Python Combinations Function – The Power To Choose

Let’s imagine this scenario. You are a fund manager who is in charge of several stocks. Your company has given you about 20 stocks to evaluate and asks you to find out what 5 stocks from the 20 you can include in your portfolio this year. You have a choice of selecting the 5 stocks which have equal probability of success. How many different selections can you make?

Ever seen a problem like this in college mathematics? Yes, it is an example of a combination problem. We see it all the time in life. In choosing what clothes to wear for the week, what combination of food to choose from a menu, or what combination of channels to watch for the week. We cannot do without combinations.

 

In simple terms, combinations can be defined as the number of possible arrangements you can make from a collection of items where the order of the selection does not matter. Combination is different from permutations because in permutations the order of selection matters.

Let me not bore you with the mathematical details. Let’s go straight to how python allows you to use the power of combinations.

How Python combinations work

To carry out combinations in python you need to import one function, the combinations function from the python itertools module. You can use the code: from itertools import combinations. Very simple. With that you are good to go.

The syntax for the python combinations function is: itertools.combinations(iterable, r) where iterable is the collection you want to select from and r is the number of possible arrangements you want to make from the collection. Note that r should not be greater than the length of the iterable otherwise python combinations function will return an empty object. When you call the combinations function, it returns a combinations object which is an iterator. You can cast the iterator to a list or set to extract the elements of the combination or the arrangements.

Now that the syntax is done, let’s solve the fund manager’s problem we started with.

The problem the fund manager is faced with is that out of 20 stocks he has to select 5 without order since they are all equally probable of success. How many selections or arrangements can he make?

I have included comments in the code above so you can follow along on the logic behind how it was applied. On line 1, we imported combinations from itertools module. That means we are good to go. On line 3, using range function, we created a collection or sequence of 20 items. So easy. On line 4 we called the combinations function and passed the collection or sequence as its first argument and then 5, the arrangements we are making, as its second positional argument. The python combinations function returned a combinations object which is an iterator, and in the next line we cast the iterator to a list so we can extract the items in it. But not to worry, we are not investing in any of the stocks yet, we just want to know how many selections the fund manager can make. So, on the last line we called length function on the list and it gave us the answer: 15504 possible arrangements of the stocks. I bet, the fund manager needs more than a voodoo priest to decide on what arrangement of stocks to choose.

I believe that right now you understand how the python combinations function works. But am not going to leave you without one more example. I so love this one because I use it often on a weekly basis.

For example, Michael loves eating 5 types of foods but he can only choose three of them every day. If the order he chooses each meal is not important, how does he choose. Also, how many choices can he make? This is just easy, right? Let’s do it.

It’s so easy, not so? I believe you can read and follow along with the code above. It’s one of the easiest codes I’ve written this week. If you look at the food choices, you would notice that rice stands out prominently. Well, because order doesn’t matter it makes no difference if rice is at the beginning of a choice or the end for each day. Why you get the print out is because combinations prints out the arrangements based on the order it finds the items in the sequence or collection. If you want a different order, you can sort the food list. Try it out on your machine and see.

Now, let me give you a bonus tip. The combinations function in python makes it possible for you to do a calculation that before now took a very lot of processing to carry out. That is calculating the powerset of a set. Before I discovered the combinations function, I used to calculate powerset of a set based on an algorithm that was of exponential complexity. You get what I mean? It took a lot of time but when I discovered combinations function, all that stress was put to rest.

How to calculate powerset of a set using python combinations function.

The powerset of a set, S, can be defined as the set consisting of all subsets of S, including the empty set and S itself. So, that’s the mathematical definition and that is the result we expect to have in our code.

