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ytzi
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the-stack-dedup-gpt2

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""" expval1(basis::SingleBlockBasis, op::AbstractMatrix, i::Int, wf::AbstractVector{Float64}) Calculate the expectation value of the one-body operator `op` at site `i` in the state `wf`. """ function expval1(basis::SingleBlockBasis{N}, op::AbstractMatrix, i::Int, wf::AbstractVector{Float64}) where {N} w = Array{Int}(undef, 2N) result = 0.0 for col in 1:basis.block.size v = basis.block.vectors[col] li = v[2i-1] mi = v[2i] coli = li*(li+1) + mi + 1 rowi = 0 for lip in 0:basis.l_max for mip in (-lip):lip rowi += 1 op[rowi, coli] == 0.0 && continue w .= v w[2i-1] = lip w[2i] = mip haskey(basis.block.lookup, w) || continue result += wf[basis.block.lookup[w]] * wf[col] * op[rowi, coli] end end end result end """ expval2(basis::SingleBlockBasis, op_i::AbstractMatrix, i::Int, op_j::AbstractMatrix, j::Int, wf::AbstractVector{Float64}) Calculate the expectation value of the two-body operator composed of `op_i` at site `i` and `op_j` at site `j` in the state `wf`. """ function expval2(basis::SingleBlockBasis{N}, op_i::AbstractMatrix, i::Int, op_j::AbstractMatrix, j::Int, wf::AbstractVector{Float64}) where {N} w = Array{Int}(undef, 2N) result = 0.0 for col in 1:basis.block.size v = basis.block.vectors[col] li = v[2i-1] mi = v[2i] coli = li*(li+1) + mi + 1 lj = v[2j-1] mj = v[2j] colj = lj*(lj+1) + mj + 1 rowi = 0 for lip in 0:basis.l_max for mip in (-lip):lip rowi += 1 op_i[rowi, coli] == 0.0 && continue rowj = 0 for ljp in 0:basis.l_max for mjp in (-ljp):ljp rowj += 1 op_j[rowj, colj] == 0.0 && continue w .= v w[2i-1] = lip w[2i] = mip w[2j-1] = ljp w[2j] = mjp haskey(basis.block.lookup, w) || continue result += wf[basis.block.lookup[w]] * wf[col] * op_i[rowi, coli] * op_j[rowj, colj] end end end end end result end
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struct HSDEMatrixQ{T,M<:AbstractMatrix{T},V<:AbstractVector{T}} <: AbstractMatrix{T} A::M b::V c::V am::Int64 an::Int64 end """ HSDEMatrixQ(A::M, b::V, c::V) where {T,M<:AbstractMatrix{T},V<:AbstractVector{T}} """ function HSDEMatrixQ(A::M, b::V, c::V) where {T,M<:AbstractMatrix{T},V<:AbstractVector{T}} am, an = size(A) @assert size(b,1) == am @assert size(c,1) == an HSDEMatrixQ{T,M,V}(A, b, c, am, an) end Base.size(Q::HSDEMatrixQ) = (Q.am+Q.an+1, Q.am+Q.an+1) # Fallback, to have both defined Base.show(io::IO, mt::MIME"text/plain", Q::T) where {T<:HSDEMatrixQ} = Base.show(io, Q) function Base.show(io::IO, Q::T) where {T<:HSDEMatrixQ} m,n = size(Q) println(io, "$(m)x$(m) $T:") println(io, "[0 A' c;\n -A 0 b;\n -c' -b' 0]") println(io,"where A:") Base.show(io, Q.A) println(io,"\nb:") Base.show(io, Q.b) println(io,"\nc:") Base.show(io, Q.c) end # Q of size #(n,n) (n,m) (n,1) #(m,m) (m,m) (m,1) #(1,n) (1,m) (1,1) function mul!(Y::AbstractArray{T,1}, Q::HSDEMatrixQ{T,M,V}, B::AbstractArray{T,1}) where {T,M<:AbstractArray{T,2},V<:AbstractArray{T,1}} @assert size(Q) == (size(Y,1), size(B,1)) y1 = view(Y, 1:Q.an) y2 = view(Y, (Q.an+1):(Q.an+Q.am)) b1 = view(B, 1:Q.an) b2 = view(B, (Q.an+1):(Q.an+Q.am)) b3 = B[Q.am+Q.an+1] A = Q.A b = Q.b c = Q.c mul!(y1, transpose(A), b2) mul!( y2, A, b1) #TODO maybe switch back for readability? y1 .+= b3.*c # y1 .= y1 .+ c.*b3 y2 .-= b3.*b # y2 .= b.*b3 .- y2 y2 .= .-y2 Y[Q.am+Q.an+1] = - dot(c, b1) - dot(b,b2) return Y end function mul!(Y::AbstractArray{T,1}, Q::Transpose{T, QMat}, B::AbstractArray{T,1}) where {T, M<:AbstractArray{T,2},V<:AbstractArray{T,1}, QMat<:HSDEMatrixQ{T,M,V}} mul!(Y,Q.parent,B) Y .= .-Y return Y end struct HSDEMatrix{T,QType<:AbstractMatrix{T}} <: AbstractMatrix{T} Q::QType cgdata::CGdata{T} end """ HSDEMatrix(Q::HSDEMatrixQ) """ HSDEMatrix(Q::QType) where {T, QType<:AbstractMatrix{T}} = HSDEMatrix{T,QType}(Q, CGdata(2*size(Q)[1])) """ HSDEMatrix(A::M, b::V, c::V) where {T,M<:AbstractMatrix{T},V<:AbstractVector{T}} """ function HSDEMatrix(A::M, b::V, c::V) where {T,M<:AbstractMatrix{T},V<:AbstractVector{T}} Q = HSDEMatrixQ(A, b, c) HSDEMatrix(Q, CGdata(2*size(M.Q)[1])) end function Base.size(M::HSDEMatrix) m, n = size(M.Q) return (2m,2m) end # Try fallback Base.show(io::IO, mt::MIME"text/plain", M::T) where {T<:HSDEMatrix} = Base.show(io, M) function Base.show(io::IO, M::T) where {T<:HSDEMatrix} m,n = size(M) println(io, "$(m)x$(m) $T:") println(io, "[I Q';\n Q -I]") println(io,"where Q:") Base.show(io, M.Q) end """ solve argmin_y{||x-y||₂}, s.t. Q*u==v, where [u;v] == x """ function prox!(y::AbstractVector, A::HSDEMatrix, x::AbstractVector) tol = size(A,2)*eps() max_iters = 1000 cgdata = A.cgdata if cgdata.firstrun.x #Pointer, has this been initialized cgdata.xinit .= x #Works as guess since Q square cgdata.firstrun.x = false end #Since y is the initial guess, use previous solution y .= cgdata.xinit #solve [I Q';Q -I][u^(k+1);μ] = [u^k;v^k] conjugategradient!(y, A, x, cgdata.r, cgdata.p, cgdata.z, tol = tol, max_iters = max_iters) #Save initial guess for next prox! cgdata.xinit .= y m, n = size(A.Q) #Let v=Q*u u = view(y,1:m) v = view(y,(m+1):2m) mul!(v, A.Q, u) return 0.0 end function mul!(Y::AbstractArray{T,1}, M::HSDEMatrix{T,QType}, B::AbstractArray{T,1}) where {T,QType<:AbstractMatrix{T}} m2 = size(M)[1] mQ = size(M.Q)[1] @assert (m2,m2) == (size(Y,1), size(B,1)) y1 = view(Y, 1:mQ) y2 = view(Y, (mQ+1):(2mQ)) b1 = view(B, 1:mQ) b2 = view(B, (mQ+1):(2mQ)) mul!(y1, transpose(M.Q), b2) mul!( y2, M.Q, b1) @inbounds y1 .= y1 .+ b1 @inbounds y2 .= y2 .- b2 return Y end mul!(Y::AbstractArray{T,1}, Q::Transpose{T, QMat}, B::AbstractArray{T,1}) where {T, QType<:AbstractMatrix{T}, QMat<:HSDEMatrix{T,QType}} = mul!(Y, Q.parent, B)
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function Error(experimental_data_array,simulation_time_array,simulation_state_array,output_index,species_symbol,data_dictionary) # initialize - total_error_value = Inf # Interpole the simulation data to the experimental time scale - interpolated_simulated_trajectory = np.interp(experimental_data_array[:,1],simulation_time_array,simulation_state_array[:,output_index]); # Compute the error - # Error is the sum of a shape function, a scale function and a background_copy_number function - # Compute the shape term - scaled_data_array = alpha_scaling(experimental_data_array[:,2]); scaled_simulation_array = alpha_scaling(interpolated_simulated_trajectory); shape_term = sum((scaled_data_array - scaled_simulation_array).^2); # Compute the initial value term - background_copy_number_dictionary = data_dictionary["background_copy_number_dictionary"]; background_copy_number = background_copy_number_dictionary[species_symbol] cell_volume = data_dictionary["cell_volume"]; av_number = data_dictionary["av_number"]; initial_concentration = background_copy_number*(1/av_number)*(1/cell_volume)*1e9; # nM initial_value_term = ((interpolated_simulated_trajectory[1] - initial_concentration)^2); # Compute the scale term - simulated_fold_change = (interpolated_simulated_trajectory)./(interpolated_simulated_trajectory[1]); scale_term = sum((simulated_fold_change - experimental_data_array[:,2]).^2); # @show (interpolated_simulated_trajectory[1],initial_concentration,interpolated_simulated_trajectory[end],species_symbol) # @show (shape_term,scale_term,initial_value_term) # compute the total error - total_error_value = shape_term+scale_term+initial_value_term; # return - return total_error_value end function Error_E2F(experimental_data_array,simulation_time_array,simulation_state_array,output_index,species_symbol,data_dictionary) # initialize - total_error_value = Inf # Interpole the simulation data to the experimental time scale - interpolated_simulated_trajectory = np.interp(experimental_data_array[:,1],simulation_time_array,simulation_state_array[:,output_index]); # Compute G1G0 - simulated_G1G0 = 1./interpolated_simulated_trajectory; # Compute the shape term - scaled_data_array = alpha_scaling(experimental_data_array[:,2]); scaled_simulation_array = alpha_scaling(simulated_G1G0); shape_term = sum((scaled_data_array - scaled_simulation_array).^2); # Compute the initial value term - background_copy_number_dictionary = data_dictionary["background_copy_number_dictionary"]; background_copy_number = background_copy_number_dictionary[species_symbol] cell_volume = data_dictionary["cell_volume"]; av_number = data_dictionary["av_number"]; initial_concentration = background_copy_number*(1/av_number)*(1/cell_volume)*1e9; # nM initial_value_term = ((interpolated_simulated_trajectory[1] - initial_concentration)^2); # Compute the fold change term - scaling_factor = simulated_G1G0[1]; simulated_fold_change = (simulated_G1G0)./(scaling_factor); scale_term = sum((simulated_fold_change - experimental_data_array[:,2]).^2); # compute the total error - total_error_value = shape_term+scale_term+initial_value_term; # return - return total_error_value; end function alpha_scaling(data_array) # get the dimension - number_of_points = length(data_array) # what is the min index? min_index = indmin(data_array) min_value = data_array[min_index] # What is the max index? max_index = indmax(data_array) max_value = data_array[max_index] # compute the scaling - scaled_array = Float64[] for index = 1:number_of_points value = data_array[index] scaled_value = (value - min_value)/(max_value - min_value) push!(scaled_array,scaled_value) end return scaled_array end
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1,267
""" vbmf_sparse_parameters Compound type for vbmf_sparse computation. Contains the following fields:\n L::Int - number of rows of the original matrix M::Int - number of columns of the original matrix H::Int - internal product dimension MH::Int - dimension of vec(A') H1::Int - number of columns of B that should belong to a contaminated class labels::Array{Int64, 1} - which columns of Y are labeled as non-contaminated AHat::Array{Float64, 2} - mean value of A, size (M, H) ATVecHat::Array{Float64, 1} - mean value of vec(A^T), size (MH,1) SigmaATVec::Array{Float64, 2} - covariance of vec(A^T), size (MH, MH) diagSigmaATVec::Array{Float64,1} - diagonal of covariance of vec(A^T), size (MH) invSigmaATVec::Array{Float64, 2} - inverse of covariance of vec(A^T), size (MH, MH) SigmaA::Array{Float64,2} - covariance of A matrix, size (H, H) BHat::Array{Float64, 2} - mean value of B, size (L, H) SigmaB::Array{Float64, 2} - covariance of B matrix, size (H, H) CA::Array{Float64, 1} - diagonal of the prior covariance of vec(A^T), size (MH) alpha0::Float64 - shape of CA gamma prior beta0::Float64 - scale of CA gamma prior alpha::Float64 - shape of CA gamma posterior beta::Array{Float64,1} - scale of CA gamma posterior, size (MH, 1) CB::Array{Float64, 1} - diagonal of prior covariance of B, size (H) gamma0::Float64 - shape of CB gamma prior delta0::Float64 - scale of CB gamma prior gamma::Float64 - shape of CB posterior delta::Array{Float64,1} - scale of CB posterior, size (H) sigmaHat::Float64 - variance of data - homoscedastic case eta0::Float64 - shape of sigma gamma prior zeta0::Float64 - scale of sigma gamma prior eta::Float64 - shape of homoscedastic sigma posterior zeta::Float64 - scale of homoscedastic sigma posterior sigmaVecHat::Array{Float64,1} - variance of data (rows) - heteroscedastic case, size (L) etaVec::Array{Float64,1} - shape of heteroscedastic sigma posterior, size (L) zetaVec::Array{Float64,1} - scale of heteroscedastic sigma posterior, size (L) YHat::Array{Float64, 2} - estimate of Y, size (L, M) trYTY::Float64 - trace(Y^T*Y), saved so that it does not have to be recomputed """ type vbmf_sparse_parameters L::Int M::Int H::Int MH::Int H1::Int labels::Array{Int64,1} AHat::Array{Float64, 2} ATVecHat::Array{Float64, 1} SigmaATVec::Array{Float64, 2} diagSigmaATVec::Array{Float64,1} invSigmaATVec::Array{Float64, 2} SigmaA::Array{Float64,2} BHat::Array{Float64, 2} SigmaB::Array{Float64, 2} CA::Array{Float64, 1} alpha0::Float64 beta0::Float64 alpha::Float64 beta::Array{Float64,1} CB::Array{Float64, 1} gamma0::Float64 delta0::Float64 gamma::Float64 delta::Array{Float64,1} sigmaHat::Float64 eta0::Float64 zeta0::Float64 eta::Float64 zeta::Float64 sigmaVecHat::Array{Float64,1} etaVec::Array{Float64,1} zetaVec::Array{Float64,1} YHat::Array{Float64, 2} trYTY::Float64 vbmf_sparse_parameters() = new() end """ vbmf_sparse_init(Y::Array{Float64,2}, H::Int; ca::Float64 = 1.0, alpha0::Float64 = 1e-10, beta0::Float64 = 1e-10, cb::Float64 = 1.0, gamma0::Float64 = 1e-10, delta0::Float64 = 1e-10, sigma::Float64 = 1.0, eta0::Float64 = 1e-10, zeta0::Float64 = 1e-10, H1::Int = 0, labels::Array{Int64,1} = Array{Int64,1}()) Returns an initialized structure of type vbmf_sparse_parameters. """ function vbmf_sparse_init(Y::Array{Float64,2}, H::Int; ca::Float64 = 1.0, alpha0::Float64 = 1e-10, beta0::Float64 = 1e-10, cb::Float64 = 1.0, gamma0::Float64 = 1e-10, delta0::Float64 = 1e-10, sigma::Float64 = 1.0, eta0::Float64 = 1e-10, zeta0::Float64 = 1e-10, H1::Int = 0, labels::Array{Int64,1} = Array{Int64,1}()) params = vbmf_sparse_parameters() L, M = size(Y) params.L, params.M = L, M params.H = H params.MH = M*H params.H1 = H1 params.labels = labels params.AHat = randn(M, H) # now set zeroes to where the columns of Y labeled as non-infected are # in A, these are rows params.AHat[labels, end-H1+1:end] = 0.0 params.ATVecHat = reshape(params.AHat', M*H) params.SigmaATVec = eye(M*H, M*H) params.diagSigmaATVec = ones(M*H) params.invSigmaATVec = eye(M*H, M*H) params.SigmaA = zeros(H, H) params.BHat = randn(L, H) params.SigmaB = zeros(H, H) params.CA = ca*ones(M*H) params.alpha0 = alpha0 params.beta0 = beta0 params.alpha = params.alpha0 + 0.5 params.beta = beta0*ones(M*H) params.CB = cb*ones(H) params.gamma0 = gamma0 params.delta0 = delta0 params.gamma = gamma0 + L/2 params.delta = delta0*ones(H) params.sigmaHat = sigma params.eta0 = eta0 params.zeta0 = zeta0 params.eta = eta0 + L*M/2 params.zeta = zeta0 params.sigmaVecHat = sigma*ones(L) params.etaVec = (eta0 + M/2)*ones(L) params.zetaVec = zeta0*ones(L) params.YHat = params.BHat*params.AHat' params.trYTY = traceXTY(Y, Y) return params end """ copy(params_in::vbmf_sparse_parameters) Copy function for vbmfa_sparse_parameters. """ function copy(params_in::vbmf_sparse_parameters) params = vbmf_sparse_parameters() for field in fieldnames(params_in) setfield!(params, field, copy(getfield(params_in, field))) end return params end """ updateA!(Y::Array{Float64,2}, params::vbmf_sparse_parameters; full_cov::Bool, diag_var::Bool = false) Updates mean and covariance of vec(A^T) and also of the A matrix. If full_cov is true, then inverse of full covariance matrix is computed, otherwise just the diagonal is estimated. """ function updateA!(Y::Array{Float64,2}, params::vbmf_sparse_parameters; full_cov::Bool = false, diag_var::Bool = false) # either just the diagonal or the full covariance if full_cov # compute the inverse of covariance if diag_var params.invSigmaATVec = kron(eye(params.M), (params.BHat'*diagm(params.sigmaVecHat)*params.BHat + params.L*mean(params.sigmaVecHat)*params.SigmaB)) + diagm(params.CA) else params.invSigmaATVec = params.sigmaHat*kron(eye(params.M), (params.BHat'*params.BHat + params.L*params.SigmaB)) + diagm(params.CA) end params.SigmaATVec = inv(params.invSigmaATVec) params.diagSigmaATVec = diag(params.SigmaATVec) # now the mean if diag_var params.ATVecHat = params.SigmaATVec*reshape(params.BHat'*diagm(params.sigmaVecHat)*Y, params.H*params.M) else params.ATVecHat = params.sigmaHat*params.SigmaATVec*reshape(params.BHat'*Y, params.H*params.M) end # now compute the covariance of AHat params.SigmaA = zeros(params.H, params.H) for m in 1:params.M params.SigmaA += view(params.SigmaATVec, (m-1)*params.H+1:m*params.H, (m-1)*params.H+1:m*params.H) end else # this updates just the diagonal of the covariance of A # compute the inverse of covariance # only use for large problems, for small problems the full covariance is more precise if diag_var for h in 1:params.H # instead of the full matrix multiplication as above, we compute just # the diagonal params.diagSigmaATVec[h] = norm2(params.BHat[:,h].*params.sigmaVecHat) + params.L*mean(params.sigmaVecHat)*params.SigmaB[h,h] end else # this is computed in case the variance of data is homoscedastic for h in 1:params.H # instead of the full matrix multiplication as above, we compute just # the diagonal params.diagSigmaATVec[h] = params.sigmaHat*norm2(params.BHat[:,h]) + params.L*params.SigmaB[h,h] end end # we filled just the first H elements, so we just copy it to fill the rest params.diagSigmaATVec[params.H+1:end] = repeat(params.diagSigmaATVec[1:params.H], inner = params.M-1) # also add the CA vector params.diagSigmaATVec += params.CA # finally, invert it params.diagSigmaATVec = 1./params.diagSigmaATVec # now the mean if diag_var params.ATVecHat = params.diagSigmaATVec.*reshape(params.BHat'*diagm(params.sigmaVecHat)*Y, params.H*params.M) else params.ATVecHat = params.sigmaHat*params.diagSigmaATVec.*reshape(params.BHat'*Y, params.H*params.M) end # now compute the covariance of AHat params.SigmaA = zeros(params.H, params.H) for m in 1:params.M params.SigmaA += diagm(view(params.diagSigmaATVec, (m-1)*params.H+1:m*params.H)) end end # now set zeroes to where the columns of Y labeled as non-infected are # in A, these are rows params.AHat = reshape(params.ATVecHat, params.H, params.M)' params.AHat[params.labels, end-params.H1+1:end] = 0.0 params.ATVecHat = reshape(params.AHat', params.M*params.H) end """ updateB!(Y::Array{Float64,2}, params::vbmf_sparse_parameters, diag_var::Bool = false) Updates mean and covariance of the B matrix. """ function updateB!(Y::Array{Float64,2}, params::vbmf_sparse_parameters; diag_var::Bool = false) # first, compute the inverse of the covariance if diag_var params.SigmaB = diagm(params.CB) + mean(params.sigmaVecHat)*(params.AHat'*params.AHat + params.SigmaA) #invert it params.SigmaB = inv(params.SigmaB) # now compute the mean params.BHat = diagm(params.sigmaVecHat)*Y*params.AHat*params.SigmaB else params.SigmaB = diagm(params.CB) + params.sigmaHat*(params.AHat'*params.AHat + params.SigmaA) #invert it params.SigmaB = inv(params.SigmaB) params.BHat = params.sigmaHat*Y*params.AHat*params.SigmaB end end """ updateYHat!(params::vbmf_sparse_parameters) Updates estimate of Y. """ function updateYHat!(params::vbmf_sparse_parameters) params.YHat = params.BHat*params.AHat' end """ updateCA!(params::vbmf_sparse_parameters) Updates the estimate of CA. """ function updateCA!(params::vbmf_sparse_parameters) params.beta = params.beta0*ones(params.M*params.H) + 1/2*(params.ATVecHat.*params.ATVecHat + params.diagSigmaATVec) params.CA = params.alpha*ones(params.M*params.H)./params.beta end """ updateCB!(params::vbmf_sparse_parameters) Updates the estimate of CB. """ function updateCB!(params::vbmf_sparse_parameters) for h in 1:params.H params.delta[h] = params.delta0 + 1/2*(params.BHat[:,h]'*params.BHat[:,h])[1] + 1/2*params.SigmaB[h,h] params.CB[h] = params.gamma/params.delta[h] end end """ updateSigma!(Y::Array{Float64,2}, params::vbmf_sparse_parameters, diag_var::Bool = false) Updates estimate of the measurement variance. """ function updateSigma!(Y::Array{Float64,2}, params::vbmf_sparse_parameters; diag_var::Bool = false) if diag_var for l in 1:params.L params.zetaVec[l] = params.zeta0 + 1/2*norm2(Y[l,:]) - sum(Y[l,:].*(params.AHat*params.BHat[l,:])) + 1/2*traceXTY(params.AHat'*params.AHat + params.SigmaA, params.BHat[l,:]*params.BHat[l,:]' + params.SigmaB) params.sigmaVecHat[l] = params.etaVec[l]/params.zetaVec[l] end else params.zeta = params.zeta0 + 1/2*params.trYTY - traceXTY(params.BHat, Y*params.AHat) + 1/2*traceXTY(params.AHat'*params.AHat + params.SigmaA, params.BHat'*params.BHat + params.L*params.SigmaB) params.sigmaHat = params.eta/params.zeta end end """ vbmf_sparse!(Y::Array{Float64, 2}, params::vbmf_sparse_parameters, niter::Int, eps::Float64 = 1e-6, est_var = false, full_cov::Bool = false, logdir = "", desc = "") Computes variational bayes matrix factorization of Y = AB' + E. Independence of A and B is assumed. Estimate of variance sigma can be either a scalar or a vector - for row variance estimation. ARD property is imposed upon columns of B and also upon vec(A) through gamma prior CB and CA and estimation of covariance. The prior model is following: p(Y|A,B) = N(Y|BA^T, 1/sigma*I) or p(Y|A,B) = N(Y|BA^T, inv(diag(sigma))) p(vec(A^T)) = N(vec(A^T)|0, invCA), CA = diag(c_a) p(B_h) = MN(B|0, I, invCB), CB = diag(c1, ..., cH) p(CA) = G(C_A| alpha0, beta0) p(CB) = G(C_B| gamma0, delta0) The params argument with initialized data is modified and contains the resulting estimates after the algorithm stops. """ function vbmf_sparse!(Y::Array{Float64, 2}, params::vbmf_sparse_parameters, niter::Int; eps::Float64 = 1e-6, diag_var::Bool = false, full_cov::Bool = false, logdir = "", desc = "", verb = false, est_cb::Bool = true) priors = Dict() # create the log dictionary loging = false if logdir !="" loging = true logVar = create_log(params) end # choice of convergence control variable convergence_var = :BHat #convergence_var = :dY if convergence_var == :dY old = Y - params.YHat else old = copy(getfield(params, convergence_var)) end d = eps + 1.0 # delta i = 1 # run the loop for a given number of iterations while ((i <= niter) && (d > eps)) updateA!(Y, params, full_cov = full_cov, diag_var = diag_var) updateB!(Y, params, diag_var = diag_var) updateCA!(params) if est_cb updateCB!(params) end updateSigma!(Y, params, diag_var = diag_var) if loging update_log!(logVar, params) end # check convergence if convergence_var == :dY updateYHat!(params) d = delta(Y - params.YHat, old) old = Y - params.YHat else d = delta(getfield(params, convergence_var), old) old = copy(getfield(params, convergence_var)) end i += 1 end # finally, compute the estimate of Y updateYHat!(params) # convergence info if verb print("Factorization finished after ", i-1, " iterations, eps = ", d, "\n") end # save inputs and outputs if loging println("Saving outputs and inputs under ", logdir, "/") save_log(logVar, Y, priors, logdir, desc = desc) end return d end """ vbmf_sparse(Y::Array{Float64, 2}, params_in::vbmf_sparse_parameters, niter::Int, eps::Float64 = 1e-6, est_var = false, full_cov::Bool = false, logdir = "", desc = "") Calls vbmf_sparse!() but copies the params_in argument so that it is not modified and can be reused. """ function vbmf_sparse(Y::Array{Float64, 2}, params_in::vbmf_sparse_parameters, niter::Int; eps::Float64 = 1e-6, diag_var::Bool = false, full_cov::Bool = false, logdir = "", desc = "", verb = false, est_cb::Bool = true) # make a copy of input params params = copy(params_in) # run the algorithm d = vbmf_sparse!(Y, params, niter, eps = eps, diag_var = diag_var, full_cov = full_cov, logdir = logdir, desc = desc, verb = verb, est_cb = est_cb) return params, d end """ lowerBound(Y::Array{Float64,2}, params::vbmf_sparse_parameters) Compute the lower bound for logarithm of data distribution. """ function lowerBound(Y::Array{Float64,2}, params::vbmf_sparse_parameters) L = 0.0 # E[lnp(Y|params)] L += - params.L*params.M/2*ln2pi + params.L*params.M/2*gammaELn(params.eta, params.zeta) L += - params.sigmaHat/2*(params.trYTY - 2*traceXTY(params.BHat, Y*params.AHat) + traceXTY(params.AHat'*params.AHat + params.SigmaA, params.BHat'*params.BHat + params.L*params.SigmaB)) # E[lnp(vec(A'))] L += - params.MH/2*ln2pi + 1/2*sum(map(gammaELn, params.alpha*ones(size(params.beta)), params.beta)) L += - (1/2*params.CA'*(params.ATVecHat.^2 + params.diagSigmaATVec))[1] # E[lnp(B)] L += - params.L*params.H/2*ln2pi L += params.L/2*sum(map(gammaELn, params.gamma*ones(size(params.delta)), params.delta)) L += - 1/2*traceXTY(diagm(params.CB), (params.BHat'*params.BHat + params.L*params.SigmaB)) # E[lnp(sigma)] L += params.eta0*log(params.zeta0) - lgamma(params.eta0) L += (params.eta0 - 1)*gammaELn(params.eta, params.zeta) - params.zeta0*params.sigmaHat # E[lnp(CA)] L += params.MH*(params.alpha0*log(params.beta0) - lgamma(params.alpha0)) L += (params.alpha0 - 1)*sum(map(gammaELn, params.alpha*ones(size(params.beta)), params.beta)) L += - params.beta0*sum(params.CA) # E[lnp(CB)] L += params.H*(params.gamma0*log(params.delta0) - lgamma(params.gamma0)) L += (params.gamma0 - 1)*sum(map(gammaELn, params.gamma*ones(size(params.delta)), params.delta)) L += - params.gamma0*sum(params.CB) # H(vec(A')) L += normalEntropy(params.diagSigmaATVec) # H(B) L += normalEntropy(kron(params.SigmaB,eye(params.L))) # H(sigma) L += gammaEntropy(params.eta, params.zeta) # H(CA) L += sum(map(gammaEntropy, params.alpha*ones(size(params.beta)), params.beta)) # H(CB) L += sum(map(gammaEntropy, params.gamma*ones(size(params.delta)), params.delta)) return L end """ lowerBoundTrimmed(Y::Array{Float64,2}, params_in::vbmf_sparse_parameters, trim = 1e-1) AHat with abs(AHat[i,j]) < trim and the corresponding elements of CA are not involved in the computation. """ function lowerBoundTrimmed(Y::Array{Float64,2}, params_in::vbmf_sparse_parameters, trim = 1e-1) params = copy(params_in) trim_inds = (abs(params.ATVecHat) .> trim) params.ATVecHat = params.ATVecHat[trim_inds] params.MH = size(params.ATVecHat)[1] params.beta = params.beta[trim_inds] params.CA = params.CA[trim_inds] params.diagSigmaATVec = params.diagSigmaATVec[trim_inds] return lowerBound(Y, params) end
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2.231332
8,062
using HamiltonianABC using Base.Test using Parameters using Distributions using Cubature using StatsBase import HamiltonianABC: logdensity, simulate! # use consistent random seed for unit tests srand(UInt32[0x8c736cc0, 0x63556b2b, 0x808f767c, 0xc912a668]) include("utilities.jl") include("test-mcmc.jl") # toy model: exponential through normal include("test-toyexponential.jl") # toy model: G-and-K through normal mixture include("EM_GaussianMixtureModel.jl") include("g_and_k_quantile.jl")
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2.97006
167
@testset "167.two-sum-ii-input-array-is-sorted.jl" begin @test two_sum_167([2, 7, 11, 15], 9) == [1, 2] @test two_sum_167([2, 3, 4], 6) == [1, 3] @test two_sum_167([-1, 0], -1) == [1, 2] end
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1.897196
107
using Base.Test using BCBI_base @testset "Test Adding and Precompiling Packages" begin println("--------------------------------") println("Testin Base Pkgs") println("--------------------------------") @test length(BCBI_base.add(BCBI_base.base_pkgs)) == 0 println("--------------------------------") println("Testing Plotting Pkgs") println("--------------------------------") @test length(BCBI_base.add(BCBI_base.plotting_pkgs)) == 0 println("--------------------------------") println("Testing Datasets Pkgs") println("--------------------------------") @test length(BCBI_base.add(BCBI_base.datasets_pkg)) == 0 println("--------------------------------") println("Testing Python/R Pkgs") println("--------------------------------") @test length(BCBI_base.add(BCBI_base.external_pkgs)) == 0 println("--------------------------------") println("Testing Clone Pkgs") println("--------------------------------") @test length(BCBI_base.clone(BCBI_base.clone_pkgs)) == 0 println("--------------------------------") println("Testing Checkout Pkgs") println("--------------------------------") @test length(BCBI_base.checkout(BCBI_base.checkout_pkgs)) == 0 println("--------------------------------") println("Precompiling Installed Pkgs") println("--------------------------------") @test length(using_all()) == 0 BCBI_base.clean_up() @test check_installed() == 0 end
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3.377273
440
#= Copyright (c) 2015, Intel Corporation Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. =# export set_debug_level, remove_line_nodes debug_level = parse(Int, get(ENV, "LATTE_DEBUG_LEVEL", "0")) function debugp(level::Int, args...) global debug_level if debug_level >= level println(args...) end end function set_debug_level(level::Int) global debug_level debug_level = level end macro recurse_unless(pred) quote if !$pred return ASTWALK_RECURSE end end end function isexpr(node) isa(node, Expr) end function line_node_remover(node, cbdata, index, top_level, read) if isa(node, LineNumberNode) || isa(node, Expr) && node.head == :line return ASTWALK_REMOVE end return ASTWALK_RECURSE end function remove_line_nodes(ast) AstWalk(ast, line_node_remover, nothing) end function Base.contains(symbol::Symbol, value::AbstractString) return contains(string(symbol), string(value)) end """ Replaces symbols in keys(cdata) with cbdata[symbol] """ function symbol_replacer(node, cbdata, index, top_level, read) if isa(node, Symbol) if haskey(cbdata, node) return cbdata[node] end elseif isa(node, Expr) && node.head == :return return AstWalk(node.args[1], symbol_replacer, cbdata) elseif isa(node, Expr) && node.head in [:loophead, :loopend, :parallel_loophead, :parallel_loopend] for i in 1:length(node.args) if isa(node.args[i], Array) node.args[i] = map!((x) ->AstWalk(x, symbol_replacer, cbdata), node.args[i]) elseif isa(node.args[i], Set) # skip else node.args[i] = AstWalk(node.args[i], symbol_replacer, cbdata) end end return node end ASTWALK_RECURSE end function replace_symbols(block::Vector{Any}, mapping::Dict{Symbol, Any}) [AstWalk(s, symbol_replacer, mapping) for s in block] end """ Inline a function with a set of arguments `args`. """ function inline(func, args) ast = Base.uncompressed_ast(func.code) env = Dict{Symbol, Any}() for (closure_var, value) in zip(ast.args[2][2], func.env) if isa(value, Box) # FIXME: Hack for Boxed vars that should be unboxed properly value = value.contents end env[closure_var[1]] = value end for (param, arg) in zip(ast.args[1], args) env[param.args[1]] = arg end # Replace arguments and escaped variables with their values AstWalk(ast.args[3], symbol_replacer, env) end intel_runtime = "/home/truongle/pse-hpc/intel-julia/intel-runtime/lib/libintel-runtime.so" double_buffer = true # @eval ccall((:pert_init,$intel_runtime), Cint, (Cint,), convert(Cint, double_buffer)) # @eval function pert_shutdown() # ccall((:pert_shutdown, $intel_runtime), Cint, ()) # end # atexit(pert_shutdown) using Base.LinAlg import Base.LinAlg: BlasInt @eval function new_sgemm(order, transA, transB, M::BlasInt, N::BlasInt, K::BlasInt, alpha::Float32, Ap::Ptr{Float32}, lda::BlasInt, Bp::Ptr{Float32}, ldb::BlasInt, beta::Float32, Cp::Ptr{Float32}, ldc::BlasInt) ccall((:new_sgemm_LD, $intel_runtime), Ptr{Void}, (UInt8, UInt8, UInt8, BlasInt, BlasInt, BlasInt, Float32, Ptr{Float32}, BlasInt, Ptr{Float32}, BlasInt, Float32, Ptr{Float32}, BlasInt), order, transA, transB, M, N, K, alpha, Ap, lda, Bp, ldb, beta, Cp, ldc) end @eval function insert_task(obj, flag) ccall((:insert_task, $intel_runtime), BlasInt, (Ptr{Void},Cint), obj, flag) end elty = Float32 const blas = Base.libblas_name function gemm!(transA::Char, transB::Char, M::Int, N::Int, K::Int, alpha::Float32, A::Ptr{Float32}, lda::Int, B::Ptr{Float32}, ldb::Int, beta::Float32, C::Ptr{Float32}, ldc::Int) # lda = (transA == 'N') ? M : K # ldb = (transB == 'N') ? K : N # ldc = M CblasNoTrans = 111 CblasTrans = 112 _transA = transA == 'N' ? CblasNoTrans : CblasTrans _transB = transB == 'N' ? CblasNoTrans : CblasTrans CblasColMajor = 102 ccall((:cblas_sgemm, blas), Void, (Clonglong, Clonglong, Clonglong, Clonglong, Clonglong, Clonglong, Float32, Ptr{Float32}, Clonglong, Ptr{Float32}, Clonglong, Float32, Ptr{Float32}, Clonglong), CblasColMajor, _transA, _transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc) end function tree_cleaner(node, cbdata, index, top_level, read) if isa(node, Expr) # Don't collapse :block nodes for nodes that need one as args[2] if node.head == :for || node.head == :function #|| node.head == :if new_args = [] for arg in node.args[2].args result = clean_tree(arg) if !(isa(result, Symbol) && result == :nothing) if isa(result, Array) append!(new_args, result) else push!(new_args, result) end end end node.args[2].args = new_args return node # Collapse all other block nodes # TODO: There are probably other blocks we shouldn't collapse elseif node.head == :block map!(clean_tree, node.args) return node end end return ASTWALK_RECURSE end function clean_tree(ast) ast = AstWalk(ast, tree_cleaner, nothing) remove_line_nodes(ast) end function log_info(args...) _time = string(Libc.strftime("%d-%b %H:%M:%S",time())," - ") if LATTE_MPI rank = @eval ccall((:get_rank, $libComm), Cint, ()) Base.info(_time, "RANK $rank: ", args...) else Base.info(_time, args...) end end function _omp_get_thread_num() return ccall((:omp_get_thread_num, "libiomp"), Cint, ()) end for head in [:(:for), :(:call), :(:function), :(:ref)] fn_name = symbol(:is_,@eval $head) @eval function $fn_name(expr::Expr) return expr.head == $head end @eval function $fn_name(expr::Any) return false end end function is_call_target(call_node::Expr, name::Symbol) target = call_node.args[1] if isa(target, GlobalRef) return target.name == name else return target == name end end is_colon(expr::Expr) = expr.head == :(:) is_colon(expr::Any) = false is_num(value::Any) = isa(value, Number) # BEGIN - :for helpers # These are utility methods for working with :for ast nodes. function get_loopvar(for_node::Expr) @assert is_for(for_node) "Expected a :for expression" for_node.args[1].args[1] end function get_loop_length(for_node::Expr) @assert is_for(for_node) "Expected a :for expression" return :(length($(for_node.args[1].args[2]))) end function get_loop_start(for_node::Expr) @assert is_for(for_node) "Expected a :for expression" for_node.args[1].args[2].args[1] end # END - :for helpers @noinline function _REMOVE_THIS_LINE() return 0 end function exp{T}(arg::T) ccall((:exp, "libm"), T, (T, ), arg) end function log{T}(arg::T) ccall((:log, "libm"), T, (T, ), arg) end function tanh{T}(arg::T) ccall((:tanh, "libm"), T, (T, ), arg) end macro NotImplemented() return :(throw("Not Implemented")) end function remove_temp_nodes(ast) function walker(node, cbdata, index, top_level, read) if isa(node, Expr) && node.head == :(=) && isa(node.args[2], Expr) && node.args[2].head == :call && isa(node.args[2].args[1], GlobalRef) && node.args[2].args[1].mod == Latte && node.args[2].args[1].name == :_REMOVE_THIS_LINE return ASTWALK_REMOVE elseif isa(node, Expr) && node.head in [:parallel_loophead, :parallel_loopend, :loophead, :loopend] return node end ASTWALK_RECURSE end AstWalk(ast, walker, nothing) end @noinline function pointer(args...) Base.pointer(args...) end function transform_to_raw_array(ast) function walker(node, cbdata, index, top_level, read) if isa(node, Expr) && node.head == :call if isa(node.args[1], GlobalRef) if node.args[1].name == :arrayref node.args[1] = :raw_arrayref return node elseif node.args[1].name == :arrayset node.args[1] = :raw_arrayset return node elseif node.args[1].name == :pointer node.args[1] = :raw_pointer return node end end elseif isa(node, Expr) && node.head in [:parallel_loophead, :parallel_loopend, :loophead, :loopend] return node end ASTWALK_RECURSE end AstWalk(ast, walker, nothing) end """ Defines @expr only if LATTE_MPI is enabled """ macro latte_mpi(args...) if length(args) == 2 LATTE_MPI ? esc(args[1]) : args[2] elseif length(args) == 1 LATTE_MPI ? esc(args[1]) : nothing else @assert false end end
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# https://docs.julialang.org/en/v1/base/parallel/ using Test function learn_Tasks() ## Core.Task # Task from a function a() = sum([i for i in 1:1000]) b = Task(a) ## Base.@task b = @task a() @test false == istaskstarted(b) schedule(b) yield() @test true == istaskstarted(b) @test true == istaskdone(b) x = fetch(b) @show x end learn_Tasks() nothing
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include("$(pwd())/startup.jl") #fp = "./ExampleFiles/STOFDATA/" # All files in this path will be processed fp = "/media/wiebke/Extreme SSD/PSM_vs_PTR3/Data/apiTOFdata/CLOUD10/run1734_02/" filefilterRegexp = r"\.h5$" #rf = "./ExampleFiles/STOFDATA/2017-05-24_12h50m39_NH4.h5" # The mass scale from this file defines the mass scale of all rf = "$(fp)APi4_Data_2015.10.20-13h34m44s.h5" #masslist = MasslistFunctions.loadMasslist("./ExampleFiles/MASSLISTS/exampleMasslistSTOF.csv") masslist = MasslistFunctions.loadMasslist("$(fp)MassList_NO3-_AP_03-01-20.csv") cr = [37 137] # alternatively: use an auto generated masslist # masslistMasses, masslistElements, masslistElementsMasses, masslistCompositions = createMassList(C=0:20, O=0:20, N=0:1, allowRadicals=false) # (masslistMasses, masslistElements, masslistElementsMasses, masslistCompositions) = masslist s = (masslistMasses.>0) .& ( masslistMasses.<600) masslistMasses = masslistMasses[s] masslistCompositions = masslistCompositions[s,:] ####################### END OF SETTINGS ############################################################### ####################### Processing sequence ########################################################### correctMassScaleAndExtractSumSpec( fp, masslistMasses, masslistElements, masslistElementsMasses, masslistCompositions, rf, cr, filefilterRegexp=filefilterRegexp, onlyUseAverages = true, plotControlMass = true, recalibInterval = 300, resolution = 1500, firstNFiles=0, lastNFiles = 0 ) baselineAndPeakshape( fp, peakshapeRegions=4, peakshapeRegionStretch=1, peakshapeQuantileValue = 0.2, peakfindingNoiseThresholdValue = 2, peakfindingSignalLimit=0.01 ) mtrx = deconvolute( fp, calcTransposed = false )
