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Addressing type conversion in inits #266

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Mar 13, 2025
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Addressing type conversion in inits #266

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62 changes: 31 additions & 31 deletions src/esn/esn_inits.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ end

# error for sparse inits with no SparseArrays.jl call

function throw_sparse_error(return_sparse)
function throw_sparse_error(return_sparse::Bool)
if return_sparse && !haskey(Base.loaded_modules, :SparseArrays)
error("""\n
Sparse output requested but SparseArrays.jl is not loaded.
Expand All @@ -18,7 +18,7 @@ end

## scale spectral radius

function scale_radius!(reservoir_matrix, radius)
function scale_radius!(reservoir_matrix::AbstractMatrix, radius::AbstractFloat)
rho_w = maximum(abs.(eigvals(reservoir_matrix)))
reservoir_matrix .*= radius / rho_w
if Inf in unique(reservoir_matrix) || -Inf in unique(reservoir_matrix)
Expand All @@ -27,6 +27,7 @@ function scale_radius!(reservoir_matrix, radius)
Increase res_size or increase sparsity.\n
""")
end
return reservoir_matrix
end

### input layers
Expand Down Expand Up @@ -70,7 +71,7 @@ function scaled_rand(rng::AbstractRNG, ::Type{T}, dims::Integer...;
scaling=T(0.1)) where {T <: Number}
res_size, in_size = dims
layer_matrix = (DeviceAgnostic.rand(rng, T, res_size, in_size) .- T(0.5)) .*
(T(2) * scaling)
(T(2) * T(scaling))
return layer_matrix
end

Expand Down Expand Up @@ -124,9 +125,8 @@ function weighted_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
q = floor(Int, res_size / in_size)

for idx in 1:in_size
layer_matrix[((idx - 1) * q + 1):((idx) * q), idx] = (DeviceAgnostic.rand(
rng, T, q) .-
T(0.5)) .* (T(2) * scaling)
sc_rand = (DeviceAgnostic.rand(rng, T, q) .- T(0.5)) .* (T(2) * T(scaling))
layer_matrix[((idx - 1) * q + 1):((idx) * q), idx] = sc_rand
end

return return_init_as(Val(return_sparse), layer_matrix)
Expand Down Expand Up @@ -179,7 +179,7 @@ function informed_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
random_row_idx = idxs[DeviceAgnostic.rand(rng, T, 1:end)]
random_clm_idx = range(1, state_size; step=1)[DeviceAgnostic.rand(rng, T, 1:end)]
input_matrix[random_row_idx, random_clm_idx] = (DeviceAgnostic.rand(rng, T) -
T(0.5)) .* (T(2) * scaling)
T(0.5)) .* (T(2) * T(scaling))
end

for idx in 1:num_for_model
Expand All @@ -189,7 +189,7 @@ function informed_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
random_clm_idx = range(state_size + 1, in_size; step=1)[DeviceAgnostic.rand(
rng, T, 1:end)]
input_matrix[random_row_idx, random_clm_idx] = (DeviceAgnostic.rand(rng, T) -
T(0.5)) .* (T(2) * scaling)
T(0.5)) .* (T(2) * T(scaling))
end

return input_matrix
Expand Down Expand Up @@ -287,9 +287,9 @@ function bernoulli(rng::AbstractRNG, ::Type{T}, res_size::Int, in_size::Int;
for idx in 1:res_size
for jdx in 1:in_size
if DeviceAgnostic.rand(rng, T) < p
input_matrix[idx, jdx] = weight
input_matrix[idx, jdx] = T(weight)
else
input_matrix[idx, jdx] = -weight
input_matrix[idx, jdx] = -T(weight)
end
end
end
Expand All @@ -312,7 +312,7 @@ function irrational(rng::AbstractRNG, ::Type{T}, res_size::Int, in_size::Int;
for idx in 1:res_size
for jdx in 1:in_size
random_number = DeviceAgnostic.rand(rng, T)
input_matrix[idx, jdx] = random_number < 0.5 ? -weight : weight
input_matrix[idx, jdx] = random_number < 0.5 ? -T(weight) : T(weight)
end
end

Expand Down Expand Up @@ -638,7 +638,7 @@ function rand_sparse(rng::AbstractRNG, ::Type{T}, dims::Integer...;
throw_sparse_error(return_sparse)
lcl_sparsity = T(1) - sparsity #consistency with current implementations
reservoir_matrix = sparse_init(rng, T, dims...; sparsity=lcl_sparsity, std=std)
scale_radius!(reservoir_matrix, radius)
reservoir_matrix = scale_radius!(reservoir_matrix, T(radius))
return return_init_as(Val(return_sparse), reservoir_matrix)
end

