三維矢量有限元-矩量法電磁場分析程序。
EMAP5 is a full-wave electromagnetic field solver that combines the method of moments (MOM) with a vector finite element method (VFEM). It employs the finite element method (FEM) to analyze a dielectric volume, and employs the method of moments (MoM) to solve for currents on the surface of (or external to) this volume. The two methods are coupled through the fields on the dielectric surface.
多項式曲線擬合 任意介數(shù) Purpose - Least-squares curve fit of arbitrary order
working in C++ Builder 2007 as a template class,
using vector<FloatType> parameters.
Added a method to handle some EMathError exceptions.
If do NOT want to use this just call PolyFit2 directly.
usage: Call PolyFit by something like this.
CPolyFit<double> PolyFitObj
double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A)
where X and Y are vectors of doubles which must have the same size and
A is a vector of doubles which must be the same size as the number of
coefficients required.
returns: The correlation coefficient or -1 on failure.
produces: A vector (A) which holds the coefficients.
This string-include defines all string functions as inline functions. Use gcc. It also assumes ds=es=data space, this should be normal. Most of the string-functions are rather heavily hand-optimized,
see especially strtok,strstr,str[c]spn. They should work, but are not
very easy to understand. Everything is done entirely within the register
set, making the functions fast and clean.
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
This Two-Category Classifier Using Discriminant Functions to
separeate two classes. The Classifier is designed on classes which
has two feature vectors and other case it has one feature vector.
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space
DIM, the number of centres in the mixture model and the type of the
mixture model, and returns a data structure MIX.
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