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2.66129
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export fit # Default solvers # TODO: in the future, have cases where if the things are too big, take another # default. Also should check if p > n in which case should do dual stuff (or # other appropriate alternative) # Linear, Ridge _solver(::GLR{L2Loss,<:L2R}, np::NTuple{2,Int}) = Analytical() # Logistic, Multinomial _solver(::GLR{LogisticLoss,<:L2R}, np::NTuple{2,Int}) = LBFGS() _solver(::GLR{MultinomialLoss,<:L2R}, np::NTuple{2,Int}) = LBFGS() # Lasso, ElasticNet, Logistic, Multinomial function _solver(glr::GLR{<:SmoothLoss,<:ENR}, np::NTuple{2,Int}) (is_l1(glr.penalty) || is_elnet(glr.penalty)) && return FISTA() @error "Not yet implemented." end # Robust, Quantile _solver(::GLR{<:RobustLoss,<:L2R}, np::NTuple{2,Int}) = LBFGS() # Fallback NOTE: should revisit bc with non-smooth, wouldn't work probably # PGD/PSGD depending on how much data there is _solver(::GLR, np::NTuple{2,Int}) = @error "Not yet implemented." """ $SIGNATURES Fit a generalised linear regression model using an appropriate solver based on the loss and penalty of the model. A method can, in some cases, be specified. """ function fit(glr::GLR, X::AbstractMatrix{<:Real}, y::AVR; solver::Solver=_solver(glr, size(X))) check_nrows(X, y) n, p = size(X) p += Int(glr.fit_intercept) # allocate cache for temporary computations of size n/p # which are frequent but otherwise un-important so that # we can reduce the overall number of allocations # these are const Refs defined when the module is loaded c = glr.loss isa MultinomialLoss ? maximum(y) : 0 allocate(n, p, c) # effective call to fit routine θ = _fit(glr, solver, X, y) # de-allocate cache deallocate() return θ end
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2.680885
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# quadratic function export Quadratic ### ABSTRACT TYPE abstract type Quadratic <: ProximableFunction end is_convex(f::Quadratic) = true is_smooth(f::Quadratic) = true is_quadratic(f::Quadratic) = true fun_name(f::Quadratic) = "Quadratic function" ### CONSTRUCTORS """ **Quadratic function** Quadratic(Q, q; iterative=false) For a matrix `Q` (dense or sparse, symmetric and positive semidefinite) and a vector `q`, returns the function ```math f(x) = \\tfrac{1}{2}\\langle Qx, x\\rangle + \\langle q, x \\rangle. ``` By default, a direct method (based on Cholesky factorization) is used to evaluate `prox!`. If `iterative=true`, then `prox!` is evaluated approximately using an iterative method instead. """ function Quadratic(Q::M, q::V; iterative=false) where {M, V} if iterative == false QuadraticDirect(Q, q) else QuadraticIterative(Q, q) end end ### INCLUDE CONCRETE TYPES include("quadraticDirect.jl") include("quadraticIterative.jl")
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# # """ # Take one step of iLQR algorithm (non-allocating) # """ # function step!(solver::iLQRSolver, J) # Z = solver.Z # state_diff_jacobian!(solver.G, solver.model, Z) # discrete_jacobian!(solver.∇F, solver.model, Z) # cost_expansion!(solver.Q, solver.G, solver.obj, solver.model, solver.Z) # ΔV = backwardpass!(solver) # forwardpass!(solver, ΔV, J) # end # # # """ # $(SIGNATURES) # Calculates the optimal feedback gains K,d as well as the 2nd Order approximation of the # Cost-to-Go, using a backward Riccati-style recursion. (non-allocating) # """ # function backwardpass!(solver::StaticiLQRSolver{T,QUAD}) where {T,QUAD<:QuadratureRule} # n,m,N = size(solver) # # # Objective # obj = solver.obj # model = solver.model # # # Extract variables # Z = solver.Z; K = solver.K; d = solver.d; # G = solver.G # S = solver.S # Q = solver.Q # # # Terminal cost-to-go # S.xx[N] = Q.xx[N] # S.x[N] = Q.x[N] # # # Initialize expecte change in cost-to-go # ΔV = @SVector zeros(2) # # # k = N-1 # while k > 0 # ix = Z[k]._x # iu = Z[k]._u # # # fdx = G[k+1]'solver.∇F[k][ix,ix]*G[k] # # fdu = G[k+1]'solver.∇F[k][ix,iu] # fdx,fdu = dynamics_expansion(solver.∇F[k], G[k], G[k+1], model, Z[k]) # # fdx, fdu = dynamics_expansion(QUAD, model, Z[k]) # # Qx = Q.x[k] + fdx'S.x[k+1] # Qu = Q.u[k] + fdu'S.x[k+1] # Qxx = Q.xx[k] + fdx'S.xx[k+1]*fdx # Quu = Q.uu[k] + fdu'S.xx[k+1]*fdu # Qux = Q.ux[k] + fdu'S.xx[k+1]*fdx # # if solver.opts.bp_reg_type == :state # Quu_reg = Quu + solver.ρ[1]*fdu'fdu # Qux_reg = Qux + solver.ρ[1]*fdu'fdx # elseif solver.opts.bp_reg_type == :control # Quu_reg = Quu + solver.ρ[1]*I # Qux_reg = Qux # end # # # Regularization # if solver.opts.bp_reg # vals = eigvals(Hermitian(Quu_reg)) # if minimum(vals) <= 0 # @warn "Backward pass regularized" # regularization_update!(solver, :increase) # k = N-1 # ΔV = @SVector zeros(2) # continue # end # end # # # Compute gains # K[k] = -(Quu_reg\Qux_reg) # d[k] = -(Quu_reg\Qu) # # # Calculate cost-to-go (using unregularized Quu and Qux) # S.x[k] = Qx + K[k]'*Quu*d[k] + K[k]'* Qu + Qux'd[k] # S.xx[k] = Qxx + K[k]'*Quu*K[k] + K[k]'*Qux + Qux'K[k] # S.xx[k] = 0.5*(S.xx[k] + S.xx[k]') # # # calculated change is cost-to-go over entire trajectory # ΔV += @SVector [d[k]'*Qu, 0.5*d[k]'*Quu*d[k]] # # k -= 1 # end # # regularization_update!(solver, :decrease) # # return ΔV # # end # # # """ # $(SIGNATURES) # Simulate forward the system with the optimal feedback gains from the iLQR backward pass. # (non-allocating) # """ # function rollout!(solver::StaticiLQRSolver{T,Q}, α) where {T,Q} # Z = solver.Z; Z̄ = solver.Z̄ # K = solver.K; d = solver.d; # # Z̄[1].z = [solver.x0; control(Z[1])] # # temp = 0.0 # # # for k = 1:solver.N-1 # δx = state_diff(solver.model, state(Z̄[k]), state(Z[k])) # ū = control(Z[k]) + K[k]*δx + α*d[k] # set_control!(Z̄[k], ū) # # # Z̄[k].z = [state(Z̄[k]); control(Z[k]) + δu] # Z̄[k+1].z = [discrete_dynamics(Q, solver.model, Z̄[k]); # control(Z[k+1])] # # temp = norm(Z̄[k+1].z) # if temp > solver.opts.max_state_value # return false # end # end # return true # end
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1.712547
2,136
using Games using Test include("test_pure_nash.jl") include("test_repeated_game.jl") include("test_normal_form_game.jl") include("test_random.jl") include("test_support_enumeration.jl") include("generators/runtests.jl")
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2.810127
79
@testset "461.hamming-distance.jl" begin @test hamming_distance(1, 4) == 2 @test hamming_distance(235, 63626) == 8 end
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2.423077
52
abstract type Drift{R,W} <: CellRule{R,W} end
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2.3
20
using TrajectoryOptimization using BenchmarkTools using TrajOptPlots using Plots using LinearAlgebra using MeshCat vis = Visualizer() open(vis) solver = DIRCOLSolver(Problems.Quadrotor()...,integration=HermiteSimpson) solve!(solver) cost(solver) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Double Integrator solver = ALTROSolver(Problems.DoubleIntegrator()...) benchmark_solve!(solver) iterations(solver) # 8 plot(solver) # Pendulum solver = ALTROSolver(Problems.Pendulum()...) benchmark_solve!(solver) iterations(solver) # 19 delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Cartpole solver = ALTROSolver(Problems.Cartpole()...) benchmark_solve!(solver) iterations(solver) # 40 delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Acrobot solver = ALTROSolver(Problems.Acrobot()...) benchmark_solve!(solver) iterations(solver) # 50 delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Parallel Park solver = ALTROSolver(Problems.DubinsCar(:parallel_park)...) benchmark_solve!(solver) iterations(solver) # 13 delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Three Obstacles solver = ALTROSolver(Problems.DubinsCar(:three_obstacles)...) benchmark_solve!(solver) iterations(solver) # 20 delete!(vis) add_cylinders!(vis, solver, robot_radius=get_model(solver).radius) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Escape solver = ALTROSolver(Problems.DubinsCar(:escape)..., infeasible=true, R_inf=0.1) benchmark_solve!(solver, samples=1, evals=1) iterations(solver) # 13 delete!(vis) set_mesh!(vis, get_model(solver)) add_cylinders!(vis, solver, robot_radius=get_model(solver).model.radius, height=0.2) visualize!(vis, solver) A = rand(10,10) typeof(view(A,1:0,1:0)) # Zig-zag solver = ALTROSolver(Problems.Quadrotor(:zigzag)...) benchmark_solve!(solver) iterations(solver) # 15 delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver) # Barrell Roll solver = ALTROSolver(Problems.YakProblems()...) benchmark_solve!(solver) iterations(solver) # 17 max_violation(solver) delete!(vis) set_mesh!(vis, get_model(solver)) visualize!(vis, solver)
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2.437989
895
using LinearAlgebra using FermiCG using Printf using Arpack using Test using OrderedCollections @testset "EST" begin atoms = [] push!(atoms,Atom(1,"H",[0, 0, 0.1])) push!(atoms,Atom(2,"H",[0, 1,-1])) push!(atoms,Atom(3,"H",[0, 1, 1])) push!(atoms,Atom(4,"H",[0, 2, 0])) push!(atoms,Atom(5,"H",[0, 4, 0])) push!(atoms,Atom(6,"H",[0, 5,-1])) push!(atoms,Atom(7,"H",[0, 5, 1])) push!(atoms,Atom(8,"H",[0, 6, 0])) #basis = "6-31g" basis = "sto-3g" na = 4 nb = 4 mol = Molecule(0,1,atoms,basis) mf = FermiCG.pyscf_do_scf(mol) nbas = size(mf.mo_coeff)[1] ints = FermiCG.pyscf_build_ints(mol,mf.mo_coeff, zeros(nbas,nbas)); e_fci, d1_fci, d2_fci = FermiCG.pyscf_fci(ints,na,nb) # @printf(" FCI Energy: %12.8f\n", e_fci) FermiCG.pyscf_write_molden(mol,mf.mo_coeff,filename="scf.molden") C = mf.mo_coeff rdm_mf = C[:,1:2] * C[:,1:2]' Cl = FermiCG.localize(mf.mo_coeff,"lowdin",mf) FermiCG.pyscf_write_molden(mol,Cl,filename="lowdin.molden") S = FermiCG.get_ovlp(mf) U = C' * S * Cl println(" Build Integrals") flush(stdout) ints = FermiCG.orbital_rotation(ints,U) println(" done.") flush(stdout) clusters = [(1:4),(5:8)] init_fspace = [(2,2),(2,2)] clusters = [Cluster(i,collect(clusters[i])) for i = 1:length(clusters)] display(clusters) rdm1 = zeros(size(ints.h1)) rdm1a = rdm_mf*.5 rdm1b = rdm_mf*.5 rdm1a = rdm1 rdm1b = rdm1 display(rdm1a) display(rdm1b) #e_cmf, D1,D2,temp,temp2 = FermiCG.cmf_ci(ints, clusters, init_fspace, rdm1a, verbose=0) #Da = D1 #Db = D1 e_cmf, U, Da, Db = FermiCG.cmf_oo(ints, clusters, init_fspace, rdm1, rdm1, max_iter_oo=40, verbose=0, gconv=1e-6, method="bfgs") ints = FermiCG.orbital_rotation(ints,U) rdm1a = Da rdm1b = Db display(Da) println() display(Db) #for ci in clusters # ints_i = subset(ints, ci.orb_list, rdm1a, rdm1b) # print(ints_i.h1) #end cb_cmf = FermiCG.compute_cluster_eigenbasis(ints, clusters, verbose=1, max_roots=5,rdm1a=Da,rdm1b=Db,init_fspace=init_fspace) cb_est = FermiCG.compute_cluster_est_basis(ints, clusters, rdm1a, rdm1b, thresh_schmidt=1e-4, init_fspace=init_fspace) clustered_ham = FermiCG.extract_ClusteredTerms(ints, clusters) cluster_ops = FermiCG.compute_cluster_ops(cb_est, ints); ci_vector = FermiCG.ClusteredState(clusters) FermiCG.expand_to_full_space!(ci_vector, cb_est, na, nb) display(ci_vector,thresh=-1) ovlp = OrderedDict() for ci in clusters for (key,value) in cb_est[ci.idx] println(key) #display(cb_est[ci.idx][key]) ovlp[ci.idx,key] = cb_cmf[ci.idx].basis[key]'*cb_est[ci.idx].basis[key] end end H = FermiCG.build_full_H(ci_vector, cluster_ops, clustered_ham) #display(size(H)) #display(H) #println() display(ci_vector,root=1) e,v = Arpack.eigs(H, nev = 8, which=:SR) for ei in e @printf(" Energy: %18.12f\n",real(ei)) end ref_e = [-10.18725871 -10.15805362 -10.14789239 -10.12277607 -10.11982911 -10.11553898 -10.03259372 -10.02109892] @test isapprox(e, ref_e, atol=1e-6) S_1_42 =[0.616641; 0.766495; 0.027335; 0.176888; 0.013518] @test isapprox(abs.(ovlp[1,(4,2)]), abs.(S_1_42), atol=1e-4) S_1_41 = [ 0.935067 -0.307226; 0.208181 0.478093; -0.272371 -0.777974; -0.090131 0.267947] @test isapprox(abs.(ovlp[1,(4,1)]), abs.(S_1_41), atol=1e-4) S_1_33 = [-0.32085 -0. 0.851868; -0. 0.616641 0. ; 0. -0.766495 -0. ; -0.711982 -0. -0.110697; -0.137815 0. -0.329198] @test isapprox(abs.(ovlp[1,(3,3)]), abs.(S_1_33), atol=1e-4) S_1_23 = [ 0.674829 0.229042 -0.228784 0.656271 -0.061039 0. -0.024558 -0. ; -0.635893 0.420242 0.073237 0.548461 0.306119 -0. -0.023685 -0. ; -0. -0. 0. 0. -0. -0.935067 -0. -0.307226; 0.036266 -0.099898 0.164285 0.035816 -0.0381 -0. -0.387746 -0. ; 0.226091 0.792939 0.421423 -0.369864 -0.06511 -0. -0.002342 0. ] @test isapprox(abs.(ovlp[1,(2,3)]) , abs.(S_1_23), atol=1e-4) S_2_42 = [-0.032297; 0.981557; -0.012785; -0.187085; -0.004896] @test isapprox(abs.(ovlp[2,(4,2)]) , abs.(S_2_42), atol=1e-4) S_2_41 = [ 0.944586 0.281996; 0.011955 -0.030939; -0.318901 0.908279; 0.07692 0.30749 ;] @test isapprox(abs.(ovlp[2,(4,1)]) , abs.(S_2_41), atol=1e-4) S_2_24 = [ 0.032297; -0.981557; -0.012785; 0.187085; -0.004896] @test isapprox(abs.(ovlp[2,(2,4)]) , abs.(S_2_24), atol=1e-4) end
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1.674799
2,992
using Test using LogicCircuits using ProbabilisticCircuits include("helper/plain_logic_circuits.jl") @testset "probabilistic circuit nodes" begin c1 = little_3var() @test isdisjoint(linearize(ProbCircuit(c1)), linearize(ProbCircuit(c1))) p1 = ProbCircuit(c1) lit3 = children(children(p1)[1])[1] # traits @test p1 isa ProbCircuit @test p1 isa PlainSumNode @test children(p1)[1] isa PlainMulNode @test lit3 isa PlainProbLiteralNode @test issum(p1) @test ismul(children(p1)[1]) @test GateType(lit3) isa LiteralGate @test length(mul_nodes(p1)) == 4 # methods @test num_parameters(p1) == 10 # extension methods @test literal(lit3) === literal(children(children(c1)[1])[1]) @test variable(left_most_descendent(p1)) == Var(3) @test ispositive(left_most_descendent(p1)) @test !isnegative(left_most_descendent(p1)) @test num_nodes(p1) == 15 @test num_edges(p1) == 18 @test num_parameters_node(p1) == 2 r1 = fully_factorized_circuit(ProbCircuit,10) @test num_parameters(r1) == 2*10+1 @test length(mul_nodes(r1)) == 1 # compilation tests @test_throws Exception compile(ProbCircuit, true) v1, v2, v3 = literals(ProbCircuit, 3) r = v1[1] * 0.3 + 0.7 * v1[2] @test r isa PlainSumNode @test all(children(r) .== [v1[1], v1[2]]) @test all(ProbabilisticCircuits.params(r) .≈ log.([0.3, 0.7])) @test r * v2[1] isa PlainMulNode @test num_children(v1[1] * v2[1] * v3[1]) == 3 @test num_children(v1[1] + v2[1] + v3[1]) == 3 end
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2.171745
722
@testset "logseries dist" begin @test logseriescdf(0.01)[1:3] ≈ [0.0, 0.994992, 0.999967] atol=1.0e-5 @test logseriescdf(0.000000000001)[1:5] ≈ [0., 1., 1., 1., 1.] atol=1.0e-2 #@test logseriesquantile(0.9, [0.25, 0.5, 0.75]) == [1, 2, 5] Random.seed!(43) w = logseriescdf(0.4) v = pmap(i -> findlast(w .< i), rand(1000000)) @test mean(v) ≈ 1.304 atol=1.0e-2 @test std(v) ≈ 0.687 atol=1.0e-2 @test skewness(v) ≈ 3.1 atol=1.0e-2 @test kurtosis(v) ≈ 13.5 atol=1.0 end @testset "stable levy dist" begin Random.seed!(43) @test levyel(2., rand(), rand()) ≈ 0.18170339379413047 Random.seed!(43) @test tiltedlevygen(0.2, 2.; rng = Random.GLOBAL_RNG) ≈ 0.00409133 atol=1.0e-5 Random.seed!(43) @test tiltedlevygen(0.6, 2.; rng = Random.GLOBAL_RNG) ≈ 0.036822009 atol=1.0e-5 end @testset "nested copulas data generators" begin @test Ginv(0.5, 0.5) ≈ 1.2732395447351625 @test InvlaJ(4, 0.5) ≈ 0.7265625 @test sampleInvlaJ(0.5, 0.5) == 1 @test sampleInvlaJ(0.5, 0.8) == 8 Random.seed!(43) w = logseriescdf(1-exp(-4.)) @test elInvlaF(4., 2., w; rng = Random.GLOBAL_RNG) == 7 Random.seed!(43) @test elInvlaF(4., .5, w; rng = Random.GLOBAL_RNG) == 16 @test nestedfrankgen(4., 3., 1, w; rng = Random.GLOBAL_RNG) == 6 end
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module Unification using InteractiveUtils using Logging include("bindings.jl") include("unify.jl") end
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module Day09 import ..data_dir # from parent module input = read(joinpath(data_dir, "day09"), String) export part1, part2 """ Horrendous logic for converting this string of integers to a matrix. There must be a better way, but I can't find it. """ function string_block_to_matrix(string_block::AbstractString) as_arrays = split.(split(string_block, "\n"), "") as_int_arrays = [parse.(Int64, array) for array in as_arrays] # Transposing changes the type! Matrix(transpose(hcat(as_int_arrays...))) end function out_of_bounds(heatmap::Matrix{Int64}, row::Int64, column::Int64) heatmap_height, heatmap_width = size(heatmap) row < 1 || row > heatmap_height || column < 1 || column > heatmap_width end function identify_neighbours(heatmap::Matrix{Int64}, row::Int64, column::Int64) candidates = [ (row - 1, column), (row + 1, column), (row, column - 1), (row, column + 1) ] filter(candidate -> !out_of_bounds(heatmap, candidate...), candidates) end function risk_level(heatmap::Matrix{Int64}, row::Int64, column::Int64) value = heatmap[row, column] # matrices are indexed row then column if value == 9 return (0) end neighbours = identify_neighbours(heatmap, row, column) is_minimum = all(value < heatmap[neighbour...] for neighbour in neighbours) is_minimum * (1 + value) # non-minima don't get a risk value end function add_to_basin( heatmap::Matrix{Int64}, unclaimed::Vector{Tuple{Int64,Int64}}, row::Int64, column::Int64 ) # can't pop by value, so I need to get the index index_in_unclaimed = findfirst(x -> x == (row, column), unclaimed) if isnothing(index_in_unclaimed) # already claimed by another path return Set{Tuple{Int64,Int64}}() end in_basin = Set([popat!(unclaimed, index_in_unclaimed)]) neighbours = [neighbour for neighbour in identify_neighbours(heatmap, row, column) if neighbour in unclaimed] if length(neighbours) == 0 return (in_basin) end for neighbour in neighbours in_basin = in_basin ∪ add_to_basin(heatmap, unclaimed, neighbour...) end in_basin end function part1(input = input) heatmap = string_block_to_matrix(input) n_rows, n_cols = size(heatmap) total_risk_level = 0 for row = 1:n_rows, column = 1:n_cols total_risk_level += risk_level(heatmap, row, column) end total_risk_level end """ Two things make this second part manageable: * I didn't need to consider the actual values of the heatmap, as long as I knew which ones were 9 and which ones aren't * Every non-9 element of the heatmap is in exactly one basin """ function part2(input = input) heatmap = string_block_to_matrix(input) n_rows, n_cols = size(heatmap) unclaimed = [(row, column) for row in 1:n_rows, column in 1:n_cols if heatmap[row, column] != 9] basins = Vector{Set{Tuple{Int64,Int64}}}() while length(unclaimed) > 0 push!(basins, add_to_basin(heatmap, unclaimed, unclaimed[1]...)) end basin_sizes = map(x -> length(x), basins) last(sort(basin_sizes), 3) |> prod end end #module
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# =========================================================================== # VOLUME CONTROL BLOCKETTES function blk_010!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) v > 1 && println("") close(sio) return nothing end function blk_011!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) if v > 1 println("") if v > 2 nsta = stream_int(sio, 3) println(" "^16, lpad("STA", 5), " | SEQ") println(" "^16, "------|-------") @inbounds for i = 1:nsta sta = String(fastread(sio, 5)) seq = stream_int(sio, 6) println(" "^16, sta, " | ", seq) end end end close(sio) return nothing end function blk_012!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) if v > 1 N = stream_int(sio, 4) println(", N_SPANS = ", N) end close(sio) return nothing end # =========================================================================== # ABBREVIATION CONTROL HEADERS # [30] is out of scope for SeisIO function blk_030!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) if v > 1 desc = string_field(sio) code = stream_int(sio, 4) fam = stream_int(sio, 3) println(", CODE ", code, ", DESC ", desc, ", FAM ", fam) if v > 2 ND = stream_int(sio, 2) @inbounds for i = 1:ND desc = string_field(sio) println(" "^18, desc) end end end close(sio) return nothing end function blk_031!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) k = stream_int(sio, 4) class = fastread(sio) desc = string_field(sio) if v > 1 units = stream_int(sio, 3) println(", CODE ", k, ", CLASS ", Char(class), ", DESC ", desc, ", UNITS ", units) end close(sio) comments[k] = replace(desc, "," => "-") return nothing end function blk_032!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) if v > 1 code = stream_int(sio, 2) pub = string_field(sio) date = string_field(sio) name = string_field(sio) println(", CODE ", code, ", REFERENCE ", pub, ", DATE ", date, ", PUBLISHER ", name) end close(sio) return nothing end function blk_033!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) code = stream_int(sio, 3) desc = string_field(sio) close(sio) v > 1 && println(", CODE ", code, ", DESC ", desc) abbrev[code] = desc return nothing end function blk_034!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) code = stream_int(sio, 3) name = string_field(sio) if v > 1 desc = string_field(sio) println(", CODE ", code, ", NAME ", name, ", DESC ", desc) end close(sio) units_lookup[code] = fix_units(name) return nothing end function blk_041!(io::IO, nb::Int64, v::Integer, units::Bool) sio = blk_string_read(io, nb, v) resp_lookup_key = stream_int(sio, 4) skip_string!(sio) symm_code = fastread(sio) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) #= NF can be completely wrong here. Two issues: (1) The SEED manual quietly suggests writing only part of "symmetric" FIR filters to file; in which case NF is not the length of the vector we need. =# NF = stream_int(sio, 4) if symm_code == 0x42 NF = div(NF, 2)+1 elseif symm_code == 0x43 NF = div(NF, 2) end #= (2) buried in the SEED manual, it's noted that the maximum size of a non-data blockette length is 9999 chars, and type [41] can exceed it. In this case, the sample files tell me that NF is dead wrong; workaround is to read as many FIR values as possible and fix with append! of next packet. We've already used 7 chars on blockette type & size; rest are in sio. =# p = fastpos(sio) NF_true = min(NF, div(9992-p, 14)) if v > 1 println(", KEY ", resp_lookup_key, ", SYMM ", Char(symm_code)) if v > 2 println(" "^16, "units in = ", units_lookup[uic]) println(" "^16, "units out = ", units_lookup[uoc]) end println(" "^16, "p = ", p, ", NF = ", NF, ", NF_true = ", NF_true) end #= This appears to fix it but results in some very odd behavior. =# F = Array{Float64, 1}(undef, NF_true) @inbounds for i = 1:NF_true fast_readbytes!(sio, BUF.hdr_old, 14) setindex!(F, buf_to_double(BUF.hdr_old, 14), i) end close(sio) if v > 2 [println("F[", i, "] = ", F[i]) for i = 1:NF_true] end # Process to resps if haskey(responses, resp_lookup_key) R = responses[resp_lookup_key][1] append!(R.b, F) else resp = CoeffResp(F, Float64[]) ui = units ? fix_units(units_lookup[uic]) : "" uo = units ? fix_units(units_lookup[uoc]) : "" responses[resp_lookup_key] = (resp, ui, uo) end return nothing end function blk_043!(io::IO, nb::Int64, v::Integer, units::Bool) sio = blk_string_read(io, nb, v) resp_lookup_key = stream_int(sio, 4) skip_string!(sio) fastskip(sio, 1) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) fast_readbytes!(sio, BUF.hdr_old, 12) A0 = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) F0 = buf_to_double(BUF.hdr_old, 12) NZ = stream_int(sio, 3) Z = Array{ComplexF64, 1}(undef, NZ) @inbounds for i = 1:NZ fast_readbytes!(sio, BUF.hdr_old, 12) rr = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) ii = buf_to_double(BUF.hdr_old, 12) Z[i] = complex(rr, ii) fastskip(sio, 24) end NP = stream_int(sio, 3) P = Array{ComplexF64,1}(undef, NP) @inbounds for i = 1:NP fast_readbytes!(sio, BUF.hdr_old, 12) rr = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) ii = buf_to_double(BUF.hdr_old, 12) P[i] = complex(rr, ii) fastskip(sio, 24) end close(sio) if v > 1 println("A0 = ", string(A0), "F0 = ", string(F0)) if v > 2 println(" "^16, "NZ = ", NZ, ":") @inbounds for i = 1:NZ println(" "^16, Z[i]) end println(" "^16, "NP = ", NP, ":") @inbounds for i = 1:NP println(" "^16, P[i]) end end end # Process to resps R = PZResp64(a0 = A0, f0 = F0, z = Z, p = P) ui = units ? fix_units(units_lookup[uic]) : "" uo = units ? fix_units(units_lookup[uoc]) : "" responses[resp_lookup_key] = (R, ui, uo) return nothing end function blk_044!(io::IO, nb::Int64, v::Integer, units::Bool) sio = blk_string_read(io, nb, v) resp_lookup_key = stream_int(sio, 4) skip_string!(sio) fastskip(sio, 1) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) NN = stream_int(sio, 4) N = Array{Float64,1}(undef, NN) @inbounds for i = 1:NN fast_readbytes!(sio, BUF.hdr_old, 12) N[i] = buf_to_double(BUF.hdr_old, 12) fastskip(sio, 12) end ND = stream_int(sio, 4) D = Array{Float64,1}(undef, ND) @inbounds for i = 1:ND fast_readbytes!(sio, BUF.hdr_old, 12) D[i] = buf_to_double(BUF.hdr_old, 12) fastskip(sio, 12) end close(sio) if v > 1 println(", KEY ", resp_lookup_key) if v > 2 println(" "^16, "units in code = ", uic) println(" "^16, "units out code = ", uoc) println(" "^16, "NN = ", NN, ":") @inbounds for i = 1:NN println(" "^16, N[i]) end println(" "^16, "ND = ", ND, ":") @inbounds for i = 1:min(ND) println(" "^16, D[i]) end end end if units responses[resp_lookup_key] = (CoeffResp(N, D), fix_units(units_lookup[uic]), fix_units(units_lookup[uoc]) ) else responses[resp_lookup_key] = (CoeffResp(N, D), "", "") end return nothing end function blk_047!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) resp_lookup_key = stream_int(sio, 4) skip_string!(sio) fast_readbytes!(sio, BUF.hdr_old, 10) fs = buf_to_double(BUF.hdr_old, 10) fac = stream_int(sio, 5) os = stream_int(sio, 5) fast_readbytes!(sio, BUF.hdr_old, 11) delay = buf_to_double(BUF.hdr_old, 11) fast_readbytes!(sio, BUF.hdr_old, 11) corr = buf_to_double(BUF.hdr_old, 11) close(sio) if v > 1 println(", KEY ", resp_lookup_key) if v > 2 println(" "^16, "fs = ", fs) println(" "^16, "decimation factor = ", fac) println(" "^16, "decimation offset = ", os) println(" "^16, "delay = ", delay) println(" "^16, "delay correction applied = ", corr) end end responses[resp_lookup_key] = Blk47(fs, delay, corr, fac, os) return nothing end function blk_048!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) resp_lookup_key = stream_int(sio, 4) skip_string!(sio) fast_readbytes!(sio, BUF.hdr_old, 12) gain = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) fg = buf_to_double(BUF.hdr_old, 12) nv = stream_int(sio, 2) # channel histories are not in the scope of SeisIO @inbounds for i = 1:nv # fastskip(sio, 24) skip_string!(sio) # should cover the 24-Byte channel history end close(sio) if v > 1 println(", KEY ", resp_lookup_key) if v > 2 println(" "^16, "gain = ", gain, " (f = ", fg, " Hz)") end end responses[resp_lookup_key] = Blk48(gain, fg) return nothing end function blk_050(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) # Station fill!(BUF.hdr, 0x00) p = pointer(BUF.hdr) fast_unsafe_read(sio, p, 5) #= I make the assumption here that channel coordinates are correctly set in blockette 52; if this is not true, then :loc will not be set. This shortcut makes support for multiplexing impossible, though blockette-50 multiplexing has never been encountered in my test files (or in any files of the ObsPy test suite.) Multiplexing would be annoying as one would need to define new SeisData objects S_subnet of length = n_subchans and use append!(S, S_subnet), rather than push!(S, C); indexing would be a mess. But it's doable in theory. =# fastskip(sio, 33) site_name = strip(string_field(sio)) fastskip(sio, 9) ts = string_field(sio) te = string_field(sio) uc = fastread(sio) # Network p = pointer(BUF.hdr, 11) fast_unsafe_read(sio, p, 2) close(sio) if v > 1 println(", ID = ", String(copy(BUF.id))) if v > 2 println(" "^16, "site name = ", site_name) println(" "^16, "start date = ", ts) println(" "^16, "end date = ", te) println(" "^16, "update code = ", Char(uc)) end end return site_name end # =========================================================================== # STATION CONTROL BLOCKETTES # not necessary function blk_051!(io::IO, nb::Int64, v::Integer) sio = blk_string_read(io, nb, v) if v > 1 println("") if v > 2 ts = string_field(sio) te = string_field(sio) k = stream_int(sio, 4) comment_level = stream_int(sio, 6) println(" "^16, "ts = ", ts) println(" "^16, "te = ", te) println(" "^16, "comment code key #", k) println(" "^16, "comment level = ", comment_level) end end close(sio) return nothing end function blk_052!(io::IO, nb::Int64, C::SeisChannel, ts_req::Int64, te_req::Int64, v::Integer) sio = blk_string_read(io, nb, v) # loc p = pointer(BUF.hdr, 6) fast_unsafe_read(sio, p, 2) # cha p = pointer(BUF.hdr, 8) fast_unsafe_read(sio, p, 3) fastskip(sio, 4) inst = stream_int(sio, 3) skip_string!(sio) units_code = stream_int(sio, 3) fastskip(sio, 3) # lat, lon, el, dep, az, inc fast_readbytes!(sio, BUF.hdr_old, 10) lat = buf_to_double(BUF.hdr_old, 10) fast_readbytes!(sio, BUF.hdr_old, 11) lon = buf_to_double(BUF.hdr_old, 11) fast_readbytes!(sio, BUF.hdr_old, 7) el = buf_to_double(BUF.hdr_old, 7) fast_readbytes!(sio, BUF.hdr_old, 5) dep = buf_to_double(BUF.hdr_old, 5) fast_readbytes!(sio, BUF.hdr_old, 5) az = buf_to_double(BUF.hdr_old, 5) fast_readbytes!(sio, BUF.hdr_old, 5) inc = 90.0 - buf_to_double(BUF.hdr_old, 5) fastskip(sio, 6) # fs fast_readbytes!(sio, BUF.hdr_old, 10) fs = buf_to_double(BUF.hdr_old, 10) # don't really need max. drift; skip_string passes it over skip_string!(sio) # ts, te ts = parse_resp_date(sio, BUF.uint16_buf) te = parse_resp_date(sio, BUF.uint16_buf) if te == -56504908800000000 te = 19880899199000000 end close(sio) if v > 1 println(", ID = ", String(copy(BUF.hdr)), ", INST = ", inst) if v > 2 println(" "^16, "lat = ", lat, ", lon = ", lon, ", z = ", el, ", dep = ", dep, ", θ = ", az, ", ϕ = ", inc) println(" "^16, "fs = ", fs) println(" "^16, "ts = ", u2d(div(ts, sμ))) println(" "^16, "te = ", u2d(div(te, sμ))) end end if ts ≤ te_req && te ≥ ts_req update_hdr!(BUF) C.id = getfield(BUF, :id_str) C.fs = fs C.loc = GeoLoc(lat = lat, lon = lon, el = el, dep = dep, az = az, inc = inc) C.resp = MultiStageResp(12) C.misc["ts"] = ts C.misc["te"] = te C.misc["timespan"] = string(u2d(div(ts, 1000000))) * " : " * string(u2d(div(te, 1000000))) C.misc["inst"] = get(abbrev, inst, "") units = get(units_lookup, units_code, "") C.units = fix_units(units) skipping = false else if v > 1 println(" "^16, "Skipping ", String(copy(BUF.hdr)), " (not in requested time range)") end skipping = true end return skipping end function blk_053(io::IO, nb::Int64, v::Integer, R::MultiStageResp, units::Bool) sio = blk_string_read(io, nb, v) tft = Char(fastread(sio)) stage = stream_int(sio, 2) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) fast_readbytes!(sio, BUF.hdr_old, 12) A0 = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) F0 = buf_to_double(BUF.hdr_old, 12) NZ = stream_int(sio, 3) Z = Array{ComplexF64,1}(undef, NZ) @inbounds for i = 1:NZ fast_readbytes!(sio, BUF.hdr_old, 12) rr = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) ii = buf_to_double(BUF.hdr_old, 12) Z[i] = complex(rr, ii) fastskip(sio, 24) end NP = stream_int(sio, 3) P = Array{ComplexF64,1}(undef, NP) @inbounds for i = 1:NP fast_readbytes!(sio, BUF.hdr_old, 12) rr = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) ii = buf_to_double(BUF.hdr_old, 12) P[i] = complex(rr, ii) fastskip(sio, 24) end close(sio) if v > 1 println(", TFT ", tft, ", STAGE ", stage) if v > 2 println(" "^16, "units in code #", uic) println(" "^16, "units out code #", uoc) println(" "^16, "A0 = ", A0) println(" "^16, "F0 = ", F0) println(" "^16, "NZ = ", NZ, ":") @inbounds for i = 1:NZ println(" "^16, Z[i]) end println(" "^16, "NP = ", NP, ":") @inbounds for i = 1:NP println(" "^16, P[i]) end end end if stage > length(R.fs) append!(R, MultiStageResp(6)) end resp = PZResp64(a0 = A0, f0 = F0, z = Z, p = P) ui = units ? fix_units(units_lookup[uic]) : "" uo = units ? fix_units(units_lookup[uoc]) : "" R.stage[stage] = resp R.i[stage] = ui R.o[stage] = uo return stage end function blk_054(io::IO, nb::Int64, v::Integer, R::MultiStageResp, units::Bool) sio = blk_string_read(io, nb, v) fastskip(sio, 1) stage = stream_int(sio, 2) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) NN = stream_int(sio, 4) N = Array{Float64,1}(undef, NN) @inbounds for i = 1:NN fast_readbytes!(sio, BUF.hdr_old, 12) N[i] = buf_to_double(BUF.hdr_old, 12) fastskip(sio, 12) end ND = stream_int(sio, 4) D = Array{Float64,1}(undef, ND) @inbounds for i = 1:ND fast_readbytes!(sio, BUF.hdr_old, 12) D[i] = buf_to_double(BUF.hdr_old, 12) fastskip(sio, 12) end close(sio) if v > 1 println(", STAGE ", stage) if v > 2 println(" "^16, "units in code = ", uic) println(" "^16, "units out code = ", uoc) println(" "^16, "NN = ", NN, ":") @inbounds for i = 1:NN println(" "^16, N[i]) end println(" "^16, "ND = ", ND, ":") @inbounds for i = 1:ND println(" "^16, D[i]) end end end if stage > length(R.fs) append!(R, MultiStageResp(6)) end R.stage[stage] = CoeffResp(N, D) if units @inbounds R.i[stage] = fix_units(units_lookup[uic]) @inbounds R.o[stage] = fix_units(units_lookup[uoc]) end return stage end function blk_057(io::IO, nb::Int64, v::Integer, R::MultiStageResp) sio = blk_string_read(io, nb, v) stage = stream_int(sio, 2) fast_readbytes!(sio, BUF.hdr_old, 10) fs = buf_to_double(BUF.hdr_old, 10) fac = stream_int(sio, 5) os = stream_int(sio, 5) fast_readbytes!(sio, BUF.hdr_old, 11) delay = buf_to_double(BUF.hdr_old, 11) fast_readbytes!(sio, BUF.hdr_old, 11) corr = buf_to_double(BUF.hdr_old, 11) close(sio) if v > 1 println(", STAGE #", stage) if v > 2 println(" "^16, "fs = ", fs) println(" "^16, "decimation factor = ", fac) println(" "^16, "decimation offset = ", os) println(" "^16, "delay = ", delay) println(" "^16, "delay correction applied = ", corr) end end @inbounds R.fs[stage] = fs @inbounds R.delay[stage] = delay @inbounds R.corr[stage] = corr @inbounds R.fac[stage] = fac @inbounds R.os[stage] = os return stage end function blk_058(io::IO, nb::Int64, v::Integer, C::SeisChannel) sio = blk_string_read(io, nb, v) stage = stream_int(sio, 2) fast_readbytes!(sio, BUF.hdr_old, 12) if stage == 0 C.gain = buf_to_double(BUF.hdr_old, 12) close(sio) if v > 1 println(", STAGE #", stage) end return stage else C.resp.gain[stage] = buf_to_double(BUF.hdr_old, 12) fast_readbytes!(sio, BUF.hdr_old, 12) C.resp.fg[stage] = buf_to_double(BUF.hdr_old, 12) if v > 1 println(", STAGE #", stage) if v > 2 println(" "^16, "gain = ", C.resp.gain[stage], " (f = ", C.resp.fg[stage], " Hz)") end end # station history is not in the scope of SeisIO close(sio) return stage end end # Not in scope of SeisIO function blk_059!(io::IO, nb::Int64, v::Integer, C::SeisChannel, units::Bool) sio = blk_string_read(io, nb, v) v > 1 && println("") if units ts = parse_resp_date(sio, BUF.uint16_buf) te = parse_resp_date(sio, BUF.uint16_buf) if te == -56504908800000000 te = 19880899199000000 end k = stream_int(sio, 4) t_str = string("comment,", ts, ",", te, ",", comments[k]) if v > 2 println(u2d(div(ts, 1000000)), "–", u2d(div(te, 1000000)), ": ", comments[k]) end note!(C, t_str) end close(sio) return nothing end # Assign dictionary elements with response info in the 41-49 blockettes function blk_060(io::IO, nb::Int64, v::Integer, R::MultiStageResp) sio = blk_string_read(io, nb, v) nstg = stream_int(sio, 2) if v > 1 println(", # STAGES = ", nstg) end for i = 1:nstg seq = stream_int(sio, 2) nr = stream_int(sio, 2) for j = 1:nr k = stream_int(sio, 4) rr = get(responses, k, "") if v > 2 printstyled(" "^16, " assigning response to stage #", seq, ":\n", color=:green, bold=true) println(rr) end if isa(rr, Blk48) @inbounds R.gain[seq] = rr.gain @inbounds R.fg[seq] = rr.fg elseif isa(rr, Blk47) @inbounds R.fs[seq] = rr.fs @inbounds R.delay[seq] = rr.delay @inbounds R.corr[seq] = rr.corr @inbounds R.fac[seq] = rr.fac @inbounds R.os[seq] = rr.os elseif typeof(rr[1]) <: InstrumentResponse (length(R.fs) < seq) && append!(R, MultiStageResp(6)) @inbounds R.stage[seq] = rr[1] @inbounds R.i[seq] = rr[2] @inbounds R.o[seq] = rr[3] end end end close(sio) return nstg end function blk_061(io::IO, nb::Int64, v::Integer, R::MultiStageResp, units::Bool) sio = blk_string_read(io, nb, v) stage = stream_int(sio, 2) skip_string!(sio) symm_code = Char(fastread(sio)) uic = stream_int(sio, 3) uoc = stream_int(sio, 3) NF = stream_int(sio, 4) F = Array{Float64,1}(undef, NF) @inbounds for i = 1:NF fast_readbytes!(sio, BUF.hdr_old, 14) F[i] = buf_to_double(BUF.hdr_old, 14) end close(sio) if v > 1 println(", STAGE #", stage, " , SYMM = ", Char(symm_code)) if v > 2 println(" "^16, "units in code #", uic) println(" "^16, "units out code #", uoc) println(" "^16, "NF = ", NF) @inbounds for i = 1:NF println(" "^16, F[i]) end end end (length(R.fs) < stage) && append!(R, MultiStageResp(6)) @inbounds R.stage[stage] = CoeffResp(F, Float64[]) if units @inbounds R.i[stage] = fix_units(units_lookup[uic]) @inbounds R.o[stage] = fix_units(units_lookup[uoc]) end return stage end
