thinkinjava2English
Thinking in Java,
2nd Edition, Release 11
To be published by Prentice-Hall mid-June, 2000
Bruce Eckel, President,
MindView, Inc.
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How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training SAmples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
高效的k-means算法實現，使用了k-d樹與局部搜索等提高k-means算法的執行效率，同時包含示例代碼，用c++代碼實現。 Effecient implementation of k-means algorith, k-d tree and local search strategy are implementd to improve the effeciency, SAmples are included to show how to use it. All codes are implemented in C++.
The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input SAmples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂，據高手說，非常有用。
The files in this directory comprise ANSI-C language reference implementations
of the CCITT (International Telegraph and Telephone Consultative Committee)
G.711, G.721 and G.723 voice compressions. They have been tested on Sun
SPARCstations and passed 82 out of 84 test vectors published by CCITT
(Dec. 20, 1988) for G.721 and G.723. [The two remaining test vectors,
which the G.721 decoder implementation for u-law SAmples did not pass,
may be in error because they are identical to two other vectors for G.723_40.]
This Telecommunication Standard [TS] describes the detailed mapping from input blocks of 160 speech SAmples in
13-bit uniform PCM format to encoded blocks of 95, 103, 118, 134, 148, 159, 204, and 244 bits and from encoded
blocks of 95, 103, 118, 134, 148, 159, 204, and 244 bits to output blocks of 160 reconstructed speech SAmples
