Ce serveur Gitlab sera éteint le 30 juin 2020, pensez à migrer vos projets vers les serveurs gitlab-research.centralesupelec.fr et gitlab-student.centralesupelec.fr !

Commit 7673e985 by Dahua Lin

introduce probs method

parent 85a66306
 ... ... @@ -199,7 +199,7 @@ export ncategories, # the number of categories in a Categorical distribution pdf, # probability density function (ContinuousDistribution) pmf, # probability mass function (DiscreteDistribution) priorprobs, # prior probabilities probs, # Get the vector of probabilities quantile, # inverse of cdf (defined for p in (0,1)) qqbuild, # build a paired quantiles data structure for qqplots sampler, # create a Sampler object for efficient samples ... ...
 ... ... @@ -49,13 +49,13 @@ length(d::MultivariateMixture) = length(d.components[1]) size(d::MatrixvariateMixture) = size(d.components[1]) components(d::MixtureModel) = d.components priorprobs(d::MixtureModel) = d.prior.prob probs(d::MixtureModel) = d.prior.prob component_type{VF,VS,C}(d::MixtureModel{VF,VS,C}) = C function mean(d::UnivariateMixture) cs = components(d) p = priorprobs(d) p = probs(d) m = 0.0 for i = 1:length(cs) pi = p[i] ... ... @@ -68,7 +68,7 @@ end function mean(d::MultivariateMixture) cs = components(d) p = priorprobs(d) p = probs(d) m = zeros(length(d)) for i = 1:length(cs) pi = p[i] ... ... @@ -81,7 +81,7 @@ end function var(d::UnivariateMixture) cs = components(d) p = priorprobs(d) p = probs(d) K = length(cs) means = Array(Float64, K) m = 0.0 ... ... @@ -109,7 +109,7 @@ end function show(io::IO, d::MixtureModel) cs = components(d) pr = priorprobs(d) pr = probs(d) K = length(cs) println(io, "MixtureModel{\$(component_type(d))}(K = \$K)") Ks = min(K, 8) ... ... @@ -128,7 +128,7 @@ end function _mixpdf1(d::MixtureModel, x) cs = components(d) p = priorprobs(d) p = probs(d) v = 0.0 for i = 1:length(cs) pi = p[i] ... ... @@ -141,7 +141,7 @@ end function _mixpdf!(r::DenseArray, d::MixtureModel, x) cs = components(d) p = priorprobs(d) p = probs(d) fill!(r, 0.0) t = Array(Float64, size(r)) for i = 1:length(cs) ... ... @@ -170,7 +170,7 @@ function _mixlogpdf1(d::MixtureModel, x) # cs = components(d) p = priorprobs(d) p = probs(d) K = length(cs) lp = Array(Float64, K) m = -Inf # m <- the maximum of log(p(cs[i], x)) + log(pri[i]) ... ... @@ -195,7 +195,7 @@ end function _mixlogpdf!(r::DenseArray, d::MixtureModel, x) cs = components(d) p = priorprobs(d) p = probs(d) K = length(cs) n = length(r) Lp = Array(Float64, n, K) ... ...
 ... ... @@ -20,6 +20,8 @@ end length(d::Multinomial) = length(d.prob) probs(d::Multinomial) = d.prob mean(d::Multinomial) = d.n .* d.prob function var(d::Multinomial) ... ...
 ... ... @@ -13,6 +13,7 @@ immutable Categorical <: DiscreteUnivariateDistribution end ncategories(d::Categorical) = d.K probs(d::Categorical) = d.prob ### handling support ... ...
 ... ... @@ -10,7 +10,7 @@ function test_mixture(g::UnivariateMixture, n::Int, ns::Int) end cs = components(g) pr = priorprobs(g) pr = probs(g) @assert length(cs) == length(pr) # mean ... ... @@ -59,7 +59,7 @@ function test_mixture(g::MultivariateMixture, n::Int, ns::Int) end cs = components(g) pr = priorprobs(g) pr = probs(g) @assert length(cs) == length(pr) # mean ... ...
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!