搜索资源列表
SGeMS-2.0
- For Windows systems, only Visual C++ 6 质统计分析软件代码-The Stanford Geostatistical Modeling Software (SGeMS) SGeMS is a software for 3D geostatistical modeling. It implements many of the classical geostatistics algorithms, as well as new d
generador
- This the code for a function generator (sin, cosin, triangle, pulse), you can variate the amplitud and the frequency, it is developed on ICCAVR, for any doubts I have the simulation on Proteus too.-This is the code for a function generator (sin, cosi
P0610
- 程序代码说明神经网络的实例 ,确实是个好-. the problem of multi-variate collinearity with an example based on neural network
Classifier_gaussian
- The code is for Classification using Gaussian Method on the Multi Variate Dataset with no missing values.
fc319646f828
- Evaluate the multi-variate density with mean vector m and covariance matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance matrix C for the input vector x.
TP_Ondelettes
- Evaluate the multi-variate density with mean vector m and covariance matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance matrix C for the input vector x.
MC_Knock_Out
- 障碍期权定价的模特卡洛模拟 用antithetic variate 法减小标准误差-Monte Carlo Simulation of a Knock Out contract, using antithetic variate method to reduce the standard error.
PDV-algorithm
- 用于分类的主判别变量算法(principal discriminant variate algorithm) - principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sampl
GMM_dynamics_position_orientation
- Gribovskaya, E., Khansari Zadeh, S. M. and Billard, A. (2010) Learning Nonlinear Multi-Variate Motion Dynamics for Real-Time Position and Orientation Control of Robotic Manipulators. International Journal of Robotics Research. [Infoscience]
Bayes_Classifier
- Bayes classifier Step 1. : Generate an arbitrary 3-class dataset with bi-variate Gaussian distribution. Step 2. : If three arbitrary samples are given as follows, determine to which class (as each class is generated by Step 1) each sample s
Maximum-Likelihood-Estimation
- Maximum Likelihood Estimation Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation An arbitrary 3-class dataset is given (by Dataset.mat) and t
NB_for_text_classification
- 文本分类：朴素贝叶斯分类器例子，采用Multi-Variate Bernoulli Event Model。一个文件为训练，一个文件为测试，采用20newsgroups数据集。-Text classification: Naive Bayes classifier example, the use of Multi-Variate Bernoulli Event Model. A file for training, a file for testing, using 20newsgroups
ppcatss
- Performs piecewise bottom-up segmentation of multi-variate time-series.
Two-Variate-Function
- 使用BP神经网络实现二元函数的逼近问题，包含训练样本，无测试集-Using BP neural network to achieve the approximation of the two function function, including the training samples, no test set
Dependence-estimate-of-bivariate-variables-in-r.r
- DEPENDENCE ESTIMATION OF COPULA ( BIVARIATE AND TRI-VARIATE )VARIABLE IN R -DEPENDENCE ESTIMATION OF COPULA ( BIVARIATE AND TRI-VARIATE )VARIABLE IN R
gauss distribution
- 用C语言产生服从高斯分布的随机数，可以由用户指定并输入高斯分布的均值和方差(to use C program get gaussian distribution variates)
RK4
- Runge Kutta for multi variate functions with comparison from ODE45