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2.118763
9,801
using MeshSignedDistance using Test @testset "MeshSignedDistance.jl" begin # Write your tests here. end
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3.114286
35
""" Quadrature information Most spectral-element operations require calculation of integrals and derivatives. The `QuadType` structure stores the required information when calculating integrals and derivatives """ type QuadType{T<:Number} Q::Int z::Vector{T} w::Vector{T} D::Matrix{T} end """ Constructs quadrature rules for Gauss-type quadrature. """ function QuadType{T<:Number,QT<:QUADRATURE_TYPE}(q::Int, ::Type{QT}=GLJ, ::Type{T}=Float64, a=0, b=0) z = Jacobi.qzeros(QT, q, a, b, T) w = Jacobi.qweights(QT, z, a, b) D = Jacobi.qdiff(QT, z, a, b) QuadType(q, z, w, D) end """ Creates the ith Lagrange polynomial from a vector of nodes Explicit construction of Lagrange polynomials from the nodes. This function builds a `Poly` object. * `i` Index specifying which Lagrange polynomial should be returned. * `z` Vector containing the nodes of the Lagrange polynomials. """ function lagrange_poly{T<:Number}(i::Integer, z::AbstractVector{T}) np = length(z) y = zeros(T, np-1) for j = 1:(i-1) y[j] = z[j] end for j = (i+1):np y[j-1] = z[j] end den = one(T) for j = 1:(np-1) den = den * (z[i] - y[j]) end p = poly(y) return p/den end """ Build Lagrange basis from any nodes Probably not the fastest algorithm. But this won't be seriously used. """ function QuadType{T<:Number}(x::AbstractVector{T}) # Generic numbers: np = length(x) z = zeros(T,np) for i = 1:np z[i] = x[i] end w = zeros(T, np) D = zeros(T, np, np) for i = 1:np p = lagrange_poly(i, z) pint = polyint(p) pder = polyder(p) w[i] = polyval(pint, one(T)) - polyval(pint, -one(T)) # Compute the rows of the derivative matrix for k = 1:np D[k,i] = polyval(pder, z[k]) end end QuadType(np, z, w, D) end
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2.238152
844
#!/usr/bin/env julia # Comprehension and Generators # https://teaching.healthtech.dtu.dk/22110/index.php/Comprehension_and_Generators # Ex 1 matrix1 = readdlm("data/matrix1", '\t', Int, '\n') matrix2 = readdlm("data/matrix2", '\t', Int, '\n') dot = matrix1*matrix2 matrix_product(a,b) = [sum([a[i,r]*b[r,j] for r = 1:size(a,2)]) for i = 1:size(a,1), j = 1:size(b,2)] matrix_product(matrix1,matrix2) # Ex 2 function _read_experiment(f, acc_match) status = Float64[] for line = eachline(f) line_split = split(line, "\t") acc = string(line_split[2]) if line_split[1] == "COL_CLASSES" status = [parse(Int64, x) for x in line_split[4:end]] elseif acc == acc_match nums = [parse(Float64, x) for x in line_split[4:end]] return nums[status .== 0], nums[status .== 1] end end return Float64[], Float64[] end function read_experiment(acc_search, outfilename) out = open(outfilename, "w") cancer, control = open(f->_read_experiment(f, acc_search), "data/dna-array.dat") if length(cancer) == 0 && length(control) == 0 println("Accession number not found.") close(out) return nothing end min_len = min(length(cancer), length(control)) for i = 1:min_len println(out, cancer[i], "\t", control[i]) end if length(cancer) > length(control) for i = (min_len+1):length(cancer) println(out, cancer[i]) end else for i = (min_len+1):length(control) println(out, "\t", cancer[i]) end end close(out) end println("Enter accession number:") read_experiment(chomp(readline()), "data/dna-array-subset.dat")
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2.177891
787
using DICOMClient using HTTP using Test @testset "version" begin @test DICOMClient.package_version() isa VersionNumber @test DICOMClient.package_version() > v"0" end @testset "_add_trailing_slash" begin @test DICOMClient._add_trailing_slash(HTTP.URI("https://juliahealth.org")) == HTTP.URI("https://juliahealth.org/") @test DICOMClient._add_trailing_slash(HTTP.URI("https://juliahealth.org/")) == HTTP.URI("https://juliahealth.org/") end @testset "parse_multipart_image" begin @test_throws ArgumentError DICOMClient.parse_multipart_image(Val(:jpeg), UInt8[0xff, 0xd8, 0xff, 0xd9, 0xff, 0xd8]) end
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2.555556
243
struct WangLi3SMil_AConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_ACache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_A,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_AConstantCache() function alg_cache(alg::WangLi3SMil_A,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_ACache(u,uprev,k,k₁,tmp) end struct WangLi3SMil_BConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_BCache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_B,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_BConstantCache() function alg_cache(alg::WangLi3SMil_B,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_BCache(u,uprev,k,k₁,tmp) end struct WangLi3SMil_CConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_CCache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_C,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_CConstantCache() function alg_cache(alg::WangLi3SMil_C,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_CCache(u,uprev,k,k₁,tmp) end struct WangLi3SMil_DConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_DCache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_D,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_DConstantCache() function alg_cache(alg::WangLi3SMil_D,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_DCache(u,uprev,k,k₁,tmp) end struct WangLi3SMil_EConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_ECache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_E,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_EConstantCache() function alg_cache(alg::WangLi3SMil_E,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_ECache(u,uprev,k,k₁,tmp) end struct WangLi3SMil_FConstantCache <: StochasticDiffEqConstantCache end @cache struct WangLi3SMil_FCache{uType,rateType} <: StochasticDiffEqMutableCache u::uType uprev::uType k::rateType k₁::rateType tmp::uType end alg_cache(alg::WangLi3SMil_F,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{false}}) = WangLi3SMil_FConstantCache() function alg_cache(alg::WangLi3SMil_F,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,jump_rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,uprev,f,t,dt,::Type{Val{true}}) k = zero(rate_prototype) k₁ = zero(rate_prototype) tmp = zero(u) WangLi3SMil_FCache(u,uprev,k,k₁,tmp) end
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2.208313
2,021
pillars(numpill, dist, width) = max(0, numpill-1)*100*dist + max(0, numpill-2)*width
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2.044444
45
module FighterJets export FighterJet using Base: Bool, Float64 #FigherJet is avery fast vehicle with powerful weapons. It mutable struct FighterJet "power status: true - on, false - off" power::Bool "current direction in radians" direction::Float64 "current position coordinate (x, y)" position::Tuple{Float64, Float64} end # Import generic functions from include("../../Vehicles/src/Vehicles.jl") using .Vehicles: power_on!, power_off!, turn!, move!, position!, Vehicles # Implementation of Vehicle interface function power_on!(fj::FighterJet) fj.power =true println("Powered on: ", fj) nothing end function power_off!(fj::FighterJet) fj.power = false println("Powered off: ", fj) nothing end function turn!(fj::FighterJet, direction) fj.direction = direction println("Changed direction to ", direction, ": ", fj) nothing end function move!(fj::FighterJet, distance) x, y = fj.position dx = round(distance * cos(fj.direction), digits = 2) dy = round(distance * sin(fj.direction), digits = 2) fj.position = (x + dx, y + dy) println("Moved (", dx, ",", dy, "): ", fj) nothing end function position(fj::FighterJet) fj.position end end # module
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using Pkg dependencies = [ "GraphRecipes", "LightGraphs", "SimpleWeightedGraphs", "GraphPlot", "MetaGraphs", "Documenter", "StatsBase", "LinearAlgebra", "Statistics", "Colors", "Plots", "LaTeXStrings", "Cairo", "Compose", "SparseArrays", "QuadGK", "SatelliteToolbox", "GR"] Pkg.add(dependencies) Pkg.update(dependencies)
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## Definition of Link types and methods for link, invlink and μη, the derivative of μ w.r.t. η abstract Link immutable IdentityLink <: Link end immutable InverseLink <: Link end immutable LogLink <: Link end immutable LogitLink <: Link end link(::IdentityLink,μ) = μ invlink(::IdentityLink,η) = η μη(::IdentityLink,η) = one(η) link(::LogitLink,μ) = log(μ/(one(μ)-μ)) invlink(::LogitLink,η) = inv(one(η) + exp(-η)) μη(::LogitLink,η) = (ee = exp(-η); ee/abs2(one(η)+ee)) link(::LogLink,μ) = log(μ) invlink(::LogLink,η) = exp(η) μη(::LogLink,η) = exp(η) link(::InverseLink,μ) = inv(μ) invlink(::InverseLink,η) = inv(η) μη(::InverseLink,η) = -inv(abs2(η))
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using Compat, Calculus, QuadGK using Test: @test function psm(nip) @test nip >= 1 # Integrate to compute coefficient in rhs rhs(n, nip) = QuadGK.quadgk((x)-> x^nip, -n, n)[1] local hn if nip == 1 w = [1.0] elseif nip == 2 w = [1/2, 1/2] else if isodd(nip) n = floor(Int, nip/2) hn = range(-n, stop=n, length=nip) else n = floor(Int, 2*(nip/2)-1) hn = range(-n, stop=n, length=nip) end m = ones(nip, nip) for i in 0:nip-1 for j in 1:nip m[i+1, j] = (hn[j])^i end end v = [rhs(n, ni) for ni in 0:nip-1] w = m \ v iseven(nip) && (w = w / 2.0) end w' end function nc(func, lb, ub, nip) @test ub > lb @test nip >= 1 if nip == 1 h = ub - lb x = [lb] elseif nip ==2 h = ub - lb x = [lb, ub] else h = (ub - lb)/(nip-1) x = collect(range(lb, stop=ub, length=nip)) end w = psm(nip) res = h * w *func.(x) (w, res) end lb = pi/4 ub = pi/2 func = sin nip = 7 w, res = nc(func, lb, ub, nip) @test res[1] ≈ QuadGK.quadgk(sin, pi/4, pi/2)[1]
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# some utility functions const Γ = gamma function generalized_binomial(α::T, n::S) where {T,S <: Integer} R = Base.promote_op(/,T,S) out = one(R) for i in n:-1:1 out *= α/i α -= 1 end out end ## memoized version of the above @memoize function generalized_binomial′(a, k::Int) k < 0 && return NaN * one(a) k == 0 && return one(a)/1 generalized_binomial′(a,k-1) * (a-k+1)/k end ## x⁽ⁱ⁾ is falling (x)⋅(x-1)⋯(x-n+1) function Pochhammer(::Val{:falling}, z::T,n::Int) where {T} iszero(n) && return one(T) out = z for i in 1:n-1 z -= 1 out *= z end out end ## (x)ᵢ is rising (x)⋅(x+1)⋯(x+n-1) = Γ(z+n)/Γ(z) function Pochhammer(::Val{:rising}, z::T,n::Int) where {T} iszero(n) && return one(T) out = z for i in 1:n-1 z += 1 out *= z end out end # default is (x)ᵢ Pochhammer(z,n::Int) = Pochhammer(Val(:rising), z, n) # (x)_n/n! Pochhammer_factorial(z, n) = Pochhammer_factorial(Val(:rising), z, n) function Pochhammer_factorial(::Val{:rising}, z, n) iszero(n) && return one(z)/1 prod((z+i)/(n-i) for i in 0:n-1) end ## Alternative to HypergeometricFunctions.jl so that ## variables can be inserted into the numerator """ pFq(as, bs, z; [maxevals=1000]) Compute the generalized [hypergeometric](https://en.wikipedia.org/wiki/Generalized_hypergeometric_function) function. The `HypergeometricFunction.jl` package would normally be used for such calculations, but this one accepts polynomial values for the `as` and `z` values. # Example From [mathworld](https://mathworld.wolfram.com/HypergeometricFunction.html) ```jldoctest julia> using Polynomials, SpecialPolynomials julia> import SpecialPolynomials: pFq, Pochhammer julia> pFq((1/3,2/3), 5/6, 27/32) ≈ 8/5 true julia> pFq([1/4, 1/2], [3/4], 80/81; maxevals=2000) ≈ 9/5 true julia> x = variable() Polynomial(x) julia> n = 5 5 julia> pFq((-n,n+1), 1, (1-x)/2) ≈ basis(Legendre,n)(x) true julia> α, β, n = 1/2, 1/2, 5; julia> Pochhammer(α+1,n)/factorial(n) * pFq((-n,n+α+β+1), α+1, (1-x)/2) ≈ basis(Jacobi{α,β}, n)(x) true ``` """ function pFq(as, bs, z; maxevals=1000) n = 1 acc = trm = one(z) p,q = length(as), length(bs) # element in `as` is negative integer or 0 && converges # p ≤ q && converges # p = q + 1 |x| < 1 && converge # p > q + 1 && diverges while n < maxevals a = isempty(as) ? 1 : prod(as) b = isempty(bs) ? 1 : prod(bs) iszero(a) && return acc iszero(b) && return Inf # check on b trm *= a /b * z /n acc += trm as = plus_1(as) bs = plus_1(bs) n += 1 end if (p > q + 1 || (p == q+1 && abs(z) < 1)) return acc else NaN*acc end end ## tuples @inline plus_1(as) = map(x->x+1, as) # use HypergeometricFunction.mFn if possible function pFq(as::Tuple{A,B}, bs::Tuple{C}, z::AbstractFloat) where {A,B,C} HypergeometricFunctions._₂F₁(as[1],as[2],bs[1],z) end function pFq(as::Tuple{A,B,C}, bs::Tuple{D,E}, z::AbstractFloat) where {A,B,C,D,E} HypergeometricFunctions._₃F₂(as[1],as[2],as[3],bs[1],bs[2],z) end ## Try to speed up quadgk by not specializing on F mutable struct Wrapper F end (F::Wrapper)(x) = F.F(x) _quadgk(f, a, b) = quadgk(Wrapper(f), a, b)[1] const ∫ = _quadgk checked_div(a, b) = (iszero(a) && iszero(b)) ? a : a/b
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## Display graph with weigthed edges Lwidth=decisionTree.Edges.Weight*19+1 Lwidth=10*Lwidth/maximum(Lwidth) graphPlot=plot(decisionTree,"Layout','layered','NodeLabel",nodeLabel,... "LineWidth",Lwidth) toHL=find(decisionTree.Nodes.Sum~=0) for HL=1:length(toHL) highlight[graphPlot,toHL[HL],"Marker','s','NodeColor','r','MarkerSize",decisionTree.Nodes.Sum[toHL[HL]]+5] end toHL=find(decisionTree.Nodes.Sum==maximum(decisionTree.Nodes.Sum)) highlight[graphPlot,toHL,"NodeColor','y"]
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187
immutable ConfidenceInterval lower::Float64 upper::Float64 end
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""" Computing in the category of finite sets and functions, and its skeleton. """ module FinSets export FinOrd, FinOrdFunction, FinOrdFunc, FinOrdVector, force, terminal, product, equalizer, pullback, limit, initial, coproduct, coequalizer, pushout, colimit using AutoHashEquals using DataStructures: IntDisjointSets, union!, find_root using ...GAT using ...Theories: Category import ...Theories: dom, codom, id, compose, ⋅, ∘, terminal, product, equalizer, initial, coproduct, coequalizer using ..ShapeDiagrams # Category of finite ordinals ############################# """ Finite ordinal (natural number). An object in the category of finite ordinals, which is the skeleton of the category of finite sets. """ @auto_hash_equals struct FinOrd n::Int end """ Function between sets in the form of finite ordinals. A morphism in the category of finite ordinals, which is the skeleton of the category of finite sets. The function can be defined implicitly by an arbitrary Julia function, in which case it is evaluated lazily, or explictly by a vector of integers. In the latter case, the function (1↦1, 2↦3, 3↦2, 4↦3), for example, is represented by the vector [1,3,2,3]. """ abstract type FinOrdFunction end FinOrdFunction(f, dom::FinOrd, codom::FinOrd) = FinOrdFunction(f, dom.n, codom.n) """ Function in FinOrd defined by an arbitrary Julia function. To be evaluated lazily unless forced. """ @auto_hash_equals struct FinOrdFunc <: FinOrdFunction func::Function dom::Int codom::Int end FinOrdFunction(f::Function, dom::Int, codom::Int) = FinOrdFunc(f, dom, codom) (f::FinOrdFunc)(x) = f.func(x) """ Function in FinOrd represented explicitly by a vector. """ @auto_hash_equals struct FinOrdVector{T<:AbstractVector{Int}} <: FinOrdFunction func::T codom::Int end FinOrdFunction(f::AbstractVector) = FinOrdVector(f, maximum(f)) FinOrdFunction(f::AbstractVector, codom::Int) = FinOrdVector(f, codom) function FinOrdFunction(f::AbstractVector, dom::Int, codom::Int) @assert length(f) == dom FinOrdVector(f, codom) end (f::FinOrdVector)(x) = f.func[x] """ Force evaluation of lazy function or relation. """ force(f::FinOrdFunction) = FinOrdVector(map(f, 1:dom(f).n), codom(f).n) force(f::FinOrdVector) = f Base.collect(f::FinOrdFunction) = force(f).func """ Category of finite ordinals and functions. """ @instance Category(FinOrd, FinOrdFunction) begin dom(f::FinOrdFunction) = FinOrd(f.dom) codom(f::FinOrdFunction) = FinOrd(f.codom) id(A::FinOrd) = FinOrdFunction(identity, A, A) function compose(f::FinOrdFunction, g::FinOrdFunction) @assert codom(f) == dom(g) FinOrdFunction(compose_impl(f,g), dom(f), codom(g)) end end dom(f::FinOrdVector) = FinOrd(length(f.func)) compose_impl(f::FinOrdFunction, g::FinOrdFunction) = g ∘ f compose_impl(f::FinOrdVector, g::FinOrdVector) = g.func[f.func] # Limits ######## terminal(::Type{FinOrd}) = FinOrd(1) function product(A::FinOrd, B::FinOrd) m, n = A.n, B.n indices = CartesianIndices((m, n)) π1 = FinOrdFunction(i -> indices[i][1], m*n, m) π2 = FinOrdFunction(i -> indices[i][2], m*n, n) Span(π1, π2) end function product(Xs::Vector{<:FinOrd}) ns = Int[X.n for X in Xs] indices = CartesianIndices(tuple(ns...)) apex = prod(ns) πs = [FinOrdFunction(i -> indices[i][j],apex,ns[j]) for j in 1:length(ns)] Cone(FinOrd(apex),πs) end function equalizer(f::FinOrdFunction, g::FinOrdFunction) @assert dom(f) == dom(g) && codom(f) == codom(g) m = dom(f).n FinOrdFunction(filter(i -> f(i) == g(i), 1:m), m) end function equalizer(fs::Vector{<:FinOrdFunction}) @assert length(fs) >= 1 f1 = fs[1] frest = fs[2:end] @assert all(dom(f) == dom(f1) && codom(f) == codom(f1) for f in frest) m = dom(f1).n FinOrdFunction(filter(i -> all(f1(i) == f(i) for f in frest), 1:m),m) end """ Pullback of cospan of functions between finite ordinals. TODO: This logic is completely generic. Make it independent of FinOrd. """ function pullback(cospan::Cospan{<:FinOrdFunction,<:FinOrdFunction}) f, g = left(cospan), right(cospan) prod = product(dom(f), dom(g)) π1, π2 = left(prod), right(prod) eq = equalizer(π1⋅f, π2⋅g) Span(eq⋅π1, eq⋅π2) end function limit(d::Diagram{<:FinOrd, <:FinOrdFunction}) p = product(d.obs) n = apex(p).n satisfy((s,t,g),x) = g(leg(p,s)(x)) == leg(p,t)(x) f = FinOrdFunction(filter(i -> all(satisfy(h,i) for h in d.homs), 1:n), n) Cone(dom(f),[compose(f,leg(p,i)) for i in 1:length(d.obs)]) end # Colimits ########## initial(::Type{FinOrd}) = FinOrd(0) function coproduct(A::FinOrd, B::FinOrd) m, n = A.n, B.n ι1 = FinOrdFunction(1:m, m, m+n) ι2 = FinOrdFunction(m+1:m+n, n, m+n) Cospan(ι1, ι2) end function coproduct(Xs::Vector{<:FinOrd}) ns = Int[X.n for X in Xs] base = sum(ns) offsets = [0,cumsum(ns)...] πs = [FinOrdFunction((1:ns[j]) .+ offsets[j],ns[j],base) for j in 1:length(ns)] Cocone(FinOrd(base),πs) end function coequalizer(f::FinOrdFunction, g::FinOrdFunction) @assert dom(f) == dom(g) && codom(f) == codom(g) m, n = dom(f).n, codom(f).n sets = IntDisjointSets(n) for i in 1:m union!(sets, f(i), g(i)) end h = [ find_root(sets, i) for i in 1:n ] roots = unique!(sort(h)) FinOrdFunction([ searchsortedfirst(roots, r) for r in h], length(roots)) end function coequalizer(fs::Vector{<:FinOrdFunction}) @assert length(fs) >= 1 f1 = fs[1] frest = fs[2:end] @assert all(dom(f) == dom(f1) && codom(f) == codom(f1) for f in frest) m,n = dom(f1).n, codom(f1).n sets = IntDisjointSets(n) for i in 1:m for f in frest union!(sets, f1(i), f(i)) end end h = [ find_root(sets, i) for i in 1:n ] roots = unique!(sort(h)) FinOrdFunction([searchsortedfirst(roots, r) for r in h], length(roots)) end """ Pushout of span of functions between finite ordinals. TODO: This logic is completely generic. Make it independent of FinOrd. """ function pushout(span::Span{<:FinOrdFunction,<:FinOrdFunction}) f, g = left(span), right(span) coprod = coproduct(codom(f), codom(g)) ι1, ι2 = left(coprod), right(coprod) coeq = coequalizer(f⋅ι1, g⋅ι2) Cospan(ι1⋅coeq, ι2⋅coeq) end function colimit(d::Diagram{<:FinOrd, <:FinOrdFunction}) cp = coproduct(d.obs) n = base(cp).n sets = IntDisjointSets(n) for (s,t,h) in d.homs for i in 1:d.obs[s].n union!(sets,leg(cp,s)(i),leg(cp,t)(h(i))) end end h = [ find_root(sets, i) for i in 1:n ] roots = unique!(sort(h)) m = length(roots) f = FinOrdFunction([searchsortedfirst(roots, r) for r in h], m) Cocone(FinOrd(m),[compose(leg(cp,i),f) for i in 1:length(d.obs)]) end end
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2.456271
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import NMRDataSetup import NMRSpectraSimulator import NMRSpecifyRegions include("../src/NMRCalibrate.jl") import .NMRCalibrate using LinearAlgebra using FFTW import PyPlot import BSON import JLD #import Clustering import Statistics import Random include("./helpers/final_helpers.jl") include("./helpers/solute_calibrate.jl") include("./helpers/loop_entries1.jl") PyPlot.close("all") fig_num = 1 Random.seed!(25) PyPlot.matplotlib["rcParams"][:update](["font.size" => 22, "font.family" => "serif"]) p_set = Vector{String}(undef, 0) m_set = Vector{Vector{String}}(undef, 0) w_set = Vector{Vector{Float64}}(undef, 0) #setupcalibrateserine!(p_set, m_set, w_set) setupcalibrateentries1!(p_set, m_set, w_set) #setupcalibrateentriesNamJan2022!(p_set, m_set, w_set) projects_dir = "/home/roy/MEGAsync/outputs/NMR/calibrate/final" base_path_JLD = "/home/roy/Documents/repo/NMRData//src/input/molecules" cs_config_path = "/home/roy/Documents/repo/NMRData/src/input/reduced_cs_config.txt" function loopscript(p_name_set, m_names_set, w_set, max_iters, projects_dir, base_path_JLD, cs_config_path) for i = 1:length(p_name_set) project_name = p_name_set[i] molecule_names = m_names_set[i] w = w_set[i] calibratesolute(project_name, molecule_names, w; max_iters = max_iters, projects_dir = projects_dir, base_path_JLD = base_path_JLD, cs_config_path = cs_config_path) println() end end ### batch. max_iters = 50000 loopscript(p_set, m_set, w_set, max_iters, projects_dir, base_path_JLD, cs_config_path) ### end batch. # #### singular. # # project_name = "Nam2022_Serine" # # molecule_names = ["D-(+)-Glucose"; "L-Serine";] # # w = [1.0; 1.0] # # # println("Now on $(project_name)") # max_iters = 50000 # #max_iters = 5 # include("solute_calibrate.jl") # println() # ### end singular.
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using Flux using DiffEqSensitivity using DifferentialEquations using LinearAlgebra using Statistics using Distributions using Plots using Random # set random seed for reproducible results Random.seed!(1) ## ----- setup true solution ------------------------------------ # define function for RHS of lorenz system function lorenz!(du, u, p, t) x,y,z = u σ,ρ,β = p du[1] = σ*(y - x) du[2] = x*(ρ - z) - y du[3] = x*y - β*z end θ = [10.0, 28.0, (8/3)] # true parameter values dt = 0.01 # time step for simulation (this may be unnecessary) tend = 10.0 # max time for total simulation ts_sim = (0.0, tend) u0True = [1.0, 1.0, 1.0] # true initial condition # integrate to obtain the true solution prob_true = ODEProblem(lorenz!, u0True, ts_sim, θ, saveat=dt) sol_true = solve(prob_true) ## ----- setup observation data ------------------------------------ # set up observation uncertainties σ_m = 0.15 # i.e. observation std # set up observation error covariance matrix R = σ_m^2 .* I(3) # set up details for measurement points dt_m = 0.2 tm_end = 2.0 # final observation time ts_m = dt_m:dt_m:tm_end # define observation operator function h(u) w = u return w end # populate observation vector w = hcat([h(sol_true(tᵢ))+rand(MvNormal(zeros(3), R)) for tᵢ ∈ ts_m]...) ## ----- perform the assimilation ------------------------------------ # initial condition guess u0a = [2.0, 3.0, 4.0] # set up problem for (bad) forecast prob = ODEProblem(lorenz!, u0a, (0.0,tend), θ) sol_forecast = solve(prob) function timeindex(t, Δt) return Int(round(t/Δt)) + 1 end # for some reason I need to collect the matrices first Rinv = collect(inv(R)) u0b = copy(u0a) σ_b = 0.1 # model error std B = σ_b^2 .* I(3) # initial model error covariance Binv = collect(inv(B)) function loss() res = Array(solve(ODEProblem(lorenz!, u0a, (0.0,tm_end), θ), saveat=dt_m:dt_m:tm_end)) # ℓ = 0.5*(u0a-u0b)'*Binv*(u0a-u0b) + 0.5*sum((w[:,i]-h(res[:,i]))'*Rinv*(w[:,i]-h(res[:,i])) for i ∈ 1:size(res,2)) ℓ = 0.5*sum((w[:,i]-h(res[:,i]))'*Rinv*(w[:,i]-h(res[:,i])) for i ∈ 1:size(res,2)) return ℓ end u0a = [2.0, 3.0, 4.0] loss() # let's do the optimization data = Iterators.repeated((), 500) opt = ADAM(0.1) cb = function () display(loss()) end Flux.train!(loss, Flux.params(u0a), data, opt, cb=cb) u0a prob = ODEProblem(lorenz!, u0a, (0.0, tend), θ) sol_analysis = solve(prob) var_names = ["x(t)", "y(t)", "z(t)"] plots = [] for i ∈ 1:3 p = plot(sol_true, vars=(0,i), color=:black, alpha=0.75, linewidth=2, label="true system") plot!(p, ts_m, w[i, :], seriestype=:scatter, color="light green", label="observation" ) plot!(p, sol_forecast, vars=(0, i), linestyle=:dashdot, color="purple", label="forecast") plot!(p, sol_analysis, vars=(0,i), color="royal blue", alpha=1, linewidth = 1, label="analysis") xlabel!(p, "t") ylabel!(p, var_names[i]) push!(plots, p) end plot(plots..., layout=(3,1)) savefig("4dvar_using_flux.svg") savefig("4dvar_using_flux.png")
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2.332569
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using DrWatson quickactivate(@__DIR__, "AccuracyAtTopPrimal_oms") using Experiments using Experiments: AccuracyAtTopPrimal, DatasetProvider using DatasetProvider: Ionosphere, Spambase, Gisette, MNIST, FashionMNIST, CIFAR10, CIFAR20, CIFAR100, SVHN2, HEPMASS # ------------------------------------------------------------------------------------------ # Model settings # ------------------------------------------------------------------------------------------ τ = [0.01, 0.05] K = [1, 3, 5, 10, 15, 20] β = [10, 1, 0.1, 0.01, 0.001, 0.0001] λ = [0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5] surrogate = hinge models = vcat( Model(PatMat; τ, β, surrogate), Model(PatMatNP; τ, β, surrogate), Model(TopPush; λ), Model(TopPushK; K), Model(τFPL; τ, λ, surrogate), Model(TopMean; τ, λ, surrogate), Model(Grill; τ, λ, surrogate), Model(GrillNP; τ, λ, surrogate), ) # ------------------------------------------------------------------------------------------ # Datasets with 10 seeds # ------------------------------------------------------------------------------------------ datasets = [ Data(Ionosphere), Data(Spambase), Data(Gisette; batchsize = 512), Data(MNIST; poslabels = 1, batchsize = 512), Data(FashionMNIST; poslabels = 1, batchsize = 512), Data(CIFAR10; poslabels = 1, batchsize = 512), Data(CIFAR20; poslabels = 1, batchsize = 512), Data(CIFAR100; poslabels = 1, batchsize = 512), Data(SVHN2; poslabels = 1, batchsize = 512), ] train = [Train(; seed = seed, iters = 10000, saveat = 500, optimiser = ADAM, step = 0.01, ) for seed in 1:10] run_simulations(datasets, train, models) # HEPMASS train_hep = [Train(; seed = seed, iters = 10000, saveat = 10000, optimiser = ADAM, step = 0.01, ) for seed in 1:10] run_simulations(Data(HEPMASS; batchsize = 512), train_hep, models) # ------------------------------------------------------------------------------------------ # Datasets with 1 seeds # ------------------------------------------------------------------------------------------ datasets_1 = reduce(vcat, [[ Data(MNIST; poslabels = l, batchsize = 512), Data(FashionMNIST; poslabels = l, batchsize = 512), Data(CIFAR10; poslabels = l, batchsize = 512), Data(CIFAR20; poslabels = l, batchsize = 512), Data(CIFAR100; poslabels = l, batchsize = 512), Data(SVHN2; poslabels = l + 1, batchsize = 512), ] for l in 0:9]) train_1 = Train(; seed = 1, iters = 10000, saveat = 500, optimiser = ADAM, step = 0.01, ) run_simulations(datasets_1, train_1, models) # ------------------------------------------------------------------------------------------ # Benchmarks # ------------------------------------------------------------------------------------------ datasets_bench = [ Data(Ionosphere), Data(Spambase), Data(Gisette; batchsize = 512), Data(MNIST; poslabels = 1, batchsize = 512), Data(FashionMNIST; poslabels = 1, batchsize = 512), Data(CIFAR10; poslabels = 1, batchsize = 512), Data(SVHN2; poslabels = 1, batchsize = 512), Data(HEPMASS; batchsize = 512), ] table = run_benchmarks(datasets_bench; force = true)
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@testset "AWSException" begin function _test_exception( ex::AWSException, expected::AbstractDict, msg::String ) @test ex.code == expected["code"] @test ex.message == ex.info[msg] == expected["message"] @test String(ex.cause.response.body) == expected["body"] @test ex.cause.status == expected["status_code"] @test ex.cause.response.headers == expected["headers"] end cases = [ ("Error", "message"), ("Error", "Message"), ("Errors", "message"), ("Errors", "Message"), ] @testset "XMLRequest $err -- $msg" for (err, msg) in cases expected = Dict( "code"=>"NoSuchKey", "message"=>"The resource you requested does not exist", "resource"=>"/mybucket/myfoto.jpg", "requestId"=>"4442587FB7D0A2F9", "headers"=>["Content-Type" => "application/xml"], "status_code"=>400, ) body = """ <?xml version="1.0" encoding="UTF-8"?> <$err> <Code>$(expected["code"])</Code> <$msg>$(expected["message"])</$msg> <Resource>$(expected["resource"])</Resource> <RequestId>$(expected["requestId"])</RequestId> </$err> """ expected["body"] = body # This does not actually send a request, just creates the object to test with req = HTTP.Request("GET", "https://amazon.ca", expected["headers"], expected["body"]) resp = HTTP.Response(expected["status_code"], expected["headers"]; body=expected["body"], request=req) ex = AWSException(HTTP.StatusError(expected["status_code"], resp)) _test_exception(ex, expected, msg) @test ex.info["Resource"] == expected["resource"] @test ex.info["RequestId"] == expected["requestId"] end @testset "XMLRequest - Invalid XML" begin expected = Dict( "body"=>"InvalidXML", "headers"=>["Content-Type" => "application/xml"], "status_code"=>404, ) req = HTTP.Request("GET", "https://amazon.ca", expected["headers"], expected["body"]) resp = HTTP.Response(expected["status_code"], expected["headers"]; body=expected["body"], request=req) ex = AWSException(HTTP.StatusError(expected["status_code"], resp)) @test ex.code == "404" end @testset "JSON Request -- $msg" for msg in ["message", "Message"] expected = Dict( "code" => "InvalidSignatureException", "message" => "Signature expired: ...", "headers" => ["Content-Type" => "application/x-amz-json-1.1"], "status_code" => 400, ) body = """ { "__type": "$(expected["code"])", "$msg": "$(expected["message"])" } """ expected["body"] = body # This does not actually send a request, just creates the object to test with req = HTTP.Request("GET", "https://amazon.ca", expected["headers"], body) resp = HTTP.Response(expected["status_code"], expected["headers"]; body=body, request=req) ex = AWSException(HTTP.StatusError(expected["status_code"], resp)) _test_exception(ex, expected, "$msg") @test ex.info["__type"] == expected["code"] end end
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using Base.Test using JuLIP, ForwardDiff using JuLIPMaterials using StaticArrays MST = JuLIPMaterials CB = MST.CauchyBorn CLE = MST.CLE FD = ForwardDiff Id = one(SMatrix{3,3,Float64}) println("------------------------------------------------------------") println(" Testing Simple Lattice Cauchy--Born Implementation") println("------------------------------------------------------------") at = bulk(:Fe) r0 = rnn(:Fe) calc = LennardJones(σ = r0) * C2Shift(2.7*r0) set_calculator!(at, calc) variablecell!((at)) # minimise!(at) println("Constructing Wcb . . .") W = CB.Wcb(at, calc) println("generate a cauchy Born potential . . . ") println("check W, grad(W..) evaluate ok . . .") @test W(Id) == energy(at) / det(defm(at)) @test CB.grad(W, Id) isa AbstractMatrix println("Finite-difference consistency test") F = Id + (rand(3,3) - 0.5) * 0.01 W0 = W(F) dW0 = CB.grad(W, F)[:] errors = [] for p = 2:10 h = .1^p dWh = zeros(9) for i = 1:9 F[i] += h dWh[i] = (W(F) - W0) / h F[i] -= h end push!(errors, norm(dWh-dW0, Inf)) @printf(" %2d | %.3e \n", p, errors[end]) end passed = minimum(errors) <= 1e-3 * maximum(errors) if passed println("passed") else warn("""It seems the finite-difference test has failed, which indicates that there is an inconsistency between the function and gradient evaluation. Please double-check this manually / visually. (It is also possible that the function being tested is poorly scaled.)""") end # ------ some more random tests left-over from debugging ------- # setup a Cauchy-Born model for bulk Fe atu = bulk(:Fe) r0 = rnn(:Fe) calc = LennardJones(σ = r0) * C2Shift(2.7*r0) set_calculator!(atu, calc) variablecell!(atu) println("Constructing Wcb . . .") W = CB.Wcb(atu, calc, normalise = :atoms) energy(atu) W(Id) @test CB.grad(W, Id) ≈ -virial(atu) @test stress(atu) == CB.grad(W, Id) / det(cell(atu)) at1 = deepcopy(atu) F0 = defm(at1) for n = 1:5 F = Id + 0.1 * rand(3,3) set_defm!(at1, F * F0) @test energy(calc, at1) == W(F) end
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2.521632
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using VLConstraintBasedModelGenerationUtilities # setup path to sequence file - path_to_protein_sequence_file = "/Users/jeffreyvarner/Desktop/julia_work/VLConstraintBasedModelGenerationUtilities.jl/test/data/P-MZ373340.fasta" # load - result = build_protein_table(path_to_protein_sequence_file) |> check
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using SortingAlgorithms raw_rel = readdlm("C:\\Users\\bernhard.konig\\Documents\\ASync\\irobot\\data_tmp\\eraw_rel.csv"); orig = deepcopy(raw_rel[:]); r = deepcopy(orig);i = zeros(Int, length(r)); @benchmark mysortperm!(i, r) r2 = deepcopy(orig);i2 = zeros(Int, length(r2)); @benchmark sortperm!(i2, r2) @assert isequal(r2[i2], r[i]) isequal(i,i2) r = deepcopy(orig); a = sortperm(r) r = deepcopy(orig); a1, b1 = mysortperm(r) isequal(a,a1) isequal(raw_rel[a],b1) r = deepcopy(orig); @btime sort!(r); r = deepcopy(orig); @btime sort!(r); r = deepcopy(orig); @btime sort!(r, alg=QuickSort); r = deepcopy(orig); @btime sort!(r, alg=RadixSort); r = deepcopy(orig); @btime sortperm(r); r = deepcopy(orig); @btime mysortperm(r); r = deepcopy(orig); @btime sortperm(r, alg=QuickSort); r = deepcopy(orig); @benchmark mysortperm(r) r = deepcopy(orig); @benchmark sortperm(r, alg=QuickSort) r = deepcopy(orig);i = zeros(Int, length(r)); @btime sortperm!(i, r, alg=QuickSort); size(orig) xx = rand(length(orig));@btime sortperm(xx); xx = rand(length(orig));@btime sort!(xx); export mysortperm function mysortperm!(ii, A) n = length(A) # ii = Array(Int,n) for i = 1:n ii[i] = i end B = copy(A) quicksort!(B, ii) # return ii, B return nothing end # function mysortperm! function mysortperm(A) n = length(A) ii = Array(Int, n) for i = 1:n ii[i] = i end B = copy(A) quicksort!(B, ii) return ii, B end # function mysortperm function quicksort!(A, order, i=1, j=length(A)) # modified from: # http://rosettacode.org/wiki/Sorting_algorithms/Quicksort#Julia if j > i if j - i <= 50 # Insertion sort for small groups is faster than Quicksort InsertionSort!(A, order, i, j) return A end # pivot = A[rand(i:j)] # random element of A pivot = A[ div(i + j, 2) ] left, right = i, j while left <= right while A[left] < pivot left += 1 end while A[right] > pivot right -= 1 end if left <= right A[left], A[right] = A[right], A[left] order[left], order[right] = order[right], order[left] left += 1 right -= 1 end end # left <= right quicksort!(A, order, i, right) quicksort!(A, order, left, j) end # j > i return A end # function quicksort! function InsertionSort!(A, order, ii=1, jj=length(A)) for i = ii + 1:jj j = i - 1 temp = A[i] itemp = order[i] while true if j == ii - 1 break end if A[j] <= temp break end A[j + 1] = A[j] order[j + 1] = order[j] j -= 1 end A[j + 1] = temp order[j + 1] = itemp end # i return end # function InsertionSort!
