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Understanding Pseudocode and Computational Processes

This lecture discusses pseudocode, representation of algorithms, input and output processes, conditional operations, loops, and computational processes.

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Understanding Pseudocode and Computational Processes

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  1. Lecture 3 Pseudocode(S&G, §2.2) CS 100 - Lecture 3

  2. Representation of Algorithms • Natural languages (English, Spanish, Chinese, …) • Formal programming languages (Java, Pascal, C++, …) • Pseudocode • “a special set of English language constructs modeled to look like the statements available in most programming languages” • compromise between natural & formal languages CS 100 - Lecture 3

  3. Sequential Operations computingagent interface input output computation external world CS 100 - Lecture 3

  4. Example:Get a value for r, the radius of the circle General form:Get values for “variable”, “variable”, … “variable” means “any variable” (e.g., r) Input Operation Get a value for r 25 17 25 r CS 100 - Lecture 3

  5. Example:Print the value of Area General form:Print the values of “variable”, “variable”, … Output Operation Print the value of Area 26 26 Area CS 100 - Lecture 3

  6. Example:Print the message ‘Sorry, no answer’ General form:Print the message ‘message’ Output Operation (2) Print the message‘Sorry, no answer’ Sorry, noanswer CS 100 - Lecture 3

  7. Example:Set the value of Area to  r2 General form:Set the value of “variable” to “arithmetic expression” compute3.14…  (252) Computational Operation  = 3.14… = 1963 1963 177 25 Area r 2 CS 100 - Lecture 3

  8. 2 cm 3 cm Computation isPhysically Instantiated   (2 cm/2)2 3 cm 13.55 g/cm3 = 127.7 g Hg 127.7 g CS 100 - Lecture 3

  9. Computational Processesin General • Computation takes place in a medium • in which patterns can be represented • by means of which they can be manipulated • Example computational media: • electrical signals & charges • light intensity, fluid pressure, molecular structure, quantum state, gear position (old), abacus beads, paper, blackboard, etc. CS 100 - Lecture 3

  10. Input Processes in General • An input process translates an external physical situation into the computer’s internal representational medium • Example physical situations: • position of mouse or joystick • position of key (depressed or not) • signal from light sensor • etc. CS 100 - Lecture 3

  11. Output Processes in General • An output process translates the computer’s internal medium of representation into an external physical effect • Example physical effects: • characters on display screen or printer • graphics images • position of robot arm • etc. CS 100 - Lecture 3

  12. true/falsecondition if false if true do one set ofoperations do another setof operations continue Conditional Operation CS 100 - Lecture 3

  13. General: If “true/false condition” is true then “first set of algorithmic operations” Else (or Otherwise) “second set of algorithmic operations” (rest of algorithm) Example: If (ci 10) then Set the value of ci to (ci – 10) Set the value of carry to 1 Else Set the value of carry to 0 Conditional Statement CS 100 - Lecture 3

  14. true/falsecondition if false if true do nothing do one set ofoperations continue One-Branch Conditional If “true/false condition” is true then “first set of algorithmic operations” (rest of algorithm) CS 100 - Lecture 3

  15. Computer-Inspired Conditionals in Legal Drafting Subsection (a). IF AND ONLY IF (1) (A) A person has threatened or attempted suicide or to inflict serious bodily harm on himself, OR (B) The person has threatened or attempted homicide or other violent behavior, OR (C) The person has placed others in reasonable fear of violent behavior and serious physical harm to them, OR (D) The person is unable to avoid severe impairment or injury from specific risks, AND (2) There is a substantial likelihood that such harm will occur, THEN (3) The person poses a “substantial likelihood of serious harm” for purposes of subsection (b). — Tenn. Code Ann. sect. 33-6-104(a) (1991) CS 100 - Lecture 3

  16. do loop body if not, repeat test if done if done, continue Basic Loop (Trailing Decision) CS 100 - Lecture 3

  17. terminationcondition loopbody Iteration (Trailing Decision) Repeat steps i to j until “a true/false condition” becomes true step i: operation step i + 1: operation … step j: operation (rest of algorithm) CS 100 - Lecture 3

  18. Example Loop • Set the value of count to 1 • Repeat steps 3 to 5 until (count > 100) • Set square to (count count) • Print the values of count and square • Add 1 to count CS 100 - Lecture 3

  19. Alternate Notation Repeat until “a true/false condition” becomes true “a set of algorithmic operations” End of loop CS 100 - Lecture 3

  20. test if more if not and repeat if more do loop body continue with rest of algorithm Leading Decision Loop While “a true/false condition” remains true do “a set of algorithmic operations” End of loop CS 100 - Lecture 3

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