Posts Tagged ‘ distance ’

Where hiding if you don’t want to get wet ?

April 5, 2012
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Where hiding if you don’t want to get wet ?

Following the previous post, two additional remarks. Following a comment by @cosi, I have investigated quickly a binomial fit to the distribution of the number of people not getting wet, with a fixed number of players on the field. It looks like it...

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B*tchin’ six dimensional 6-cube. The rainbow colours and…

July 1, 2011
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B*tchin’ six dimensional 6-cube. The rainbow colours and…

B*tchin’ six dimensional 6-cube. The rainbow colours and glass panes really help this visualisation.  Examples of 6-dimensional things If it’s hard to envision 6 dimensions, consider this: the possible tunings of a guitar constitute a 6-dimensio...

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B*tchin’ six dimensional 6-cube. The rainbow colours and…

July 1, 2011
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B*tchin’ six dimensional 6-cube. The rainbow colours and…

B*tchin’ six dimensional 6-cube. The rainbow colours and glass panes really help this visualisation.  Examples of 6-dimensional things If it’s hard to envision 6 dimensions, consider this: the possible tunings of a guitar constitute a 6-dimensio...

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Europe Data set:> eurodist                 Athens Barcelona…

July 3, 2010
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Europe
Data set:> eurodist                 Athens Barcelona…

Europe Data set:> eurodist                 Athens Barcelona Brussels Calais Cherbourg Cologne CopenhagenBarcelona         3313                                                   

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Repulsive dots pattern, the difference of distance

June 14, 2010
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Repulsive dots pattern, the difference of distance

What if you wanted to randomly place objects into a field, and the more objects you had, the more they rejected newcomers placed nearby? To find out, I setup a simulation. The code, shown at the end, isn’t all that interesting, and the plots shown below aren’t all that special. I think there is one

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Example 7.19: find the closest pair of observations

December 28, 2009
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Example 7.19: find the closest pair of observations

Suppose we need to find the closest pair of observations on some variable x. For example, we might be concerned that some data had been accidentally duplicated. We return the ID's of the two closest observations, and their distance from each other. In both languages, we'll first create the data, then sort it, recognizing that the...

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