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I am new to algorithms. What is the difference between the design of algorithms and the analysis of algorithms?
The design of an algorithm is the process of inventing the algorithm. You work out what steps to take, the order in which to take them, etc. (Think of it like writing the code for the algorithm). The analysis of an algorithm is where you work out mathematically how efficient it is, prove that it's correct in all cases, etc.
Think of the design as writing the code and the analysis as justifying why that code works and why it's efficient.
Algorithm Design is a specific instructions for completing a task.
They've also been called "recipes".Perhaps a more accurate description would be that algorithms design are patterns for completing a task in an efficient way.
Analysis of Algorithms is the determination of the amount of resources (such as time and storage) necessary to execute them.usually described as (time complexity) and storage locations (space complexity) of an algorithm and stated as a function relating the input length to the number of steps.
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Let's suppose that androids which are physically alike humans are a reality.
What would be an algorithm to make it interact with human beings if we want it to:
1) be indistinguishable from regular people in behavior
2) be as equally friendly to everyone as possible?
I understand that it is very hard to write an algorithm like that. I can, however, imagine an android simulating human behavior fairly well with some sort of machine learning technique.
But how would we train it? The act of collecting data would also be a big big problem.
Which machine learning technique would be ideal?
If you consider requirement 1 to be a hard requirement, such an algorithm would beat the Turing Test at least to some extent, so it would be a pretty advanced (world-class) algorithm.
Your problem basically equates to beating the Turing Test, so check the linked article to see the scientific literature produced by people working on this problem.
Assuming massive data availability and processing power are basically unbounded, I believe an Artificial Neural Network would be the best runner-up to base such an algorithm on.
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I am in a quest of understanding majority of important algorithm that SO community has used in read world applciations. I know a ready list can be extracted from wiki page. But, i am interested only those algorithm or problem that community has faced either in their projects or asked in interviews. Few lines on that algorithm will also be helpful.
I am looking beyond the generic algorithm D&C, DP, Greedy...
If you are interesting about optimization problems which can be used any computer applications such as network and socket programming these could be useful for you;
NearbyNeighbour
Munkres
Hungarian
BruteForce
Min&Max Finding Algorithms
Ant and Bee Colonies Algorithms
General Genetic Algorithm etc.
I totally advice you to search all aboves but genetic algorithms and ant colonies algorithm are asked many interviewers.
I hope that helps.
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I am getting ahead on next semester classes and just had a question about bigO notation. What is the time factor measured in? Is it a measure of milliseconds, nanoseconds or just an arbitrary measure based upon the amount of inputs, n, used to compare different versions of algorithims?
It kinda sorta depends on how exactly you define the notation (there are a many different definitions that ultimately describe the same thing). We defined it on turing machines, there time would be defined as the number of computation steps performed. On real machines, it'd be similar - for instance, the number of atomic instructions performed. As some of the comments have pointed out, the unit of time doesn't really matter anyway because what's measured is the asymptotic performance, that is, how the performance changes with increasing input sizes.
Note that this isn't really a programming question and probably not a good fit for the site. More of a CompSci thing, but i think the compsci stackexchange site is meant for post graduates.
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I have the test results on Weka and in the some of data sets there is not much difference between them when using J48 and decisionStump as an algorithm.
How could J48 algorithm have no statistically significant difference to DecisionStump algorithm when comparing by accuracy (percent correct)? Can we find the relation by examining algorithms or structure of data?
DecisionStump is intended to be a very basic building block for other classifiers, but perhaps your data happen to be adequately modeled with a simple classifier, in which case J48 will be unable to find a clever answer that is better. Degenerate cases of this are:
1) DecisionStump always produces the right answer because one of the predictors in fact completely predicts the right answer.
2) All of the predictors are completely useless, in which case DecisionStump is no different than everything else.
I'm not at all surprised because I keep seeing studies that say that no one model was spectacularly better than the others. See e.g. the abstract at http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.6753. Usually logistic regression is one of the "good enough" classifiers.
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I am trying to understand McCabe's Cyclomatic number and I learned what it is actually for i.e. it is used to indicate the complexity of a program. It directly measures the number of linearly independent paths through a program's source code. (Read from wikipedia)
But I want to know which software entity and attribute it really measures.
Cyclomatic Complexity, CC, is measured at the granularity of the function or method. Sometimes it is summed up for a class and called the Weighted Method Count, WMC, i.e. the sum of the CC's for every method in the class.
Cyclomatic complexity analyses the code. looks for Loops and branches you have in code and assumes that greater the loops and branches, complex the code.
Complexity is then linked to maintainability. its assumed that higher the complexity difficult it gets to maintain.
This is used for methods and classes to measure the complexity .
Complexity number 3 is not bad for a method,If it is greater than 3,then it is eligible for refactoring.
It encourages to write small methods so that there is high posibility for code reuse.