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PlotSphereIntensity(azimuth, elevation)
PlotSphereIntensity(azimuth, elevation, intensity)
h = PlotSphereIntensity(...)
Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated.
Output:
h - a handle to the patch object
The axes are also plotted:
positive x axis is red
positive y axis is green
positive z axis is blue
標(biāo)簽：
PlotSphereIntensity
elevation
azimuth
intensity
上傳時(shí)間：
2014-01-15
上傳用戶：ruan2570406
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Matrix Transposition and Multiplication
It is a MIPS assembly program that does the following: given two matrices, M1 and M2, first transpose M2 to obtain M2tran. Then multiply M1 and M2tran.
標(biāo)簽：
Multiplication
Transposition
following
assembly
上傳時(shí)間：
2016-05-03
上傳用戶：kernaling
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The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants.
The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).
The version 2.0 (February 2005) handles sparse matrices and contains an example
標(biāo)簽：
discrete-time
resolution
functions
Decision
上傳時(shí)間：
2014-01-01
上傳用戶：xuanjie
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％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％
標(biāo)簽：
multidimensional
estimation
algorithm
Gaussian
上傳時(shí)間：
2013-12-03
上傳用戶：我們的船長(zhǎng)
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The CoinUtils project is a collection of open-source utilities developed and used by a variety of other projects in the COIN-OR repository. The project includes classes for storing and manipulating sparse matrices and vectors, performing matrix factorization, parsing input files in standard formats, building representations of mathematical programs, comparing floating point numbers with a tolerance, performing simple presolve operations, and warm starting algorithms for mathematical programs, among others.
標(biāo)簽：
open-source
collection
CoinUtils
developed
上傳時(shí)間：
2013-12-19
上傳用戶：xmsmh
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this directory
contains the following:
* The acdc algorithm for finding the
approximate general (non-orthogonal)
joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices.
[acdc.m]
* The acdc algorithm for finding the
same for a set of Symmetric matrices.
[acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version
[acdc.m], rather than the Symmetric version[acdc_sym] is preferable.
* A function that finds an initial guess
for acdc by applying hard-whitening
followed by Cardoso s orthogonal joint
diagonalizer. Note that acdc may also
be called without an initial guess,
in which case the initial guess is set by default to the identity matrix.
The m-file includes the joint_diag
function (by Cardoso) for performing
the orthogonal part.
[init4acdc.m]
標(biāo)簽：
approximate
directory
algorithm
the
上傳時(shí)間：
2014-01-17
上傳用戶：hanli8870
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The kernel-ica package is a Matlab program that implements the Kernel
ICA algorithm for independent component analysis (ICA). The Kernel ICA
algorithm is based on the minimization of a contrast function based on
kernel ideas. A contrast function measures the statistical dependence
between components, thus when applied to estimated components and
minimized over possible demixing matrices, components that are as
independent as possible are found.
標(biāo)簽：
independent
kernel-ica
implements
algorithm
上傳時(shí)間：
2014-01-17
上傳用戶：yiwen213
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Quaternions are hypercomplex numbers (that is generalizations of the complex numbers to higher dimensions than two). For an introduction, refer to the Wikipedia article on Quaternions.
Quaternion toolbox for Matlab® extends Matlab® to allow calculation with matrices of quaternions in almost the same way that one calculates with matrices of complex numbers. This is achieved by defining a private type to represent quaternion matrices and overloadings of many standard Matlab® functions. The toolbox supports real and complex quaternions (that is quaternions with four real or complex components).
標(biāo)簽：
numbers
generalizations
hypercomplex
Quaternions
上傳時(shí)間：
2014-11-27
上傳用戶：jhksyghr
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密碼學(xué)界牛人Victor Shoup用C++編寫數(shù)論類庫(kù)。
NTL is a high-performance, portable C++ library providing data structures and algorithms for arbitrary length integers for vectors, matrices, and polynomials over the integers and over finite fields and for arbitrary precision floating point arithmetic.
NTL provides high quality implementations of state-of-the-art algorithms for:
* arbitrary length integer arithmetic and arbitrary precision floating point arithmetic
* polynomial arithmetic over the integers and finite fields including basic arithmetic, polynomial factorization, irreducibility testing, computation of minimal polynomials, traces, norms, and more
* lattice basis reduction, including very robust and fast implementations of Schnorr-Euchner, block Korkin-Zolotarev reduction, and the new Schnorr-Horner pruning heuristic for block Korkin-Zolotarev
* basic linear algebra over the integers, finite fields, and arbitrary precision floating point numbers.
標(biāo)簽：
high-performance
providing
portable
library
上傳時(shí)間：
2014-01-04
上傳用戶：exxxds
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SuperLU is a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations on high performance machines. The library is written in C and is callable from either C or Fortran. The library routines will perform an LU decomposition with partial pivoting and triangular system solves through forward and back substitution. The LU factorization routines can handle non-square matrices but the triangular solves are performed only for square matrices. The matrix columns may be preordered (before factorization) either through library or user supplied routines. This preordering for sparsity is completely separate from the factorization. Working precision iterative refinement subroutines are provided for improved backward stability. Routines are also provided to equilibrate the system, estimate the condition number, calculate the relative backward error, and estimate error bounds for the refined solutions.
標(biāo)簽：
nonsymmetric
solution
SuperLU
general
上傳時(shí)間：
2017-02-20
上傳用戶：lepoke