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# verbose flag const VERBOSE = Ref(false) """ set_verbose(::Bool) For debugging - set flag to print macro expansions """ function set_verbose(verbose::Bool) VERBOSE[] = verbose end
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71
module ProcessingStd import GLFW using ModernGL using GLUtil using GLText using GLWindow using Events using Color using Tau include("constants.jl") export animate, coordSystem export size, Height, Width, displayHeight, displayWidth export focused, cursor, noCursor # export frameCount, frameRate # export createShape, loadShape export arc, ellipse, line, point, quad, rect, triangle export bezier # export bezierDetail, bezierPoint, bezierTangent, curve, curveDetail, curvePoint, curveTangent, curveTightness export strokeWeight, strokeCap, strokeJoin, noSmooth, smooth export ellipseMode, rectMode # export beginShape, endShape, vertex # export bezierVertex, curveVertex, quadraticVertex # export shape, shapeMode export mouseButton, mouseClicked, mouseDragged, mouseMoved, mousePressed export mouseReleased, mouseWheel, mouseX, mouseY, pmouseX, pmouseY export background, fill, noFill, colorMode, noStroke, stroke # export applyMatrix export popMatrix, printMatrix, pushMatrix, resetMatrix, rotate export scale, shearX, shearY, translate export alpha, blue, brightness, color, green, hue, lerpColor, red, saturation # export createImage export image, loadImage, noTint # export imageMode, tint, requestImage # export texture, textureMode, textureWrap # export blend, copy, filter, get, loadPixels, set, updatePixels # export blendMode # export createGraphics export createFont, text, textFont # export loadFont export textSize, textWidth # export textAlign, textLeading, textMode # export textAscent, textDescent # state variables # bgCol = RGB(0.94, 0.92, 0.9) #pleasant default background color from Jeff Bezanson's Fractals iJulia notebook (http://nbviewer.ipython.org/url/beowulf.csail.mit.edu/18.337/fractals.ipynb) # fillStuff = true #should drawing elements be filled or not? # fillCol = RGB(0.7,0.7,0.7) #color to be used for filling elements # strokeStuff = true #should drawing elements be stroked or not? # strokeCol = RGB(0,0,0) #color to be used for stroking elements # h = 275 #height of display window # w = 275 #width of display window # left = -1 #scaling of left-hand of x-axis in plotting coordinate system # right = 1 #scaling of right-hand of x-axis in plotting coordinate system # top = 1 #scaling of top-hand of x-axis in plotting coordinate system # bottom = -1 #scaling of bottom-hand of x-axis in plotting coordinate system # cMode = "RGB" #what color space are we using for colors? # title = "Processing.jl" #window title # ellipseMode = CENTER #specify ellipses center, width, and height when drawing it by default # rectMode = CORNER #specficy rects upper-left corner coordinate and its width and height when drawing it by default # cmX # cmY # pmX # pmY # mouse1Pressed # mouse2Pressed # mouse3Pressed # mouse1Released # mouse2Released # mouse3Released # mouse1Dragged # state structure type stateStruct bgCol::ColorValue fillStuff::Bool fillCol::ColorValue strokeStuff::Bool strokeCol::ColorValue h::Int w::Int left::Float32 right::Float32 top::Float32 bottom::Float32 cMode::String title::String tintStuff::Bool ellipseMode::Int rectMode::Int cmX::Int cmY::Int pmX::Int pmY::Int mouse1Pressed::Bool mouse2Pressed::Bool mouse3Pressed::Bool mouse1Released::Bool mouse2Released::Bool mouse3Released::Bool mouse1Dragged::Bool end # initialize state structure and open drawing window state = stateStruct(RGB(0.94, 0.92, 0.9), true, RGB(0.7,0.7,0.7), true, RGB(0,0,0), 275, 275, -1., 1., 1., -1., "RGB", "Processing.jl", false, CENTER, CORNER, 0, 0, 0, 0, false, false, false, true, true, true, false) export state function size(w, h) state.w = w state.h = h GLFW.Init() @osx_only begin GLFW.WindowHint(GLFW.CONTEXT_VERSION_MAJOR, 3) GLFW.WindowHint(GLFW.CONTEXT_VERSION_MINOR, 2) GLFW.WindowHint(GLFW.OPENGL_PROFILE, GLFW.OPENGL_CORE_PROFILE) GLFW.WindowHint(GLFW.OPENGL_FORWARD_COMPAT, GL_TRUE) end state.window = GLFW.CreateWindow(state.w, state.h, "Processing.jl") GLFW.MakeContextCurrent(state.window) end function size() GLFW.Init() @osx_only begin GLFW.WindowHint(GLFW.CONTEXT_VERSION_MAJOR, 3) GLFW.WindowHint(GLFW.CONTEXT_VERSION_MINOR, 2) GLFW.WindowHint(GLFW.OPENGL_PROFILE, GLFW.OPENGL_CORE_PROFILE) GLFW.WindowHint(GLFW.OPENGL_FORWARD_COMPAT, GL_TRUE) end state.window = GLFW.CreateWindow(state.w, state.h, "Processing.jl") GLFW.MakeContextCurrent(state.window) end # special Processing.jl animate() command for smoother animations function animate() GLFW.SwapBuffers(state.window) GLFW.PollEvents() end # allow user to control coordinate system function coordSystem(left, right, top, bottom) Base.Graphics.set_coords(cr, 0, 0, state.w, state.h, left, right, top, bottom) end # Environment # exported environment variables displayHeight = tcl("winfo", "screenwidth", win) displayWidth = tcl("winfo", "screenheight", win) function cursor(cursorType) if cursorType == ARROW elseif cursorType == CROSS elseif cursorType == HAND elseif cursorType == MOVE elseif cursorType == TEXT elseif cursorType == WAIT end end function focused() if return false else return true end end #frameCount #frameRate function Height() (pixelwidth::Float64, pixelheight::Float64) = GLFW.GetFramebufferSize(window) return pixelheight end function noCursor() end function Width() (pixelwidth::Float64, pixelheight::Float64) = GLFW.GetFramebufferSize(window) return pixelwidth end # Shape #createShape #loadShape ## 2D Primitives function arc(xcent, ycent, ellipseW, ellipseH, angle1, angle2, mode) save(cr) move_to(cr, xcent, ycent) scale(cr, ellipseW, ellipseH) new_sub_path(cr) if mode == OPEN arc(cr, 0, 0, 1, angle1, angle2) elseif mode == PIE end if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end if state.fillStuff set_source(cr, state.fillCol) fill(cr) end if mode == CHORD move_to(cos(angle1), sin(angle1)) line_to(cos(angle2), sin(angle2)) end restore(cr) end function ellipse(xcent, ycent, ellipseW, ellipseH) save(cr) if state.ellipseMode == RADIUS move_to(cr, xcent, ycent) scale(cr, ellipseW/2, ellipseH/2) elseif state.ellipseMode == CENTER move_to(cr, xcent, ycent) scale(cr, ellipseW, ellipseH) elseif state.ellipseMode == CORNER move_to(cr, xcent+ellipseW/2, ycent+ellipseH/2) scale(cr, ellipseW, ellipseH) elseif state.ellipseMode == CORNERS move_to(cr, xcent, ycent) scale(cr, ellipseW, ellipseH) end new_sub_path(cr) arc(cr, 0, 0, 1, 0, 2*pi) if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end if state.fillStuff set_source(cr, state.fillCol) fill(cr) end restore(cr) end function line(x1, y1, x2, y2) move_to(cr,x1,y1) line_to(cr,x2,y2) if state.strokeStuff set_source(cr, state.strokeCol) stroke(cr) end end function point(x, y) move_to(cr,x,y) dx, dy = device_to_user_distance!(cr,[1., 0.]) rectangle(cr,x,y,dx,dx) if state.strokeStuff set_source(cr, state.strokeCol) stroke(cr) end end function quad(x1, y1, x2, y2, x3, y3, x4, y4) move_to(cr, x1, y1) line_to(cr, x2, y2) line_to(cr, x3, y3) line_to(cr, x4, y4) close_path(cr) if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end if state.fillStuff set_source(cr, state.fillCol) fill(cr) end end function rect(xtopleft, ytopleft, width, height) if state.rectMode == CORNER rectangle(cr, xtopleft, ytopleft, width, height) elseif state.rectMode == CORNERS # in this case, width and height are # reinterpreted as (x,y) coords of # bottom-right corner rectangle(cr, xtopleft, ytopleft, width-xtopleft, height-ytopleft) elseif state.rectMode == CENTER rectangle(cr, xtopleft-width/2, ytopleft-height/2, width, height) elseif state.rectMode == RADIUS rectangle(cr, xtopleft-width/2, ytopleft-height/2, width/2, height/2) end if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end if state.fillStuff set_source(cr, state.fillCol) fill(cr) end end function triangle(x1,y1,x2,y2,x3,y3) move_to(cr, x1, y1) line_to(cr, x2, y2) line_to(cr, x3, y3) close_path(cr) if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end if state.fillStuff set_source(cr, state.fillCol) fill(cr) end end ## Curves function bezier(x1, y1, x2, y2, x3, y3, x4, y4) move_to(cr, x1, y1); curve_to(cr, x2, y2, x3, y3, x4, y4); if state.strokeStuff set_source(cr, state.strokeCol) stroke_preserve(cr) end end #bezierDetail #bezierPoint #bezierTangent #curve #curveDetail #curvePoint #curveTangent #curveTightness ## Attributes function ellipseMode(eMode) state.ellipseMode = eMode end function noSmooth() end function rectMode(rMode) state.rectMode = rMode end function smooth() end function strokeCap(capType) if capType == ROUND elseif capType == SQUARE elseif capType == PROJECT end end function strokeJoin(joinType) if joinType == MITER elseif joinType == BEVEL elseif joinType == ROUND end end function strokeWeight(newWeight) end ## Vertex # function beginShape() # # end #bezierVertex #curveVertex # function endShape() # # end #quadraticVertex # function vertex(x,y) # # end ## Loading & Displaying #shape #shapeMode ## Input # Mouse function mouseButton() if state.mouse1Pressed return LEFT elseif state.mouse2Pressed return CENTER elseif state.mouse3Pressed return RIGHT end end function mouseClicked() end function mouseDragged() end function mouseMoved() end function mousePressed() return pressed = GLFW.GetMouseButton(window, GLFW.MOUSE_BUTTON_LEFT) == GLFW.PRESS end function mouseReleased() end function mouseWheel() end function mouseX() (mx, my) = GLFW.GetCursorPos(window) return mx end function mouseY() (mx, my) = GLFW.GetCursorPos(window) return my end function pmouseX() return state.pmX end function pmouseY() return state.pmY end ## Transform #applyMatrix() function popMatrix() end function printMatrix() end function pushMatrix() end function resetMatrix() end function rotate(ang) end function scale(sx, sy) end #shearX() #shearY() function translate(x, y) end # Color ## Setting function background(r, g, b, a) state.bgCol = RGB(r, g, b) glClearColor(r, g, b, a) glClear(GL_COLOR_BUFFER_BIT) end function colorMode(mode::String) state.cMode = mode end function fill(r, g, b, a) if state.fillStuff == false state.fillStuff = true end if state.cMode == "RGB" state.fillCol = RGB(r, g, b) else end end function noFill() state.fillStuff = false end function noStroke() state.strokeStuff = false end function stroke(r, g, b, a) if state.strokeStuff == false state.strokeStuff = true end if state.cMode == "RGB" state.strokeCol = RGB(r, g, b) else end end ## Creating & Reading function alpha(c::ColorValue) return c.a end function blue(c::ColorValue) return c.b end function brightness(c::ColorValue) hsv = convert(HSV, c) return hsv.v end function color(r, g, b) return RGB(r, g, b) end function green(c::ColorValue) return c.g end function hue(c::ColorValue) hsv = convert(HSV, c) return hsv.h end function lerpColor(c1::ColorValue, c2::ColorValue, amt::Float32) return weighted_color_mean(amt, c1, c2) end function red(c::ColorValue) return c.r end function saturation(c::ColorValue) hsv = convert(HSV, c) return hsv.s end # Image #function createImage(x, y, colorSpace::String) # #end ## Loading & Displaying function image(img, x, y, w, h) end #imageMode function loadImage(fileName::String) return end function noTint() state.tintStuff = false end #requestImage #function tint() # #end ## Textures #texture #textureMode #textureWrap ## Pixels #blend #copy #filter #get #loadPixels #set #updatePixels # Rendering # function blendMode() # # end #createGraphics # Typography ## Loading & Displaying function createFont(fontName::String, fontSize::Float32) end #loadFont function text(str::String, x, y) end function textFont(fontName::String) end ## Attributes #function textAlign() # #end #textLeading #textMode function textSize(fontSize) end function textWidth(str::String) end ## Metrics #function textAscent(str::String) # extents = scaled_font_extents(cr, str) # return extents[1] #end #function textDescent(str::String) # extents = scaled_font_extents(cr, str) # return extents[2] #end end # module Processing
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2.409593
5,525
using GeometryBasics using GeometryBasics: Mesh using Colors # # Meshes in 3d space # "Compute a mesh from points (vertices) and faces" function mesh3d(ps,fs; vertexColors = nothing ) pps = [Point(ps[i,1],ps[i,2],ps[i,3]) for i in 1:size(ps)[1]] ffs = [TriangleFace(fs[i,1],fs[i,2],fs[i,3]) for i in 1:size(fs)[1]] if(vertexColors != nothing) return meta(Mesh(pps, ffs),vertexColors=vertexColors) end Mesh(pps, ffs) end "Compute Mesh from the coordinates of triangles" function mesh3d(faces_coords::Array{<:Real,2}) nps = Point{3, Float64}[] nfs = NgonFace{3,Int}[] normals = Point{3,Float64}[] vcnt = 1 for i in 1:size(faces_coords,1) p1 = faces_coords[i,1:3] p2 = faces_coords[i,4:6] p3 = faces_coords[i,7:9] push!(nps, Point(p1[1],p1[2],p1[3])) push!(nps, Point(p2[1],p2[2],p2[3])) push!(nps, Point(p3[1],p3[2],p3[3])) push!(nfs, TriangleFace(vcnt,vcnt+1,vcnt+2)) vcnt = vcnt + 3 end Mesh(nps,nfs) end "Compute line segments from vertices coordinates and edges" function edges3d(ps,es; color=RGBA(1,1,1,1), linewidth=1) xs = ps[:,1] ys = ps[:,2] zs = ps[:,3] xes = transpose(xs[es]) yes = transpose(ys[es]) zes = transpose(zs[es]) xes = vec(xes) yes = vec(yes) zes = vec(zes) MeshCat.LineSegments(Point.(xes,yes,zes),LineBasicMaterial(color=color, linewidth=linewidth)) end "Compute line segments from the edges coordinates" function edges3d(es_ps::Array{<:Real,2}; color=RGBA(1,1,1,1), linewidth=1) cnt = 2*size(es_ps,1) x = zeros(cnt) y = zeros(cnt) z = zeros(cnt) x[1:2:cnt] = es_ps[:,1] x[2:2:cnt] = es_ps[:,4] y[1:2:cnt] = es_ps[:,2] y[2:2:cnt] = es_ps[:,5] z[1:2:cnt] = es_ps[:,3] z[2:2:cnt] = es_ps[:,6] mat = LineBasicMaterial(color=color, linewidth=linewidth) LineSegments(Point.(x,y,z),mat) end
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1.934542
993
# SAM Reader # ========= mutable struct Reader{S <: TranscodingStream} <: BioGenerics.IO.AbstractReader state::State{S} header::Header end function Reader(state::State{S}) where {S <: TranscodingStream} rdr = Reader(state, Header()) cs, ln, f = readheader!(rdr.state.stream, rdr.header, (sam_machine_header.start_state, rdr.state.linenum)) rdr.state.state = sam_machine_body.start_state rdr.state.linenum = ln rdr.state.filled = false return rdr end """ SAM.Reader(input::IO) Create a data reader of the SAM file format. # Arguments * `input`: data source """ function Reader(input::IO) if input isa TranscodingStream return Reader(State(input, 1, 1, false)) end stream = TranscodingStreams.NoopStream(input) return Reader(State(stream, 1, 1, false)) end function Base.eltype(::Type{<:Reader}) return Record end function BioGenerics.IO.stream(reader::Reader) return reader.state.stream end """ header(reader::Reader)::Header Get the header of `reader`. """ function header(reader::Reader)::Header return reader.header end function Base.close(reader::Reader) if reader.state.stream isa IO close(reader.state.stream) end return nothing end function index!(record::MetaInfo) stream = TranscodingStreams.NoopStream(IOBuffer(record.data)) found = index!(stream, record) if !found throw(ArgumentError("invalid SAM metadata")) end return record end function index!(record::Record) stream = TranscodingStreams.NoopStream(IOBuffer(record.data)) found = index!(stream, record) if !found throw(ArgumentError("invalid SAM record")) end return record end function Base.iterate(reader::Reader, nextone::Record = Record()) if BioGenerics.IO.tryread!(reader, nextone) === nothing return nothing end return copy(nextone), empty!(nextone) end """ read!(rdr::Reader, rec::Record) Read a `Record` into `rec`; overwriting or adding to existing field values. It is assumed that `rec` is already initialized or empty. """ function Base.read!(rdr::Reader, record::Record) cs, ln, found = readrecord!(rdr.state.stream, record, (rdr.state.state, rdr.state.linenum)) rdr.state.state = cs rdr.state.linenum = ln rdr.state.filled = found if found return record end if cs == 0 || eof(rdr.state.stream) throw(EOFError()) end throw(ArgumentError("malformed SAM file")) end
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2.673448
934
using DistributedControl using Test @testset "DistributedControl.jl" begin # Write your tests here. end
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3.30303
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# -------------------------------------------------------------------------- # ACE1.jl: Julia implementation of the Atomic Cluster Expansion # Copyright (c) 2019 Christoph Ortner <christophortner0@gmail.com> # Licensed under ASL - see ASL.md for terms and conditions. # -------------------------------------------------------------------------- @testset "PIPotential" begin #--- using ACE1, JuLIP, Test using ACE1: combine #--- @info("Testing :Any Potential to evaluate energy, forces, virial") basis = ACE1.Utils.rpi_basis(species = :Any, N = 3, r0 = 2.7, rcut = 5.0, maxdeg = 8) c = ACE1.Random.randcoeffs(basis) V = combine(basis, c) #--- at1 = rattle!(bulk(:Fe, cubic=true) * (2,2,2), 0.1) at2 = deepcopy(at1); at2.Z[:] .= AtomicNumber(:Al) at3 = deepcopy(at1); at2.Z[1:3:end] .= AtomicNumber(:Al) for f in (energy, forces, virial) println(@test f(V, at1) ≈ f(V, at2) ≈ f(V, at3)) end #--- @info(" ... check that a non-Any potential will fail this test") basis1 = ACE1.Utils.rpi_basis(species = :Fe, N = 3, r0 = 2.7, rcut = 5.0, maxdeg = 8) V1 = combine(basis1, c) # same coefficients println(@test energy(V1, at1) ≈ energy(V, at1)) for at in (at2, at3) println(@test ( try energy(V1, at2) false catch true end )) end #--- end
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2.508637
521
using Try: Try, Ok, Err using ...TryExperimental: ConcreteResult Maybe.ok(result::Ok) = Some{Try.oktype(result)}(result.value) Maybe.ok(::Err) = nothing function Maybe.ok(result::ConcreteResult) value = result.value if value isa Ok return Maybe.ok(value) else return nothing end end Maybe.err(::Ok) = nothing Maybe.err(result::Err) = Some{Try.errtype(result)}(result.value) function Maybe.err(result::ConcreteResult) value = result.value if value isa Err return Maybe.err(value) else return nothing end end
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2.546667
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using CartesianGP using Base.Test reload("Utilities.jl") @test output_mask(1) == convert(BitString,0b11) @test output_mask(2) == convert(BitString,0b1111) @test output_mask(3) == convert(BitString,0b11111111) @test output_mask(4) == convert(BitString,0b1111111111111111) @test std_input_context(1) == BitString[0b10] @test std_input_context(2) == BitString[0b1100,0b1010] @test std_input_context(3) == BitString[0b11110000,0b11001100,0b10101010] @test std_input_context(4) == BitString[0b1111111100000000,0b1111000011110000,0b1100110011001100,0b1010101010101010]
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2.565611
221
""" ReLU() (l::ReLU)(x) = max(0,x) Rectified Linear Unit function. """ struct ReLU <: Activation end (l::ReLU)(x) = relu.(x) """ Sigm() (l::Sigm)(x) = sigm(x) Sigmoid function """ struct Sigm <: Activation end (l::Sigm)(x) = sigm.(x) """ Tanh() (l::Tanh)(x) = tanh(x) Tangent hyperbolic function """ struct Tanh <: Activation end (l::Tanh)(x) = tanh.(x) """ ELU() (l::ELU)(x) = elu(x) -> Computes x < 0 ? exp(x) - 1 : x Exponential Linear Unit nonlineariy. """ struct ELU <: Activation end (l::ELU)(x) = elu.(x) """ LeakyReLU(α=0.2) (l::LeakyReLU)(x) -> Computes x < 0 ? α*x : x """ mutable struct LeakyReLU <: Activation α::AbstractFloat LeakyReLU(alpha::AbstractFloat=0.2) = new(alpha) end (l::LeakyReLU)(x) = relu.(x) .+ l.α*min.(0,x) """ Dropout(p=0) Dropout Layer. `p` is the droput probability. """ mutable struct Dropout <: Activation p::Real end Dropout(;p=0) = Dropout(p) (l::Dropout)(x; enable=true) = enable ? dropout(x,l.p) : x """ LogSoftMax(dims=:) (l::LogSoftMax)(x) Treat entries in x as as unnormalized log probabilities and return normalized log probabilities. dims is an optional argument, if not specified the normalization is over the whole x, otherwise the normalization is performed over the given dimensions. In particular, if x is a matrix, dims=1 normalizes columns of x and dims=2 normalizes rows of x. """ struct LogSoftMax <: Activation dims::Union{Integer,Colon} end LogSoftMax(;dims=:) = LogSoftMax(dims) (l::LogSoftMax)(x) = logp(x;dims=l.dims) """ SoftMax(dims=:) (l::SoftMax)(x) Treat entries in x as as unnormalized scores and return softmax probabilities. dims is an optional argument, if not specified the normalization is over the whole x, otherwise the normalization is performed over the given dimensions. In particular, if x is a matrix, dims=1 normalizes columns of x and dims=2 normalizes rows of x. """ struct SoftMax <: Activation dims::Union{Integer,Colon} end SoftMax(;dims=:) = SoftMax(dims) (l::SoftMax)(x) = softmax(x;dims=l.dims) """ LogSumExp(dims=:) (l::LogSumExp)(x) Compute log(sum(exp(x);dims)) in a numerically stable manner. dims is an optional argument, if not specified the summation is over the whole x, otherwise the summation is performed over the given dimensions. In particular if x is a matrix, dims=1 sums columns of x and dims=2 sums rows of x. """ struct LogSumExp <: Activation dims::Union{Integer,Colon} end LogSumExp(;dims=:) = LogSumExp(dims) (l::LogSumExp)(x) = logsumexp(x;dims=l.dims)
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2.543393
1,014
using TidalFluxConfigurations, ADCPDataProcessing, PIEMetData, TidalFluxPlots, TidalFluxExampleData, Plots using Base.Test TidalFluxConfigurations.config[:_ADCPDATA_DIR] = Pkg.dir("TidalFluxExampleData","data","adcp") TidalFluxConfigurations.config[:_METDATA_DIR] = Pkg.dir("TidalFluxExampleData","data","met") creek = Creek{:sweeney}() deps = parse_deps(creek) ad1 = load_data(deps[1]) plot(ad1) cs = parse_cs(creek) csd = load_data(cs) # Test the cross-section plot plot(csd) h1,Q1 = computedischarge(ad1,csd) # Test a few Quantities plot(h1) plot(Q1) cals = parse_cals(creek) cald = load_data(cals[1]) plot(cald) calscatter([cald])
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2.362637
273
module IWSLT using Fetch using DataDeps using ..Datasets: Dataset import ..Datasets: testfile, devfile, trainfile import ..Datasets: token_freq, get_vocab export IWSLT2016 function __init__() iwslt2016_init() end include("./iwslt2016.jl") end
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2.50495
101
module StructsOfArrays export StructOfArrays immutable StructOfArrays{T,N,U<:Tuple} <: AbstractArray{T,N} arrays::U end @generated function StructOfArrays{T}(::Type{T}, dims::Integer...) (!isleaftype(T) || T.mutable) && return :(throw(ArgumentError("can only create an StructOfArrays of leaf type immutables"))) isempty(T.types) && return :(throw(ArgumentError("cannot create an StructOfArrays of an empty or bitstype"))) N = length(dims) arrtuple = Tuple{[Array{T.types[i],N} for i = 1:length(T.types)]...} :(StructOfArrays{T,$N,$arrtuple}(($([:(Array($(T.types[i]), dims)) for i = 1:length(T.types)]...),))) end StructOfArrays(T::Type, dims::Tuple{Vararg{Integer}}) = StructOfArrays(T, dims...) Base.linearindexing{T<:StructOfArrays}(::Type{T}) = Base.LinearFast() @generated function Base.similar{T}(A::StructOfArrays, ::Type{T}, dims::Dims) if isbits(T) && length(T.types) > 1 :(StructOfArrays(T, dims)) else :(Array(T, dims)) end end Base.convert{T,S,N}(::Type{StructOfArrays{T,N}}, A::AbstractArray{S,N}) = copy!(StructOfArrays(T, size(A)), A) Base.convert{T,S,N}(::Type{StructOfArrays{T}}, A::AbstractArray{S,N}) = convert(StructOfArrays{T,N}, A) Base.convert{T,N}(::Type{StructOfArrays}, A::AbstractArray{T,N}) = convert(StructOfArrays{T,N}, A) Base.size(A::StructOfArrays) = size(A.arrays[1]) Base.size(A::StructOfArrays, d) = size(A.arrays[1], d) @generated function Base.getindex{T}(A::StructOfArrays{T}, i::Integer...) Expr(:block, Expr(:meta, :inline), Expr(:new, T, [:(A.arrays[$j][i...]) for j = 1:length(T.types)]...)) end @generated function Base.setindex!{T}(A::StructOfArrays{T}, x, i::Integer...) quote $(Expr(:meta, :inline)) v = convert(T, x) $([:(A.arrays[$j][i...] = getfield(v, $j)) for j = 1:length(T.types)]...) x end end end # module
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2.272289
830
mutable struct DataTable dims :: Dict grid_dims :: Dict grid_dims2_str :: Dict data_units :: Dict missing_idx1 :: Dict # with shape (z, x, y) missing_idx2 :: Dict # with shape (x, y, z) flags :: Dict # What are flags for? function DataTable(; Nx, Ny, Nz, mask_sT :: Union{Nothing, AbstractArray{Float64, 3}} = nothing, mask_T :: Union{Nothing, AbstractArray{Float64, 3}} = nothing, ) N1 = 1 Nyp1 = Ny+1 Nzp1 = Nz+1 dims = Dict( "N1" => N1, "Nx" => Nx, "Ny" => Ny, "Nz" => Nz, "Nyp1" => Nyp1, "Nzp1" => Nzp1, ) grid_dims = Dict( :T => (Nz, Nx , Ny ), :U => (Nz, Nx , Ny ), :V => (Nz, Nx , Nyp1), :W => (Nzp1, Nx , Ny ), :sT => (N1, Nx, Ny ), :sU => (N1, Nx, Ny ), :sV => (N1, Nx, Nyp1), :SCALAR => (N1, N1, N1), :cW => (Nzp1, N1, N1), :cT => (Nz, N1, N1), ) # This is used for RecordTool output # Notice that this is oriented dimension so z is the last one grid_dims2_str = Dict( :T => ("Nx", "Ny", "Nz"), :U => ("Nx", "Ny", "Nz"), :V => ("Nx", "Nyp1", "Nz"), :W => ("Nx", "Ny", "Nzp1"), :sT => ("Nx", "Ny", "N1"), :sU => ("Nx", "Ny", "N1"), :sV => ("Nx", "Nyp1", "N1"), :SCALAR => ("N1", "N1", "N1"), :cW => ("N1", "N1", "Nzp1",), :cT => ("N1", "N1", "Nz",), ) data_units = Dict() flags = Dict() missing_idx1 = Dict( :sT => (mask_sT == nothing) ? nothing : mask_sT .== 0, :T => (mask_T == nothing) ? nothing : mask_T .== 0, :U => nothing, :V => nothing, :W => nothing, :UV => nothing, nothing => nothing, ) missing_idx2 = Dict() for (k,v) in missing_idx1 missing_idx2[k] = (v != nothing) ? permutedims(v, [2, 3, 1]) : nothing end return new( dims, grid_dims, grid_dims2_str, data_units, missing_idx1, missing_idx2, flags, ) end end function regVariable!( dt :: DataTable, id :: Union{Symbol, String}, grid :: Symbol, mask :: Union{Symbol, Nothing}, data :: AbstractArray{T}, ) where T N1 = dt.dims["N1"] Nx = dt.dims["Nx"] Ny = dt.dims["Ny"] Nz = dt.dims["Nz"] Nyp1 = dt.dims["Nyp1"] Nzp1 = dt.dims["Nzp1"] if haskey(dt.data_units, id) throw(ErrorException("Error: variable id " * String(id) * " already exists.")) end dim = dt.grid_dims[grid] dtype = eltype(data) if ! (dtype in (Float64, Int64)) throw(ErrorException("Invalid data type. Only Float64 and Int64 are accepted")) end if Tuple(dim) != size(data) println("Expect ", dim) println("Get ", size(data)) throw(ErrorException("Provided data does not have correct dimension: " * string(id))) end if dtype != T throw(ErrorException("dtype and provided data does not match.")) end dt.data_units[id] = DataUnit( dt, id, grid, mask, data, ) dt.flags[id] = 0 end
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module CharFuncPricing export DefaultCharFunc, CharFunc abstract type CharFunc{MT,CR} end struct DefaultCharFunc{MT,CR} <: CharFunc{MT,CR} #model type, return type (e.g. Complex or acb) model::MT end @inline model(cf::DefaultCharFunc) = cf.model @inline oneim(cf::CharFunc) = 1im @inline oneim(cf::CharFunc{MT,Complex{T}}) where {MT,T} = 1im @inline oneim(cf::CharFunc{MT,Complex}) where {MT} = 1im @inline Base.zero(cf::CharFunc{MT,Complex}) where {MT} = Base.zero(Float64) @inline Base.zero(cf::CharFunc{MT,Complex{BigFloat}}) where {MT} = Base.zero(BigFloat) @inline const_pi(cf::CharFunc{MT,Complex{T}}) where {MT,T} = T(pi) @inline const_pi(cf::CharFunc{MT,Complex}) where {MT} = Float64(pi) include("lambertw.jl") include("Heston.jl") include("SchobelZhu.jl") include("Cos.jl") include("Flinn.jl") include("Filon.jl") include("NemoCharFunc.jl") include("GaussLobatto.jl") include("AndersenLake.jl") end
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using GraphPlot using Plots pyplot() using PyPlot pygui(false) include("loadbuild.jl") include("graphing.jl") include("beliefprop.jl") include("Psis.jl") EDGEFILE = "data/save.feather" BIASFILE = "data/bias.csv" plotdir = "results/plots/" figext = "eps" problem_types = ["bias", "fake"] epsilon = 0.44 MAXITER = 2 Plots.PyPlot.rc("lines", lw=10) # Plots.PyPlot.rc("font", family="monospace") # Plots.PyPlot.rc("font", weight="bold") Plots.PyPlot.rc("font", size=22) if EDGEFILE == "data/save.feather" by_domain_flag = true elseif EDGEFILE == "data/save2.feather" by_domain_flag = false end save = loadSave(EDGEFILE) # size(unique(save[:sdom])) #println(size(unique(vcat(save[:rsrc],save[:rdest])))) bias = loadBias(BIASFILE) cats = buildWeb(save, by_domain_flag) #only mutual links for "a" edgeunion = countunion(cats["a"], cats["img"]) @time for x in ["link", "script"] countunion!(edgeunion, cats[x]) end sets = [("union", 1)] #[("union",10)], [("mut", 1000), ("mut", 50), ("mut", 2), ("mut", 1), ("a", 1000), ("a", 500),("a", 100), ("img", 200), ("img", 50)] #s2p = Dict{String, Array}() roc_x = Vector{Float64}() roc_y = Vector{Float64}() k = 3 #number of folds for (sn, set) in enumerate(sets) edgetype = set[1] if edgetype == "union" edgeset = edgeunion else edgeset = cats[set[1]] end # if ~by_domain_flag # domainmap = Dict{String, String}() # for j in edgeset # domainmap[j.first] = split(j.first, '/')[1] # for i in j.second # domainmap[i.first] = split(i.first, '/')[1] # end # end # end G, s2i, i2s = constructGraph(edgeset, minsamp=set[2], giant=false, by_domain=by_domain_flag) #G2, s2i2, i2s2 = constructGraph(edgeset, minsamp=set[2], giant=true, by_domain=by_domain_flag) mindeg = minimum([length([x for x in all_neighbors(G, v) if x != v]) for v in 1:nv(G)]) println("Minimum Degree: $mindeg") maxdeg = maximum(degree(G)) println("Maximum Degree: $maxdeg") # lx, ly = spring_layout(G) #lx2, ly2 = spring_layout(G2) for key in problem_types p = Plots.plot() plot!(p, [0, 1], [0, 1], line=:dash, label="") println(key*string(set)) try mkdir(plotdir*string(set[1])) catch x end try mkdir(plotdir*string(set[1])*"/"*string(set[2])) catch x end mydir = plotdir*string(set[1])*"/"*string(set[2])*"/" # plotGraph(bias, G, s2i, i2s, lx, ly, color=key, # path=mydir*key*"train.png") # ϕ = makeBeliefs(bias, G, s2i, i2s, key) # pr, b, lodds = beliefprop(G, ϕ, Psis(0.44), 1); # plotGraph(bias, G, s2i, i2s, lx, ly, color=b[:,1], # path=mydir*key*"prop.png") # plotGraph(bias, G2, s2i2, i2s2, lx2, ly2, color=key, # path=mydir*key*"train_main.png") # ϕ = makeBeliefs(bias, G2, s2i2, i2s2, key) # pr, b, lodds = beliefprop(G2, ϕ, Psis(0.44), 1); # plotGraph(bias, G2, s2i2, i2s2, lx2, ly2, color=b[:,1], # path=mydir*key*"prop_main.png") folds = makeFolds(G, k) # makeFolds(G, k, i2s, domainmap) aucs = [] for f in 1:k println("Fold " *string(f)) ϕ = makeBeliefs(bias, G, s2i, i2s, key, folds=folds, fold=f) pr, b, lodds = beliefprop(G, ϕ, Psis(epsilon), MAXITER); # for v in 1:nv(G) # if folds[v] == f # site = i2s[v] # if ~haskey(s2p, site) # s2p[site] = ones(1, size(sets, 1)+2)./2.0 # s2p[site][size(sets, 1)+1] = reality(bias, site, "bias") # s2p[site][size(sets, 1)+2] = folds[v] # end # s2p[site][sn] = b[v] # end # end roc_x, roc_y, acc, lvls, cms = getROC(bias, G, s2i, i2s, key, b, folds=folds, fold=f) auc = AUC(roc_x, roc_y) #println("Max "*string(maximum(acc))*" at "*string(lvls[find(x->x==maximum(acc), acc)[1]])) println("AUC : "*string(auc)) best_cutoff = collect(keys(acc))[indmax(collect(values(acc)))] println("best cutoff threshold : "*string(best_cutoff)) println("confusion matrix (best threshold) : ") show(cms[best_cutoff]) println("") println("confusion matrix (0.5 threshold) : ") show(cms[0.5]) println("") #println(typeof(roc_x)) #println(typeof(roc_y)) #println(roc_x) #println(roc_y) plot!(p, roc_x, roc_y,label="Fold $f (AUC=$(@sprintf("%.3f", auc)))") push!(aucs, auc) end avg_auc = sum(aucs)/k title!("Receiver Operating Characteristic (Average AUC=$(@sprintf("%.3f", avg_auc)))") println("Avg AUC : "*string(avg_auc)) figpath = mydir*key*"_ROC_curve.$figext" println("Saving figure to: $figpath") savefig(figpath) open(mydir*"score.txt", "w") do f write(f, string(sum(aucs)/k)) end end end # for site in keys(s2p) # s2p[site][10] = reality(bias, site, "bias") # end # s2p # s2p["cnn.com"] # s2p["bluebirdbanter.com"] # using JSON # open("metadata.json", "w") do f # write(f, JSON.json(s2p)) # end
