Mixing
normalizing by using gaussian distribution for negative and positive numbers and feed in Min,Max...
$begingroup$
I'm dealing in Python with a dataset which has 6 million float numbers belongs to 3 main parameters A, B , C and I map them in 24x20 matrices for each cycle and I plot them 480-values by 480-values. Initially I normalized during plotting A & B between [-1,+1] and C between [-40, +150] during HeatMap plotting and before that I applied Max,Min normalization by below formula:
Normalization data between [a,b] = (b-a)(X-Xmin/Xmax-Xmin)+a
Then we checked out video of stream of images and we found out that the normalization was not good enough and that's why it's not that much realistic the behaviour changes of A,B,C in images. Actually the normalization we use should be applied by Gaussian distribution function to get rid of some unwanted values (Noise)!!
Due to few extraordinary values as Min and Max from lists of parameters could just happened few times and it distracted our normalization and resulted in seeing no difference in Matrix full of value of 25 with matrix full of values in range of -300 after plotting with HeatMap !! Then we started to apply Gaussian distribution on negative and positive numbers of each parameters A,B,C and took real Max & Min for feeding above-mentioned formula from Mean values of Gaussian function for positive numbers & negative numbers respectively!
it means:
Xmin = Mean of gaussian for negative numbers
Xmax = Mean of gaussian for positive numbers
then calculated confidence intervals for each for negative and positive sides and neglect outer by using : μ ± σ * k we got plots of A,B,C and we realized we cancelled huge noise!
Right now I'm wondering what would be happened if we just apply gaussian normal distribution in whole values of our parameters A,B,C based on below principle:
(1/σ√2π)exp(−(x−μ)^2 /2σ^2) just by computing μ , σ
Did we do extra effort? and just one should be assigned through all data? what's the advantage of what we did by applying 2 times for + and - values and fed them into Max,Min normalization formula?
I asked this question here to get different mathematics point of views beside of programming to have right vision.
Waiting for your kind answers
normal-distribution matlab gaussian-elimination python noise
asked Jan 17 at 17:59
Mario Mario
11
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add a comment |
$begingroup$
I'm dealing in Python with a dataset which has 6 million float numbers belongs to 3 main parameters A, B , C and I map them in 24x20 matrices for each cycle and I plot them 480-values by 480-values. Initially I normalized during plotting A & B between [-1,+1] and C between [-40, +150] during HeatMap plotting and before that I applied Max,Min normalization by below formula:
Normalization data between [a,b] = (b-a)(X-Xmin/Xmax-Xmin)+a
Then we checked out video of stream of images and we found out that the normalization was not good enough and that's why it's not that much realistic the behaviour changes of A,B,C in images. Actually the normalization we use should be applied by Gaussian distribution function to get rid of some unwanted values (Noise)!!
Due to few extraordinary values as Min and Max from lists of parameters could just happened few times and it distracted our normalization and resulted in seeing no difference in Matrix full of value of 25 with matrix full of values in range of -300 after plotting with HeatMap !! Then we started to apply Gaussian distribution on negative and positive numbers of each parameters A,B,C and took real Max & Min for feeding above-mentioned formula from Mean values of Gaussian function for positive numbers & negative numbers respectively!
it means:
Xmin = Mean of gaussian for negative numbers
Xmax = Mean of gaussian for positive numbers
then calculated confidence intervals for each for negative and positive sides and neglect outer by using : μ ± σ * k we got plots of A,B,C and we realized we cancelled huge noise!
Right now I'm wondering what would be happened if we just apply gaussian normal distribution in whole values of our parameters A,B,C based on below principle:
(1/σ√2π)exp(−(x−μ)^2 /2σ^2) just by computing μ , σ
Did we do extra effort? and just one should be assigned through all data? what's the advantage of what we did by applying 2 times for + and - values and fed them into Max,Min normalization formula?
I asked this question here to get different mathematics point of views beside of programming to have right vision.
