法國cromda編寫的新版本MATRICE 2（矩陣和矢量運(yùn)算單元）。
// ----------------------------------------------------------
// 12-01-02 : MODIFIED Matrice to Matrice2 (Delphi 6)
// All routines now operate on rectangular matrix, except (InvMat and SysLin)
// No more need to use the InitMat procedure (suppressed) :
// - the routines detect automaticaly the dimensions of matrix and Vector
// - error code MatDimNul is generated if zero lines or column in matrix and Vector (See DimensionMatrice and DimensionVecteur)
// - error code MatMauvDim is generated if the dimensions of matrix/Vector don t allow valid result
// -
// The result matrix is dimensioned automaticaly
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(xiàng)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 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.
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.