To get the powerset of any iterable from the combinations function, we will use the following code:

from itertools import combinations, chain

def powerset(iterable):
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) 
                      for r in range(len(s)+1))

Notice that this time we are not only importing combinations but also the chain function. The meat of the code lies in the last line of the powerset function. What is happening there is that using a generator expression we are creating combinations with the arrangements, r, going from 0 , 1, 2… to the length of the iterable. This makes sure we are creating arrangements for every combination of the powerset. The generator expression outputs a combination object which is an iterator. To extract the elements we have to cast it to a chain object, which is also an iterator and then cast the result of the function to a list or any other iterable. The casting to a list was done in the example below. Note that the elements will be arranged in tuples since they are combinations of sometimes more than one object. It’s so elegant. No more lengthy and time consuming code.

Let’s try it with a working example.

The code above will print out the powerset of the list, num. Cool, right?

Experiment with these functions to your heart’s delight. They demonstrate the power of python.

Happy pythoning.

First Walking Microscopic Robots (Nanobots) To Change The World

Although it has been said several times that the future of nanoscale technology with nanobots is immense, each day researchers continue to expand it. Recently, in a first of its kind, a Cornell University-led collaboration has manufactured the first microscopic robot that can walk. The details seem like a plot from a science fiction story.

 

The collaboration is led by Itai Cohen, professor of physics, Paul McEuen, the John A. Newman Professor of Physical Science – both in the College of Arts and Sciences – and their former postdoctoral researcher Marc Miskin, who is now an assistant professor at the University of Pennsylvania. The engineers are not new to producing nanoscale creations. To their name they already have a microscopic nanoscale sensor along with graphene-based origami machines.

The microscopic robots are made with semiconductor components that allow them to be controlled and made to walk with electronic signals. The robots have a brain and torso, and legs. They are 5 microns thick, 40 microns wide, and 40-70 microns in length. A micron is 1 millionth of a metre. The torso and the brain were the easy part. They are made of simple circuits manufactured from silicone photovoltaics. But the legs were completely innovative and they consist of four electrochemical actuators.

According to McEuen, the technology for the brains and the torso already existed, so they had no problem with it except for the legs. “But the legs did not exist before,” McEuen said. “There were no small, electrically activatable actuators that you could use. So we had to invent those and then combine them with the electronics.”

The legs were made of strips of platinum. They were deposited by atomic layer deposition and lithography, with the strips being just some dozen atoms thick. Then these strips of platinum are capped by layers of titanium. So, how did they make these legs to walk? By applying a positive charge to the platinum. When this is done, negative ions from the solution surrounding the surface of the platinum are adsorbed to the surface and they neutralize the charge. Neutralization makes the platinum to expand and the strips bend. Because the strips are ultrathin, they can bend on neutralization without breaking. To enable three dimensional motion control, rigid polymer panels were patterned on top of the strips. The panels were made to have gaps and these gaps made the legs to function like knees or ankles, enabling the legs to move in a controlled manner with generated motion.

A paper describing this technology titled: “Electronically integrated, mass-manufactured, microscopic robots,” has been published in the August 26 edition of Nature.

The future applications of this technology is immense. Since the size of the electronically controlled microscopic robots is that of a paramecium, one day when they are more sophisticated, they could be inserted into the human body to carry out some functions like cleaning up clogged veins and arteries, or even analyzing the human brain. Also this first production will become a template for the production of even more complex versions in the future. This initial mcroscopic robot is just a simple machine but imagine how sophisticated and computational complex it will be when it is installed with complicated electronics and onboard computers. Furthermore, to produce the robots do not take much in terms of time and resources because they are silicone-based and the technology already exists. So we could see the possibility of mass-produced robots like this being used in technology and medicine to the benefit of the human race. In fact the benefits are immense when one calculates the economics involved.

“Controlling a tiny robot is maybe as close as you can come to shrinking yourself down. I think machines like these are going to take us into all kinds of amazing worlds that are too small to see,” said Miskin, the study’s lead author.

The frontiers of nanobot technology is expanding by the day. With these mass produced robots in the market, I see a solution in the offing for various medical and technological challenges. This is an innovative nanobot.

Material for this post was taken from the Cornell University Website.