[PD] cumulative distribution?

[david golightly]

Are you just looking to produce a bell curve aka normal/gaussian 
distibution?  Because [;tabname cosinesum 512 1 1( does that just fine.


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>From: pete mcpartlan <petemcpartlan at yahoo.co.uk>
>To: Tebjan Halm <tebjan at gmx.de>, pd-list <pd-list at iem.at>
>Subject: Re: [PD] cumulative distribution?
>Date: Wed, 09 Nov 2005 15:52:27 +0000
>
>thanks Tebjan,
>
>but...its not half a cosine because it has to flatten out at both ends.
>
>and secondly i have no idea how to take an equation like that and implement 
>that in pd... maybe i didnt explain that i'm not too good at all that maths 
>stuff...
>
>thanks,
>
>pete
>
>Tebjan Halm wrote:
>
>> >>(cos(x) + 1) * c with x inside the range -Pi to 0 and c is a constant
>> >>that defines the output range of the curve 0 to c ...
>>
>>
>>sorry, the output range will be 0 to 2*c ... because the cos range -1 to 1
>>gets shifted upwards by the +1 to 0..2 and c scales this range ...
>>
>>pete mcpartlan schrieb:
>>
>>>hello,
>>>
>>>i need help with a maths problem... i am trying to plot a cumulative 
>>>distribution curve to weight random. I have a [random] that feeds into a 
>>>chain of [moses], sililar to the markov chain example but what i want to 
>>>do is have a table dump into the right inlet of each moses changing the 
>>>weighting. so far so good. what i need help with is the curve which needs 
>>>to make it more likely for the next result to be near the  same position. 
>>>the attatched patch has an array with the sort of function it should 
>>>be... like an s stetched at both ends... is there a way i can do this 
>>>with expr? or am i going to have to type out a list for each state? i'm 
>>>sure this is probably quite a simple maths problem... but beyond me... or 
>>>other ideas? might it be simpler to have a longer array with the curve is 
>>>then plotted at different points back into the array... but considering 
>>>i'm probably going to have 16+ of these and other stuff i want to make it 
>>>as simple as possible....
>>>
>>>thanks in advance and apologies for rambling a bit..
>>>
>>>pete
>>>
>>
>>
>
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