Core Java 2中awt高級(jí)特性圖像處理中顯示Mandebrot Set的小例子
標(biāo)簽： Mandebrot Core Java awt
上傳時(shí)間： 2014-01-06
上傳用戶：chenjjer
一個(gè)delphi下使用的壓縮組件 TCompress Component Set V8
標(biāo)簽： TCompress Component delphi Set
上傳時(shí)間： 2017-02-13
上傳用戶：大融融rr
A set of C++ and Matlab routines implementing the surfacelet transform surfacelet的一個(gè)非常好用的工具箱
標(biāo)簽： surfacelet implementing transform routines
上傳時(shí)間： 2014-01-24
上傳用戶：tyler
for binder and can be set as any mode
上傳時(shí)間： 2013-12-09
上傳用戶：JIUSHICHEN
Input : A set S of planar points Output : A convex hull for S Step 1: If S contains no more than five points, use exhaustive searching to find the convex hull and return. Step 2: Find a median line perpendicular to the X-axis which divides S into SL and SR SL lies to the left of SR . Step 3: Recursively construct convex hulls for SL and SR. Denote these convex hulls by Hull(SL) and Hull(SR) respectively. Step 4: Apply the merging procedure to merge Hull(SL) and Hull(SR) together to form a convex hull. Time complexity: T(n) = 2T(n/2) + O(n) = O(n log n)
標(biāo)簽： contains Output convex planar
上傳時(shí)間： 2017-02-19
上傳用戶：wyc199288
Huffman codes In telecommunication, how do we represent a set of messages, each with an access frequency, by a sequence of 0’s and 1’s? To minimize the transmission and decoding costs, we may use short strings to represent more frequently used messages. This problem can by solved by using an extended binary tree which is used in the 2- way merging problem.
標(biāo)簽： telecommunication represent messages Huffman
上傳時(shí)間： 2014-01-04
上傳用戶：x4587
aiParts is a set of C++ classes that can be used to develop artificial intelligence for multi-decision problems. It includes classes that implement the High-Hope technique and some sample programs.
標(biāo)簽： intelligence multi-decisi artificial aiParts
上傳時(shí)間： 2017-02-20
上傳用戶：徐孺
A Simplex is an immutable set of vertices
標(biāo)簽： immutable vertices Simplex set
上傳時(shí)間： 2014-01-16
上傳用戶：CHINA526
TMS320C55x DSP Mnemonic Instruction Set Reference Guide (Rev. G).pdf
標(biāo)簽： Instruction Reference Mnemonic Guide
上傳時(shí)間： 2017-02-24
上傳用戶：PresidentHuang
The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesian network for discrete variables in which the conditional probability tables are specified by logistic regression models. Logistic regression can be used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Nominal variables are modeled with multinomial logistic regression, whereas the category probabilities of ordered variables are modeled through a cumulative or adjacent-categories response function. Variables can be observed, partially observed, or hidden.
標(biāo)簽： estimating parameters functions defining
上傳時(shí)間： 2014-12-05
上傳用戶：天誠(chéng)24
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