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Project: Worlds

generate multivariate correlated pseudorandom scalar attributes

in order to quickly create complex domains
a world creator should be able to create populations with multivariate correlated pseudorandom scalar attributes

Scenarios
weakly-correlated scalar feature pair
Given
a feature "height" with mean 80 and standard deviation 20
And
a feature "weight" with mean 150 and standard deviation 15
And
"height" and "weight" are weakly correlated
When
a large population is sampled
Then
"height" and "weight" should be weakly correlated
And
the empirical rule should hold
strongly-correlated scalar feature pair
Given
a feature "height" with mean 80 and standard deviation 40
And
a feature "weight" with mean 150 and standard deviation 30
And
"height" and "weight" are strongly correlated
When
a large population is sampled
Then
"height" and "weight" should be strongly correlated
And
the empirical rule should hold
arbitrarily-correlated scalar feature pair
Given
a feature "height" with mean 80 and standard deviation 45
And
a feature "weight" with mean 160 and standard deviation 50
And
"height" and "weight" are 30% correlated
When
a large population is sampled
Then
"height" and "weight" should be 30% correlated
And
the empirical rule should hold
multiple features with covariance matrix
Given
a feature "height" with mean 80 and standard deviation 25
And
a feature "weight" with mean 150 and standard deviation 30
And
a feature "foot_size" with mean 10 and standard deviation 3
And
"height" and "weight" are strongly correlated
And
"weight" and "foot_size" are weakly correlated
And
"height" and "foot_size" are 90% correlated
When
a large population is sampled
Then
"height" and "weight" should be strongly correlated
And
"weight" and "foot_size" should be weakly correlated
And
"height" and "foot_size" should be 90% correlated
And
the empirical rule should hold

Last published about 7 years ago by jweissman.