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MisterGeeky
MisterGeeky
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MisterGeeky's query-to-list challenges:

MisterGeeky's query to list challenges:It is up to you whether you interpret these challenges as following a pattern.

Challenge 1:- Given an array of random numbers and a target number, sample pairs in the array whose sum is closest to the target.

It is up to you whether you interpret these challenges as following a patternMinimize the error function. To explain this challenge, I need elucidate on the random list.

Challenge 1:L = [8.76, 7.89, 6.98, 9.99]

That's just a handmade random list. Yours can be auto- Given an arraygenerated of random numbers andany length.

Say target = 42.

Find a target number, sample pairs in the arraytuple made from cartesian LxL whose sum of elements is closest to the target. Minimize

These are obviously not the error functionbest words to pose the challenge with, they will be refined. How close is this to golf?

Challenge 1.5:- Extend the above problem with all known operations, and even increase the number of numbers that can be taken at a time to reach the target.

Challenge 2op = ['*', '**', '^', '/', '+', '-']

Assume usual meaning for each, ** is sometimes for exponentiation but you can use it for tetration, similar to knuth's up arrow notation. '^' is either XOR or exponentiation. You have the liberty to choose operators. I meant operators known to you in general. Just normal arithmetic should even do.

op = ['+', '-', '*', '/']

I also allow any binary function that doesn't trivially output the target value.

Challenge 2:- Find Adjacent elementslist of anAdjacent Elements to a given element or element position in any 4-dimensional matrix. (ie, m rows* n columns * p sheets * 4th dim whatever)

Even I'm not sure how this is golf. But I typically like to think there's only one approach that's the correct answer. Feel free to argue with me on that :D The idea is that this knowledge gives you a starting point to find n-neighbours of a cell location.

So, you typically have 8 directions of search (4 pure and 4 combined). You start with listing the immediate neighbours

Challenge 2.5:- Find specified number of adjacent elements of a point in n-space. You are free to either sample at random, use rank of the point or use minimum euclidean distances.

Challenge 3:- build a simple recommenderHere, takesyou find n-neighbors of a querygiven item. Points for creativity.

Challenge 3:- build a simple recommender, takes a query and outputs a list (recommender as a suggestions lister is not easily defineable)

Challenge 3.5:- improve recommendation efficiency (too advanced for golf)

Third challenge is anything that combines knowledge from first and outputs a listsecond challenge.

Challenge 3.5:- improve recommendation efficiency..

The above are to be refined as golfing challenges, shortest code is the best code.

Please comment your thoughts below. Thanks.

MisterGeeky's query to list challenges:

It is up to you whether you interpret these challenges as following a pattern.

Challenge 1:- Given an array of random numbers and a target number, sample pairs in the array whose sum is closest to the target. Minimize the error function.

Challenge 1.5:- Extend the above problem with all known operations, and even increase the number of numbers that can be taken at a time to reach the target.

Challenge 2:- Find Adjacent elements of an 4-dimensional matrix. (ie, m rows* n columns * p sheets * 4th dim whatever)

Challenge 2.5:- Find specified number of adjacent elements of a point in n-space. You are free to either sample at random, use rank of the point or use minimum euclidean distances.

Challenge 3:- build a simple recommender, takes a query and outputs a list

Challenge 3.5:- improve recommendation efficiency

The above are to be refined as golfing challenges, shortest code is the best code.

Please comment your thoughts below. Thanks.

MisterGeeky's query-to-list challenges:

It is up to you whether you interpret these challenges as following a pattern.

Challenge 1:- Given an array of random numbers and a target number, sample pairs in the array whose sum is closest to the target.

Minimize the error function. To explain this challenge, I need elucidate on the random list.

L = [8.76, 7.89, 6.98, 9.99]

That's just a handmade random list. Yours can be auto-generated of any length.

Say target = 42.

Find a tuple made from cartesian LxL whose sum of elements is closest to target.

These are obviously not the best words to pose the challenge with, they will be refined. How close is this to golf?

Challenge 1.5:- Extend the above problem with all known operations, and even increase the number of numbers that can be taken at a time to reach the target.

op = ['*', '**', '^', '/', '+', '-']

Assume usual meaning for each, ** is sometimes for exponentiation but you can use it for tetration, similar to knuth's up arrow notation. '^' is either XOR or exponentiation. You have the liberty to choose operators. I meant operators known to you in general. Just normal arithmetic should even do.

op = ['+', '-', '*', '/']

I also allow any binary function that doesn't trivially output the target value.

Challenge 2:- Find list of Adjacent Elements to a given element or element position in any 4-dimensional matrix. (ie, m rows* n columns * p sheets * 4th dim whatever)

Even I'm not sure how this is golf. But I typically like to think there's only one approach that's the correct answer. Feel free to argue with me on that :D The idea is that this knowledge gives you a starting point to find n-neighbours of a cell location.

So, you typically have 8 directions of search (4 pure and 4 combined). You start with listing the immediate neighbours

Challenge 2.5:- Find specified number of adjacent elements of a point in n-space. You are free to either sample at random, use rank of the point or use minimum euclidean distances.

Here, you find n-neighbors of a given item. Points for creativity.

Challenge 3:- build a simple recommender, takes a query and outputs a list (recommender as a suggestions lister is not easily defineable)

Challenge 3.5:- improve recommendation efficiency (too advanced for golf)

Third challenge is anything that combines knowledge from first and second challenge.

...

The above are to be refined as golfing challenges, shortest code is the best code.

Please comment your thoughts below. Thanks.

Source Link
MisterGeeky
MisterGeeky
  • 101
  • 2

MisterGeeky's query to list challenges:

It is up to you whether you interpret these challenges as following a pattern.

Challenge 1:- Given an array of random numbers and a target number, sample pairs in the array whose sum is closest to the target. Minimize the error function.

Challenge 1.5:- Extend the above problem with all known operations, and even increase the number of numbers that can be taken at a time to reach the target.

Challenge 2:- Find Adjacent elements of an 4-dimensional matrix. (ie, m rows* n columns * p sheets * 4th dim whatever)

Challenge 2.5:- Find specified number of adjacent elements of a point in n-space. You are free to either sample at random, use rank of the point or use minimum euclidean distances.

Challenge 3:- build a simple recommender, takes a query and outputs a list

Challenge 3.5:- improve recommendation efficiency

The above are to be refined as golfing challenges, shortest code is the best code.

Please comment your thoughts below. Thanks.