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slower calculate_u but less allocs #42

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35 changes: 29 additions & 6 deletions src/likelihood.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -39,18 +39,19 @@ function loopiloop!(myAmica::MultiModelAmica, y_rho)
(n,_) = size(myAmica.models[1].source_signals)

for h in 1:M #run along models
Threads.@threads for i in 1:n #run along components
for i in 1:n #run along components
Q = calculate_Q(myAmica.models[h], i, y_rho)
calculate_u!(myAmica.models[h], @view(Q[:, n, :]),i)
calculate_Lt!(myAmica.models[h], @view(Q[:, n, :]))
end
end
end

@views function calculate_Q(myAmica::SingleModelAmica, i, y_rho)

@views function calculate_Q!(myAmica::SingleModelAmica, Q,i, y_rho)
(n,N) = size(myAmica.source_signals)
m = myAmica.m
Q = zeros(m,N)
#Q = zeros(m,N)

for j in 1:m
Q[j,:] .= log(myAmica.learnedParameters.proportions[j,i]) + 0.5 * log(myAmica.learnedParameters.scale[j,i]) .+ logpfun(myAmica.learnedParameters.shape[j,i], y_rho[i, :, j])
Expand All @@ -59,14 +60,36 @@ end
return Q
end

function calculate_Q(myAmica::SingleModelAmica, i, y_rho)
(n,N) = size(myAmica.source_signals)
m = myAmica.m
Q = zeros(m,N)

for k in 1:n
for j in 1:m
Q[j,k] = log(myAmica.learnedParameters.proportions[j,i]) +
0.5 * log(myAmica.learnedParameters.scale[j,i]) +
logpfun(myAmica.learnedParameters.shape[j,i], y_rho[i, k, j])
end
end

return Q
end

#calculates u but saves it into z. MultiModel also uses the SingleModel version
@views function calculate_u!(myAmica::SingleModelAmica, Q, i)
function calculate_u!(myAmica::SingleModelAmica, Q, i)
m = size(myAmica.learnedParameters.scale,1)
#scratch_Q = similar(@view(Q[1,:]))
#scratch_Q = similar(Q)
if m > 1
for j in 1:m
myAmica.z[i, :, j] .= (1 ./ sum(optimized_exp(Q .- Q[j, :]'), dims=1))[:]
# i is component/channel
for j in 1:m # 1:3 m mixtures
# I think it is very hard to optimize the Q .- Q[j,:] inner term
sum!( @view(myAmica.z[i,:,j]),optimized_exp(Q .- @view(Q[j, :])')')

end
end
myAmica.z[i,:,:] .= 1 ./ @view(myAmica.z[i,:,:])
end

#Applies location and scale parameter to source signals (per generalized Gaussian)
Expand Down