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""" struct ElementFEAInfo{dim, T} Kes::AbstractVector{<:AbstractMatrix{T}} fes::AbstractVector{<:AbstractVector{T}} fixedload::AbstractVector{T} cellvolumes::AbstractVector{T} cellvalues::CellValues{dim, T} facevalues::FaceValues{<:Any, T} metadata::Metadata black::AbstractVector white::AbstractVector varind::AbstractVector{Int} cells end An instance of the `ElementFEAInfo` type stores element information such as: - `Kes`: the element stiffness matrices, - `fes`: the element load vectors, - `cellvolumes`: the element volumes, - `cellvalues` and `facevalues`: two `Ferrite` types that facilitate cell and face iteration and queries. - `metadata`: that stores degree of freedom (dof) to node mapping, dof to cell mapping, etc. - `black`: a `BitVector` such that `black[i]` is 1 iff element `i` must be part of any feasible design. - `white`: a `BitVector` such that `white[i]` is 1 iff element `i` must never be part of any feasible design. - `varind`: a vector such that `varind[i]` gives the decision variable index of element `i`. - `cells`: the cell connectivities. """ @params struct ElementFEAInfo{dim,T} Kes::AbstractVector{<:AbstractMatrix{T}} fes::AbstractVector{<:AbstractVector{T}} fixedload::AbstractVector{T} cellvolumes::AbstractVector{T} cellvalues::CellValues{dim,T,<:Any} facevalues::FaceValues{<:Any,T,<:Any} metadata::Metadata black::AbstractVector white::AbstractVector varind::AbstractVector{Int} cells::Any end function Base.show(io::Base.IO, ::MIME"text/plain", efeainfo::ElementFEAInfo) return print( io, "ElementFEAInfo: Kes |$(length(efeainfo.Kes))|, fes |$(length(efeainfo.fes))|, fixedload |$(length(efeainfo.fixedload))|, cells |$(length(efeainfo.cells))|", ) end """ ElementFEAInfo(sp, quad_order=2, ::Type{Val{mat_type}}=Val{:Static}) where {mat_type} Constructs an instance of `ElementFEAInfo` from a stiffness problem `sp` using a Gaussian quadrature order of `quad_order`. The element matrix and vector types will be: 1. `SMatrix` and `SVector` if `mat_type` is `:SMatrix` or `:Static`, the default, 2. `MMatrix` and `MVector` if `mat_type` is `:MMatrix`, or 3. `Matrix` and `Vector` otherwise. The static matrices and vectors are more performant and GPU-compatible therefore they are used by default. """ function ElementFEAInfo( sp, quad_order=2, ::Type{Val{mat_type}}=Val{:Static} ) where {mat_type} Kes, weights, dloads, cellvalues, facevalues = make_Kes_and_fes( sp, quad_order, Val{mat_type} ) element_Kes = convert( Vector{<:ElementMatrix}, Kes; bc_dofs=sp.ch.prescribed_dofs, dof_cells=sp.metadata.dof_cells, ) fixedload = Vector(make_cload(sp)) assemble_f!(fixedload, sp, dloads) cellvolumes = get_cell_volumes(sp, cellvalues) cells = sp.ch.dh.grid.cells return ElementFEAInfo( element_Kes, weights, fixedload, cellvolumes, cellvalues, facevalues, sp.metadata, sp.black, sp.white, sp.varind, cells, ) end """ struct GlobalFEAInfo{T, TK<:AbstractMatrix{T}, Tf<:AbstractVector{T}, Tchol} K::TK f::Tf cholK::Tchol end An instance of `GlobalFEAInfo` hosts the global stiffness matrix `K`, the load vector `f` and the cholesky decomposition of the `K`, `cholK`. """ @params mutable struct GlobalFEAInfo{T} K::AbstractMatrix{T} f::AbstractVector{T} cholK::Any qrK::Any end function Base.show(::IO, ::MIME{Symbol("text/plain")}, ::GlobalFEAInfo) return println("TopOpt global FEA information") end """ GlobalFEAInfo(::Type{T}=Float64) where {T} Constructs an empty instance of `GlobalFEAInfo` where the field `K` is an empty sparse matrix of element type `T` and the field `f` is an empty dense vector of element type `T`. """ GlobalFEAInfo(::Type{T}=Float64) where {T} = GlobalFEAInfo{T}() function GlobalFEAInfo{T}() where {T} return GlobalFEAInfo(sparse(zeros(T, 0, 0)), zeros(T, 0), cholesky(one(T)), qr(one(T))) end """ GlobalFEAInfo(sp::StiffnessTopOptProblem) Constructs an instance of `GlobalFEAInfo` where the field `K` is a sparse matrix with the correct size and sparsity pattern for the problem instance `sp`. The field `f` is a dense vector of the appropriate size. The values in `K` and `f` are meaningless though and require calling the function `assemble!` to update. """ function GlobalFEAInfo(sp::StiffnessTopOptProblem) K = initialize_K(sp) f = initialize_f(sp) return GlobalFEAInfo(K, f) end function GlobalFEAInfo( K::Union{AbstractSparseMatrix,Symmetric{<:Any,<:AbstractSparseMatrix}}, f ) chol = cholesky(spdiagm(0 => ones(size(K, 1)))) qrfact = qr(spdiagm(0 => ones(size(K, 1)))) return GlobalFEAInfo{eltype(K),typeof(K),typeof(f),typeof(chol),typeof(qrfact)}( K, f, chol, qrfact ) end """ GlobalFEAInfo(K, f) Constructs an instance of `GlobalFEAInfo` with global stiffness matrix `K` and load vector `f`. """ function GlobalFEAInfo(K, f) chol = cholesky(Matrix{eltype(K)}(I, size(K)...)) qrfact = qr(Matrix{eltype(K)}(I, size(K)...)) return GlobalFEAInfo(K, f, chol, qrfact) end """ get_cell_volumes(sp::StiffnessTopOptProblem{dim, T}, cellvalues) Calculates an approximation of the element volumes by approximating the volume integral of 1 over each element using Gaussian quadrature. `cellvalues` is a `Ferrite` struct that facilitates the computation of the integral. To initialize `cellvalues` for an element with index `cell`, `Ferrite.reinit!(cellvalues, cell)` can be called. Calling `Ferrite.getdetJdV(cellvalues, q_point)` then computes the value of the determinant of the Jacobian of the geometric basis functions at the point `q_point` in the reference element. The sum of such values for all integration points is the volume approximation. """ function get_cell_volumes(sp::StiffnessTopOptProblem{dim,T}, cellvalues) where {dim,T} dh = sp.ch.dh cellvolumes = zeros(T, getncells(dh.grid)) for (i, cell) in enumerate(CellIterator(dh)) reinit!(cellvalues, cell) cellvolumes[i] = sum( Ferrite.getdetJdV(cellvalues, q_point) for q_point in 1:Ferrite.getnquadpoints(cellvalues) ) end return cellvolumes end
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]
2.591771
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# use Julia1 for this using PyPlot # using StatPlots using Statistics using CSV type_list = [Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64,Float64] # table = CSV.read("logs/test_gconvert.csv") # table = CSV.read("logs/net_transition_discount_vary_reference_solver_training.csv",types=type_list,weakrefstrings=false,rows=10) # table = CSV.read("logs/n_vary_reference_solver_training.csv") table = CSV.read("logs/mturk_trusted_condition_1.csv") # table = CSV.read("logs/mturk_candidate_condition_4.csv") # for c in names(table) # println(typeof(table[c])) # end tbl_ary = convert(Array{Float64},table[:,1:22]) println(typeof(tbl_ary)) # normalize all data # for i = 1:size(tbl_ary,2) # tbl_ary[:,i] = tbl_ary[:,i]./maximum(tbl_ary[:,i]) # end col_list = [] # detect nearly identical columns and leave them out # for i = 1:size(tbl_ary,2) # if sum(isequal.(diff(tbl_ary[:,i]),0.0))/length(tbl_ary[:,i]) > 0.40 # # this is an (nearly) identical column # continue # else # push!(col_list,i) # end # end println(col_list) println(size(tbl_ary)) fig,ax = PyPlot.subplots(2,1) fig.set_size_inches(8,8) fsize = 15 # ax[1].scatter(tbl_ary[:,3],tbl_ary[:,21],marker=".",c=:black,label="Monte-Carlo Results") # ax[1].scatter(tbl_ary[:,3],tbl_ary[:,21],c=tbl_ary[:,12]) ax[1].hlines(y=0,xmin=0,xmax=1,colors=:gray,label="rwd=0.0") ax[1].set_title("transition probability vs. reward",size=fsize) ax[1].set_xlabel("transition probability",size=fsize) ax[1].set_ylabel("Reward",size=fsize) ax[1].legend() mean_rwd = [] for u in unique(tbl_ary[:,12]) uniqueN = tbl_ary[:,12].==u append!(mean_rwd,mean(tbl_ary[:,21][uniqueN])) end mean_sort = sortperm(unique(tbl_ary[:,12])) ax[2].scatter(tbl_ary[:,12],tbl_ary[:,21],marker=".",c=:black,label="Monte-Carlo Results") ax[2].plot(unique(tbl_ary[:,12])[mean_sort],mean_rwd[mean_sort],c=:blue,label="Mean Reward") ax[2].hlines(y=0,xmin=16,xmax=43,colors=:gray,label="rwd=0.0") ax[2].set_title("N vs. mean reward",size=fsize) ax[2].set_xlabel("N",size=fsize) ax[2].set_ylabel("Reward",size=fsize) ax[2].legend() fig.tight_layout() savefig("raw_data.pdf",transparent=true,dpi=300) # fig2,ax2 = PyPlot.subplots(1,1) # fig.set_size_inches(5,5) # fsize = 15 # # ax2.scatter(tbl_ary[:,3],tbl_ary[:,12],c=tbl_ary[:,21]) # ax2.set_title("ptrans vs N",size=fsize) # ax2.set_xlabel("ptrans",size=fsize) # ax2.set_ylabel("N",size=fsize) # # fig3,ax3 = PyPlot.subplots(1,1) # fig3.set_size_inches(5,5) # fsize = 15 # # mean_rwd = [] # for u in unique(tbl_ary[:,12]) # uniqueN = tbl_ary[:,12].==u # # append!(mean_rwd,mean(tbl_ary[:,21][uniqueN])) # end # # ax3.scatter(mean_rwd,unique(tbl_ary[:,12])) # ax3.set_title("mean reward vs N",size=fsize) # ax3.set_xlabel("mean reward",size=fsize) # ax3.set_ylabel("N",size=fsize) # # fig4,ax4 = PyPlot.subplots(1,1) # fig4.set_size_inches(5,5) # fsize = 15
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2.144027
1,465
using Bolt 𝕡 = CosmoParams() bg = Background(𝕡) 𝕣 = Bolt.RECFAST(bg=bg, Yp=𝕡.Y_p, OmegaB=𝕡.Ω_b) # 𝕣 = Bolt.Peebles() ih = IonizationHistory(𝕣, 𝕡, bg) Nz = 1000 dz = 10000/float(Nz) z = (10000 - dz):(-dz):0.0 ## using PyPlot clf() plot(z, ih.Tmat.(z2x.(z)), "-", label=raw"$T_{\mathrm{mat}}$") plot(z, ih.Trad.(z2x.(z)), "--", label=raw"$T_{\mathrm{rad}}$") yscale("log") xscale("log") legend() ylim(1, 2e4) xlim(10, 10000) xlabel("redshift") ylabel("temperature [K]") gcf()
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1.828244
262
### A Pluto.jl notebook ### # v0.17.2 using Markdown using InteractiveUtils # ╔═╡ 4b348c51-c844-4e64-85e4-a4f7ea952fc9 using Pkg, DrWatson # ╔═╡ 75ce54b9-b801-412a-b70e-ab5222df137f begin using Random using StatsBase using Distributions using StatsPlots using StatsFuns using Logging using CSV using DataFrames using Optim using MCMCChains using Turing using StatisticalRethinking end # ╔═╡ e803d172-2b8a-43b4-b99d-307e64cf29d5 md"# Chapter 9. Markov Chain Monte Carlo." # ╔═╡ 77a70403-fa95-4b13-b886-415d1a91f15a begin default(labels=false) Logging.disable_logging(Logging.Warn); end # ╔═╡ f9ff23ea-fdf2-4d48-bb84-98f40db8603e md"## 9.1 Good King Markov and his island kingdom." # ╔═╡ 821bcd65-4ab5-43e8-837e-8bdf96f24edd md"### Code 9.1" # ╔═╡ 0ccba2cd-c10c-4713-80e1-77e4a3e9c85c begin Random.seed!(1) num_weeks = 10^5 positions = [] current = 10 end; # ╔═╡ b71b59cb-b9ed-4a18-a713-400d8fb48517 for i ∈ 1:num_weeks # record current position push!(positions, current) # flip coin to generate proposal proposal = current + sample([-1, 1]) # handle loops around proposal < 1 && (proposal = 10) proposal > 10 && (proposal = 1) # move? prob_move = proposal / current rand() < prob_move && (current = proposal) end # ╔═╡ 62852231-aebe-43bc-a591-ff3c79651739 md"### Code 9.2" # ╔═╡ 2f603bc2-938d-45b4-a9ed-60daa5847af5 scatter(positions[1:100], xlab="week", ylab="island") # ╔═╡ 3f745e69-690e-4ab9-934f-b05f24f20a98 md"### Code 9.3" # ╔═╡ a88970a9-341c-4e92-891b-6057dbac1e01 histogram(positions, xlab="island", ylab="number of weeks") # ╔═╡ e7569635-07ab-4b69-8014-f26d42955fce md"## 9.2 Metropolis algorithms" # ╔═╡ d4991ad9-4341-47e8-aee4-9c35cbe28e35 md"### Code 9.4" # ╔═╡ 9322d404-9ebd-4dd8-985d-8322f4f77571 begin D = 10 T = 1000 Y = rand(MvNormal(zeros(D), ones(D)), T) Rd = sqrt.(sum.(eachcol(Y.^2))) density(Rd) end # ╔═╡ 7d94a7f1-7ce5-4f6a-ba72-0a8a9cde44e9 md"## 9.3 Hamiltonian Monte Carlo" # ╔═╡ 19d13362-93a0-4a93-b3bb-6e30d3d934a3 md"### Code 9.5" # ╔═╡ 29a1a866-522b-4354-866e-9f121ba378b9 begin Random.seed!(7) x = rand(Normal(), 50) y = rand(Normal(), 50) x = standardize(ZScoreTransform, x) y = standardize(ZScoreTransform, y); end; # ╔═╡ e3cc237b-9997-41b6-8298-1b79e4444662 function U(q::Vector{Float64}; a=0, b=1, k=0, d=1)::Float64 μy, μx = q U = sum(normlogpdf.(μy, 1, y)) + sum(normlogpdf.(μx, 1, x)) U += normlogpdf(a, b, μy) + normlogpdf(k, d, μx) -U end # ╔═╡ cb41155d-43a6-4fef-932f-13cabcdf907a md"### Code 9.6" # ╔═╡ 7d29cbaf-2b27-4b4e-8dee-74a049408fba function ∇U(q::Vector{Float64}; a=0, b=1, k=0, d=1)::Vector{Float64} μy, μx = q G₁ = sum(y .- μy) + (a - μy) / b^2 # ∂U/∂μy G₂ = sum(x .- μx) + (k - μx) / d^2 # ∂U/∂μx [-G₁, -G₂] end # ╔═╡ 9d674865-3fed-48a6-a970-d997ae9d7443 md"### Codes 9.8 - 9.10 (before 9.7 to define HMC2 function)" # ╔═╡ aa9be028-c3dc-4e4a-9405-86be642540ca function HMC2(U, ∇U, ϵ::Float64, L::Int, current_q::Vector{Float64}) q = current_q p = rand(Normal(), length(q)) # random flick - p is momentum current_p = p # make a half step for momentum at the beginning p -= ϵ .* ∇U(q) ./ 2 # initialize bookkeeping - saves trajectory qtraj = [q] ptraj = [p] # Alternate full steps for position and momentum for i ∈ 1:L q += @. ϵ * p # full step for the position # make a full step for the momentum except at the end of trajectory if i != L p -= ϵ * ∇U(q) push!(ptraj, p) end push!(qtraj, q) end # Make a half step for momentum at the end p -= ϵ * ∇U(q) / 2 push!(ptraj, p) # negate momentum at the end of trajectory to make the proposal symmetric p = -p # evaluate potential and kinetic energies at the start and the end of trajectory current_U = U(current_q) current_K = sum(current_p.^2)/2 proposed_U = U(q) proposed_K = sum(p.^2)/2 # accept or reject the state at the end of trajectory, returning either # the position at the end of the trajectory or the initial position accept = (rand() < exp(current_U - proposed_U + current_K - proposed_K)) if accept current_q = q end (q=current_q, traj=qtraj, ptraj=ptraj, accept=accept) end # ╔═╡ e61d81ed-761a-450b-9ea0-1398a9ac8e22 md"### Code 9.7" # ╔═╡ a74b283d-febc-40b1-9654-3d63b7d014b2 begin Random.seed!(1) Q = (q=[-0.1, 0.2],) pr = 0.3 step1 = 0.03 L = 11 n_samples = 4 p = scatter([Q.q[1]], [Q.q[2]], xlab="μx", ylab="μy") for i ∈ 1:n_samples Q = HMC2(U, ∇U, step1, L, Q.q) if n_samples < 10 cx, cy = [], [] for j ∈ 1:L K0 = sum(Q.ptraj[j].^2)/2 plot!( [Q.traj[j][1], Q.traj[j+1][1]], [Q.traj[j][2], Q.traj[j+1][2]], lw=1+2*K0, c=:black, alpha=0.5 ) push!(cx, Q.traj[j+1][1]) push!(cy, Q.traj[j+1][2]) end scatter!(cx, cy, c=:white, ms=3) end scatter!([Q.q[1]], [Q.q[2]], shape=(Q.accept ? :circle : :rect), c=:blue) end p end # ╔═╡ 4dc8aa1b-7544-4a80-9109-a2c9040d9321 md"## 9.4 Easy HMC: ulam" # ╔═╡ e6a3d465-57d2-40aa-b71d-8cca5db234d9 md"### Code 9.11" # ╔═╡ ff957f53-dbc7-4885-9797-61d10b8dac15 begin d = CSV.read(sr_datadir("rugged.csv"), DataFrame) dd = d[completecases(d, :rgdppc_2000),:] dd[:,:log_gdp] = log.(dd.rgdppc_2000); dd[:,:log_gdp_std] = dd.log_gdp / mean(dd.log_gdp) dd[:,:rugged_std] = dd.rugged / maximum(dd.rugged) dd[:,:cid] = @. ifelse(dd.cont_africa == 1, 1, 2); end; # ╔═╡ 06d5c8b7-7a87-4ddb-9c30-d6d623a71814 md"### Code 9.12" # ╔═╡ e29edd1a-890d-4e21-a221-d4311bd845d3 r̄ = mean(dd.rugged_std); # ╔═╡ 200f0e88-b30f-4aac-a733-a25f5a52c5d8 @model function model_m8_3(rugged_std, cid, log_gdp_std) σ ~ Exponential() a ~ MvNormal([1, 1], 0.1) b ~ MvNormal([0, 0], 0.3) μ = @. a[cid] + b[cid] * (rugged_std - r̄) log_gdp_std ~ MvNormal(μ, σ) end # ╔═╡ fbcda425-2e88-4a49-b24b-17894231642e begin m8_3 = optimize(model_m8_3(dd.rugged_std, dd.cid, dd.log_gdp_std), MAP()) m8_3_df = DataFrame(sample(m8_3, 1000)) PRECIS(m8_3_df) end # ╔═╡ f67d530a-e56d-408c-8f21-d4e37e8dab42 md"### Code 9.13" # ╔═╡ 60a185df-0656-4a46-b301-388407073ab6 md"#### For Turing this is not needed" # ╔═╡ 9d6afd05-6570-44e0-8b49-5e4bc7e8a76f begin dat_slim = dd[!,[:log_gdp_std, :rugged_std, :cid]] describe(dat_slim) end # ╔═╡ cfe67e9f-41bb-419f-96ba-282926de31a2 md"### Code 9.14" # ╔═╡ 8409b9f4-7fa5-4f38-9b38-ae34e4c60e83 @model function model_m9_1(rugged_std, cid, log_gdp_std) σ ~ Exponential() a ~ MvNormal([1, 1], 0.1) b ~ MvNormal([0, 0], 0.3) μ = @. a[cid] + b[cid] * (rugged_std - r̄) log_gdp_std ~ MvNormal(μ, σ) end # ╔═╡ 4e94cf59-81eb-4b99-b048-502f54faee15 md"#### One chain will be produced by default" # ╔═╡ 76782d05-9e32-424c-a206-8081e0fdd146 m9_1 = sample(model_m8_3(dd.rugged_std, dd.cid, dd.log_gdp_std), NUTS(), 1000); # ╔═╡ e8dd780e-b94d-413c-9b0b-0476817b2094 md"### Code 9.15" # ╔═╡ b80e5ff3-445a-465c-9665-697f5b0b897e PRECIS(DataFrame(m9_1)) # ╔═╡ b6057787-3948-482c-be7e-69c4231386b6 md"### Code 9.16" # ╔═╡ d438bddd-9138-410e-ab98-40f7c90e780f md"#### For this to use multiple cores, julia has to be started with `--threads 4` parameter, otherwise chains will be sampled sequentially" # ╔═╡ 3f4a3b7c-2db7-4d7b-800a-c14968a02855 m9_1_4 = sample(model_m8_3(dd.rugged_std, dd.cid, dd.log_gdp_std), NUTS(), MCMCThreads(), 1000, 4); # ╔═╡ 2d2aa3ef-8fb7-4c69-8869-42e1939dc950 md"### Code 9.17" # ╔═╡ 61b5e6a8-3baa-4101-9a21-5d49af43bfe7 md"#### This shows combined chains statistics. To get information about individual chains, use `m9_1[:,:,1]`" # ╔═╡ 35f25d1e-234b-481f-9303-3c5973ab1228 m9_1 # ╔═╡ 4e20ad94-3011-4b89-b29e-8a793e733e03 md"### Code 9.18" # ╔═╡ ed026592-6cb3-40a3-9f0a-565e14038114 PRECIS(DataFrame(m9_1[:,:,1])) # ╔═╡ b25c0b7a-4469-4945-be28-bfb2c22103ca md"### Code 9.19" # ╔═╡ 8a0295cc-4f0c-475a-a8c6-ccf2b99ed00f @df DataFrame(m9_1) corrplot(cols(1:5), seriestype=:scatter, ms=0.2, size=(950, 800), bins=30, grid=false) # ╔═╡ 580db5a8-550a-42d1-9aef-c95f4099a3fa md"### Code 9.20" # ╔═╡ 0bd7dbfe-0689-40d4-8fd0-1fd372b2edc2 traceplot(m9_1) # ╔═╡ 534f0c53-da8b-4da5-81d4-c0ab3ceb270b md"### Code 9.21" # ╔═╡ a28ee7bb-d9d7-43ea-833f-6460a2cf7579 histogram(m9_1) # ╔═╡ 4535c71f-be7c-41e8-940b-57289c83e509 md"## 9.5 Care and feeding of your Markov chain." # ╔═╡ f39cd55f-ed8f-487e-885e-f1e16b78d9dd md"### Codes 9.22 - 9.23" # ╔═╡ cd971faa-315a-4cea-a11a-0cb28f3e3997 let # To make it diverting with Turing, it was needed to increase exp() argument. Random.seed!(1) y = [-1., 1.] @model function model_m9_2(y) α ~ Normal(0, 1000) σ ~ Exponential(1/0.0001) y ~ Normal(α, σ) end global m9_2 = sample(model_m9_2(y), NUTS(), 1000) m9_2_df = DataFrame(m9_2) PRECIS(m9_2_df) end # ╔═╡ a9febef3-06d0-4552-8590-2f165ce36547 md"### Code 9.23" # ╔═╡ 7b2588d2-d86d-44fb-9895-d6452875bc47 plot( traceplot(m9_2), histogram(m9_2), size=(900, 500) ) # ╔═╡ cc97f4d9-ad00-4d2c-b520-2071b9cf86f6 md"### Code 9.24" # ╔═╡ 631bb03b-00a5-466a-be0d-36f86a9721aa Random.seed!(2) # ╔═╡ 95dc66d6-660a-4b0c-b89d-487a22dfec82 @model function model_m9_3(y) α ~ Normal(1, 10) σ ~ Exponential(1) y ~ Normal(α, σ) end # ╔═╡ c5570bf1-c04d-41e3-b8ae-6e6dac45cb70 begin m9_3 = sample(model_m9_3(y), NUTS(), 1000) m9_3_df = DataFrame(m9_3) PRECIS(m9_3_df) end # ╔═╡ 8f945f6c-2561-433b-aedb-3448d2c4b419 ess_rhat(m9_3) # ╔═╡ ecd23fa3-fdd2-4ffb-a0e9-ad1feee04d22 md"### Code 9.25 - 9.26" # ╔═╡ f10e310f-165b-4b4b-b3e0-66af6430ba4f let Random.seed!(41) y = rand(Normal(), 100) Random.seed!(384) @model function model_m9_4(y) a1 ~ Normal(0, 1000) a2 ~ Normal(0, 1000) σ ~ Exponential(1) μ = a1 + a2 y ~ Normal(μ, σ) end global m9_4 = sample(model_m9_4(y), NUTS(), 1000) m9_4_df = DataFrame(m9_4) PRECIS(m9_4_df) end # ╔═╡ 4c09eda3-8f4c-4fa2-91ea-99b92cfbb422 ess_rhat(m9_4) # ╔═╡ 1f2c5378-bbcd-4fb7-9130-9c6cebb4c6d4 plot(m9_4) # ╔═╡ 517d31ba-5843-40a7-ad06-96e7a8ea8845 md"### Code 9.27" # ╔═╡ 1cc0a6c1-3fcf-4df3-a8d9-ba3454c0a37b Random.seed!(384) # ╔═╡ 9a76593b-c9a9-45cc-aca5-9877432baffd @model function model_m9_5(y) a1 ~ Normal(0, 10) a2 ~ Normal(0, 10) σ ~ Exponential(1) μ = a1 + a2 y ~ Normal(μ, σ) end # ╔═╡ 6a7ea8be-bea2-41c4-89ab-9a6bbcfba116 begin m9_5 = sample(model_m9_5(y), NUTS(), 1000) m9_5_df = DataFrame(m9_5) PRECIS(m9_5_df) end # ╔═╡ 942e3f19-a5c1-430e-a3be-097c53bc7394 ess_rhat(m9_5) # ╔═╡ 6caa4b75-9ac1-496b-a4b2-38b72b203e59 plot(m9_5) # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" Optim = "429524aa-4258-5aef-a3af-852621145aeb" Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" StatisticalRethinking = "2d09df54-9d0f-5258-8220-54c2a3d4fbee" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" [compat] CSV = "~0.9.11" DataFrames = "~1.2.2" Distributions = "~0.25.34" DrWatson = "~2.7.5" MCMCChains = "~5.0.1" Optim = "~1.5.0" StatisticalRethinking = "~4.4.3" StatsBase = "~0.33.13" StatsFuns = "~0.9.14" StatsPlots = "~0.14.29" Turing = "~0.19.0" """ # ╔═╡ 00000000-0000-0000-0000-000000000002 PLUTO_MANIFEST_TOML_CONTENTS = """ # This file is machine-generated - editing it directly is not advised [[ANSIColoredPrinters]] git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c" uuid = "a4c015fc-c6ff-483c-b24f-f7ea428134e9" version = "0.0.1" [[AbstractFFTs]] deps = ["LinearAlgebra"] git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.0.1" [[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "db0a7ff3fbd987055c43b4e12d2fa30aaae8749c" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" version = "3.2.1" [[AbstractPPL]] deps = ["AbstractMCMC"] git-tree-sha1 = "15f34cc635546ac072d03fc2cc10083adb4df680" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" version = "0.2.0" [[AbstractTrees]] git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5" 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["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "130d6b17a3a9d420d9a6b37412cae03ffd6a64ff" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.3" [[ArgCheck]] git-tree-sha1 = "dedbbb2ddb876f899585c4ec4433265e3017215a" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.1.0" [[ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" [[Arpack]] deps = ["Arpack_jll", "Libdl", "LinearAlgebra"] git-tree-sha1 = "2ff92b71ba1747c5fdd541f8fc87736d82f40ec9" uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97" version = "0.4.0" [[Arpack_jll]] deps = ["Libdl", "OpenBLAS_jll", "Pkg"] git-tree-sha1 = "e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7" uuid = "68821587-b530-5797-8361-c406ea357684" version = "3.5.0+3" [[ArrayInterface]] deps = ["Compat", "IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"] git-tree-sha1 = 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"3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23" uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89" version = "1.19.0+0" [[Wayland_protocols_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "66d72dc6fcc86352f01676e8f0f698562e60510f" uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" version = "1.23.0+0" [[WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.1" [[Widgets]] deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "80661f59d28714632132c73779f8becc19a113f2" uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62" version = "0.6.4" [[WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" version = "0.5.5" [[XML2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"] git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a" uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" version = "2.9.12+0" [[XSLT_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a" uuid = "aed1982a-8fda-507f-9586-7b0439959a61" version = "1.1.34+0" [[Xorg_libX11_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527" uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc" version = "1.6.9+4" [[Xorg_libXau_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e" uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec" version = "1.0.9+4" [[Xorg_libXcursor_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"] git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd" uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724" version = "1.2.0+4" [[Xorg_libXdmcp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4" uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" version = "1.1.3+4" [[Xorg_libXext_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3" uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3" version = "1.3.4+4" [[Xorg_libXfixes_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4" uuid = "d091e8ba-531a-589c-9de9-94069b037ed8" version = "5.0.3+4" [[Xorg_libXi_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"] git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246" uuid = "a51aa0fd-4e3c-5386-b890-e753decda492" version = "1.7.10+4" [[Xorg_libXinerama_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"] git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123" uuid = "d1454406-59df-5ea1-beac-c340f2130bc3" version = "1.1.4+4" [[Xorg_libXrandr_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"] git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631" uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484" version = "1.5.2+4" [[Xorg_libXrender_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96" uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" version = "0.9.10+4" [[Xorg_libpthread_stubs_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb" uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" version = "0.1.0+3" [[Xorg_libxcb_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6" uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" version = "1.13.0+3" [[Xorg_libxkbfile_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2" uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a" version = "1.1.0+4" [[Xorg_xcb_util_image_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97" uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b" version = "0.4.0+1" [[Xorg_xcb_util_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"] git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1" uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5" version = "0.4.0+1" [[Xorg_xcb_util_keysyms_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" version = "0.4.0+1" [[Xorg_xcb_util_renderutil_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" version = "0.3.9+1" [[Xorg_xcb_util_wm_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" [[Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" [[Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" [[Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" [[Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" [[Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.0+0" [[ZygoteRules]] deps = ["MacroTools"] git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.2" [[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" [[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" [[libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" [[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+0" [[nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" [[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" [[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" [[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" [[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" version = "0.9.1+5" """ # ╔═╡ Cell order: # ╟─e803d172-2b8a-43b4-b99d-307e64cf29d5 # ╠═4b348c51-c844-4e64-85e4-a4f7ea952fc9 # ╠═75ce54b9-b801-412a-b70e-ab5222df137f # ╠═77a70403-fa95-4b13-b886-415d1a91f15a # ╟─f9ff23ea-fdf2-4d48-bb84-98f40db8603e # ╟─821bcd65-4ab5-43e8-837e-8bdf96f24edd # ╠═0ccba2cd-c10c-4713-80e1-77e4a3e9c85c # ╠═b71b59cb-b9ed-4a18-a713-400d8fb48517 # ╟─62852231-aebe-43bc-a591-ff3c79651739 # ╠═2f603bc2-938d-45b4-a9ed-60daa5847af5 # ╟─3f745e69-690e-4ab9-934f-b05f24f20a98 # ╠═a88970a9-341c-4e92-891b-6057dbac1e01 # ╟─e7569635-07ab-4b69-8014-f26d42955fce # ╟─d4991ad9-4341-47e8-aee4-9c35cbe28e35 # ╠═9322d404-9ebd-4dd8-985d-8322f4f77571 # ╟─7d94a7f1-7ce5-4f6a-ba72-0a8a9cde44e9 # ╟─19d13362-93a0-4a93-b3bb-6e30d3d934a3 # ╠═29a1a866-522b-4354-866e-9f121ba378b9 # ╠═e3cc237b-9997-41b6-8298-1b79e4444662 # ╟─cb41155d-43a6-4fef-932f-13cabcdf907a # ╠═7d29cbaf-2b27-4b4e-8dee-74a049408fba # ╟─9d674865-3fed-48a6-a970-d997ae9d7443 # ╠═aa9be028-c3dc-4e4a-9405-86be642540ca # ╟─e61d81ed-761a-450b-9ea0-1398a9ac8e22 # ╠═a74b283d-febc-40b1-9654-3d63b7d014b2 # ╟─4dc8aa1b-7544-4a80-9109-a2c9040d9321 # ╟─e6a3d465-57d2-40aa-b71d-8cca5db234d9 # ╠═ff957f53-dbc7-4885-9797-61d10b8dac15 # ╟─06d5c8b7-7a87-4ddb-9c30-d6d623a71814 # ╠═e29edd1a-890d-4e21-a221-d4311bd845d3 # ╠═200f0e88-b30f-4aac-a733-a25f5a52c5d8 # ╠═fbcda425-2e88-4a49-b24b-17894231642e # ╟─f67d530a-e56d-408c-8f21-d4e37e8dab42 # ╟─60a185df-0656-4a46-b301-388407073ab6 # ╠═9d6afd05-6570-44e0-8b49-5e4bc7e8a76f # ╟─cfe67e9f-41bb-419f-96ba-282926de31a2 # ╠═8409b9f4-7fa5-4f38-9b38-ae34e4c60e83 # ╟─4e94cf59-81eb-4b99-b048-502f54faee15 # ╠═76782d05-9e32-424c-a206-8081e0fdd146 # ╟─e8dd780e-b94d-413c-9b0b-0476817b2094 # ╠═b80e5ff3-445a-465c-9665-697f5b0b897e # ╟─b6057787-3948-482c-be7e-69c4231386b6 # ╟─d438bddd-9138-410e-ab98-40f7c90e780f # ╠═3f4a3b7c-2db7-4d7b-800a-c14968a02855 # ╟─2d2aa3ef-8fb7-4c69-8869-42e1939dc950 # ╟─61b5e6a8-3baa-4101-9a21-5d49af43bfe7 # ╠═35f25d1e-234b-481f-9303-3c5973ab1228 # ╟─4e20ad94-3011-4b89-b29e-8a793e733e03 # ╠═ed026592-6cb3-40a3-9f0a-565e14038114 # ╟─b25c0b7a-4469-4945-be28-bfb2c22103ca # ╠═8a0295cc-4f0c-475a-a8c6-ccf2b99ed00f # ╟─580db5a8-550a-42d1-9aef-c95f4099a3fa # ╠═0bd7dbfe-0689-40d4-8fd0-1fd372b2edc2 # ╟─534f0c53-da8b-4da5-81d4-c0ab3ceb270b # ╠═a28ee7bb-d9d7-43ea-833f-6460a2cf7579 # ╟─4535c71f-be7c-41e8-940b-57289c83e509 # ╟─f39cd55f-ed8f-487e-885e-f1e16b78d9dd # ╠═cd971faa-315a-4cea-a11a-0cb28f3e3997 # ╟─a9febef3-06d0-4552-8590-2f165ce36547 # ╠═7b2588d2-d86d-44fb-9895-d6452875bc47 # ╟─cc97f4d9-ad00-4d2c-b520-2071b9cf86f6 # ╠═631bb03b-00a5-466a-be0d-36f86a9721aa # ╠═95dc66d6-660a-4b0c-b89d-487a22dfec82 # ╠═c5570bf1-c04d-41e3-b8ae-6e6dac45cb70 # ╠═8f945f6c-2561-433b-aedb-3448d2c4b419 # ╟─ecd23fa3-fdd2-4ffb-a0e9-ad1feee04d22 # ╠═f10e310f-165b-4b4b-b3e0-66af6430ba4f # ╠═4c09eda3-8f4c-4fa2-91ea-99b92cfbb422 # ╠═1f2c5378-bbcd-4fb7-9130-9c6cebb4c6d4 # ╟─517d31ba-5843-40a7-ad06-96e7a8ea8845 # ╠═1cc0a6c1-3fcf-4df3-a8d9-ba3454c0a37b # ╠═9a76593b-c9a9-45cc-aca5-9877432baffd # ╠═6a7ea8be-bea2-41c4-89ab-9a6bbcfba116 # ╠═942e3f19-a5c1-430e-a3be-097c53bc7394 # ╠═6caa4b75-9ac1-496b-a4b2-38b72b203e59 # ╟─00000000-0000-0000-0000-000000000001 # ╟─00000000-0000-0000-0000-000000000002
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1.859007
40,846
using CSV, Distributions, HypothesisTests data = CSV.read("../data/machine1.csv", header=false)[:,1] xBar, n = mean(data), length(data) sig = 1.2 alpha = 0.1 z = quantile(Normal(),1-alpha/2) println("Calculating formula: ", (xBar - z*sig/sqrt(n), xBar + z*sig/sqrt(n))) println("Using confint() function: ", confint(OneSampleZTest(xBar,sig,n),alpha))
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2.503546
141
# Automatically generated using Clang.jl wrap_c, version 0.0.0 const SDL_IMAGE_MAJOR_VERSION = 2 const SDL_IMAGE_MINOR_VERSION = 0 const SDL_IMAGE_PATCHLEVEL = 4 # Skipping MacroDefinition: SDL_IMAGE_VERSION ( X ) \ #{ ( X ) -> major = SDL_IMAGE_MAJOR_VERSION ; ( X ) -> minor = SDL_IMAGE_MINOR_VERSION ; ( X ) -> patch = SDL_IMAGE_PATCHLEVEL ; \ #} # Skipping MacroDefinition: SDL_IMAGE_COMPILEDVERSION SDL_VERSIONNUM ( SDL_IMAGE_MAJOR_VERSION , SDL_IMAGE_MINOR_VERSION , SDL_IMAGE_PATCHLEVEL ) # Skipping MacroDefinition: SDL_IMAGE_VERSION_ATLEAST ( X , Y , Z ) ( SDL_IMAGE_COMPILEDVERSION >= SDL_VERSIONNUM ( X , Y , Z ) ) #const IMG_SetError = SDL_SetError #const IMG_GetError = SDL_GetError # begin enum ANONYMOUS_1 const ANONYMOUS_1 = UInt32 const SDL_FALSE = (UInt32)(0) const SDL_TRUE = (UInt32)(1) const SDL_bool = Bool # end enum ANONYMOUS_1 # begin enum ANONYMOUS_49 const ANONYMOUS_49 = UInt32 const IMG_INIT_JPG = (UInt32)(1) const IMG_INIT_PNG = (UInt32)(2) const IMG_INIT_TIF = (UInt32)(4) const IMG_INIT_WEBP = (UInt32)(8) # end enum ANONYMOUS_49 #$(Expr(:typealias, :IMG_InitFlags, :Void))
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2.421739
460
# Constructing Gaussian quadrature weights for an # arbitrary weight function and integration bounds. # for numerical stability, we apply the usual Lanczos # Gram–Schmidt procedure to the basis {T₀,T₁,T₂,…} of # Chebyshev polynomials on [-1,1] rather than to the # textbook monomial basis {1,x,x²,…}. # evaluate Chebyshev polynomial p(x) with coefficients a[i] # by a Clenshaw recurrence. function chebeval(x, a) if length(a) ≤ 2 length(a) == 1 && return a[1] + x * zero(a[1]) return a[1]+x*a[2] end bₖ = a[end-1] + 2x*a[end] bₖ₊₁ = oftype(bₖ, a[end]) for j = lastindex(a)-2:-1:2 bⱼ = a[j] + 2x*bₖ - bₖ₊₁ bₖ, bₖ₊₁ = bⱼ, bₖ end return a[1] + x*bₖ - bₖ₊₁ end # if a[i] are coefficients of Chebyshev series, compute the coefficients xa (in-place) # of the series multiplied by x, using recurrence xTₙ = 0.5 (Tₙ₊₁+Tₙ₋₁) for n > 0 function chebx!(xa, a) resize!(xa, length(a)+1) xa .= 0 for n = 2:lastindex(a) c = 0.5*a[n] xa[n-1] += c xa[n+1] += c end if !isempty(a) xa[2] += a[1] end return xa end """ gauss(W, N, a, b; rtol=sqrt(eps), quad=quadgk) Return a pair `(x, w)` of `N` quadrature points `x[i]` and weights `w[i]` to integrate functions on the interval `(a, b)` multiplied by the weight function `W(x)`. That is, `sum(w .* f.(x))` approximates the integral `∫ W(x)f(x)dx` from `a` to `b`. This function performs `2N` numerical integrals of polynomials against `W(x)` using the integration function `quad` (defaults to `quadgk`) with relative tolerance `rtol` (which defaults to half of the precision `eps` of the endpoints). This is followed by an O(N²) calculations. So, using a large order `N` is expensive. If `W` has lots of singularities that make it hard to integrate numerically, you may need to decrease `rtol`. You can also pass in a specialized quadrature routine via the `quad` keyword argument, which should accept arguments `quad(f,a,b,rtol=_,atol=_)` similar to `quadgk`. (This is useful if your weight function has discontinuities, in which case you might want to break up the integration interval at the discontinuities.) """ function gauss(W, N, a::Real,b::Real; rtol::Real=sqrt(eps(typeof(float(b-a)))), quad=quadgk) (isfinite(a) && isfinite(b)) || throw(ArgumentError("a finite interval is required")) return _gauss(W, N, a, b, rtol, quad) end function _gauss(W, N, a, b, rtol, quad) # Uses the Lanczos recurrence described in Trefethen & Bau, # Numerical Linear Algebra, to find the `N`-point Gaussian quadrature # using O(N) integrals and O(N²) operations, applied to Chebyshev basis: xscale = 2.0/(b-a) # scaling from (a,b) to (-1,1) T = typeof(xscale) α = zeros(T, N) β = zeros(T, N) q₀ = sizehint!(T[0], N+1) # 0 polynomial wint = first(quad(W, a, b, rtol=rtol)) (wint isa Real && wint > 0) || throw(ArgumentError("weight W must be real and positive")) atol = rtol*wint q₁ = sizehint!(T[1/sqrt(wint)],N+1) # normalized constant polynomial v = copy(q₀) for n = 1:N chebx!(v, q₁) # v = x * q₁ α[n] = first(quad(a, b, rtol=rtol, atol=atol) do x t = (x-a)*xscale - 1 W(x) * chebeval(t, q₁) * chebeval(t, v) end) n == N && break for j = 1:length(q₀); v[j] -= β[n]*q₀[j]; end for j = 1:length(q₁); v[j] -= α[n]*q₁[j]; end β[n+1] = sqrt(first(quad(a, b, rtol=rtol, atol=atol) do x W(x) * chebeval((x-a)*xscale - 1, v)^2 end)) v .*= inv(β[n+1]) q₀,q₁,v = q₁,v,q₀ end # TODO: handle BigFloat etcetera — requires us to update eignewt() to # support nonzero diagonal entries. E = eigen(SymTridiagonal(α, β[2:N])) w = E.vectors[1,:] w .= wint .* abs2.(w) return ((E.values .+ 1) ./ xscale .+ a, w) end
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2.099567
