚类后的直方图和聚类后的图像直方图。(This code is the input image to get gray histogram of original image, get the clustering histogram and cluster after the image histogram. )