Tag Archive for 'PHP Factory Method'

PHP OOP & Algorithms II: What to Use

algorithm2What Are the Guidelines?

Like just about everything else in computing, there’s a certain amount of empirical testing. More importantly, however, are the general principles derived by both empirical and mathematical calculations. In the first post on algorithms and OOP, you saw an example that showed how many operations were necessary to find “Waldo,” a name near the end of an array of over 1,000 elements. One used a straight up loop and the other a binary search algorithm. Both were pretty quick; in fact it was hard to tell the difference in speed, and without seeing the little dots, you would not have seen the number of iterations required in each algorithm. You also saw that quadratic algorithms grew at rates that can quickly reach a point where processing comes to a crawl.

A Table Guide

Table 1 is derived from the 4th Edition of Algorithms (2011, p. 187) by Robert Sedgewick and Kevin Wayne. I’ve summarized it further. (Examples in the book are all in Java.) It is a rough but useful guide, and I have found it handy for a quick look-up.

Table 1: Algorithm Order of Growth

NameOrder of GrowthDescriptionExample
 constant 1statement   2 + 7
 logarithmic log Ndivide in half   binary search
 linear Nloop   find max
 linearithmic N log Ndivide & conquer   mergesort
quadratic N²double loop   check all pairs
cubic N³triple loop   check all triples
exponential2Nexhaustive search   check all subsets

Fortunately, most of the time our algorithms are pretty simple statements, single loops or recursive calls. All of the models in green are algorithms we should try and stick with. You should avoid the reds ones if possible.

What About Recursive Algorithms?

Those who love to profess a little knowledge like to say that recursion is slower than a loop. As indicated, we really don’t want to end up paying attention to small costs. Recursion is important, and using recursive algorithms is cost effective, especially since the difference in running time is negligible.

We often use recursive implementations of methods because they can lead to compact, elegant code that is easier to understand than a corresponding implementation that does not use recursion.
~Robert Sedgewick and Kevin Wayne

(A recursive example of a logarithmic algorithm is not included here, but you can find a recursive binary search in the post on binary searches.) What we need to pay attention to are the order-of-growth issues. For example, quadratic algorithms on the lower end of the scale are not really that bad, but when the N increases at a squaring rate, it’s easy to run into problems. So if you avoid quadratic algorithms, you’ll be better off when the N increases. On the other hand, whether you’re using a recursive method for a binary search, you’ll not see that much of a difference as the N increases compared to non-recursive methods.

A Strategy Design Pattern Handles Algorithms

This blog has lots of posts detailing the Strategy design patterns; so if you’re not familiar with a PHP implementation of the Strategy pattern, you might want to take a quick look at the code. To get started, play the example and download the code.
PlayDownload

In the last post on algorithms, the program used a Factory Method pattern to produce an array, and in this post, the same pattern is used and two additional array products have been added. However, instead of having the algorithm classes be more or less on their own, all of the algorithm classes have been organized into a Strategy design pattern. Figure 1 is a file diagram of the objects in the application:

Figure 1: Object groupings with Strategy and Factory Method patterns

Figure 1: Object groupings with Strategy and Factory Method patterns

If Figure 1 looks a bit daunting, it is only three object groupings. The HTML and PHP Client make a request for one of seven algorithm implementations through a Strategy pattern. The concrete strategy implementations all get their data from a “data factory.” Figure 2 provides an easier (and more accurate) way to understand what’s going on.

Figure 2: Overview of the main purposes of the objects.

Figure 2: Overview of the main purposes of the objects.

So instead of having a complex task, you only have three groupings of classes and interfaces: Those making a request (Clients), those executing operations on data (Algorithms) organized as a Strategy pattern and the data itself organized with a Factory Method (Data.)

