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How to reduce the number of lines in code?
It’s not about the actual number of lines of code in the idea because you can reduce the number of lines with something like: This is one line of code, but it’s a place where an amazing amount of things can go wrong. So I’d say focus on doing the most with the fewest statements — write as much code as you need to get things done and no more.
How to reduce code size without sacrificing performance?
Often, varying compiler options to reduce code size can enable developers to decrease the amount of on-chip and external memory an application requires without adversely affecting performance, thereby reducing the overall bill of materials (BOM).
How are compilers used to reduce code size?
Compilers offer a number of optimizations, such as loop unrolling and in-lining code, that can increase performance while increasing code size, as shown in the code samples below. The value of such optimizations is dependent upon the number of times a particular function is called.
How to reduce code size and memory cost?
Code Sample 2 offers an alternative between these two extremes. Consider two blocks of code, one part of a critical computational loop that represents a significant portion of CPU utilization for a particular algorithm and the other an initialization block that is only run once the first time an application is turned on.
Which is the best example of reduce in JavaScript?
Here are 4 common examples and 1 not-so-common example that demonstrate how to use reduce (). Most reduce () tutorials start with this example: given an array of numbers [1, 3, 5, 7], calculate the sum. Here’s how you might sum up an array with a plain old for loop.
Why do you use list in reduce function?
List prevents this by Reduce iterating though the elements of the list (the individual X i T X i matricies) and applying the binary function ‘+’ to each one. List is used in the reduce function Sum because we will also use this as a combiner function and if we didn’t use a list we would have the same problem as above.
How to make map reduce work with Hadoop?
For Hadoop/MapReduce to work we MUST figure out how to parallelize our code, in other words how to use the hadoop system to only need to make a subset of our calculations on a subset of our data. ( X T X) p × p and ( X T y) p × 1 is small enough for R to solve for β ^, thus we only need X T X, X T y to get β ^.