ecefe89e-d256-4df0-a30c-a010079c448c 32 15 12 decimal

&[DATE] &[TIME] - &[FILENAME]

M Min Max Scale Max Min - (M M min - M max M min - 106 -^+ / (* Min + :

Bar plot unicolor style, bar width user. data dt BarSize v data 2 col : t v bar t dt - 0 t dt - v t dt + v t dt + 042 mat : j 1 data rows range j 1 ≡ B data j 1 el data j 2 el bar : B B data j 1 el data j 2 el bar stack : if 11 line for B 41 line :

length 100 :

Cols 40 :

The S _j,1 stacks the 40 nested cols.

The ΣS sums the 40 nested cols

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length of 'M': user M length Cols Random :

rn j 1 M cols range S j 1 el M j col : for S sum 21 line eval :

μ rn Mean : 19.8988

rn max 24.8572

rn rn 0 μ Scale :

scale 1 :

rnd scale rn Floor μ / * :

i 1 M rows range :

½noise 0.5 - rnd + :

'rnd => vector of user scaled normal random

i rnd augment 0.0125 BarSize 1 1 sys

User ½ noise to superpose on function

i ½noise augment 0.0125 BarSize

counts specific values in a sorted vector value table count k 1 : j k table rows range table j el value ≡ index k el j : k k 1 + : 21 line No match if for index rows 31 line :

N 10 rn μ / * Floor sort :

collect all the counted values in sorted 'N' N histogram i 0 N max range x i 1 + el i : count i 1 + el i N count : 21 line for x count augment 21 line :

H N histogram :

λ M rows : μ H rows 2 / : σ M rows H rows - 24 / : 31 line

H 03132113184155236107118492102 mat

Central Limit Theorem: confirmed

H 0.125 BarSize λ x μ σ pdf.Normal * 2 1 sys

Error => "index undefined"

Some value coudn't be accounted in:

....... count(value,table) .......

F9 to regenerate. But the 'rnd'

vector is complete for export.

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http://www.embedded.com/design/configurable-systems/ 4211153/DSP-Tricks--Generating-normally-distributed-random-data-

In the development of many signal processing algorithms where it is necessary to generate normally distributed random data, a problem that is sometimes encountered is how actually to generate random data samples whose distribution follows that normal (Gaussian) curve. There’s a straightforward way to solve this problem using any software package that can generate uniformly distributed random data, as most of them do. As it turns out, there’s a principle in advanced probability theory, known as the Central Limit Theorem, that says when random data from an arbitrary distribution is summed over M samples, the probability distribution of the sum begins to approach a normal distribution as M increases. In other words, if we generate a set of N random samples that are uniformly distributed between zero and one, we can begin adding other sets of N samples to the first set. As we continue summing additional sets, the distribution of the N-element set of sums becomes more and more normal.

n random

Undocumented in f(x)

Gleichverteilte ganze Zufallszahl zwischen 0 und n-1.

Uniformly distributed random number between 0 and n In the example, we generate m random numbers between 0 and n and plot their frequency. Set 'm' 10*n

n 100 :

m 10 n * :

v 1 n range :

h n 1 matrix :

i 1 m range r n random : h r 1 + el h r 1 + el 1 + : 21 line for

data v h augment :

data 0.01 BarSize

rn h :

μ rn Mean : 10

rn max 19

rn rn 0 μ Scale :

scale 1 :

rnd scale rn Floor μ / * :

i 1 n range :

½noise 0.5 - rnd + :