[BlindMath] Strange math / logic question

Sat Aug 17 19:35:16 UTC 2024

Funny thing I was trying this with Pandas but would you do it on each row or
on the whole data frame.  I am also not sure about the answer I am getting.
Lets say I have a mask where all full squares are 1 and empty squares are 0.
I can use any character of course since it's a mask. 

When I did the Standard deviation on each row The ones with all squares full
came up with zero which is perfect.  The ones that was not full came up with
numbers like .42 and 1.42.  I am not sure how I would use that to give an
over all goodness or badness number though.

-----Original Message-----
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Subject: Re: [BlindMath] Strange math / logic question

Hi Ken,
How about the standard deviation of the number of columns filled in each
row?
Best, Chris 

-----Original Message-----
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Subject: Re: [BlindMath] Strange math / logic question

Ken,

Are you looking at evaluating the raggedness of the right edge specifically,
or just the fullness of each row?  For example, is the assumption that rows
are being filled from the left and not randomly?  If filling rows from the
left is the assumption, is that enforced?

Best regards,

Steve Jacobson

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From: BlindMath <blindmath-bounces at nfbnet.org> On Behalf Of Ken Perry via
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Subject: Re: [BlindMath] Strange math / logic question

I actually want to know how close to full and smooth it is .    If I had 20
rows in the matrix and lets say only 2 had 10 in it that should give a low
confidence  of being full but in just doing a percent that would give me a
92 %

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Sent: Saturday, August 17, 2024 3:11 AM
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Cc: ckgoodwin85 at gmail.com
Subject: Re: [BlindMath] Strange math / logic question

Hi Ken,
I'm going to reflect your problem statement back to you to see how well I've
understood it.
* You have a matrix, a square grid, where the cells get filled.  So each
cell is either full or empty.
* Each row gets filled from left to right.  Each row though may not get
completely filled.
* The last row is slightly different so it can be ignored for now.
* You want a measure of how full your matrix is

The obvious answer is just to work out the percentage of filled cells.  So
if the matrix has 100 cells and 90 are full, then confidence level is 90%.

However, I suspect that I've missed out something from your problem
statement and it isn't as simple as this otherwise you wouldn't have asked
it smiley face

Can you explain the problem a bit more?
Best, Chris

-----Original Message-----
From: BlindMath <blindmath-bounces at nfbnet.org> On Behalf Of Ken Perry via
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Sent: Saturday, August 17, 2024 1:46 AM
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Cc: kperry at blinksoft.com
Subject: [BlindMath] Strange math / logic question

I have a  strange math / logic problem.  I think I could teach a neural net
to figure this out, but I am trying to produce a formula that would give me
a number that is bigger the more columns are used.  An example would be I
have a 50 row and 20 column matrices of things.  The matrix of things will
have things in the first column all the way up to the 50th column.  If the
matrix is square and fills all the columns. ON all the rows.

.  I want a confidence level close to 100 %

The less columns are used or the more ragged the right side is I.e., if One
or more random rows are missing 20 10 or 30 etc. then I want a much lower
confidence in fact in around 30 to 50.  If a whole group is missing a lot of
columns, then again it should be exceptionally low number.

If the last row is short though I want the confidence level to still be
high.  I could chop it off before calculating and then deciding on the last
row separately.

Does anyone have an  easy method of I guess I would call it a fullness
factor right justified calculation function.  Note it doesn't matter what I
stick in the column items I just want it as a count of things starting from
the left and a confidence factor the more ragged or less full the matrix is.

Note this is not a school thing it is a software Engineering question for
something I am working on.

I am writing this in python if you have a suggestion, a library, a formula .
Let me know.

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