This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
The flpydisk sample is a floppy driver that resides in the directory \\Ntddk\Src\Storage\Fdc\Flpydsk. It is similar to a class driver in that it sits a level above the floppy disk controller in the driver stack, and brokers communication between the application level and the low-level driver. The floppy driver takes commands from the application and then calls routines in the controller which will in turn perform the actual interaction with the device. The sample compiles in 64-bit, but has not been tested in this environment. It is compatible with x86 and Alpha platforms.
REMOVE removes a TSR. It takes two command line arguments. The first is the name of TSR to be removed (or an * to remove the last one), and the second is a file name which MUST contain the interrupt vectors to be loaded when the TSR is removed.
Finds the polynomial p10 of degree less than or equal to 10 that interpolates
cos x on the interval [0, PI/2] at 11 equally spaced points. Study the error betwee
between the function and the polynomial at 41 equally spaced points over the
same interval. Repeat the latter but use your 11 points to be Chebyshevs.
This article is a very simple introduction writing a Windows Form application for the
Microsoft.NET framework using C#. The sample application demonstrates how to create and
layout controls on a simple form and the handling of mouse click events. The application
displays a form showing attributes of a file. This form is similar to the properties dialog box of a
file (Right click on a file and Click on Properties menu item). Since attributes of a file will be
shown, the sample will show how to use File IO operations in .NET framework.
This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
機(jī)頂盒界面源代碼：
## Avoid the so-called SINGAPPL to be initialized at runtime
## Used when the tuner is controlled externally by I2C and
## the PIDs forced to some specific values.
LVQ學(xué)習(xí)矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
