uladata

matlab编程风格，是老外写的，对于编写matlab程序非常有帮助。(matlab programming style, is a foreigner written procedures for the preparation of matlab very helpful.)

用于生成ECG波形，包括P波，QRS波，T波(Used to generate the ECG waveform, including the P wave, QRS wave, T wave)

最小均方误差均衡器，误码率和信噪比的关系曲线(Minimum mean square error equalizer, the relationship between BER and SNR curve)

量子遗传算法的matlab原程序，寻优精度高，收敛速度快(Quantum genetic algorithm matlab original program, the optimization of high precision, fast convergence)

This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.

粒子群优化源程序 本人已通过matlab实现 希望对大家有用(Particle swarm optimization matlab source code I have passed to achieve the desired useful to everyone)

利用第二代小波变换———提升小波变换,为第一代小波变换提供了一种新的更快速的实现方法,使得其构造不再 依赖于Fourier 变换构造,可以实现所有的第一代小波变换,提升方案把此变换过程分为分裂、预测和更新3 个阶段。(--- Second generation wavelet transform using lifting wavelet transform, for the first generation wavelet transform provides a new, more rapid implementation method, so its structure is no longer dependent on the Fourier transform structure, can achieve all of the first generation wavelet transform, enhance the program to the transformation process is divided into split, predict and update the three stages.)

这是一个脑电信号分析的matlab工具箱软件EEGLAB的中文操作手册，或许给英文不太好的同学们带来便利，希望对大家有用！(This is an EEG analysis matlab toolbox software EEGLAB of Chinese operating manual, maybe English is not good for the students to bring convenience, we want to be useful!)

最小协方差（pls）算法中的多个任务间比较的脚本，脚本由John F. Magnotti n, Nedret Billor提供 参考文献：Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review，NeuroImg，2014，(plsc，contrast between tasks)

说明：  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.)