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Commit e81a3020 by Dahua Lin

### introduce a dim function for getting sample dimension of a multivariate distribution

parent 13a56269
 ... ... @@ -82,6 +82,7 @@ export # types cumulant, # cumulants of distribution deviance, # deviance of fitted and observed responses devresid, # vector of squared deviance residuals dim, # sample dimension of multivariate distribution entropy, # entropy of distribution in nats fit, # fit a distribution to data freecumulant, # free cumulants of distribution ... ...
 ... ... @@ -19,6 +19,8 @@ Dirichlet(d::Integer, alpha::Real) = Dirichlet(fill(alpha, d)) Dirichlet(dim::Integer) = Dirichlet(ones(dim)) dim(d::Dirichlet) = length(d.alpha) function entropy(d::Dirichlet) k = length(d.alpha) en = lmnB(d) ... ...
 ... ... @@ -32,6 +32,8 @@ end Multinomial(d::Integer) = Multinomial(1, d) dim(d::Multinomial) = length(d.prob) entropy(d::Multinomial) = NumericExtensions.entropy(d.prob) function insupport{T <: Real}(d::Multinomial, x::Vector{T}) ... ...
 ... ... @@ -21,6 +21,9 @@ function MultivariateNormal(cov::Matrix{Float64}) end MultivariateNormal() = MultivariateNormal(zeros(2), eye(2)) dim(d::MultivariateNormal) = length(d.mean) function cdf{T <: Real}(d::MultivariateNormal, x::Vector{T}) k = length(d.mean) if k > 3 ... ...
 ... ... @@ -8,12 +8,14 @@ for d in [Dirichlet([100.0, 17.0, 31.0, 45.0]), MultivariateNormal([0.0, 0.0], [1.0 0.9; 0.9 1.0])] # Check that we can generate a single random draw draw = rand(d) @assert length(draw) == dim(d) # Check that draw satifies insupport() @assert insupport(d, draw) # Check that we can generate many random draws at once X = rand(d, n_samples) @assert size(X, 1) == dim(d) # Check that sequence of draws satifies insupport() @assert insupport(d, X) ... ...
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