1,848
import Base.Dates: trunc, DatePeriod, TimePeriod import Base.Dates: firstdayofweek, lastdayofweek, firstdayofmonth, lastdayofmonth, firstdayofyear, lastdayofyear, firstdayofquarter, lastdayofquarter # Truncation # TODO: Just utilize floor code for truncation? function trunc{P<:DatePeriod}(zdt::ZonedDateTime, ::Type{P}) ZonedDateTime(trunc(localtime(zdt), P), timezone(zdt)) end function trunc{P<:TimePeriod}(zdt::ZonedDateTime, ::Type{P}) local_dt = trunc(localtime(zdt), P) utc_dt = local_dt - zdt.zone.offset ZonedDateTime(utc_dt, timezone(zdt); from_utc=true) end trunc(zdt::ZonedDateTime, ::Type{Millisecond}) = zdt # Adjusters for prefix in ("firstdayof", "lastdayof"), suffix in ("week", "month", "year", "quarter") func = Symbol(prefix * suffix) @eval begin $func(dt::ZonedDateTime) = ZonedDateTime($func(localtime(dt)), dt.timezone) end end
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2.576369
347
v0 = [] v1 = [] v2 = [] v3 = [] v4 = [] function d() while length(v0) > 0 side = v0[length(v0)] if side == 0 step = v1[length(v1)] while step > 0 move!(robot, Sud) step -= 1 end pop!(v1) elseif side == 1 step = v2[length(v2)] while step > 0 move!(robot, West) step -= 1 end pop!(v2) elseif side == 2 step = v3[length(v3)] while step > 0 move!(robot, Nord) step -= 1 end pop!(v3) else step = v4[length(v4)] while step > 0 move!(robot, Ost) step -= 1 end pop!(v4) end pop!(v0) end end function f() while !isborder(robot, Nord) || !isborder(robot, West) step = 0 while !isborder(robot, Nord) move!(robot, Nord) step += 1 end push!(v0, 0) push!(v1, step) step = 0 while !isborder(robot, West) move!(robot, West) step += 1 end push!(v0, 3) push!(v4, step) end putmarker!(robot) d() while !isborder(robot, Nord) || !isborder(robot, Ost) step = 0 while !isborder(robot, Nord) move!(robot, Nord) step += 1 end push!(v0, 0) push!(v1, step) step = 0 while !isborder(robot, Ost) move!(robot, Ost) step += 1 end push!(v0, 1) push!(v2, step) end putmarker!(robot) d() while !isborder(robot, Sud) || !isborder(robot, Ost) step = 0 while !isborder(robot, Sud) move!(robot, Sud) step += 1 end push!(v0, 2) push!(v3, step) step = 0 while !isborder(robot, Ost) move!(robot, Ost) step += 1 end push!(v0, 1) push!(v2, step) end putmarker!(robot) d() while !isborder(robot, Sud) || !isborder(robot, West) step = 0 while !isborder(robot, Sud) move!(robot, Sud) step += 1 end push!(v0, 2) push!(v3, step) step = 0 while !isborder(robot, West) move!(robot, West) step += 1 end push!(v0, 3) push!(v4, step) end putmarker!(robot) d() end f()
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1.548851
1,740
using MPSGE using Test using MPSGE.JuMP.Containers @testset "MPSGE" begin @testset "TWOBYTWO (functional version)" begin m = Model() inputcoeff = add!(m, Parameter(:inputcoeff, value=2.)) endow = add!(m, Parameter(:endow, value=2.)) elascoeff = add!(m, Parameter(:elascoeff, value=2.)) outputmult = add!(m, Parameter(:outputmult, value=2.)) X = add!(m, Sector(:X)) Y = add!(m, Sector(:Y)) U = add!(m, Sector(:U)) PX = add!(m, Commodity(:PX)) PY = add!(m, Commodity(:PY)) PU = add!(m, Commodity(:PU)) PL = add!(m, Commodity(:PL)) PK = add!(m, Commodity(:PK)) RA = add!(m, Consumer(:RA, benchmark=150.)) add!(m, Production(X, 1, [Output(PX, 100)], [Input(PL, :(25 * $inputcoeff)), Input(PK, 50)])) add!(m, Production(Y, :(0.5 * $elascoeff), [Output(PY, 50)], [Input(PL, 20), Input(PK, 30)])) add!(m, Production(U, 1, [Output(PU, :(75 * $outputmult))], [Input(PX, 100), Input(PY, 50)])) add!(m, DemandFunction(RA, [Demand(PU,150)], [Endowment(PL, :(35 * $endow)), Endowment(PK, 80)])) avm = algebraic_version(m) @test typeof(avm) == MPSGE.AlgebraicWrapper solve!(m) @test value(m, :X) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 150. @test MPSGE.Complementarity.result_value(m._jump_model[:PX]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PY]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PL]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PK]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†X")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†Y")]) ≈ 20. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†X")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†Y")]) ≈ 30. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PX†U")]) ≈ 100. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PY†U")]) ≈ 50. avm2 = algebraic_version(m) @test typeof(avm2) == MPSGE.AlgebraicWrapper # For now just run these functions, we might add tests for the results # at a later point repr(MIME("text/plain"), m) repr(MIME("text/plain"), avm2) repr(MIME("text/latex"), avm2) set_fixed!(PX, true) set_value(endow, 2.2) solve!(m) @test value(m, :X) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 1.03886012 @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1.04548206 @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 157.321327225523 @test MPSGE.Complementarity.result_value(m._jump_model[:PX]) ≈ 1.0000000000 @test MPSGE.Complementarity.result_value(m._jump_model[:PY]) ≈ 1.00957658 @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1.00318206 @test MPSGE.Complementarity.result_value(m._jump_model[:PL]) ≈ 0.95346259 @test MPSGE.Complementarity.result_value(m._jump_model[:PK]) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†X")]) ≈ 52.4404424085075 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†Y")]) ≈ 21.1770570584356 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†X")]) ≈ 47.6731294622795 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†Y")]) ≈ 28.877805079685 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PX†U")]) ≈ 100.318205802571 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PY†U")]) ≈ 49.6833066029729 end @testset "TWOBYTWO (macro version)" begin m = Model() inputcoeff = @parameter(m, inputcoeff, 2.) endow = @parameter(m, endow, 2.) elascoeff = @parameter(m, elascoeff, 2.) outputmult = @parameter(m, outputmult, 2.) @sector(m, X) @sector(m, Y) @sector(m, U) @commodity(m, PX) @commodity(m, PY) @commodity(m, PU) @commodity(m, PL) @commodity(m, PK) @consumer(m, RA, benchmark=150.) @production(m, X, 1, [Output(PX, 100)], [Input(PL, :(25 * $inputcoeff)), Input(PK, 50)]) @production(m, Y, :(0.5 * $elascoeff), [Output(PY, 50)], [Input(PL, 20), Input(PK, 30)]) @production(m, U, 1, [Output(PU, :(75 * $outputmult))], [Input(PX, 100), Input(PY, 50)]) @demand(m, RA, [Demand(PU, 150)], [Endowment(PL, :(35 * $endow)), Endowment(PK, 80)]) avm = algebraic_version(m) @test typeof(avm) == MPSGE.AlgebraicWrapper solve!(m) @test value(m, :X) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 150. @test MPSGE.Complementarity.result_value(m._jump_model[:PX]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PY]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PL]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PK]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†X")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†Y")]) ≈ 20. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†X")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†Y")]) ≈ 30. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PX†U")]) ≈ 100. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PY†U")]) ≈ 50. avm2 = algebraic_version(m) @test typeof(avm2) == MPSGE.AlgebraicWrapper # For now just run these functions, we might add tests for the results # at a later point repr(MIME("text/plain"), m) repr(MIME("text/plain"), avm2) repr(MIME("text/latex"), avm2) set_fixed!(PX, true) set_value(endow, 2.2) solve!(m) @test value(m, :X) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 1.03886012 @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1.04548206 @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 157.321327225523 @test MPSGE.Complementarity.result_value(m._jump_model[:PX]) ≈ 1.0000000000 @test MPSGE.Complementarity.result_value(m._jump_model[:PY]) ≈ 1.00957658 @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1.00318206 @test MPSGE.Complementarity.result_value(m._jump_model[:PL]) ≈ 0.95346259 @test MPSGE.Complementarity.result_value(m._jump_model[:PK]) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†X")]) ≈ 52.4404424085075 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PL†Y")]) ≈ 21.1770570584356 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†X")]) ≈ 47.6731294622795 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PK†Y")]) ≈ 28.877805079685 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PX†U")]) ≈ 100.318205802571 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PY†U")]) ≈ 49.6833066029729 end @testset "TWOBYTWO (indexed version)" begin m = Model() goods = [:x, :y] factors = [:l, :k] factor = DenseAxisArray(Float64[50 50; 20 30], goods, factors) supply = DenseAxisArray(Float64[100, 50], goods) @parameter(m, endow, 1.0) Y = add!(m, Sector(:Y, indices=(goods,))) U = add!(m, Sector(:U)) PC = add!(m, Commodity(:PC, indices=(goods,))) PU = add!(m, Commodity(:PU)) PF = add!(m, Commodity(:PF, indices=(factors,))) RA = add!(m, Consumer(:RA, benchmark=150.)) for i in goods @production(m, Y[i], 1, [Output(PC[i], supply[i])], [Input(PF[:l], factor[i,:l]), Input(PF[:k], factor[i,:k])]) end @production(m, U, 1, [Output(PU, 150)], [Input(PC[:x], 100), Input(PC[:y], 50)]) @demand(m, RA, [Demand(PU, 150)], [Endowment(PF[:l], :(70 * $endow)), Endowment(PF[:k], 80.)]) solve!(m, cumulative_iteration_limit=0) @test MPSGE.Complementarity.result_value(m._jump_model[:Y][:x]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:Y][:y]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 150. @test MPSGE.Complementarity.result_value(m._jump_model[:PC][:x]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PC][:y]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:l]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:k]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†Y[x]")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†Y[y]")]) ≈ 20. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†Y[x]")]) ≈ 50. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†Y[y]")]) ≈ 30. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PC[x]†U")]) ≈ 100. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PC[y]†U")]) ≈ 50. set_fixed!(PC[:x], true) set_value(endow, 1.1) solve!(m) @test MPSGE.Complementarity.result_value(m._jump_model[:Y][:x]) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[:Y][:y]) ≈ 1.03886012 @test MPSGE.Complementarity.result_value(m._jump_model[:U]) ≈ 1.04548206 @test MPSGE.Complementarity.result_value(m._jump_model[:RA]) ≈ 157.321327225523 @test MPSGE.Complementarity.result_value(m._jump_model[:PC][:x]) ≈ 1.0000000000 @test MPSGE.Complementarity.result_value(m._jump_model[:PC][:y]) ≈ 1.00957658 @test MPSGE.Complementarity.result_value(m._jump_model[:PU]) ≈ 1.00318206 @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:l]) ≈ 0.95346259 @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:k]) ≈ 1.04880885 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†Y[x]")]) ≈ 52.4404424085075 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†Y[y]")]) ≈ 21.1770570584356 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†Y[x]")]) ≈ 47.6731294622795 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†Y[y]")]) ≈ 28.877805079685 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PC[x]†U")]) ≈ 100.318205802571 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PC[y]†U")]) ≈ 49.6833066029729 end @testset "JPMGE (Joint Production Intermediate Demand)" begin m = Model() goods = [:g1, :g2] factors = [:l, :k] sectors = [:s1, :s2] make0 = DenseAxisArray(Float64[6 2; 2 10], goods, sectors) use0 = DenseAxisArray(Float64[4 2; 2 6], goods, sectors) fd0 = DenseAxisArray(Float64[1 3; 1 1], factors, sectors) c0 = DenseAxisArray(Float64[2, 4], goods) e0 = DenseAxisArray(Float64[sum(fd0[f,:]) for f in factors], factors) @parameter(m, endow, 1.0) X = add!(m, Sector(:X, indices=(sectors,))) P = add!(m, Commodity(:P, indices=(goods,))) PF = add!(m, Commodity(:PF, indices=(factors,))) Y = add!(m, Consumer(:Y, benchmark=sum(fd0))) for j in sectors @production(m, X[j], 1, [Output(P[i], make0[i,j]) for i in goods], [[Input(P[i], use0[i,j]) for i in goods]; [Input(PF[f], fd0[f,j]) for f in factors]]) end @demand(m, Y, [Demand(P[i], c0[i]) for i in goods], [Endowment(PF[:k], e0[:k]), Endowment(PF[:l], :($(endow) * $(e0[:l]))) ]) avm = algebraic_version(m) @test typeof(avm) == MPSGE.AlgebraicWrapper solve!(m) @test MPSGE.Complementarity.result_value(m._jump_model[:X][:s1]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:X][:s2]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:P][:g1]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:P][:g2]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:l]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:k]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 6. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]‡X[s1]")]) ≈ 6. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]‡X[s1]")]) ≈ 2. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]‡X[s2]")]) ≈ 2. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]‡X[s2]")]) ≈10. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]†X[s1]")]) ≈ 4. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]†X[s1]")]) ≈ 2. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†X[s1]")]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†X[s1]")]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]†X[s2]")]) ≈ 2. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]†X[s2]")]) ≈ 6. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†X[s2]")]) ≈ 3. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†X[s2]")]) ≈ 1. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]ρY")]) ≈ 2. @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]ρY")]) ≈ 4. solve!(m) #Counter-factual set_value(endow, 1.1) set_value(Y, 6.4) set_fixed!(Y, true) solve!(m) @test MPSGE.Complementarity.result_value(m._jump_model[:X][:s1]) ≈ 0.99692562 @test MPSGE.Complementarity.result_value(m._jump_model[:X][:s2]) ≈ 1.09975731 @test MPSGE.Complementarity.result_value(m._jump_model[:P][:g1]) ≈ 1.01306317 @test MPSGE.Complementarity.result_value(m._jump_model[:P][:g2]) ≈ 0.99467892 @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:l]) ≈ 0.97665932 @test MPSGE.Complementarity.result_value(m._jump_model[:PF][:k]) ≈ 1.0513495 @test MPSGE.Complementarity.result_value(m._jump_model[:Y]) ≈ 6.4 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]‡X[s1]")]) ≈ 6.0271570795595 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]‡X[s1]")]) ≈ 1.97259368637181 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]‡X[s2]")]) ≈ 2.03066186698742 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]‡X[s2]")]) ≈ 9.96905522562002 #note - digits after 9.969 added from MPSGE.jl results bc GAMS not showing @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]†X[s1]")]) ≈ 3.98197684247759 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]†X[s1]")]) ≈ 2.02778707866119 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†X[s1]")]) ≈ 1.03260012599366 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†X[s1]")]) ≈ 0.959241925360056 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]†X[s2]")]) ≈ 1.96980110341978 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]†X[s2]")]) ≈ 6.018624497730061 #note - digits after 6.0186 added from MPSGE.jl results bc GAMS not showing @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[l]†X[s2]")]) ≈ 3.06483480444652 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("PF[k]†X[s2]")]) ≈ 0.94903406 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g1]ρY")]) ≈ 2.1058245831306 @test MPSGE.Complementarity.result_value(m._jump_model[Symbol("P[g2]ρY")]) ≈ 4.28949135539776 end end
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2.020152
8,436
@testset "Curve struct" begin @testset "Uniformly distributed values" begin n = 20 data = rand(Uniform(0,10), n) curve = CalculatedABC.ABCcurve(data) @test curve.effort == 0:0.05:1.0 expected_yield = [0, 0.10223370412290836, 0.19630374973228518, 0.2798337861487627, 0.35309289814499817, 0.4251124119444249, 0.49223359866263033, 0.5559340313195282, 0.6192318028485928, 0.6706485917196718, 0.7210686469145637, 0.7696104643520768, 0.812596101423477, 0.8466456273448749, 0.8777009051387628, 0.9076823618805581, 0.9368834040175215, 0.9647084964342312, 0.9824989229044542, 0.994333140487581, 1.0000000000000002] @test isapprox(curve.yield, expected_yield) end @testset "Exponentially distributed values" begin n = 20 data = rand(Exponential(1), n) curve = CalculatedABC.ABCcurve(data) @test curve.effort == 0:0.05:1.0 expected_yield = [0.0, 0.1559704817922999, 0.259107787045309, 0.36017085915674457, 0.4567531613173713, 0.530483819367366, 0.5905820392527759, 0.6503414525690904, 0.6912925133668514, 0.7318440937106174, 0.7704643555492486, 0.8031964684921609, 0.8353247928350085, 0.8670259991394021, 0.8976554662905372, 0.9212877248856802, 0.9431001183934831, 0.964704167187443, 0.9810003669812493, 0.9911110632975999, 1.0] @test isapprox(curve.yield, expected_yield) end @testset "SwissInhabitants data" begin data = readdlm("swissinhabitants")[:,1] curve = CalculatedABC.ABCcurve(data) @test curve.effort == 0:0.0003453038674033149:1.0 expected_yield = readdlm("expected_yield")[:,1] @test isapprox(curve.yield, expected_yield) end end
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module GenieCMS greet() = print("Hello World!") using Genie function install(dest::String; force = false) src = abspath(normpath(joinpath(@__DIR__, "..", Genie.Plugins.FILES_FOLDER))) for f in readdir(src) isdir(f) || continue Genie.Plugins.install(joinpath(src, f), dest, force = force) end end end # module
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abstract type AbstractHyperedgeCutModel end @inline (mdl::AbstractHyperedgeCutModel)(n_vertices, n_pins, n_self_nets, n_cut_nets, k) = mdl(n_vertices, n_pins, n_self_nets, n_cut_nets) struct AffineHyperedgeCutModel{Tv} <: AbstractHyperedgeCutModel α::Tv β_vertex::Tv β_pin::Tv β_self_net::Tv β_cut_net::Tv end function AffineHyperedgeCutModel(; α = false, β_vertex = false, β_pin = false, β_self_net = false, β_cut_net = false) AffineHyperedgeCutModel(promote(α, β_vertex, β_pin, β_self_net, β_cut_net)...) end @inline cost_type(::Type{AffineHyperedgeCutModel{Tv}}) where {Tv} = Tv (mdl::AffineHyperedgeCutModel)(n_vertices, n_pins, n_self_nets, n_cut_nets) = mdl.α + n_vertices * mdl.β_vertex + n_pins * mdl.β_pin + n_self_nets * mdl.β_self_net + n_cut_nets * mdl.β_cut_net struct HyperedgeCutOracle{Ti, Net, SelfNet, Mdl} <: AbstractOracleCost{Mdl} pos::Vector{Ti} net::Net selfnet::SelfNet mdl::Mdl end oracle_model(ocl::HyperedgeCutOracle) = ocl.mdl function oracle_stripe(hint::AbstractHint, mdl::AbstractHyperedgeCutModel, A::SparseMatrixCSC{Tv, Ti}; net=nothing, adj_A=nothing, kwargs...) where {Tv, Ti} @inbounds begin m, n = size(A) pos = A.colptr if net === nothing net = netcount(hint, A; kwargs...) end selfnet = selfnetcount(hint, A; kwargs...) return HyperedgeCutOracle(pos, net, selfnet, mdl) end end @inline function (cst::HyperedgeCutOracle{Ti, Mdl})(j, j′, k...) where {Ti, Mdl} @inbounds begin w = cst.pos[j′] - cst.pos[j] d = cst.net[j, j′] l = cst.selfnet[j, j′] return cst.mdl(j′ - j, w, l, d - l, k...) end end @inline function (stp::Step{Ocl})(_j, _j′, _k...) where {Ti, Mdl, Ocl <: HyperedgeCutOracle{Ti, Mdl}} @inbounds begin cst = stp.ocl j = destep(_j) j′ = destep(_j′) k = maptuple(destep, _k...) w = cst.pos[j′] - cst.pos[j] d = Step(cst.net)(_j, _j′) l = Step(cst.selfnet)(_j, _j′) return cst.mdl(j′ - j, w, l, d - l, k...) end end
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using LinearAlgebraicRepresentation Lar = LinearAlgebraicRepresentation using ViewerGL GL = ViewerGL # data input (V, FV, EV) = ([1.01181 0.215639 0.91979 0.123616 1.02252 0.226347 0.930498 0.134324 0.0458309 -0.301827 0.348275 0.0006172 0.579367 0.23171 0.881811 0.534154 -0.0521776 0.627953 -0.190635 0.489496 -0.0233884 0.656742 -0.161846 0.518285 0.27652 -0.0875132 0.52527 0.161237 0.509324 0.145291 0.758074 0.394041 0.27631 0.564484 0.0058279 0.294002 1.01221 1.30039 0.741732 1.02991; 0.160033 0.0680099 0.956278 0.864255 0.160649 0.0686266 0.956895 0.864872 -0.200245 0.102199 0.417839 0.720283 -0.35354 -0.0510965 0.264543 0.566987 0.682359 0.543901 0.0592036 -0.0792537 0.956374 0.817917 0.333219 0.194762 -0.102028 0.146722 0.324834 0.573584 -0.16916 0.0795901 0.257702 0.506452 0.452356 0.181874 1.15396 0.883481 0.816151 0.545669 1.51776 1.24728; 0.196256 0.206963 0.196872 0.20758 0.997729 1.00844 0.998346 1.00905 0.0677451 0.601282 -0.0855504 0.447986 0.502301 1.03584 0.349005 0.882542 0.159301 0.18809 0.433316 0.462105 0.797002 0.825792 1.07102 1.09981 0.1446 0.377404 0.0774682 0.310272 0.580364 0.813168 0.513232 0.746036 0.403805 1.13971 0.767599 1.5035 0.249344 0.985248 0.613139 1.34904], Array{Int64,1}[[1,2,3,4],[5,6,7,8],[1,2,5,6],[3,4,7,8],[1,3,5,7],[2,4,6,8],[9,10,11,12],[13,14,15,16],[9,10,13,14],[11,12,15,16],[9,11,13,15],[10,12,14,16],[17,18,19,20],[21,22,23,24],[17,18,21,22],[19,20,23,24],[17,19,21,23],[18,20,22,24],[25,26,27,28],[29,30,31,32],[25,26,29,30],[27,28,31,32],[25,27,29,31],[26,28,30,32],[33,34,35,36],[37,38,39,40],[33,34,37,38],[35,36,39,40],[33,35,37,39],[34,36,38,40]],Array{Int64,1}[[1,2],[3,4],[5,6],[7,8],[1,3],[2,4],[5,7],[6,8],[1,5],[2,6],[3,7],[4,8],[9,10],[11,12],[13,14],[15,16],[9,11],[10,12],[13,15],[14,16],[9,13],[10,14],[11,15],[12,16],[17,18],[19,20],[21,22],[23,24],[17,19],[18,20],[21,23],[22,24],[17,21],[18,22],[19,23],[20,24],[25,26],[27,28],[29,30],[31,32],[25,27],[26,28],[29,31],[30,32],[25,29],[26,30],[27,31],[28,32],[33,34],[35,36],[37,38],[39,40],[33,35],[34,36],[37,39],[38,40],[33,37],[34,38],[35,39],[36,40]]) cop_EV = Lar.coboundary_0(EV::Lar.Cells); cop_EW = convert(Lar.ChainOp, cop_EV); cop_FE = Lar.coboundary_1(V, FV::Lar.Cells, EV::Lar.Cells); W = convert(Lar.Points, V'); V, copEV, copFE, copCF = Lar.space_arrangement( W::Lar.Points, cop_EW::Lar.ChainOp, cop_FE::Lar.ChainOp) cc = [copEV, copFE, copCF] LarModelString = Lar.lar2obj(V::Lar.Points, cc::Lar.ChainComplex) f = open("../test/out3d.obj", "w") print(f, LarModelString); close(f) V,EVs,FVs = Lar.obj2lar("../test/out3d.obj") GL.VIEW([ GL.GLPolyhedron(V, FVs[1]) GL.GLFrame ]); V,CVs,FVs,EVs = Lar.pols2tria(V, copEV, copFE, copCF); # whole assembly GL.VIEW(GL.GLExplode(V,FVs,1.2,1.2,1.2,99,1)); GL.VIEW(GL.GLExplode(V,EVs,1.5,1.5,1.5,99,1)); GL.VIEW(GL.GLExplode(V,CVs[2:end],5,5,5,99,0.2)); GL.VIEW(GL.GLExplode(V,[CVs[1]],1,1,1,99,0.5));
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1.885363
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# dependencies using LFAToolkit using LinearAlgebra using Plots # setup p = 2 dimension = 1 mesh = Mesh1D(1.0) # operator diffusion = GalleryOperator("diffusion", p+1, p+1, mesh) # NoPreco smoother noPreco = NoPreco(diffusion) # full operator symbols numbersteps = 250 maxeigenvalue = 0 θ_min = -π/2 θ_max = 3π/2 θ_step = 2π/(numbersteps-1) θ_range = θ_min:θ_step:θ_max # compute and plot smoothing factor # setup ω = [1.00] eigenvalues = zeros(numbersteps, p) # compute for i in 1:numbersteps θ = [θ_range[i]] if abs(θ[1]) > π/512 A = computesymbols(noPreco, ω, θ) currenteigenvalues = [real(val) for val in eigvals(I-A)] eigenvalues[i, :] = currenteigenvalues end end # plot xrange = θ_range/π plot( xrange, xlabel="θ/π", xtickfont=font(12, "Courier"), eigenvalues, ytickfont=font(12, "Courier"), ylabel="λ", linewidth=3, legend=:none, title="Spectrum of NoPreco Symbol", palette=palette(:tab10) ) ylims!(min(0.0, eigenvalues...) * 1.1, max(eigenvalues...) * 1.1) savefig("noPreco_spectrum_2") ## p = 4 # setup p = 4 dimension = 1 mesh = Mesh1D(1.0) # operator diffusion = GalleryOperator("diffusion", p+1, p+1, mesh) # NoPreco smoother noPreco = NoPreco(diffusion) # full operator symbols numbersteps = 250 maxeigenvalue = 0 θ_min = -π/2 θ_max = 3π/2 θ_step = 2π/(numbersteps-1) θ_range = θ_min:θ_step:θ_max # compute and plot smoothing factor # setup ω = [1.00] eigenvalues = zeros(numbersteps, p) # compute for i in 1:numbersteps θ = [θ_range[i]] if abs(θ[1]) > π/512 A = computesymbols(noPreco, ω, θ) currenteigenvalues = [real(val) for val in eigvals(I-A)] eigenvalues[i, :] = currenteigenvalues end end # plot xrange = θ_range/π plot( xrange, xlabel="θ/π", xtickfont=font(12, "Courier"), eigenvalues, ytickfont=font(12, "Courier"), ylabel="λ", linewidth=3, legend=:none, title="Spectrum of NoPreco Symbol", palette=palette(:tab10) ) ylims!(min(0.0, eigenvalues...) * 1.1, max(eigenvalues...) * 1.1) savefig("noPreco_spectrum_4") ## p = 7 # setup p = 7 dimension = 1 mesh = Mesh1D(1.0) # operator diffusion = GalleryOperator("diffusion", p+1, p+1, mesh) # NoPreco smoother noPreco = NoPreco(diffusion) # full operator symbols numbersteps = 250 maxeigenvalue = 0 θ_min = -π/2 θ_max = 3π/2 θ_step = 2π/(numbersteps-1) θ_range = θ_min:θ_step:θ_max # compute and plot smoothing factor # setup ω = [1.00] eigenvalues = zeros(numbersteps, p) # compute for i in 1:numbersteps θ = [θ_range[i]] if abs(θ[1]) > π/512 A = computesymbols(noPreco, ω, θ) currenteigenvalues = [real(val) for val in eigvals(I-A)] eigenvalues[i, :] = currenteigenvalues end end # plot xrange = θ_range/π plot( xrange, xlabel="θ/π", xtickfont=font(12, "Courier"), eigenvalues, ytickfont=font(12, "Courier"), ylabel="λ", linewidth=3, legend=:none, title="Spectrum of NoPreco Symbol", palette=palette(:tab10) ) ylims!(min(0.0, eigenvalues...) * 1.1, max(eigenvalues...) * 1.1) savefig("noPreco_spectrum_7")
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struct ECE{B<:AbstractBinningAlgorithm,D} <: CalibrationErrorEstimator """Binning algorithm.""" binning::B """Distance function.""" distance::D end """ ECE(binning[, distance = TotalVariation()]) Estimator of the expected calibration error (ECE) for a classification model with respect to the given `distance` function using the `binning` algorithm. For classification models, the predictions ``P_{X_i}`` and targets ``Y_i`` are identified with vectors in the probability simplex. The estimator of the ECE is defined as ```math \\frac{1}{B} \\sum_{i=1}^B d\\big(\\overline{P}_i, \\overline{Y}_i\\big), ``` where ``B`` is the number of non-empty bins, ``d`` is the distance function, and ``\\overline{P}_i`` and ``\\overline{Y}_i`` are the average vector of the predictions and the average vector of targets in the ``i``th bin. By default, the total variation distance is used. The `distance` has to be a function of the form ```julia distance(pbar::Vector{<:Real}, ybar::Vector{<:Real}). ``` In particular, distance measures of the package [Distances.jl](https://github.com/JuliaStats/Distances.jl) are supported. """ ECE(binning::AbstractBinningAlgorithm) = ECE(binning, TotalVariation()) # estimate ECE function (ece::ECE)( predictions::AbstractVector{<:AbstractVector{<:Real}}, targets::AbstractVector{<:Integer}, ) @unpack binning, distance = ece # check number of samples nsamples = check_nsamples(predictions, targets) # bin predictions and labels bins = perform(binning, predictions, targets) nbins = length(bins) nbins > 0 || error("there must exist at least one bin") # compute the weighted mean of the distances in each bin # use West's algorithm for numerical stability # evaluate the distance in the first bin @inbounds begin bin = bins[1] x = distance(bin.mean_predictions, bin.proportions_targets) # initialize the estimate estimate = x / 1 # for all other bins n = bin.nsamples for i in 2:nbins # evaluate the distance bin = bins[i] x = distance(bin.mean_predictions, bin.proportions_targets) # update the estimate m = bin.nsamples n += m estimate += (m / n) * (x - estimate) end end return estimate end
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# module Octo # SQLElement abstract type SQLElement end const Structured = Array # Union{<:SQLElement, Any} struct FromItem <: SQLElement __octo_model::Type __octo_alias::Union{Symbol, Nothing} end struct SubQuery <: SQLElement __octo_query::Structured __octo_alias::Union{Symbol, Nothing} end struct SQLFunction <: SQLElement name::Symbol fields::Tuple end struct SQLFunctionName <: SQLElement name::Symbol end (f::SQLFunctionName)(args...) = SQLFunction(f.name, args) struct Field <: SQLElement clause::Union{FromItem, SubQuery, Nothing} name::Symbol end struct SQLKeyword <: SQLElement name::Symbol end """ Octo.PlaceHolder """ struct PlaceHolder <: SQLElement body::String end const PredicateValueTypes = Union{Bool, Number, String, Symbol, Day, <:SQLElement, Type{PlaceHolder}} struct Predicate <: SQLElement func::Function left::PredicateValueTypes right::PredicateValueTypes end struct SQLAlias <: SQLElement field::Union{Field, SQLFunction, Predicate} alias::Symbol end struct SQLExtract <: SQLElement field::Union{SQLKeyword, Type{DP}, Type{TP}} where DP <: DatePeriod where TP <: TimePeriod from::Union{DateTime, DP, TP, CompoundPeriod} where DP <: DatePeriod where TP <: TimePeriod end """ Octo.Raw """ struct Raw <: SQLElement string::String end struct Enclosed <: SQLElement values::Vector end struct VectorOfTuples <: SQLElement values::Vector{<:Tuple} end struct KeywordAllKeyword <: SQLElement left::SQLKeyword right::SQLKeyword end # module Octo
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#bld_parametric_sin_initial.jl: Parametric sin(): Initial observations #------------------------------------------------------------------------------- using CMDimData using CMDimData.EasyPlot #==Constants ===============================================================================# LBL_AXIS_TIME = "Time [s]" LBL_AXIS_POSITION = "Position [m]" LBL_AXIS_NORMPOSITION = "Normalized Pos [m/m]" LBL_AXIS_SPEED = "Speed [m/s]" lstylesweep = cons(:a, line = set(style=:solid, width=2)) #Default is a bit thin dfltglyph = cons(:a, glyph = set(shape=:o, size=1.5)) function fnbuild(data) plot = cons(:plot, nstrips = 3, ystrip1 = set(axislabel=LBL_AXIS_POSITION, striplabel="Sinusoidal response"), ystrip2 = set(axislabel=LBL_AXIS_NORMPOSITION, striplabel="Normalized response (All peaks should be ±1)"), ystrip3 = set(axislabel=LBL_AXIS_SPEED, striplabel="Rate of change (should be larger for higher frequencies)"), xaxis = set(label=LBL_AXIS_TIME) ) push!(plot, cons(:wfrm, data.signal, lstylesweep, label="", strip=1), cons(:wfrm, data.signal_norm, lstylesweep, label="", strip=2), cons(:wfrm, data.rate, lstylesweep, label="", strip=3), ) pcoll = push!(cons(:plotcoll, title="Parametric sin() - Initial Observations"), plot) return pcoll end #Return EasyPlotBuilder object: EasyPlot.EasyPlotBuilder(fnbuild)
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2.824153
472
export genplots, getexamples, runplotgen """ results = runplotgen() Run full generation of the plots needed for the tests in ControlSystems.jl """ function runplotgen() funcs, refs, eps = getexamples() genplots(funcs, refs, eps=eps) end """ results = genplots(funcs, refs; eps, kwargs...) Generate the plots needed for the tests in ControlSystems.jl """ function genplots(funcs, refs; eps=0.01*ones(length(refs)), kwargs...) ENV["MPLBACKEND"] = "Agg" results = Array{VisualRegressionTests.VisualTestResult,1}(undef, length(refs)) #Run/generate tests for (i,ref) in enumerate(refs) testf(fn) = png(funcs[i](), fn) results[i] = test_images(VisualTest(testf, ref); tol=eps[i], kwargs...) end results end """funcs, refs, eps = getexamples() Get the example functions and locations of reference plots needed for the tests in ControlSystems.jl """ function getexamples() tf1 = tf([1],[1,1]) tf2 = tf([1/5,2],[1,1,1]) sys = [tf1 tf2] sysss = ss([-1 2; 0 1], [1 0; 1 1], [1 0; 0 1], [0.1 0; 0 -0.2]) ws = 10.0 .^range(-2,stop=2,length=200) ts = 0:0.01:5 bodegen() = begin setPlotScale("dB") bodeplot(sys,ws) end nyquistgen() = nyquistplot(sysss,ws) sigmagen() = sigmaplot(sysss,ws) #Only siso for now nicholsgen() = nicholsplot(tf1,ws) stepgen() = stepplot(sys, ts[end], ts[2]-ts[1], l=(:dash, 4)) impulsegen() = impulseplot(sys, ts[end], ts[2]-ts[1], l=:blue) L = lqr(sysss.A, sysss.B, [1 0; 0 1], [1 0; 0 1]) lsimgen() = lsimplot(sysss, (x,i)->-L*x, ts, [1;2]) margingen() = marginplot([tf1, tf2], ws) gangoffourgen() = begin setPlotScale("log10"); gangoffourplot(tf1, [tf(1), tf(5)]) end pzmapgen() = pzmap(tf2, xlims=(-15,5)) rlocusgen() = rlocusplot(tf2) plotsdir = joinpath(dirname(pathof(ControlExamplePlots)), "..", "src", "figures") refs = ["bode.png", "nyquist.png", "sigma.png", "nichols.png", "step.png", "impulse.png", "lsim.png", "margin.png", "gangoffour.png", "pzmap.png", "rlocus.png"] funcs = [bodegen, nyquistgen, sigmagen, nicholsgen, stepgen, impulsegen, lsimgen, margingen, gangoffourgen, pzmapgen, rlocusgen] eps = 0.001*ones(length(refs)); funcs, map(s -> joinpath(plotsdir,s), refs), eps end
