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1.Program to Interface Not Implementation
2.Encapsulate What Varies
3.Depend on Abstractions, Not Concrete classes
4.Favor Composition over Inheritance
5.Strive for Loosely Coupled System
In your own words, in less than 4 lines, please explain which of these is most important and why?
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Problem: http://usaco.org/index.php?page=viewproblem2&cpid=664 (USACO 2016 Bronze December)
So is this problem generally asking how many of each letter are in all the words together; meaning it is asking to print how many a's there are in all the terms together, how many b's are all together, etc.?
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The details of the project is to get the solution of oops features from the above statement.
I have expected some related solutions for the above question.
Thank you!
features of OOP's are:
Inheritance.
Encapsulation.
Abstraction.
Polymorphism.
Method Overriding.
Method Overloading.
Objects.
Classes.
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When running:
mc.run_model(tmy_data)
which air mass model is used?
https://pvpmc.sandia.gov/modeling-steps/1-weather-design-inputs/irradiance-and-insolation-2/air-mass/
how can I change to other air mass model?
Moreover, where can I find that information (what mathematical models are created in python and how to change it, to run the: mc.run_model(tmy_data).
The ModelChain documentation states:
airmass_model (str, default 'kastenyoung1989') – Passed to location.get_airmass.
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I am working on pattern recognition program using R/python. What would be the best way to compare two or more figures and identify/recognize the similar or duplicate figures based on pattern recognition?
There are lots of papers on the internet, we can try to get the idea how to extract and process feature in a fingerprint. For instance, http://www.cse.unr.edu/~bebis/CS790Q/PaperPresentations/MinutiaeDetection.pdf
Then you can use whatever classifier you want such as support vector machine.
If you need more idea you can visit http://dermatoglyphics.org/11-basic-patterns-of-fingerprint/ to generalize
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How would I use the dataset at http://oceancolor.gsfc.nasa.gov/DOCS/DistFromCoast/ to efficiently determine the distance of a given coordinate (lat,lng) to the nearest coastline?
It's quite a large file. Is there a library that can help with processing this kind of data?