Waiting for your kind answers
normal-distribution matlab gaussian-elimination python noise
asked Jan 17 at 17:59
Mario Mario
11
$endgroup$
add a comment |
$begingroup$
I'm dealing in Python with a dataset which has 6 million float numbers belongs to 3 main parameters A, B , C and I map them in 24x20 matrices for each cycle and I plot them 480-values by 480-values. Initially I normalized during plotting A & B between [-1,+1] and C between [-40, +150] during HeatMap plotting and before that I applied Max,Min normalization by below formula:
Normalization data between [a,b] = (b-a)(X-Xmin/Xmax-Xmin)+a
Then we checked out video of stream of images and we found out that the normalization was not good enough and that's why it's not that much realistic the behaviour changes of A,B,C in images. Actually the normalization we use should be applied by Gaussian distribution function to get rid of some unwanted values (Noise)!!
Due to few extraordinary values as Min and Max from lists of parameters could just happened few times and it distracted our normalization and resulted in seeing no difference in Matrix full of value of 25 with matrix full of values in range of -300 after plotting with HeatMap !! Then we started to apply Gaussian distribution on negative and positive numbers of each parameters A,B,C and took real Max & Min for feeding above-mentioned formula from Mean values of Gaussian function for positive numbers & negative numbers respectively!
it means:
Xmin = Mean of gaussian for negative numbers
Xmax = Mean of gaussian for positive numbers
then calculated confidence intervals for each for negative and positive sides and neglect outer by using : μ ± σ * k we got plots of A,B,C and we realized we cancelled huge noise!
Right now I'm wondering what would be happened if we just apply gaussian normal distribution in whole values of our parameters A,B,C based on below principle:
(1/σ√2π)exp(−(x−μ)^2 /2σ^2) just by computing μ , σ
Did we do extra effort? and just one should be assigned through all data? what's the advantage of what we did by applying 2 times for + and - values and fed them into Max,Min normalization formula?
I asked this question here to get different mathematics point of views beside of programming to have right vision.
Waiting for your kind answers
normal-distribution matlab gaussian-elimination python noise
asked Jan 17 at 17:59
Mario Mario
11
$endgroup$
I'm dealing in Python with a dataset which has 6 million float numbers belongs to 3 main parameters A, B , C and I map them in 24x20 matrices for each cycle and I plot them 480-values by 480-values. Initially I normalized during plotting A & B between [-1,+1] and C between [-40, +150] during HeatMap plotting and before that I applied Max,Min normalization by below formula:
Normalization data between [a,b] = (b-a)(X-Xmin/Xmax-Xmin)+a
Then we checked out video of stream of images and we found out that the normalization was not good enough and that's why it's not that much realistic the behaviour changes of A,B,C in images. Actually the normalization we use should be applied by Gaussian distribution function to get rid of some unwanted values (Noise)!!
Due to few extraordinary values as Min and Max from lists of parameters could just happened few times and it distracted our normalization and resulted in seeing no difference in Matrix full of value of 25 with matrix full of values in range of -300 after plotting with HeatMap !! Then we started to apply Gaussian distribution on negative and positive numbers of each parameters A,B,C and took real Max & Min for feeding above-mentioned formula from Mean values of Gaussian function for positive numbers & negative numbers respectively!
it means:
Xmin = Mean of gaussian for negative numbers
Xmax = Mean of gaussian for positive numbers
then calculated confidence intervals for each for negative and positive sides and neglect outer by using : μ ± σ * k we got plots of A,B,C and we realized we cancelled huge noise!
Right now I'm wondering what would be happened if we just apply gaussian normal distribution in whole values of our parameters A,B,C based on below principle:
(1/σ√2π)exp(−(x−μ)^2 /2σ^2) just by computing μ , σ
Did we do extra effort? and just one should be assigned through all data? what's the advantage of what we did by applying 2 times for + and - values and fed them into Max,Min normalization formula?
I asked this question here to get different mathematics point of views beside of programming to have right vision.
Waiting for your kind answers
normal-distribution matlab gaussian-elimination python noise
normal-distribution matlab gaussian-elimination python noise
asked Jan 17 at 17:59
Mario Mario
11
asked Jan 17 at 17:59
Mario Mario
11
asked Jan 17 at 17:59
Mario Mario
11
asked Jan 17 at 17:59
Mario Mario
11
asked Jan 17 at 17:59
Mario Mario
11
11
add a comment |
add a comment |
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