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2.1862
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###### ###### `inv` ###### function frule((_, Δx), ::typeof(inv), x::AbstractArray) Ω = inv(x) return Ω, -Ω * Δx * Ω end function rrule(::typeof(inv), x::AbstractArray) Ω = inv(x) function inv_pullback(ΔΩ) return NO_FIELDS, -Ω' * ΔΩ * Ω' end return Ω, inv_pullback end ##### ##### `*` ##### function rrule(::typeof(*), A::AbstractMatrix{<:Real}, B::AbstractMatrix{<:Real}) function times_pullback(Ȳ) return (NO_FIELDS, @thunk(Ȳ * B'), @thunk(A' * Ȳ)) end return A * B, times_pullback end function rrule(::typeof(*), A::Real, B::AbstractArray{<:Real}) function times_pullback(Ȳ) return (NO_FIELDS, @thunk(dot(Ȳ, B)), @thunk(A * Ȳ)) end return A * B, times_pullback end function rrule(::typeof(*), B::AbstractArray{<:Real}, A::Real) function times_pullback(Ȳ) return (NO_FIELDS, @thunk(A * Ȳ), @thunk(dot(Ȳ, B))) end return A * B, times_pullback end ##### ##### `/` ##### function rrule(::typeof(/), A::AbstractVecOrMat{<:Real}, B::AbstractVecOrMat{<:Real}) Aᵀ, dA_pb = rrule(adjoint, A) Bᵀ, dB_pb = rrule(adjoint, B) Cᵀ, dS_pb = rrule(\, Bᵀ, Aᵀ) C, dC_pb = rrule(adjoint, Cᵀ) function slash_pullback(Ȳ) # Optimization note: dAᵀ, dBᵀ, dC are calculated no matter which partial you want _, dC = dC_pb(Ȳ) _, dBᵀ, dAᵀ = dS_pb(unthunk(dC)) ∂A = last(dA_pb(unthunk(dAᵀ))) ∂B = last(dA_pb(unthunk(dBᵀ))) (NO_FIELDS, ∂A, ∂B) end return C, slash_pullback end ##### ##### `\` ##### function rrule(::typeof(\), A::AbstractVecOrMat{<:Real}, B::AbstractVecOrMat{<:Real}) Y = A \ B function backslash_pullback(Ȳ) ∂A = @thunk begin B̄ = A' \ Ȳ Ā = -B̄ * Y' Ā = add!!(Ā, (B - A * Y) * B̄' / A') Ā = add!!(Ā, A' \ Y * (Ȳ' - B̄'A)) Ā end ∂B = @thunk A' \ Ȳ return NO_FIELDS, ∂A, ∂B end return Y, backslash_pullback end ##### ##### `\`, `/` matrix-scalar_rule ##### function rrule(::typeof(/), A::AbstractArray{<:Real}, b::Real) Y = A/b function slash_pullback(Ȳ) return (NO_FIELDS, @thunk(Ȳ/b), @thunk(-dot(Ȳ, Y)/b)) end return Y, slash_pullback end function rrule(::typeof(\), b::Real, A::AbstractArray{<:Real}) Y = b\A function backslash_pullback(Ȳ) return (NO_FIELDS, @thunk(-dot(Ȳ, Y)/b), @thunk(Ȳ/b)) end return Y, backslash_pullback end
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function getIndexes(i,j,d) if i == j return [i] elseif i > j && i > 2 return 0 else niz1 = [] niz2 = collect(i:j-1) for k in 1:d if !(k in niz2) push!(niz1,k) end end return length(niz1) == 0 ? 0 : niz1 end end function getSelectedColumns(selectedColumns,X) localX = [] if selectedColumns != 0 for i in selectedColumns push!(localX,X[:,i]) end return localX else return 0 end end X = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5] d = 10 for i in 1:d-1 for j in 1:d temp = getIndexes(i,j,d) println(temp == 0 ? "" : temp) end end
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function obtain_raw_data(base_dir::String, sha::String) data_path = joinpath(base_dir,"raw_input_data") ispath(data_path) && rm(data_path, recursive = true) mkpath(data_path) if Sys.iswindows() POWERSYSTEMSTESTDATA_URL = "https://github.com/GridMod/RTS-GMLC/archive/$(sha).zip" else POWERSYSTEMSTESTDATA_URL = "https://github.com/GridMod/RTS-GMLC/archive/$(sha).tar.gz" end tempfilename= Base.download(POWERSYSTEMSTESTDATA_URL) Sys.iswindows() && unzip_windows(tempfilename, base_dir) Sys.islinux() && unzip_unix(tempfilename, base_dir) Sys.isapple() && unzip_unix(tempfilename, base_dir) mv(joinpath(base_dir, "RTS-GMLC-$(sha)"), data_path, force=true) return data_path end function unzip_unix(filename, directory) @assert success(`tar -xvf $filename -C $directory`) "Unable to extract $filename to $directory" end function unzip_windows(filename, directory) home = (Base.VERSION < v"0.7-") ? JULIA_HOME : Sys.BINDIR @assert success(`$home/7z x $filename -y -o$directory`) "Unable to extract $filename to $directory" end
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2.494357
443
using FromFile @from "../types.jl" import MyInt @from "ops.jl" import Square int_unwrap(x::MyInt)::Int = x.value int_square_unwrap(x::MyInt)::Int = int_unwrap(Square.int_square(x))
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# This file compares approaching multi-agent models in two ways: # 1) using different Types to represent different agents. This leads to type # stability within `step!`. # 2) using a single type with some extra property `type` or `kind`, and then do # an `if`-based branching on this type to dispatch to different functions. # The result is that (2) is much faster. ################### DEFINITION 1 ############### using Agents, Random, BenchmarkTools mutable struct GridAgentOne <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool end mutable struct GridAgentTwo <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool end mutable struct GridAgentThree <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool end mutable struct GridAgentFour <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool end mutable struct GridAgentFive <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool end model1 = ABM( Union{GridAgentOne,GridAgentTwo,GridAgentThree,GridAgentFour,GridAgentFive}, GridSpace((15, 15)); warn = false, rng = MersenneTwister(42), scheduler = schedule_randomly, ) for _ in 1:50 a = GridAgentOne(nextid(model1), (1,1), rand(model1.rng), rand(model1.rng, Bool)) add_agent!(a, model1) a = GridAgentTwo(nextid(model1), (1,1), rand(model1.rng), rand(model1.rng, Bool)) add_agent!(a, model1) a = GridAgentThree(nextid(model1), (1,1), rand(model1.rng), rand(model1.rng, Bool)) add_agent!(a, model1) a = GridAgentFour(nextid(model1), (1,1), rand(model1.rng), rand(model1.rng, Bool)) add_agent!(a, model1) a = GridAgentFive(nextid(model1), (1,1), rand(model1.rng), rand(model1.rng, Bool)) add_agent!(a, model1) end agent_step!(agent::GridAgentOne, model1) = walk!(agent, rand, model1) function agent_step!(agent::GridAgentTwo, model1) agent.one += rand(model1.rng) agent.two = rand(model1.rng, Bool) end function agent_step!(agent::GridAgentThree, model1) if any(a-> a isa GridAgentTwo, nearby_agents(agent, model1)) agent.two = true walk!(agent, rand, model1) end end function agent_step!(agent::GridAgentFour, model1) agent.one += sum(a.one for a in nearby_agents(agent, model1)) end function agent_step!(agent::GridAgentFive, model1) targets = filter!(a->a.one > 0.8, collect(nearby_agents(agent, model1, 3))) idx = argmax(map(t->edistance(agent, t, model1), targets)) farthest = targets[idx] walk!(agent, sign.(farthest.pos .- agent.pos), model1) end ################### DEFINITION 2 ############### mutable struct GridAgentAll <: AbstractAgent id::Int pos::Dims{2} one::Float64 two::Bool type::Symbol end model2 = ABM( GridAgentAll, GridSpace((15, 15)); rng = MersenneTwister(42), scheduler = schedule_randomly, ) for _ in 1:50 a = GridAgentAll(nextid(model2), (1,1), rand(model2.rng), rand(model2.rng, Bool), :one) add_agent!(a, model2) a = GridAgentAll(nextid(model2), (1,1), rand(model2.rng), rand(model2.rng, Bool), :two) add_agent!(a, model2) a = GridAgentAll(nextid(model2), (1,1), rand(model2.rng), rand(model2.rng, Bool), :three) add_agent!(a, model2) a = GridAgentAll(nextid(model2), (1,1), rand(model2.rng), rand(model2.rng, Bool), :four) add_agent!(a, model2) a = GridAgentAll(nextid(model2), (1,1), rand(model2.rng), rand(model2.rng, Bool), :five) add_agent!(a, model2) end function agent_step!(agent::GridAgentAll, model2) if agent.type == :one agent_step_one!(agent, model2) elseif agent.type == :two agent_step_two!(agent, model2) elseif agent.type == :three agent_step_three!(agent, model2) elseif agent.type == :four agent_step_four!(agent, model2) else agent_step_five!(agent, model2) end end agent_step_one!(agent, model2) = walk!(agent, rand, model2) function agent_step_two!(agent, model2) agent.one += rand(model2.rng) agent.two = rand(model2.rng, Bool) end function agent_step_three!(agent, model2) if any(a-> a.type == :two, nearby_agents(agent, model2)) agent.two = true walk!(agent, rand, model2) end end function agent_step_four!(agent, model2) agent.one += sum(a.one for a in nearby_agents(agent, model2)) end function agent_step_five!(agent, model2) targets = filter!(a->a.one > 1.0, collect(nearby_agents(agent, model2, 3))) if !isempty(targets) idx = argmax(map(t->edistance(agent, t, model2), targets)) farthest = targets[idx] walk!(agent, sign.(farthest.pos .- agent.pos), model2) end end ################### Benchmarks ############### @btime step!($model1, agent_step!, dummystep, 500) @btime step!($model2, agent_step!, dummystep, 500) # Results: # 718.589 ms (11581560 allocations: 704.26 MiB) # 141.673 ms (2292318 allocations: 149.54 MiB)
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""" find_global_maximum_bfs(graph::Dict{Int64,Array{Int64,1}}) Find the total global maximum based on a comparsion between the intial vertex (`global_maximum = 0`), the current dictionary key, and the accesible vertexes from the array-list (value). # Arguments - `graph::Dict{Int64,Array{Int64,1}}`: Graph of the connected nodes """ function find_global_maximum_bfs(graph::Dict{Int64,Array{Int64,1}}) global_maximum = 0 # Going through the dictionary for (key, value) in graph # Check the keys as new global maximum if key > global_maximum global_maximum = key end # Check the values as new local maximum local_maximum = maximum(value) # Check if new local maximum becomes global maximum if local_maximum > global_maximum global_maximum = local_maximum end end return global_maximum end """ breadth_first_search(graph::Dict{Int64,Array{Int64,1}}, start::Int64) The breadth-first search (BFS) is an algorithm dedicated to traversing or searching for tree or graph data structures. It starts at a specified tree root (start) for exploring all connected neighbor nodes. The important feature is that the BFS automatically leaves the present depth and passes on to the next nodes at a deeper level. BFS is queue-based. For more information see: [https://en.wikipedia.org/wiki/Breadth-first_search](https://en.wikipedia.org/wiki/Breadth-first_search) # Arguments - `graph::Dict{Int64,Array{Int64,1}}`: Graph of the connected nodes - `start::Int64`: Startpoint (first selected vertex) of the graph-traveling process # Examples ```julia-repl julia> import ClassicAlgorithmsCollections julia> graph = Dict(1=> [2, 3], 2=> [3], 3=> [1, 4], 4=> [4]) julia> ClassicAlgorithmsCollections.breadth_first_search(graph, 3) [3, 1, 4, 2] ``` """ function breadth_first_search(graph::Dict{Int64,Array{Int64,1}}, start::Int64) # Mark all the vertices as not visited n = find_global_maximum_bfs(graph) visited = zeros(Bool, n) # Create a queue for BFS queue = zeros(Int64, false) # Create array-list for the searching solution solution = zeros(Int64, false) # Mark the source node as visited and enqueue it visited[start] = true append!(queue, start) while !isempty(queue) # Dequeue a vertex from # queue and print it start = popat!(queue, 1) append!(solution, start) # Get all adjacent vertices of the dequeued vertex s. If a adjacent # has not been visited, then mark it visited and enqueue it for i in graph[start] # Check if vertex has already been visited if visited[i] == false append!(queue, i) visited[i] = true end end end return solution end
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# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using DifferentialEquations, DiffEqCallbacks using ChainRulesCore import ForwardDiff, Zygote # helper to collect variable IdSet function indiciesForValueReferences(fmu::FMU2, refs::Array{fmi2ValueReference}) array = fmi2ValueReference[] for i in 1:length(fmu.modelDescription.valueReferences) if fmu.modelDescription.valueReferences[i] in refs push!(array, i) end end array end """ Performs something similar to `fmiDoStep` for ME-FMUs (note, that fmiDoStep is for CS-FMUs only). Event handling (state- and time-events) is supported. If you don't want events to be handled, you can disable event-handling for the NeuralFMU `nfmu` with the attribute `eventHandling = false`. Optional, additional FMU-values can be set via keyword arguments `setValueReferences` and `setValues`. Optional, additional FMU-values can be retrieved by keyword argument `getValueReferences`. Function takes the current system state array ("x") and returns an array with state derivatives ("x dot") and optionally the FMU-values for `getValueReferences`. Setting the FMU time via argument `t` is optional, if not set, the current time of the ODE solver around the NeuralFMU is used. """ function fmi2EvaluateME(comp::FMU2Component, x::Array{<:Real}, t = -1.0,#::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) _fmi2EvaluateME(comp, x, t, setValueReferences, setValues, getValueReferences) end function fmi2EvaluateME(comp::FMU2Component, x::Array{<:ForwardDiff.Dual{Tx, Vx, Nx}}, t = -1.0,#::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0) ) where {Tx, Vx, Nx} _fmi2EvaluateME_fd((Tx, Vx, Nx), (Tx, Vx, Nx), comp, x, t, setValueReferences, setValues, getValueReferences) end function fmi2EvaluateME(comp::FMU2Component, x::Array{<:ForwardDiff.Dual{Tx, Vx, Nx}}, t,#::Real, setValueReferences::Array{fmi2ValueReference}, setValues::Array{<:ForwardDiff.Dual{Tu, Vu, Nu}}, getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0) ) where {Tx, Vx, Nx, Tu, Vu, Nu} _fmi2EvaluateME_fd((Tx, Vx, Nx), (Tu, Vu, Nu), comp, x, t, setValueReferences, setValues, getValueReferences) end # ForwardDiff backend using the existing ChainRulesCore.frule # adapted from: https://discourse.julialang.org/t/chainrulescore-and-forwarddiff/61705/8 function _fmi2EvaluateME_fd(TVNx, TVNu, comp, x, t, setValueReferences, setValues, getValueReferences) #where {T, V, N} #where {T} # <:ForwardDiff.Dual} Tx, Vx, Nx = TVNx Tu, Vu, Nu = TVNu ȧrgs = [NoTangent(), NoTangent(), collect(ForwardDiff.partials(e) for e in x), ForwardDiff.partials(t), NoTangent(), collect(ForwardDiff.partials(e) for e in setValues), NoTangent()] args = [fmi2EvaluateME, comp, collect(ForwardDiff.value(e) for e in x), ForwardDiff.value(t), setValueReferences, collect(ForwardDiff.value(e) for e in setValues), getValueReferences] # ToDo: Find a good fix! #Zygote.@ignore @debug "From $(typeof(args[6]))" if typeof(args[6]) == Vector{Any} args[6] = convert(Vector{Float64}, args[6]) #Zygote.@ignore @debug "To $(typeof(args[6]))" end ȧrgs = (ȧrgs...,) args = (args...,) @assert (typeof(args[3]) == Vector{Float64}) || (typeof(args[3]) == Vector{Float32}) "After conversion, `x` is still an invalid type `$(typeof(args[3]))`." y, _, dx, _, _, du, _ = ChainRulesCore.frule(ȧrgs, args...) if Vx != Float64 Vx = Float64 end if Vu != Float64 Vu = Float64 end # original function returns [dx_1, ..., dx_n, y_1, ..., y_m] # ToDo: Add sensitivities (partials) from du -> (dx, y) [collect( ForwardDiff.Dual{Tx, Vx, Nx}(y[i], dx[i]) for i in 1:length(dx) )...] end function _fmi2EvaluateME(comp::FMU2Component, x::Array{<:Real}, t::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) @assert fmi2IsModelExchange(comp.fmu) ["fmi2EvaluateME(...): As in the name, this function only supports ME-FMUs."] setter = (length(setValueReferences) > 0) getter = (length(getValueReferences) > 0) if setter @assert length(setValueReferences) == length(setValues) ["fmi2EvaluateME(...): `setValueReferences` and `setValues` need to be the same length!"] end if t >= 0.0 fmi2SetTime(comp, t) end if setter fmi2SetReal(comp, setValueReferences, setValues) end fmi2SetContinuousStates(comp, x) y = [] if getter y = fmi2GetReal(comp, getValueReferences) end dx = fmi2GetDerivatives(comp) [dx..., y...] end function ChainRulesCore.rrule(::typeof(fmi2EvaluateME), comp::FMU2Component, x::Array{<:Real}, t::Real = -1.0, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) y = fmi2EvaluateME(comp, x, t, setValueReferences, setValues, getValueReferences) function fmi2EvaluateME_pullback(ȳ) setter = (length(setValueReferences) > 0) getter = (length(getValueReferences) > 0) if setter @assert length(setValueReferences) == length(setValues) ["ChainRulesCore.rrule(fmi2EvaluateME, ...): `setValueReferences` and `setValues` need to be the same length!"] end if t >= 0.0 fmi2SetTime(comp, t) end fmi2SetContinuousStates(comp, x) rdx = vcat(comp.fmu.modelDescription.derivativeValueReferences, getValueReferences) rx = comp.fmu.modelDescription.stateValueReferences ru = setValueReferences n_dx_x = NoTangent() n_dx_u = NoTangent() if comp.senseFunc == :full || comp.senseFunc == :directionalDerivatives || comp.senseFunc == :auto # OPTIMIZATION: compute new jacobians only if system state or time changed, otherwise return the cached one if comp.jac_x != x || comp.jac_t != t if size(comp.jac_dxy_x) != (length(rdx), length(rx)) comp.jac_dxy_x = zeros(length(rdx), length(rx)) end comp.jacobianFct(comp.jac_dxy_x, comp, rdx, rx) if size(comp.jac_dxy_u) != (length(rdx), length(ru)) comp.jac_dxy_u = zeros(length(rdx), length(ru)) end comp.jacobianFct(comp.jac_dxy_u, comp, rdx, ru) comp.jac_x = x comp.jac_t = t end n_dx_x = @thunk(comp.jac_dxy_x' * ȳ) if setter n_dx_u = @thunk(comp.jac_dxy_u' * ȳ) end elseif comp.senseFunc == :adjointDerivatives @assert false "Adjoint Derivatives not supported by FMI2." else @assert false "`senseFunc=$(comp.senseFunc)` unknown value for `senseFunc`." end f̄ = NoTangent() c̄omp = ZeroTangent() x̄ = n_dx_x t̄ = ZeroTangent() s̄etValueReferences = ZeroTangent() s̄etValues = n_dx_u ḡetValueReferences = ZeroTangent() return f̄, c̄omp, x̄, t̄, s̄etValueReferences, s̄etValues, ḡetValueReferences end return y, fmi2EvaluateME_pullback end function ChainRulesCore.frule((Δself, Δcomp, Δx, Δt, ΔsetValueReferences, ΔsetValues, ΔgetValueReferences), ::typeof(fmi2EvaluateME), comp, #::FMU2Component, x,#::Array{<:Real}, t,#::Real = -1.0, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) y = fmi2EvaluateME(comp, x, t, setValueReferences, setValues, getValueReferences) function fmi2EvaluateME_pullforward(Δx, ΔsetValues) setter = (length(setValueReferences) > 0) getter = (length(getValueReferences) > 0) if setter @assert length(setValueReferences) == length(setValues) ["ChainRulesCore.frule(fmi2EvaluateME, ...): `setValueReferences` and `setValues` need to be the same length!"] end if t >= 0.0 fmi2SetTime(comp, t) end if all(isa.(x, ForwardDiff.Dual)) xf = collect(ForwardDiff.value(e) for e in x) fmi2SetContinuousStates(comp, xf) else fmi2SetContinuousStates(comp, x) end rdx = vcat(comp.fmu.modelDescription.derivativeValueReferences, getValueReferences) rx = comp.fmu.modelDescription.stateValueReferences ru = setValueReferences n_dx_x = NoTangent() n_dx_u = NoTangent() if comp.senseFunc == :auto || comp.senseFunc == :full # OPTIMIZATION: compute new jacobians only if system state or time changed, otherwise return the cached one # ToDo: Optimize for getDirectionalDerivatives with seed vector Δx if comp.jac_x != x || comp.jac_t != t if size(comp.jac_dxy_x) != (length(rdx), length(rx)) comp.jac_dxy_x = zeros(length(rdx), length(rx)) end comp.jacobianFct(comp.jac_dxy_x, comp, rdx, rx) if size(comp.jac_dxy_u) != (length(rdx), length(ru)) comp.jac_dxy_u = zeros(length(rdx), length(ru)) end comp.jacobianFct(comp.jac_dxy_u, comp, rdx, ru) comp.jac_x = x comp.jac_t = t end n_dx_x = comp.jac_dxy_x * Δx if setter n_dx_u = comp.jac_dxy_u * ΔsetValues end elseif comp.senseFunc == :directionalDerivatives n_dx_x = fmi2GetDirectionalDerivative(comp, rdx, rx, Δx) if setter n_dx_u = fmi2GetDirectionalDerivative(comp, rdx, ru, ΔsetValues) end elseif comp.senseFunc == :adjointDerivatives @assert false "Adjoint Derivatives not supported by FMI2." else @assert false "`senseFunc=$(comp.senseFunc)` unknown value for `senseFunc`." end c̄omp = ZeroTangent() x̄ = n_dx_x t̄ = ZeroTangent() s̄etValueReferences = ZeroTangent() s̄etValues = n_dx_u ḡetValueReferences = ZeroTangent() return (c̄omp, x̄, t̄, s̄etValueReferences, s̄etValues, ḡetValueReferences) end return (y, fmi2EvaluateME_pullforward(Δx, ΔsetValues)...) end """ Performs a fmiDoStep for CS-FMUs (note, that fmiDoStep is for CS-FMUs only). Optional, FMU-values can be set via keyword arguments `setValueReferences` and `setValues`. Optional, FMU-values can be retrieved by keyword argument `getValueReferences`. Function returns the FMU-values for the optional keyword argument `getValueReferences`. The CS-FMU performs one macro step with step size `dt`. Dependent on the integrated numerical solver, the FMU may perform multiple (internal) micro steps if needed to meet solver requirements (stability/accuracy). These micro steps are hidden by FMI2. """ function fmi2DoStepCS(comp::FMU2Component, dt::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) _fmi2DoStepCS(comp, dt, setValueReferences, setValues, getValueReferences) end function fmi2DoStepCS(comp::FMU2Component, dt,#::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:ForwardDiff.Dual{Tu, Vu, Nu}} = Array{ForwardDiff.Dual{Tu, Vu, Nu}}(), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0) ) where {Tu, Vu, Nu} _fmi2DoStepCS_fd((Td, Vd, Nd), comp, dt, setValueReferences, setValues, getValueReferences) end # Helper because keyword arguments are (currently) not supported by Zygote. function _fmi2DoStepCS(comp::FMU2Component, dt::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) @assert fmi2IsCoSimulation(comp.fmu) ["fmi2DoStepCS(...): As in the name, this function only supports CS-FMUs."] @assert length(setValueReferences) == length(setValues) ["fmi2DoStepCS(...): `setValueReferences` ($(length(setValueReferences))) and `setValues` ($(length(setValues))) need to be the same length!"] if length(setValueReferences) > 0 fmi2SetReal(comp, setValueReferences, setValues) end fmi2DoStep(comp, dt) y = zeros(Float64, 0) if length(getValueReferences) > 0 y = fmi2GetReal(comp, getValueReferences) end y end # ForwardDiff backend using the existing ChainRulesCore.frule # adapted from: https://discourse.julialang.org/t/chainrulescore-and-forwarddiff/61705/8 function _fmi2DoStepCS_fd(TVNu, comp, dt, setValueReferences, setValues, getValueReferences) Tu, Vu, Nu = TVNu ȧrgs = [NoTangent(), NoTangent(), ForwardDiff.partials(dt), NoTangent(), collect(ForwardDiff.partials(e) for e in setValues), NoTangent()] args = [fmi2DoStepCS, comp, ForwardDiff.value(dt), setValueReferences, collect(ForwardDiff.value(e) for e in setValues), getValueReferences] # ToDo: Find a good fix! if typeof(args[5]) == Vector{Any} args[5] = convert(Vector{Float64}, args[5]) end ȧrgs = (ȧrgs...,) args = (args...,) y, _, _, _, du, _ = ChainRulesCore.frule(ȧrgs, args...) if Vu != Float64 Vu = Float64 end # original function returns [y_1, ..., y_m] [collect( ForwardDiff.Dual{Tu, Vu, Nu}(y[i], du[i]) for i in 1:length(du) )...] end function ChainRulesCore.rrule(::typeof(fmi2DoStepCS), comp::FMU2Component, dt::Real = -1.0, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) y = fmi2DoStepCS(comp, dt, setValueReferences, setValues, getValueReferences) function fmi2DoStepCS_pullback(ȳ) setter = (length(setValueReferences) > 0) getter = (length(getValueReferences) > 0) if setter @assert length(setValueReferences) == length(setValues) ["ChainRulesCore.rrule(fmi2DoStepCS, ...): `setValueReferences` and `setValues` need to be the same length!"] end rdx = getValueReferences rx = setValueReferences n_dx_u = ZeroTangent() if getter mat = zeros(length(rdx), length(rx)) comp.jacobianFct(mat, comp, rdx, rx) n_dx_u = @thunk(mat' * ȳ) end f̄ = NoTangent() f̄mu = ZeroTangent() d̄t = ZeroTangent() s̄etValueReferences = ZeroTangent() s̄etValues = n_dx_u ḡetValueReferences = ZeroTangent() return (f̄, f̄mu, d̄t, s̄etValueReferences, s̄etValues, ḡetValueReferences) end return y, fmi2DoStepCS_pullback end function ChainRulesCore.frule((Δself, Δcomp, Δdt, ΔsetValueReferences, ΔsetValues, ΔgetValueReferences), ::typeof(fmi2DoStepCS), comp, #::FMU2, dt,#::Real = -1.0, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0)) y = fmi2DoStepCS(comp, dt, setValueReferences, setValues, getValueReferences) function fmi2DoStepCS_pullforward(ΔsetValues) setter = (length(setValueReferences) > 0) getter = (length(getValueReferences) > 0) if setter @assert length(setValueReferences) == length(setValues) ["ChainRulesCore.frule(fmi2DoStepCS, ...): `setValueReferences` and `setValues` need to be the same length!"] end rdx = getValueReferences rx = setValueReferences n_dx_u = ZeroTangent() if getter mat = zeros(length(rdx), length(rx)) comp.jacobianFct(mat, comp, rdx, rx) n_dx_u = mat * ΔsetValues end f̄mu = ZeroTangent() d̄t = ZeroTangent() s̄etValueReferences = ZeroTangent() s̄etValues = n_dx_u ḡetValueReferences = ZeroTangent() return (f̄mu, d̄t, s̄etValueReferences, s̄etValues, ḡetValueReferences) end return (y, fmi2DoStepCS_pullforward(ΔsetValues)...) end """ fmi2InputDoStepCSOutput(comp::FMU2Component, dt::Real, u::Array{<:Real}) Sets all FMU inputs to `u`, performs a ´´´fmi2DoStep´´´ and returns all FMU outputs. """ function fmi2InputDoStepCSOutput(comp::FMU2Component, dt::Real, u::Array{<:Real}) @assert fmi2IsCoSimulation(comp.fmu) ["fmi2InputDoStepCSOutput(...): As in the name, this function only supports CS-FMUs."] fmi2DoStepCS(comp, dt, comp.fmu.modelDescription.inputValueReferences, u, comp.fmu.modelDescription.outputValueReferences) end # FMU wrappers function fmi2EvaluateME(fmu::FMU2, args...; kwargs...) fmi2EvaluateME(fmu.components[end], args...; kwargs...) end function fmi2DoStepCS(fmu::FMU2, args...; kwargs...) fmi2DoStepCS(fmu.components[end], args...; kwargs...) end function fmi2InputDoStepCSOutput(fmu::FMU2, args...; kwargs...) fmi2InputDoStepCSOutput(fmu.components[end], args...; kwargs...) end
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2.136129
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using GmshParser using Test @testset "GmshParser.jl" begin # Write your tests here. end
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2.657143
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""" Stacked layer is a abstract type and defines common behavior for containers layers that enables to access them as arrays. """ abstract type StackedLayer <: Layer end add(model::StackedLayer, layers...) = push!(model.layers, layers...) """ Sequential layer allows to chain together a number of other layers. # Usage ```julia model = Sequential(Conv2D(100),MaxPool(),Dense(10)) ``` """ struct Sequential <:StackedLayer layers::Vector Sequential(blocks...) = new([blocks...]) end function call(model::Sequential, X::Tensor) for layer in model.layers X = layer(X) end return X end """ Concurrrent layer allows for stacking a number of other layers in parallel and combining their results before returning it. This layer will stack on the second last dimension. So with 2D and 3D convolution this will be the channel layer (WxHxCxN). As a result other dimensions have to the same. """ struct Concurrent <:StackedLayer layers::Vector Concurrent(blocks...) = new([blocks...]) end function call(model::Concurrent, X::Tensor) out = [] for layer in model.layers push!(out, layer(X)) end cat(out..., dims=ndims(out[1])-1) end """ Residual Layer works like a Sequential layer, however before returning the result it will be combined with the orginal input (residual). This is a popular techique in modern neural networds since it allows for better backpropagation. This will stack on the second last dimension. So with 2D and 3D convolution this will be the channel layer (WxHxCxN) """ struct Residual <:StackedLayer layers::Vector Residual(blocks...) = new([blocks...]) end function call(model::Residual, X::Tensor) res = X for layer in model.layers X = layer(X) end cat(res, X, dims=ndims(res[1])-1) end @debug "Loaded Container modules"
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3.126095
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# Add our own pairwise function to be able to apply it on vectors function pairwise(d::PreMetric, X::AbstractVector, Y::AbstractVector) return broadcast(d, X, permutedims(Y)) end pairwise(d::PreMetric, X::AbstractVector) = pairwise(d, X, X) function pairwise!(out::AbstractMatrix, d::PreMetric, X::AbstractVector, Y::AbstractVector) return broadcast!(d, out, X, permutedims(Y)) end pairwise!(out::AbstractMatrix, d::PreMetric, X::AbstractVector) = pairwise!(out, d, X, X) function pairwise(d::PreMetric, x::AbstractVector{<:Real}) return Distances_pairwise(d, reshape(x, :, 1); dims=1) end function pairwise(d::PreMetric, x::AbstractVector{<:Real}, y::AbstractVector{<:Real}) return Distances_pairwise(d, reshape(x, :, 1), reshape(y, :, 1); dims=1) end function pairwise!(out::AbstractMatrix, d::PreMetric, x::AbstractVector{<:Real}) return Distances.pairwise!(out, d, reshape(x, :, 1); dims=1) end function pairwise!( out::AbstractMatrix, d::PreMetric, x::AbstractVector{<:Real}, y::AbstractVector{<:Real} ) return Distances.pairwise!(out, d, reshape(x, :, 1), reshape(y, :, 1); dims=1) end # Also defines the colwise method for abstractvectors function colwise(d::PreMetric, x::AbstractVector) return zeros(Distances.result_type(d, x, x), length(x)) # Valid since d(x,x) == 0 by definition end function colwise(d::PreMetric, x::ColVecs) return zeros(Distances.result_type(d, x.X, x.X), length(x)) # Valid since d(x,x) == 0 by definition end function colwise(d::PreMetric, x::RowVecs) return zeros(Distances.result_type(d, x.X, x.X), length(x)) # Valid since d(x,x) == 0 by definition end ## The following is a hack for DotProduct and Delta to still work function colwise(d::Distances.UnionPreMetric, x::ColVecs) return Distances.colwise(d, x.X, x.X) end function colwise(d::Distances.UnionPreMetric, x::RowVecs) return Distances.colwise(d, x.X', x.X') end function colwise(d::Distances.UnionPreMetric, x::AbstractVector) return map(d, x, x) end function colwise(d::PreMetric, x::ColVecs, y::ColVecs) return Distances.colwise(d, x.X, y.X) end function colwise(d::PreMetric, x::RowVecs, y::RowVecs) return Distances.colwise(d, x.X', y.X') end function colwise(d::PreMetric, x::AbstractVector, y::AbstractVector) return map(d, x, y) end
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function listFunctions(_module::Module) _names = names(_module; all=true) _functions = Function[] for _name in _names _function = getfield(_module, _name) if ( typeof(_function) <: Function && !occursin("#",string(_name)) && !(string(_function) in ["eval","include"]) ) push!(_functions, _function) end end _functions end function listMethods(_module::Module) _functions = listFunctions(_module) _methods = Method[] for _f in _functions _methodList = methods(_f) push!(_methods,_methodList.ms...) end _methods end function createDefaultArguments(method) @info "Create default arguments for method[$(method.name)]" sig = try collect(method.sig.types) catch e @warn e return nothing end popfirst!(sig) argsAsVector = [] for _type in sig arg = try createDefaultObject(getNonMissingTypeOfUnionType(_type)) catch e @warn e return nothing end push!(argsAsVector, arg) end argsAsVector end function createDefaultObject(_type::DataType) if _type <: Number return _type(0) end if _type <: String return "lorem ipsum" end if _type <: Date return today() end if _type <: Enum return first(instances(_type)) end return _type() end function createDefaultObject(::Type{Dict}) return Dict() end function invokeMethod(_function::Function, args::Vector, procID::Int64) argsTypesForPrinting = join(string.(typeof.(args)),", ") @info "# Invoke $(_function)($argsTypesForPrinting) on procID[$procID]" try future = @spawnat procID _function(args...) catch e # try-catch on the @spawnat is not enough because Exceptions on remote # computations are captured and rethrown locally. Therefore the calling # method needs to try-catch the call to this function. # see https://docs.julialang.org/en/v1/stdlib/Distributed/index.html#Distributed.RemoteException error(e) end end function invokeMethod(_method::Method, args::Vector, procID::Int64) fct = getfield(_method.module, _method.name) invokeMethod(fct, args, procID) end function invokeMethodOnAllProcs(_method::Method) args = createDefaultArguments(_method) if isnothing(args) @warn "Unable to create default arugments => Skip method[$(_method)]" return end for procID in 1:nprocs() try invokeMethod(_method, args, procID) # try-catch on the @spawnat is not enough because Exceptions on remote # computations are captured and rethrown locally # see https://docs.julialang.org/en/v1/stdlib/Distributed/index.html#Distributed.RemoteException catch e @debug "RemoteException was caught so that we can carry on." end end end function invokeMethodsOfModule(_module::Module ;excludeMethods::Vector{Symbol} = Symbol[]) methods = listMethods(_module) filter!(x-> !(x.name in excludeMethods),methods) for m in methods invokeMethodOnAllProcs(m) end end # This method exists so that we can call it even without having to test if the # argument is a Union function getNonMissingTypeOfUnionType(arg::Any) return arg end # NOTE: We cannot say that this method returns a DataType (::DataType) because # arrays are not datatypes function getNonMissingTypeOfUnionType(arg::Union) if arg.a != Missing return arg.a else return arg.b end end
