Well boys and girls this is it. the last lab, and boy was it a fun one. I did enjoy these labs and i did learn some stuff actually. which is definitely not the case for all of my class. anyways to the lab material...
Shannon's method vs. Harley method on how they measure information
Alright, well to start off, we need to know what these two guys are trying to even do. We must ask ourselves how do you measure information? Well the most simplistic form of measuring information is by representing the information in strands of zeros and ones, or binary. This encoding is a sequence of trues or falses, yes or no, or whatever you want it to represent. By doing this, you can represent information clearly, and everything has its own special code. What Shannon and Hartley are trying to accomplish is figuring our the amount of bits of binary will need to be used to encode something. In the lab we were trying to see how much code would need to be used to represent the grades a teacher is most likely to give out. Here is where the two methods are different. Basically, using Hartley's method, you take in account only what you have there as in raw data, you don't look at probability or anything else that might sway what information might be displayed. On the contrary, Shannon's method takes in account probabilities. For example if there is a 0% chance that a student can get an A in a class, Shannon would not even count it in his representation of information for that class. Hartley however would take it as part of the information even though there is no chance that it could ever happen. This difference makes Hartley's method a lot more inconsistent, and also less accurate and concise.
Well i guess this means goodbye to the labs, i guess all i can do now is thank the A.I.s and all their hard work and answering my questions even if they are colts fans. i guess that is ok.
until next time...
Chris Kremser
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