AndrewG

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10 years, 13 days

MaplePrimes Activity


These are replies submitted by AndrewG

@Markiyan Hirnyk I see this question was asked a lot but none of the answers were satisfying in my opinion.  They weren't elagantly simple. 

Isn't it strange Maple doesn't have a simple convert to scientific notation command?

@Kitonum Thanks, never thought about using Search.

@peter2108 

Using cat is similar but doesn't need typeset

plot(sin((1/180)*Pi*x), x = -180 .. 180, tickmarks = [[seq(180*i*(1/4) = cat(180*i*(1/4), `°`), i = -4 .. 4)], default])

@Carl Love k means clustering tends to be less complex and faster and considered superior for some reason.  Fuzzy c means is weighted giving more importance to stronger connected points.

Actually I didn't realize there were so many different types of clustering algorithms or different names attached to them.  The fuzzy c-means clustering is also known as soft k means.

It would them seem redundant to use RandomVariable command in the Sample statement.

@vv thanks.  I see from the help page description

Sample
The Sample command generates a random sample drawn from the distribution given by X.

RandomVariable
The RandomVariable command creates new random variable with the specified distribution.

@Carl Love Thanks for the work on that. 

I found a post on k mean clustering that was open ended.  It appears we should be able to extrapolate this code to k mean clustering?

@Markiyan Hirnyk Thanks for your example.  You have done quite a bit of work. 

 

@tomleslie thanks for the alternate.  I was hoping someone would show another way, I didn't know if the way I did it was a correct way or if maple had a command I didn't know about. 

Mathematica (which I don't know much about) creates it like this


e1:=RandomArray[MultinormalDistribution[{0,0},{{.5,0},{0,.5}}],100]

Table[{3,15}+e1[[i]],{i,100}]

To me it looks like they generate a normal distribution around the point 0,0 and then just add it to a new point.  Isn't this the same thing

RandomArray[MultinormalDistribution[{3,15},{{.5,0},{0,.5}}],100]

?

So you're saying what I created is not a general normal distribution around a specific point?  Show me how you would do it. 

 

If X and Y are normally distributed and independent (they are), this implies they are "jointly normally distributed". 

@Markiyan Hirnyk I probably should have been more specific.  I appreciate you looking into it.

Regardless, thank you Markiyan for pointing me to http://www.maplesoft.com/applications/view.aspx?SID=6352 I came across a help page that gave me an idea which produced my above result. 

So the way I created the points, are they still considered distributed normally about point [3,15] ?  or have I missed something?

 

@Markiyan Hirnyk just a two dimensional distribution

@Carl Love ok here is some sample data

[[5.37,19.50],[5.73,19.44],[4.66,20.03],[5.66,20.38],[4.22,21.97],[5.08,20.67],[5.08,19.08],[4.54,20.06],[4.35,19.82],[5.19,20.72],[4.48,19.95],[5.76,19.81],[4.15,18.68],[6.37,20.60],[5.58,21.13],[5.76,19.91],[5.85,19.02],[5.00,19.71],[5.42,20.31],[4.77,21.45],[8.61,19.48],[10.70,20.31],[10.99,20.28],[11.68,21.28],[9.12,20.77],[10.30,20.07],[10.40,21.62],[10.95,20.34],[9.79,20.29],[9.69,20.86],[10.02,21.45],[11.05,20.19],[9.20,17.96],[10.49,19.88],[9.61,19.49],[10.33,19.59],[9.29,20.94],[10.17,19.64],[10.97,20.32],[10.08,19.16]]

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