Finding range that separates series of numbers best
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I have X number of colors (lets say, red, yellow, green) which have X measurements in R, G, B, H, S and I channel. In every channel I want to find the solution that separates the measurements the best.
fx:
R channel
Red: [235 224 230 228 233 231]
Yellow: [155 145 153 157 152 148]
Green: [120 122 119 126 124 123]
...and similar in G, B, H, S and I channel
I want the output to be an interval around every color. Usually there are 25 different colors with +3000 measurements have.
How would I go about solving this issue?
BR Simon
linear-algebra matrices optimization
asked Jan 4 at 7:46
std_svstd_sv
1
$endgroup$
add a comment |
$begingroup$
I have X number of colors (lets say, red, yellow, green) which have X measurements in R, G, B, H, S and I channel. In every channel I want to find the solution that separates the measurements the best.
fx:
R channel
Red: [235 224 230 228 233 231]
Yellow: [155 145 153 157 152 148]
Green: [120 122 119 126 124 123]
...and similar in G, B, H, S and I channel
I want the output to be an interval around every color. Usually there are 25 different colors with +3000 measurements have.
How would I go about solving this issue?
BR Simon
linear-algebra matrices optimization
asked Jan 4 at 7:46
std_svstd_sv
1
$endgroup$
$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56
add a comment |
$begingroup$
I have X number of colors (lets say, red, yellow, green) which have X measurements in R, G, B, H, S and I channel. In every channel I want to find the solution that separates the measurements the best.
fx:
R channel
Red: [235 224 230 228 233 231]
Yellow: [155 145 153 157 152 148]
Green: [120 122 119 126 124 123]
...and similar in G, B, H, S and I channel
I want the output to be an interval around every color. Usually there are 25 different colors with +3000 measurements have.
How would I go about solving this issue?
BR Simon
linear-algebra matrices optimization
asked Jan 4 at 7:46
std_svstd_sv
1
$endgroup$
I have X number of colors (lets say, red, yellow, green) which have X measurements in R, G, B, H, S and I channel. In every channel I want to find the solution that separates the measurements the best.
fx:
R channel
Red: [235 224 230 228 233 231]
Yellow: [155 145 153 157 152 148]
Green: [120 122 119 126 124 123]
...and similar in G, B, H, S and I channel
I want the output to be an interval around every color. Usually there are 25 different colors with +3000 measurements have.
How would I go about solving this issue?
BR Simon
linear-algebra matrices optimization
linear-algebra matrices optimization
asked Jan 4 at 7:46
std_svstd_sv
1
asked Jan 4 at 7:46
std_svstd_sv
1
asked Jan 4 at 7:46
std_svstd_sv
1
asked Jan 4 at 7:46
std_svstd_sv
1
asked Jan 4 at 7:46
std_svstd_sv
1
1
$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56
add a comment |
$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56
$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56
add a comment |
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$begingroup$
There are many ways to formulate this problem depending on what "separates the best" means. One metric might be to minimize the number of entries put in the wrong bin. Also, in your example the ranges of colors are non-overlapping. Is this always the case?
$endgroup$
– tch
Jan 4 at 14:19
$begingroup$
The best solution for me would be the interval that creates the greatest margin to the other colors. It varies a lot, whether it overlaps, but optimally all the color channels (R, G, B, H, S, I) should be combined where at least one of the color channels should not overlap - if that makes any sense
$endgroup$
– std_sv
Jan 4 at 14:52
$begingroup$
without knowing exactly what your data looks like its hard to say the best approach. One way to do it would be compute the min and max for each color, see if you can order the colors, and then deal with overlaps by adjusting the boundary a bit. I think this post is probably better suited for stackoverflow since it seems like you will end up writing a program for this in the end.
$endgroup$
– tch
Jan 4 at 14:56