Problem Solving with Algorithms and Data Structures
Problem Solving with Algorithms and Data Structures, Release 3.0 Control constructs allow algorithmic steps to be represented in a convenient yet unambiguous way. At a minimum, algorithms require constructs that perform sequential processing, selection for decision-making, and iteration for repetitive control. As long as the language provides these
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Introduction to Weka - The University of Auckland
www.cs.auckland.ac.nzWhat is Weka? Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code.
When was the first computer invented?
www.cs.auckland.ac.nzIn 1968, Hewlett Packard began marketing the first mass-marketed PC and the first desktop computer, the HP 9100A. The first workstation Although it was never sold, the first workstation is considered to be the Xerox Alto, introduced in 1974. The computer was revolutionary for its time and included a fully functional computer, display, and mouse.
String Matching Algorithms - Auckland
www.cs.auckland.ac.nzOutlineString matchingNa veAutomatonRabin-KarpKMPBoyer-MooreOthers 1 String matching algorithms 2 Na ve, or brute-force search 3 Automaton search 4 Rabin-Karp algorithm 5 Knuth-Morris-Pratt algorithm 6 Boyer-Moore algorithm 7 Other string matching algorithms Learning outcomes: Be familiar with string matching algorithms Recommended reading:
Image Filtering - Auckland
www.cs.auckland.ac.nzMedian Filtering example The following example shows the application of a median filter to a simple one dimensional signal. A window size of three is used, with one entry immediately preceding and following each entry. ... – It defines a probability distribution for noise or data.
1 Exercises and Solutions - Auckland
www.cs.auckland.ac.nz4. Assume that each of the expressions below gives the processing time T(n) spent by an algorithm for solving a problem of size n. Select the dominant term(s) having the steepest increase in n and specify the lowest Big-Oh complexity of each algorithm. Expression Dominant term(s) O(...) 5+0.001n3 +0.025n 500n+100n1.5 +50nlog 10 n 0.3n+5n 1.5 +2 ...
Gaussian Filtering - Auckland
www.cs.auckland.ac.nzThis means we can normally limit the kernel size to contain only values within three standard deviations of the mean. 5/25/2010 7 ... This is a common first step in edge detectionThis is a common first step in edge detection. The images below have been processed with a …
Big-Oh notation: few examples - Auckland
www.cs.auckland.ac.nzthe Big-Oh condition cannot hold (the left side of the latter inequality is growing infinitely, so that there is no such constant factor c). Example 3: Prove that running time T(n) = n3 + 20n + 1 is O(n4) Proof: by the Big-Oh definition, T(n) is O(n4) if T(n) ≤ c·n4 for some n ≥ n0 . Let us check this condition: if n3 + 20n + 1 ≤ c·n4 ...
Python 3 – Turtle graphics - Auckland
www.cs.auckland.ac.nzLogo and Turtle graphics In 1967, Seymour Papert and Wally Feurzeig created an interpretive programming language called Logo. Papert added commands to Logo so that he could control a turtle robot, which drew shaped on paper, from his computer Turtle graphics is now part of Python Using the Turtle involves instructing the turtle to move on the
Image Filtering - School of Computer Science
www.cs.auckland.ac.nz5/25/2010 2 Median Filtering example The following example shows the applicati on of a median filter to a simple one dimensional signal. A window size of three is used, wi th one entry immediately preceding and
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