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ID: 23763, Dew Lab Studio v2.1

by Janez Atmapuri Makovsek Email: Anonymous

Dew Lab Studio is a comprehensive set of products for numerical, scientific, statistical and signal processing applications. It features exceptional performance and supports W32 and .NET.
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For Delphi, Version 6.0  to 10.0 77 downloads
Copyright: All rights reserved

Size: 18,383,782 bytes
Updated on Tue, 22 Nov 2005 06:18:20 GMT
Originally uploaded on Fri, 28 Oct 2005 06:50:05 GMT
SHA1 Hash: 5AE0562087C3A21840D55380A3F21AA74E06730B
MD5 Hash: 51D9CD11C38AFEFEF34FF9705C37BACF

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Dew Lab Studio is a comprehensive set of products for numerical, scientific, statistical and signal processing applications supporting W32 and .NET applications.
1.) MtxVec is an object oriented numerical library for Delphi and .NET developers with complete matrix/vector arithmetic. It features a large set of vectorized mathemathical functions which cover complex numbers, sparse matrices, math parser, probabilities, optimization unit, SVD, QR, LQ, and LU solvers, special functions, and more. All applications based on this library take advantage of CPU-specific code optimization and symmetric multiprocessing. Efficient memory and CPU cache management further enhance its performance.
2.) Signal processing package for Delphi features: IIR (butterworth, chebyshev, elliptic, bessel) and FIR (window, Remez exchange) filter designers. Over 40 included components feature: Support for streaming pipelines, frequency analyzer, higher order spectral analyzer, cross spectral analyzer, signal generator, read/write file support, audio playback and recording, and much more.
3.) The Stats Master statistical package includes: 21 different distributions (PDF, CDF and inverse CDF function), mean and variance for all 21 distributions, random generators for 18 distributions, parameter estimate for beta, binomial, exponential, gamma, geometric, normal, Poisson, continuous uniform and Weibull distributions, histograms, ogives nth-Moment, percentile, range, Interquertile Range IQR, mean, harmonic mean, goodness-of-fit tests and more..
4.)Data miner is a set of components for classification. The algorithms included cover: KNN and Naive Bayes plus a third completely new algorithm named Linear Classifier. The algorithms can work on real and discrete data and can be connected to a TDataSet descendant. They appropriately handle missing data and are
all capable of incremental learning. Suitable for artifical inteligence like applications.

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