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Session 05: C# PatternsPowerPoint Presentation

Session 05: C# Patterns

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Session 05: C# Patterns

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Session 05:C# Patterns

Algorithm Patterns:

Sweep

Search

AK IT: Softwarekonstruktion

The concept of patterns originates from architecture (Christopher Alexander, 1977):

“Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice”

(Christopher Alexander e. a.: “A Pattern Language”. Oxford University Press, New York, 1977.)

UCN/IT:

Advanced Computer Studies

- A well known and widely accepted concept in software engineering
- Developed in the early 1990s and published by Gamma e.a. (“Gang of Four”, GoF) in 1995:
“(…) design patterns (…) are descriptions of communicating objects and classes that are customized to solve a general design problem in a particular context.”

(Erich Gamma e.a.:

”Design Patterns. Elements of Reusable Object-Oriented Software”. Addison-Wesley. 1995.)

UCN/IT:

Advanced Computer Studies

- A pattern captures a proven good design:
- A pattern is based on experience
- A pattern is discovered – not invented

- It introduces a new (and higher) level of abstraction, which makes it easier:
- to talk and reason about design on a higher level
- to document and communicate design

- One doesn’t have to reinvent solutions over and over again
- Patterns facilitate reuse not only of code fragments, but of ideas.

UCN/IT:

Advanced Computer Studies

- It is often said that good skills in software construction require experience and talent
- …and neither can be taught or learned at school
- Patterns capture experience (and talent) in a way that is communicable and comprehensible
- …and hence experience can be taught (and learned)
- So we should rely heavily on patterns in our teaching

UCN/IT:

Advanced Computer Studies

- Many different problems from many different problem domains may be solved by algorithms that possess a common structure – or a common pattern.
- By abstracting and formalizing this structure it becomes a reusable pattern with all the desired properties connected to patterns.
- Patterns have names – within the field of algorithms the following – among others – may be identified:
- Sweep algorithms
- Search algorithms
- Merge algorithms
- Divide and Conquer algorithms
- Greedy algorithms
- Backtracking algorithms
- Dynamic programming etc. etc.…

UCN/IT:

Advanced Computer Studies

- Purpose:
- inspects all elements in a collection (senselessly sweeping through the collection) and doing something according to the characteristics of the current element.

- Benefits:
- separates operations depending on the collection (loop control) from operations depending on the actual problem at hand.

UCN/IT:

Advanced Computer Studies

- Examples:
- counting the number of students older than 25 years in of list of students
- increasing the value of a discount percentage by 10 on all elements with a balance of more than DKK 10,000 in a set of customers
- calculating the average number of words per sentence in a text
- etc. etc.

UCN/IT:

Advanced Computer Studies

visitedUS

a:

i

Data representation (C#):

inti; int a[];

< DO_INIT >;

inti = 0;

while ( i < a.Length) {

< DO something to a[i]) >;

i++;

} // end while

< DO_INIT >;

for (inti= 0 ; i < a.Length; i++ ) {

< DO something to a[i]) >;

} // end for

In C# a counter controlled loop may be written simpler using the foreach-statement.

UCN/IT:

Advanced Computer Studies

Counting zeros in an array:

DO_INIT:int count= 0;

DO:if(a[i] = = 0) count++;

< DO_INIT >;

for (inti= 0 ; i < a.Length; i++ ) {

< DO something to a[i]) >;

} // end for

int count= 0;

for (inti= 0 ; i < a.Length; i++){

if (a[i] = = 0)

count++;

}// end for

UCN/IT:

Advanced Computer Studies

Increasing all elements by one:

DO_INIT:no concretising is needed.

DO:a[i]++;

< DO_INIT >;

for (inti= 0 ; i < a.Length; i++ ) {

< DO something to a[i]) >;

} // end for

for (inti= 0 ; i < a.Length; i++) {

a[i]++;

} // end for

UCN/IT:

Advanced Computer Studies

- Purpose:
- The algorithm looks for an element (target, t) with some specified property in a collection

- Benefits:
- The search terminates when the first occurrence of the target is discovered
- Loop control is separated from the testing for the desired property

- Examples:
- Searching for a customer with a balance greater than DKK 10,000
- Searching for a student older than 30
- Searching for the word “algorithm” in a text.

UCN/IT:

Advanced Computer Studies

Only the abstract operations (in red) are problem specific

Notation:

- CC: Candidate Collection
- c: Element to be examined
- t:The target element

< InitialiseCC >;

boolfound= false;

while ( ! found && <CC ¹Ø > ) {

< Select c from CC >;

if ( < c==t >)

found = true;

else {

< Split CC >

}

}

The structure is general and reusable

UCN/IT:

Advanced Computer Studies

initialise:inti = 0

select:c = a[i]

CC¹ Ø:i < a.Length

split:i ++

a:

i

CC

int c;

int i= 0;

boolfound= false;

while ( !found && i<a.Length) {

c = a[i];

if (c == target)

found= true;

else

i ++;

} // end while

Conditions connected to loop control

Conditions connected to the actual search

UCN/IT:

Advanced Computer Studies

- The strategy:
- Select an element in the middle of the candidate set:
- If this is the element we are looking for – we are done
- If the target comes after the middle element, then look in the upper part (remember the collection is sorted)
- If the target comes before the middle element, then look in the lower part (again remember the collection is sorted)

- Repeat this until the target has been found or there are no more candidate elements

- Select an element in the middle of the candidate set:

UCN/IT:

Advanced Computer Studies

INITIALISE:intlow = 0;

inthigh= a.Length;

SELECT:middle= (low+high)/2

c = a[middle]

CC¹ Ø:low <= high

SPLIT:if (c<t) low= middle + 1;

elsehigh= middle – 1;

INITIALISE:int low = 0;

int high= a.length;

SELECT:middle= (low´+high)/2

c = a[i]

CC¹ Ø:low <= high

SPLIT:if (k<m) low= middle + 1;

else high:= middle – 1;

int low = 0;

int high = a.Length-1;

int c , middle;

bool found = false;

while ( ! found && low<=high ) {

middle = (high + low) / 2;

c= a[middle];

if (c == t)

found= true;

else if ( c<t )low = middle+1;

else high= middle-1;

} // end while

CC

a:

lowhigh

UCN/IT:

Advanced Computer Studies

- Please note:
- Binary search is very efficient (logarithmic in execution time), but:
- The realisation of SPLIT relies heavily on the precondition that the array is sorted.
- The realisation of SELECT requires that the data representation provides random access to elements.
- Binary search is not to be applied otherwise (don’t ever use it on linked lists)

- Binary search is very efficient (logarithmic in execution time), but:

UCN/IT:

Advanced Computer Studies