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struct IPSReactionIR pairwise :: Bool input1 :: Int # center input2 :: Int # adjacent output1 :: Int # center output2 :: Int # adjacent label :: Int # reaction index p_index :: Int # parameter index end function Base.show(io::IO, x::IPSReactionIR) i1, i2 = x.input1, x.input2 o1, o2 = x.output1, x.output2 if x.pairwise str = "$(i1) + $(i2) --> $(o1) + $(o2)" else str = "$(i1) --> $(o1)" end print(io, str) end ##### helper functions ##### function clean_expression(input_ex) ex = Expr(input_ex.head) for line in input_ex.args if line isa Expr && line.head == :tuple && length(line.args) == 2 # change 0 to :∅ to make it its own "type" clean_line = postwalk(x -> x isa Integer && x == 0 ? :∅ : x, line) # add the new line push!(ex.args, clean_line) end end # add checks to make sure each formula is well-formed return ex end # catch-all for symbols and constants function add_tokens!(tokens, sym) push!(tokens, sym) end # search through an Expr function add_tokens!(tokens, ex::Expr) for token in ex.args if isa(token, Symbol) push!(tokens, token) end end return tokens end # build a Dictionary that maps a particle type to an index function build_species_dict(ex) open_site = :∅ tokens = Symbol[] for line in ex.args formula = line.args[1] input = formula.args[1] output = formula.args[2] add_tokens!(tokens, input) add_tokens!(tokens, output) end tokens = unique(tokens) filter!(x -> (x != :+), tokens) filter!(x -> (x != :*), tokens) filter!(x -> (x != open_site), tokens) sort!(tokens) tokens = [open_site; tokens] return OrderedDict{Symbol,Int}(tokens[i] => i for i in eachindex(tokens)) end # catch-all for non-pairwise events function get_species_types(sym, dict) x = dict[sym] y = 0 return x, y end # for pairwise events function get_species_types(ex::Expr, dict) x_sym = ex.args[2] y_sym = ex.args[3] x = dict[x_sym] y = dict[y_sym] return x, y end # build a Dictionary that maps a parameter symbol to an index function build_parameters_dict(p) parameters = OrderedDict{Symbol,Int}() p_count = 0 for parameter in p if !haskey(parameters,parameter) p_count += 1 parameters[parameter] = p_count end end return parameters end # build a list of internal IPSReactionIR objects from input function encode_reaction_struct(ex::Expr, species_dict, params_dict) reactions = IPSReactionIR[] for j in eachindex(ex.args) line = ex.args[j] formula = line.args[1] parameter = line.args[2] input = formula.args[1] output = formula.args[2] is_pairwise = isa(input, Expr) # if not, then the argument is a symbol or constant type1, type2 = get_species_types(input, species_dict) type3, type4 = get_species_types(output, species_dict) reaction = IPSReactionIR(is_pairwise, type1, type2, type3, type4, j, params_dict[parameter]) push!(reactions, reaction) end return reactions end ## define a reaction set from user input macro def_reactions(inputex::Expr, p...) __def_reactions(inputex, p) end ## define a reaction set from a programatically generated list macro def_reactions(inputex::Symbol, p...) escex = esc(inputex) quote __def_reactions($escex, $p) end end # implements the body of the @def_reactions macro function __def_reactions(inputex, p) # sweep through the user's code block to clean it up # e.g. strip away the annoying line numbers that get folded in ex = clean_expression(inputex) # sweep through the user's code block to pick up # all the unique species and map them to an index species_dict = build_species_dict(ex) params_dict = build_parameters_dict(p) # translate the user's model to some intermediate representation reactions = encode_reaction_struct(ex, species_dict, params_dict) # the macro needs to return an expression # which builds a IPSReactionIR array return :($reactions) end ## enumerate the full reaction list using a given spatial structure # macro enumerate_with_nclass(r, n, d, p) # escr = esc(r) # escn = esc(n) # escd = esc(d) # escp = esc(p) # quote # __reactions_nclass($escr, $escn, $escd, $escp) # end # end # implements the body of @enumerate_with_nclass # function __reactions_nclass(initial, nbhood, d, params) # nbhood ∉ NBTYPES && error("unsupported neighborhood structure") # d > 3 && error("do you really need $(d) dimensions?") # # # determine number of particle types # L = 0 # # for reaction in initial # L = max(L, reaction.input1, reaction.input2, reaction.output1, reaction.output2) # end # # # determine maximum number of neighbors # nbmax = capacity(nbhood, d) # # # compositions = collect(multiexponents(L + 1, 2 * d)) # compositions = collect(multiexponents(L, nbmax)) # number_compositions = length(compositions) # # reactions = IPSReactionStruct[] # # for reaction in initial # for class in 1:number_compositions # # class is the integer corresponding to each composition # composition = compositions[class] # # # check for pairwise reaction # if reaction.pairwise == true # # get the number of reactants in the composition # number_reactants = composition[reaction.input2] # # # ignore this class-reaction pair if there are no suitable # # reactants in the given configuration # if number_reactants != 0 # rate = params[reaction.p_index] # # push!(reactions, IPSReactionStruct( # reaction.pairwise, # reaction.input1, # reaction.input2, # reaction.output1, # reaction.output2, # class, # neighborhood class # number_reactants * rate # local rate # ) ) # end # else # rate = params[reaction.p_index] # # push!(reactions, IPSReactionStruct( # reaction.pairwise, # reaction.input1, # reaction.input2, # reaction.output1, # reaction.output2, # class, # neighborhood class # rate / number_compositions # scaled local rate # ) ) # end # end # end # # return reactions # end ## enumerate the full reaction list using a given spatial structure macro enumerate_with_sclass(r, n, d, p) escr = esc(r) escn = esc(n) escd = esc(d) escp = esc(p) quote __reactions_sclass($escr, $escn, $escd, $escp) end end # implements the body of the @enumerate_with_sclass macro function __reactions_sclass(initial, nbhood, d, params) nbhood ∉ NBTYPES && error("unsupported neighborhood structure") d > 3 && error("do you really need $(d) dimensions?") # determine number of particle types number_types = 0 for rxn in initial number_types = max( number_types, rxn.input1, rxn.input2, rxn.output1, rxn.output2 ) end number_types -= 1 # determine maximum number of neighbors number_neighbors = capacity(nbhood, d) pairs = build_reactant_pairs(initial) isactive = build_active_set(pairs, number_types) reactant_to_class = map_reactant_to_class(pairs, number_neighbors) reactions = IPSReactionStruct[] for reaction in initial reactant_pair = (reaction.input1, reaction.input2) sampleidx = reactant_to_class[reactant_pair] if reaction.pairwise == true # iterate over the possible number of adjacent reactants for number_reactants in 1:number_neighbors rate = params[reaction.p_index] push!(reactions, IPSReactionStruct( reaction.pairwise, reaction.input1, reaction.input2, reaction.output1, reaction.output2, # need to shift index based on dimension sampleidx + number_reactants - 1, # total rate at which a particle in this class undergoes this reaction number_reactants * rate) ) end else # don't care about neighborhood composition here for the rate or the class, so we explicitly ignore it # meaning we don't need to change anything about the reaction vector rate = params[reaction.p_index] push!(reactions, IPSReactionStruct( reaction.pairwise, reaction.input1, reaction.input2, reaction.output1, reaction.output2, sampleidx, rate # total rate at which a particle in this class undergoes this reaction ) ) end end # enumerate the possible neighborhood compositions composition = collect(Vector{Int}, multiexponents(number_types + 1, number_neighbors)) # build a mapping from (l,k) to sample classes s pair_to_classes = map_pair_to_classes(composition, reactant_to_class, isactive, number_types) # group everything from our sample class enumeration enumeration = SampleClassEnumeration{d,typeof(nbhood)}(composition, pairs, reactant_to_class, pair_to_classes, isactive) return InteractingParticleSystem(reactions, isactive, enumeration) end
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37724, 11, 318, 5275, 8, 628, 220, 1441, 4225, 27362, 7841, 1548, 11964, 7, 260, 4658, 11, 318, 5275, 11, 27056, 341, 8, 198, 437, 198 ]
2.576988
3,546
""" Atomic solution for an PODE. ### Fields * `t`: time of current time step * `t̅`: time of previous time step * `q`: current solution of q * `q̅`: previous solution of q * `q̃`: compensated summation error of q * `p`: current solution of p * `p̅`: previous solution of p * `p̃`: compensated summation error of p * `v`: vector field of q * `v̅`: vector field of q̅ * `f`: vector field of p * `f̅`: vector field of p̅ """ mutable struct AtomicSolutionPODE{DT,TT} <: AtomicSolution{DT,TT} t::TT t̅::TT q::Vector{DT} q̅::Vector{DT} q̃::Vector{DT} p::Vector{DT} p̅::Vector{DT} p̃::Vector{DT} v::Vector{DT} v̅::Vector{DT} f::Vector{DT} f̅::Vector{DT} function AtomicSolutionPODE{DT, TT}(nd) where {DT <: Number, TT <: Real} new(zero(TT), zero(TT), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd), zeros(DT, nd)) end end AtomicSolutionPODE(DT, TT, nd) = AtomicSolutionPODE{DT, TT}(nd) function set_solution!(asol::AtomicSolutionPODE, sol) t, q, p = sol asol.t = t asol.q .= q asol.p .= p asol.v .= 0 asol.f .= 0 end function get_solution(asol::AtomicSolutionPODE) (asol.t, asol.q, asol.p) end function CommonFunctions.reset!(asol::AtomicSolutionPODE, Δt) asol.t̅ = asol.t asol.q̅ .= asol.q asol.p̅ .= asol.p asol.v̅ .= asol.v asol.f̅ .= asol.f asol.t += Δt end function update!(asol::AtomicSolutionPODE{DT}, y::Vector{DT}, z::Vector{DT}) where {DT} for k in eachindex(y,z) update!(asol, y[k], z[k], k) end end function update!(asol::AtomicSolutionPODE{DT}, y::DT, z::DT, k::Int) where {DT} asol.q[k], asol.q̃[k] = compensated_summation(y, asol.q[k], asol.q̃[k]) asol.p[k], asol.p̃[k] = compensated_summation(z, asol.p[k], asol.p̃[k]) end
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1.970498
983
function trimolecular_1path_diff!(du,u,p,t,dens,cons) # unpack rates and constants nᵣ,nᵣₓ,n₃ = u kₓ,k₋ₓ,k₃,k₋₃ = p k₁,k₋₁ = cons mᵣ,mₗ,mₓ,A = dens # model du[1] = dnᵣ = A*k₁*mᵣ*mₗ - k₋₁*nᵣ - A*kₓ*mₓ*nᵣ + k₋ₓ*nᵣₓ du[2] = dnᵣₓ = A*kₓ*mₓ*nᵣ - k₋ₓ*nᵣₓ - k₃*nᵣₓ + k₋₃*n₃ du[3] = dn₃ = k₃*nᵣₓ - k₋₃*n₃ end
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### A Pluto.jl notebook ### # v0.12.18 using Markdown using InteractiveUtils # ╔═╡ 92081fb8-5d90-11eb-2078-ddbf87421051 begin using ReinforcementLearning using Flux using Statistics using Plots using Distributions end # ╔═╡ 3254385c-5d90-11eb-2c99-07a3a40ae467 md""" Again, we'll describe the car rental problem with a distributional model. """ # ╔═╡ a1a5176c-5d90-11eb-2ad6-2f108e1531e2 begin const PoissonUpperBound = 10 const MaxCars= 20 const MaxMoves = 5 const CostPerMove = 2 const CarRentalCartesianIndices = CartesianIndices((0:MaxCars,0:MaxCars)) const CarRentalLinearIndices = LinearIndices((0:MaxCars,0:MaxCars)) const Actions = -MaxMoves:MaxMoves const RequestDist_1 = Poisson(3) const RequestDist_2 = Poisson(4) const ReturnDist_1 = Poisson(3) const ReturnDist_2 = Poisson(2) decode_state(s::Int) = Tuple(CarRentalCartesianIndices[s]) encode_state(s1::Int, s2::Int) = CarRentalLinearIndices[CartesianIndex(s1+1, s2+1)] decode_action(a::Int) = a - MaxMoves - 1 encode_action(a::Int) = a + MaxMoves + 1 function merge_prob(dist) merged = Dict() for (s′, r, p) in dist if haskey(merged, (s′, r)) merged[(s′, r)] += p else merged[(s′, r)] = p end end [(r, false, s′) => p for ((s′, r), p) in merged] end function nextstep(state::Int, action::Int) (s1, s2), a = decode_state(state), decode_action(action) move = a > 0 ? min(a, s1) : max(a, -s2) reward = -CostPerMove*abs(move) s1′, s2′ = min(s1 - move, MaxCars), min(s2 + move, MaxCars) merge_prob( ( encode_state( min(max(s1′-req_1, 0)+ret_1, MaxCars), min(max(s2′-req_2, 0)+ret_2, MaxCars) ), reward + (min(s1′, req_1) + min(s2′, req_2)) * 10, ( pdf(RequestDist_1, req_1) * pdf(RequestDist_2, req_2) * pdf(ReturnDist_1, ret_1) * pdf(ReturnDist_2, ret_2) ) ) for req_1 in 0:PoissonUpperBound, req_2 in 0:PoissonUpperBound, ret_1 in 0:PoissonUpperBound, ret_2 in 0:PoissonUpperBound ) end struct CarRentalEnvModel <: AbstractEnvironmentModel dist end function CarRentalEnvModel() CarRentalEnvModel( Dict( (s,a) => nextstep(s,a) for s in 1:(MaxCars+1)^2 for a in 1:length(Actions) ) ) end (m::CarRentalEnvModel)(s, a) = m.dist[(s, a)] RLBase.state_space(m::CarRentalEnvModel) = Base.OneTo((MaxCars+1)^2) RLBase.action_space(m::CarRentalEnvModel) = Base.OneTo(length(Actions)) end # ╔═╡ 20e3c8d6-5d92-11eb-1591-9911e62805bd model = CarRentalEnvModel() # ╔═╡ 0c099578-5d92-11eb-2a8b-2f4889026185 V = TabularVApproximator(n_state=length(state_space(model)), opt=Descent(1.0)) # ╔═╡ dda27350-5d94-11eb-1a82-c302513da719 p = TabularPolicy(;table=Dict(s=>1 for s in state_space(model)), n_action=length(action_space(model))) # ╔═╡ 021f9b90-5d95-11eb-303a-ed2fb3de3d08 policy_iteration!(;V=V, π=p, model=model, γ=0.9, max_iter=300) # ╔═╡ 8bbe08e6-5d98-11eb-0c02-4712c4659830 heatmap(0:MaxCars, 0:MaxCars, reshape([decode_action(p(x)) for x in state_space(model)], 1+MaxCars,1+MaxCars)) # ╔═╡ 91d90bf6-5d98-11eb-23a5-4dfc2839a707 heatmap(0:MaxCars, 0:MaxCars, reshape(V.table, 1+MaxCars,1+MaxCars)) # ╔═╡ Cell order: # ╟─3254385c-5d90-11eb-2c99-07a3a40ae467 # ╠═92081fb8-5d90-11eb-2078-ddbf87421051 # ╠═a1a5176c-5d90-11eb-2ad6-2f108e1531e2 # ╠═20e3c8d6-5d92-11eb-1591-9911e62805bd # ╠═0c099578-5d92-11eb-2a8b-2f4889026185 # ╠═dda27350-5d94-11eb-1a82-c302513da719 # ╠═021f9b90-5d95-11eb-303a-ed2fb3de3d08 # ╠═8bbe08e6-5d98-11eb-0c02-4712c4659830 # ╠═91d90bf6-5d98-11eb-23a5-4dfc2839a707
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const _fact_tab64 = SafeInt64.(Base._fact_table64) const _fact_tab128 = SafeUInt128.(Base._fact_table128) function factorial_lookup(n::S, table, lim) where {S<:Union{SafeSigned, SafeUnsigned}} n < 0 && throw(DomainError(n, "`n` must not be negative.")) n > lim && throw(OverflowError(string(n, "! is too large to fit in a $S"))) n == 0 && return one(n) @inbounds f = table[n] return oftype(n, f) end Base.factorial(n::SafeInt128) = factorial_lookup(n, _fact_tab128, 33) Base.factorial(n::SafeUInt128) = factorial_lookup(n, _fact_tab128, 34) Base.factorial(n::Union{SafeInt64,SafeUInt64}) = factorial_lookup(n, _fact_tab64, 20) if Int === Int32 Base.factorial(n::Union{SafeInt8,SafeUInt8,SafeInt16,SafeUInt16}) = factorial(SafeInt32(n)) Base.factorial(n::Union{SafeInt32,SafeUInt32}) = factorial_lookup(n, _fact_tab64, 12) else Base.factorial(n::Union{SafeInt8,SafeUInt8,SafeInt16,SafeUInt16,SafeInt32,SafeUInt32}) = factorial(SafeInt64(n)) end
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#! /usr/bin/env julia using Benchmark using PolygonClipping using DataFrames function intersection1() polya = Polygon() push!(polya, Vertex(0.38717397323666614,0.9175286859623484)) push!(polya, Vertex(0.22863911382759805,0.97369797312849)) push!(polya, Vertex(0.411735284885411,0.67925592862104)) push!(polya, Vertex(0.4760881945244875,0.6150149154240558)) push!(polya, Vertex(0.05897662471214904,0.8393947489758318)) push!(polya, Vertex(0.1138425905192968,0.4935408034874309)) push!(polya, Vertex(0.5369414505688628,0.20213738997953312)) push!(polya, Vertex(0.8894594154609499,0.9924712388095045)) push!(polya, Vertex(0.9256610367848219,0.9190955722799556)) push!(polya, Vertex(0.6895872059237731,0.43384802891551)) push!(polya, Vertex(0.9087449635347744,0.47063971708226404)) push!(polya, Vertex(0.9757793404848136,0.7322250783012572)) push!(polya, Vertex(0.12339214488107642,0.11118441062092432)) push!(polya, Vertex(0.6140766278925023,0.7892323008875815)) push!(polya, Vertex(0.9080611442010653,0.8482511305518234)) push!(polya, Vertex(0.10274769769723902,0.38869064624120186)) push!(polya, Vertex(0.6082870387345407,0.9944088302986258)) push!(polya, Vertex(0.3641405100568089,0.6359528425953462)) push!(polya, Vertex(0.6835875991611398,0.6110124950747555)) push!(polya, Vertex(0.7131748261757693,0.8537241667688524)) push!(polya, Vertex(0.47313568651228555,0.84090661977734)) push!(polya, Vertex(0.6122874995061984,0.4397916059818694)) push!(polya, Vertex(0.9378622844157258,0.6928400362996152)) push!(polya, Vertex(0.37988141528846486,0.3924066347636528)) push!(polya, Vertex(0.37612785711290586,0.1262573371450586)) push!(polya, Vertex(0.2855066522487957,0.34807448520950257)) push!(polya, Vertex(0.3226665347220963,0.7530552496358307)) push!(polya, Vertex(0.6260310508679137,0.9624257683012829)) push!(polya, Vertex(0.5090596161323337,0.6293681446635448)) push!(polya, Vertex(0.15373073711802787,0.699604530492266)) push!(polya, Vertex(0.5916127221754168,0.5134368361443975)) push!(polya, Vertex(0.5476501761702322,0.12025320226897085)) push!(polya, Vertex(0.7364982683875068,0.6573742771739695)) push!(polya, Vertex(0.3564770108133659,0.6190436269899608)) push!(polya, Vertex(0.1942877121254134,0.7460859067795749)) push!(polya, Vertex(0.48141346512614325,0.33226336281429103)) push!(polya, Vertex(0.029892986776481,0.2174442631248703)) push!(polya, Vertex(0.6110877370822994,0.6597597834785822)) push!(polya, Vertex(0.6492858228562519,0.6866871593335842)) push!(polya, Vertex(0.7810487364244136,0.6630376297746976)) push!(polya, Vertex(0.7517037010056422,0.5481097354087443)) push!(polya, Vertex(0.32595306165531923,0.827698654052716)) push!(polya, Vertex(0.0946562934579609,0.06511398366340826)) push!(polya, Vertex(0.4016822086257372,0.2633273020831326)) push!(polya, Vertex(0.14840039175150355,0.26329155785679026)) push!(polya, Vertex(0.660892803459532,0.14824001535942988)) push!(polya, Vertex(0.7270462350880156,0.9616603642178803)) push!(polya, Vertex(0.20565290637531142,0.45438554764185524)) push!(polya, Vertex(0.01226767618359137,0.4346542090688914)) push!(polya, Vertex(0.8443656165997122,0.6218309213080848)) push!(polya, Vertex(0.36543895623038214,0.575693893269152)) push!(polya, Vertex(0.2057450566974688,0.41382632848748657)) push!(polya, Vertex(0.6578438918849019,0.018731630167831304)) push!(polya, Vertex(0.9251751209532304,0.27030612773533713)) push!(polya, Vertex(0.3698114785252209,0.08714925463572976)) push!(polya, Vertex(0.8310050243916558,0.19245735232136196)) push!(polya, Vertex(0.09311535434229778,0.5708872306538855)) push!(polya, Vertex(0.2275489167423912,0.10333866005215286)) push!(polya, Vertex(0.5691689464441063,0.07917751066510315)) push!(polya, Vertex(0.9441999955891076,0.08863956272995877)) push!(polya, Vertex(0.06944908047821619,0.9580858557547942)) push!(polya, Vertex(0.13317769494718235,0.30579396918400126)) push!(polya, Vertex(0.8916959800005415,0.49287891827534636)) push!(polya, Vertex(0.05313909148370777,0.568467861010775)) push!(polya, Vertex(0.8839793776888005,0.3922614940305833)) push!(polya, Vertex(0.29947054407245455,0.13067176985025442)) push!(polya, Vertex(0.6412436143042177,0.05964508351161513)) push!(polya, Vertex(0.9907882336943714,0.18490674849468958)) push!(polya, Vertex(0.3164116203917908,0.6915262366567434)) push!(polya, Vertex(0.670340857966748,0.2494086348537079)) push!(polya, Vertex(0.9516002726498964,0.5877410895223771)) push!(polya, Vertex(0.37859268972845683,0.5498068716998208)) push!(polya, Vertex(0.4585342971405306,0.009925253379711174)) push!(polya, Vertex(0.825358328321574,0.02436860679566233)) push!(polya, Vertex(0.2944481729419708,0.35565821826071664)) push!(polya, Vertex(0.9479518990808133,0.022876736181673296)) push!(polya, Vertex(0.32890511871298855,0.5903117675591205)) push!(polya, Vertex(0.13434078534002403,0.8043761349960912)) push!(polya, Vertex(0.36305742044837475,0.8074864926312384)) push!(polya, Vertex(0.37006152533035075,0.810010072925335)) push!(polya, Vertex(0.30245125983035126,0.531982907605898)) push!(polya, Vertex(0.6061475382439927,0.3591482019894219)) push!(polya, Vertex(0.4318224796352559,0.6906958337163789)) push!(polya, Vertex(0.5484989647598377,0.5979198296052988)) push!(polya, Vertex(0.3283124258298571,0.550335718203822)) push!(polya, Vertex(0.731217871868109,0.694746265475011)) push!(polya, Vertex(0.33503956272666824,0.7135843285873211)) push!(polya, Vertex(0.4458879602562089,0.8271969180995566)) push!(polya, Vertex(0.7868344276375905,0.9746067374413514)) push!(polya, Vertex(0.12173043080454882,0.8576124078521512)) push!(polya, Vertex(0.5907417299265332,0.6830068306826631)) push!(polya, Vertex(0.2927496535507432,0.9373149390911051)) push!(polya, Vertex(0.8550155012852447,0.9303058527010857)) push!(polya, Vertex(0.30123570126170174,0.5735420574883527)) push!(polya, Vertex(0.8967395912778122,0.018901254929095)) push!(polya, Vertex(0.6950306441092162,0.32167381094779923)) push!(polya, Vertex(0.8612213482620878,0.8954192932054237)) push!(polya, Vertex(0.755007334348081,0.6441892302099188)) push!(polya, Vertex(0.5129282217003186,0.6163889826956628)) push!(polya, Vertex(0.5956877205258515,0.207078358084255)) polyb = Polygon() push!(polyb, Vertex(0.45922312991074965,0.8634708765893035)) push!(polyb, Vertex(0.3432506505322517,0.7610444169585342)) push!(polyb, Vertex(0.7745733205429546,0.6432459662157686)) push!(polyb, Vertex(0.6626248852675782,0.3756202023478652)) push!(polyb, Vertex(0.11391157584189071,0.1175951719793249)) push!(polyb, Vertex(0.47290352518324696,0.2521388807609466)) push!(polyb, Vertex(0.975042508309663,0.31523396145745664)) push!(polyb, Vertex(0.6645972006883816,0.39747175992018424)) push!(polyb, Vertex(0.4033012612046003,0.6697684324194775)) push!(polyb, Vertex(0.7478015750621811,0.26064185515728866)) push!(polyb, Vertex(0.5408637693590228,0.6848821125508033)) push!(polyb, Vertex(0.20715997569592037,0.6570627238206466)) push!(polyb, Vertex(0.23093726175916385,0.8287208212251969)) push!(polyb, Vertex(0.6024572633721841,0.8204147304496012)) push!(polyb, Vertex(0.7025482053945702,0.6316840227377571)) push!(polyb, Vertex(0.15103563731064762,0.5804190220758261)) push!(polyb, Vertex(0.9887757894210867,0.9772711166739021)) push!(polyb, Vertex(0.6251517614575921,0.38220971314131513)) push!(polyb, Vertex(0.23163449016202686,0.7955298748003772)) push!(polyb, Vertex(0.008749232698707532,0.7038966642300737)) push!(polyb, Vertex(0.36552065983260706,0.007266458811545862)) push!(polyb, Vertex(0.8431274574795424,0.975174390993478)) push!(polyb, Vertex(0.9674040599289924,0.7546437140411728)) push!(polyb, Vertex(0.6299023469869027,0.5941113636172484)) push!(polyb, Vertex(0.3651692839350955,0.9412811548927036)) push!(polyb, Vertex(0.46700978293718576,0.6372087264115178)) push!(polyb, Vertex(0.09441852929672212,0.712398255967287)) push!(polyb, Vertex(0.6594117537641844,0.8556959981933909)) push!(polyb, Vertex(0.41749612512996004,0.7726528031089717)) push!(polyb, Vertex(0.8230135867808732,0.9270969466572365)) push!(polyb, Vertex(0.04389833237303109,0.5609640399741183)) push!(polyb, Vertex(0.4125116220480387,0.5160396701522856)) push!(polyb, Vertex(0.8836833851491399,0.7590081568215858)) push!(polyb, Vertex(0.5428845419686217,0.5433520734475961)) push!(polyb, Vertex(0.5473279623543394,0.48707911841886875)) push!(polyb, Vertex(0.504975646406884,0.6269770507544459)) push!(polyb, Vertex(0.6692031351652192,0.8269983773582317)) push!(polyb, Vertex(0.5665824658182701,0.28971587253261544)) push!(polyb, Vertex(0.6979596641936274,0.6963299758066004)) push!(polyb, Vertex(0.41393013487701724,0.02515192656365972)) push!(polyb, Vertex(0.4146094682050434,0.7760774006253788)) push!(polyb, Vertex(0.916841516397837,0.3812664317124159)) push!(polyb, Vertex(0.5694746452391368,0.021859026116122227)) push!(polyb, Vertex(0.5811595135265466,0.2757689306043134)) push!(polyb, Vertex(0.4894054388068505,0.35643214512810095)) push!(polyb, Vertex(0.5595554636066462,0.5848619132982462)) push!(polyb, Vertex(0.055595115965147324,0.08067075687112912)) push!(polyb, Vertex(0.8201820592938383,0.5690753436287503)) push!(polyb, Vertex(0.1630668008610887,0.21593074025144188)) push!(polyb, Vertex(0.3818523211281113,0.49892970559056926)) push!(polyb, Vertex(0.5879509816156174,0.7091657227073489)) push!(polyb, Vertex(0.7906807122153934,0.2210363105879154)) push!(polyb, Vertex(0.5593694790678512,0.4640752428580712)) push!(polyb, Vertex(0.6012166172350997,0.31394337995190624)) push!(polyb, Vertex(0.5626052673349125,0.8689869237375565)) push!(polyb, Vertex(0.9062078706328689,0.5926285281119317)) push!(polyb, Vertex(0.2116818069931432,0.7097461450894131)) push!(polyb, Vertex(0.23462798149426778,0.6347647741466378)) push!(polyb, Vertex(0.8333332026965838,0.4044637675513083)) push!(polyb, Vertex(0.38151281356770417,0.5842041828199334)) push!(polyb, Vertex(0.15019329095984024,0.4211489213025983)) push!(polyb, Vertex(0.8360328648037809,0.023292893005907978)) push!(polyb, Vertex(0.31874416444804177,0.11721397482037488)) push!(polyb, Vertex(0.39155608346680637,0.0016294336268325438)) push!(polyb, Vertex(0.49241666713123067,0.827810647049537)) push!(polyb, Vertex(0.28852655549271455,0.5134511690270223)) push!(polyb, Vertex(0.6320220046603813,0.8851410292212929)) push!(polyb, Vertex(0.9740042546342045,0.18256471264167917)) push!(polyb, Vertex(0.5780722916281602,0.20879214268020507)) push!(polyb, Vertex(0.6201054810476829,0.09914904930053448)) push!(polyb, Vertex(0.6219009431337508,0.1516866613709651)) push!(polyb, Vertex(0.34716243679494574,0.006595091110359164)) push!(polyb, Vertex(0.6890583759094768,0.3440450342993846)) push!(polyb, Vertex(0.9778512374676402,0.5744641432773667)) push!(polyb, Vertex(0.0033568324875912214,0.6148369261343873)) push!(polyb, Vertex(0.34832869925563625,0.40428761834628935)) push!(polyb, Vertex(0.2907179321707041,0.5340135773079302)) push!(polyb, Vertex(0.12479109273224198,0.8885089407774147)) push!(polyb, Vertex(0.9876184891473105,0.5950350229751693)) push!(polyb, Vertex(0.2006836897721096,0.44523290012385375)) push!(polyb, Vertex(0.24482339423302024,0.11468063465242362)) push!(polyb, Vertex(0.28181652810828073,0.2035021093756293)) push!(polyb, Vertex(0.06733186631795296,0.45001635767596593)) push!(polyb, Vertex(0.21527661471388138,0.3520300299617407)) push!(polyb, Vertex(0.5927741449744783,0.8525914294328816)) push!(polyb, Vertex(0.6258613640600956,0.43656539966226804)) push!(polyb, Vertex(0.5910220014222105,0.3521522088352207)) push!(polyb, Vertex(0.6106632086832191,0.5346375695717998)) push!(polyb, Vertex(0.34566121714575027,0.5845480914914436)) push!(polyb, Vertex(0.8691587357382462,0.32313446735351126)) push!(polyb, Vertex(0.3861919843487014,0.12972240893508835)) push!(polyb, Vertex(0.37769973424020375,0.051694328774281484)) push!(polyb, Vertex(0.4552128830552533,0.16435305304622982)) push!(polyb, Vertex(0.16131619574158784,0.3971212509168853)) push!(polyb, Vertex(0.24572158657159515,0.8610180990083223)) push!(polyb, Vertex(0.7284331633547654,0.7187589226042599)) push!(polyb, Vertex(0.03504548735067359,0.6029694383465394)) push!(polyb, Vertex(0.9995434477131571,0.3499686099551058)) push!(polyb, Vertex(0.8042033719306321,0.17999362049770196)) push!(polyb, Vertex(0.7467041955244049,0.3309946873417935)) results = intersection(polya, polyb) end outfile = Pkg.dir("PolygonClipping")*"/perf/intersection1_benchmarks.csv" out = benchmark(intersection1, "Clipping", "Intersection 1", 10) if isfile(outfile) previous = readtable(outfile) out = vcat(previous, out) end writetable(outfile, out)
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function _is_within_range(query_start, query_length, target_start, target_length) target_start <= query_start <= query_start - 1 + query_length <= target_start - 1 + target_length end abstract type Overlap end struct ExactMatch <: Overlap end struct LeftNeighbor <: Overlap end struct RightNeighbor <: Overlap end struct Disjunct <: Overlap end struct Within <: Overlap end struct WithinFromStart <: Overlap end struct WithinToEnd <: Overlap end struct CompleteOverlap <: Overlap end struct LeftOverlap <: Overlap end struct RightOverlap <: Overlap end function _determine_overlap(query_start, query_length, target_start, target_length) query_end = query_start - 1 + query_length target_end = target_start - 1 + target_length if query_start == target_start && query_length == target_length ExactMatch() elseif query_end == target_start - 1 LeftNeighbor() elseif query_start == target_end + 1 RightNeighbor() elseif query_end < target_start || target_end < query_start Disjunct() elseif target_start == query_start <= query_end < target_end WithinFromStart() elseif target_start < query_start <= query_end == target_end WithinToEnd() elseif target_start < query_start <= query_end < target_end Within() elseif query_start < target_start <= query_end <= target_end LeftOverlap() elseif target_start <= query_start <= target_end < query_end RightOverlap() elseif query_start < target_start <= target_end < query_end CompleteOverlap() else println(query_start, " ", query_length, " ", target_start, " ", target_length) @assert false end end
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""" simpledown Compute the mesh topology structures that correspond to just the downward adjacency between the tetrahedra and the vertices. Memory is used only to store the incidence relation and the locations of the vertices. """ module simpledown using StaticArrays using MeshCore: nvertices, nshapes, manifdim, attribute using MeshCore: ir_skeleton, ir_bbyfacets, ir_transpose using MeshSteward: vtkwrite, T4block using BenchmarkTools using Test include("usedbytes.jl") function test() n = 3 membytes = 0; summembytes = 0 @info "Initial (3, 0)" @time connectivity = T4block(1.0, 2.0, 3.0, n*7, n*9, n*10, :a; intbytes = 4) ir30 = connectivity @show "($(manifdim(ir30.left)), $(manifdim(ir30.right)))" @show (nshapes(ir30.left), nshapes(ir30.right)) @show membytes = usedbytes(ir30._v) summembytes += membytes geom = attribute(ir30.right, "geom") @show membytes = usedbytes(geom.v) summembytes += membytes # Print the total number of megabytes used to store the database @show summembytes/2^20 # vtkwrite("speedtest1", connectivity) true end end using .simpledown # using BenchmarkTools # @btime simpledown.test() simpledown.test()
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function hello() # println("Hello, World!") return "Hello, World!" end
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using CharFuncPricing, TaylorSeries @testset "CumulantsJoshi" begin κ = 4.0 θ = 0.25 σ = 1.0 ρ = -0.5 v0 = 0.01 τ = 0.01 params = HestonParams{Float64}(v0, κ, θ, ρ, σ) cf = DefaultCharFunc{HestonParams{Float64},Taylor1{Complex}}(params) t = Taylor1(Float64, 4) cft = CharFuncPricing.evaluateLogCharFuncCui(cf, t, τ) c1, c2, c4 = computeCumulants(params, τ) println(c1, " ", c2 / 2, " ", c4 / (2 * 3 * 4), " ", cft) @test isapprox(imag(cft.coeffs[2]), c1, atol = 1e-12) @test isapprox(-real(cft.coeffs[3]) * 2, c2, atol = 1e-12) @test isapprox(cft.coeffs[5] * 2 * 3 * 4, c4, atol = 1e-16) end @testset "CumulantsAlan" begin κ = 4.0 θ = 0.25 σ = 1.0 ρ = -0.5 v0 = 0.04 τ = 1.0 params = HestonParams{Float64}(v0, κ, θ, ρ, σ) cf = DefaultCharFunc{HestonParams{Float64},Taylor1{Complex}}(params) t = Taylor1(Float64, 4) cft = CharFuncPricing.evaluateLogCharFuncCui(cf, t, τ) c1, c2, c4 = computeCumulants(params, τ) println(c1, " ", c2 / 2, " ", c4 / (2 * 3 * 4), " ", cft) @test isapprox(imag(cft.coeffs[2]), c1, atol = 1e-12) @test isapprox(-real(cft.coeffs[3]) * 2, c2, atol = 1e-12) @test isapprox(cft.coeffs[5] * 2 * 3 * 4, c4, atol = 1e-15) end @testset "CumulantsNearZeroKappa" begin #Low accuracy with small kappa due to powers of kappa in the denominator. κ = 0.02 θ = 0.25 σ = 1.0 ρ = -0.5 v0 = 0.04 τ = 1.0 params = HestonParams{Float64}(v0, κ, θ, ρ, σ) cf = DefaultCharFunc{HestonParams{Float64},Taylor1{Complex}}(params) t = Taylor1(Float64, 4) cft = CharFuncPricing.evaluateLogCharFuncCui(cf, t, τ) c1, c2, c4 = computeCumulants(params, τ) println(c1, " ", c2 / 2, " ", c4 / (2 * 3 * 4), " ", cft) @test isapprox(imag(cft.coeffs[2]), c1, atol = 1e-12) @test isapprox(-real(cft.coeffs[3]) * 2, c2, atol = 1e-3) @test isapprox(cft.coeffs[5] * 2 * 3 * 4, c4, atol = 1e-3) end
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using DrWatson @quickactivate "NonlinearDynamicsTextbook" include(srcdir("colorscheme.jl")) using InteractiveDynamics, DynamicalSystems import GLMakie xmax = 1.0 xmin = -1.0 ymin = -1.0 ymax = 1.0 d = 50 const a = 1.4 const b = 0.3 ics = [ SVector(x, y) for x in range(xmin, xmax; length = d) for y in range(ymin, ymax; length = d) ] f1(u) = SVector(u[1], 1 - a*u[1]^2 + u[2]) f2(u) = SVector(b*u[1], u[2]) f3(u) = SVector(u[2], u[1]) function three_phases(ics) p1 = f1.(ics) p2 = f2.(p1) p3 = f3.(p2) return p1, p2, p3 end function linear_transition!(o, p1, p2, steps, io) for i in 1:steps # assumes we start with o[] = p1 o[] = p1 .+ (p2 .- p1) .* i/steps GLMakie.recordframe!(io) # sleep(0.01) # change to record frame end end iterations = 10 steps = 20 fig = GLMakie.Figure(); display(fig) ax = GLMakie.Axis(fig[1,1]) o = GLMakie.Observable(ics) GLMakie.scatter!(ax, o; color = COLORS[1], strokewidth = 0.5, strokecolor = :black, markersize = 5 ) ax.limits = ((-2.5,2.5),(-1.5,1.5)) GLMakie.record(fig, string(@__FILE__)[1:end-2]*"mp4"; framerate = 30) do io for j in 1:iterations p1, p2, p3 = three_phases(ics) linear_transition!(o, ics, p1, steps, io) GLMakie.recordframe!(io) linear_transition!(o, p1, p2, steps, io) GLMakie.recordframe!(io) linear_transition!(o, p2, p3, steps, io) GLMakie.recordframe!(io) global ics = p3 GLMakie.recordframe!(io) end end
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