Figure 2 shows a simplified version of what the program does. The Context class considerably eases the requesting process. The HTML UI passes the name of the requested concrete strategy to the Client, and the Client uses the name as a parameter in a Context method. The Client is not bound to any of the concrete strategies because the strategy classes are handled by a Context object and method. (Click continue to see the PHP implementations of the algorithms.)
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PHP OOP & Algorithms I: An Introduction

quadAvoiding the Misinformed

Programmers often spend more time un-doing bad information than using good information. One of the comments that set me off recently was someone “explaining” to others on a blog why PHP was not an object oriented language. Then he continued to blather on about the difference between compiled and interpreted languages. Whether or not a language is compiled or not has nothing to do with whether or not it is an object oriented language. Having interfaces, classes and communication between objects are the key criteria of an OOP language, and certainly since PHP5 has been a full-fledged OOP language. (We PHPers should not feel singled out because I recently saw post where a Java programmer pronounced that neither Python nor Perl were OOP, and she was “informed” otherwise by irate Python programmers. Perl has been OOP since V5.) So here I am again wasting time grumbling about people who don’t know what they’re talking about.

Instead of frothing at the mouth over the misinformed, I decided to spent more time with the well-informed. To renew my acquaintance with algorithms I began reading Algorithms 4th Ed. (2011) by Sedgewick and Wayne. Quickly, I learned some very basic truths about algorithms that had been only vaguely floating around in my head. First and foremost are the following:

Bad programmers worry about the code.
Good programmers worry about data structures and their relationships.
Linus Torvalds (Creator of Linux)

Since we’ve been spending time on this blog acting like good programmers, that was reassuring. In this post, I’d like to look at two things that are important for developing algorithms: 1) What to count as a “cost” in developing algorithms, and 2) Identifying good and bad algorithmic models. First, though, play and download the example. Using two different algorithms, a logarithmic and a linear (both pretty good ones), I’ve added “dots” to loop iterations to visually demonstrate the difference between a logarithmic algorithm (binary search) and a linear algorithm (loop). The “expense” of the algorithm can be seen in the number of dots generated.
PlayDownload

The example is a pretty simple one. However, since this blog is about PHP Design Patterns, I added a little Factory Method. The two algorithm classes act like clients making requests through the factory for a big string array with over 1,000 first names. Figure 1 shows the file diagram:

Figure 1: File diagram for use of Factory Method by two algorithm clients.

Figure 1: File diagram for use of Factory Method by two algorithm clients.

In looking at the file diagram, you may be thinking, “Why didn’t you use a Strategy pattern coupled with that Factory Method?” I thought about it, but then decided you could do it yourself. (Why should I have all the fun?)

Lesson 1: Leave the Bag of Pennies

The first lesson I learned in Bank Robbery 101 was to leave the bag of pennies. They’re just not worth it. Speed is everything in a bank robbery, and so you pay attention to how to get the most with the least time. The same thing applies to analyzing algorithms. For example, an object (as compared to an integer, boolean or string) has an overhead of 16 bytes. I have actually seen posts intoning, “objects are expensive…” Just to be clear,

Objects are not expensive. Iterations are expensive, quadratic algorithms are expensive.

In evaluating an algorithm you need to see how many operations must be completed or the size and nature of the N. An N made of up strings is different than an N made up of Booleans or integers. A quadratic (N²) and cubic (N³) algorithm are among the worst. They’re gobbling up kilo- or megabytes, and so that 16 bytes seems pretty silly to worry about. So instead of seeing an algorithm weight expressed as N² + 84 bytes, you’ll just see it expressed as ~N². (When you see a ~ (tilde) in an algorithm, it denotes ‘approximately.’) Another way of understanding the ~ is to note, They left the bag of pennies.

Lesson 2: Watch out for Nested Loops; they’re Quadratic!

I’ve never liked nested loops, and while I admit that I’ve used them before, I just didn’t like them. They were hard to unwind and refactor, and they always seemed to put a hiccup in the run-time. Now I know why I don’t like them; they’re quadratic.

Quadratic algorithms have the following feature: When the N doubles, the running time increases fourfold.

An easy way to understand the problem with quadradics is to consider a simple matrix or table. Suppose you start with a table of 5 rows and 5 columns. You would create 5² cells—25 cells. Now if you double the number to 10, 10² cells = 100. That’s 4 x 25. Double that 10 to 20 and your have 20² or 400. A nested loop has that same quality as your N increases. If both your inner and outer loop N increases, you’re heading for a massive slowdown.

Algorithms, OOP and Design Patterns are Mutually Exclusive

An important fact to remember is that good algorithms do not guarantee good OOP. Likewise, good OOP does not mean good algorithms. Good algorithms make your code execute more efficiently and effectively. Good OOP makes your programs easier to reuse, update, share and change. Using them together is the ultimate goal of a great program.

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