Expand Down Expand Up @@ -697,7 +697,7 @@ function delay_line(rng::AbstractRNG, ::Type{T}, dims::Integer...;
"""

for idx in first(axes(reservoir_matrix, 1)):(last(axes(reservoir_matrix, 1)) - 1)
reservoir_matrix[idx + 1, idx] = weight
reservoir_matrix[idx + 1, idx] = T(weight)
end

return return_init_as(Val(return_sparse), reservoir_matrix)
Expand Down Expand Up @@ -756,8 +756,8 @@ function delay_line_backward(rng::AbstractRNG, ::Type{T}, dims::Integer...;
throw_sparse_error(return_sparse)
reservoir_matrix = DeviceAgnostic.zeros(rng, T, dims...)
for idx in first(axes(reservoir_matrix, 1)):(last(axes(reservoir_matrix, 1)) - 1)
reservoir_matrix[idx + 1, idx] = weight
reservoir_matrix[idx, idx + 1] = fb_weight
reservoir_matrix[idx + 1, idx] = T(weight)
reservoir_matrix[idx, idx + 1] = T(fb_weight)
end
return return_init_as(Val(return_sparse), reservoir_matrix)
end
Expand Down Expand Up @@ -819,18 +819,18 @@ function cycle_jumps(rng::AbstractRNG, ::Type{T}, dims::Integer...;
reservoir_matrix = DeviceAgnostic.zeros(rng, T, dims...)

for idx in first(axes(reservoir_matrix, 1)):(last(axes(reservoir_matrix, 1)) - 1)
reservoir_matrix[idx + 1, idx] = cycle_weight
reservoir_matrix[idx + 1, idx] = T(cycle_weight)
end

reservoir_matrix[1, res_size] = cycle_weight
reservoir_matrix[1, res_size] = T(cycle_weight)

for idx in 1:jump_size:(res_size - jump_size)
tmp = (idx + jump_size) % res_size
if tmp == 0
tmp = res_size
end
reservoir_matrix[idx, tmp] = jump_weight
reservoir_matrix[tmp, idx] = jump_weight
reservoir_matrix[idx, tmp] = T(cycle_weight)
reservoir_matrix[tmp, idx] = T(cycle_weight)
end

return return_init_as(Val(return_sparse), reservoir_matrix)
Expand Down Expand Up @@ -947,7 +947,7 @@ function pseudo_svd(rng::AbstractRNG, ::Type{T}, dims::Integer...;
return_diag::Bool=false) where {T <: Number}
throw_sparse_error(return_sparse)
reservoir_matrix = create_diag(rng, T, dims[1],
max_value;
T(max_value);
sorted=sorted,
reverse_sort=reverse_sort)
tmp_sparsity = get_sparsity(reservoir_matrix, dims[1])
Expand Down Expand Up @@ -1003,10 +1003,10 @@ function create_qmatrix(rng::AbstractRNG, ::Type{T}, dim::Number,
qmatrix[idx, idx] = 1.0
end

qmatrix[coord_i, coord_i] = cos(theta)
qmatrix[coord_j, coord_j] = cos(theta)
qmatrix[coord_i, coord_j] = -sin(theta)
qmatrix[coord_j, coord_i] = sin(theta)
qmatrix[coord_i, coord_i] = cos(T(theta))
qmatrix[coord_j, coord_j] = cos(T(theta))
qmatrix[coord_i, coord_j] = -sin(T(theta))
qmatrix[coord_j, coord_i] = sin(T(theta))
return qmatrix
end

Expand Down Expand Up @@ -1192,7 +1192,7 @@ function build_cycle(::Val{false}, rng::AbstractRNG, ::Type{T}, res_size::Int;
reservoir_matrix[idx, jdx] = T(randn(rng))
end
end
scale_radius!(reservoir_matrix, radius)
reservoir_matrix = scale_radius!(reservoir_matrix, T(radius))
return reservoir_matrix
end

Expand All @@ -1204,7 +1204,7 @@ function build_cycle(::Val{true}, rng::AbstractRNG, ::Type{T}, res_size::Int;
reservoir_matrix[perm[idx], perm[idx + 1]] = T(randn(rng))
end
reservoir_matrix[perm[res_size], perm[1]] = T(randn(rng))
scale_radius!(reservoir_matrix, radius)
reservoir_matrix = scale_radius!(reservoir_matrix, T(radius))
if cut_cycle
cut_cycle_edge!(reservoir_matrix, rng)
end
Expand Down Expand Up @@ -1270,14 +1270,14 @@ function double_cycle(rng::AbstractRNG, ::Type{T}, dims::Integer...;
reservoir_matrix = DeviceAgnostic.zeros(rng, T, dims...)

for uidx in first(axes(reservoir_matrix, 1)):(last(axes(reservoir_matrix, 1)) - 1)
reservoir_matrix[uidx + 1, uidx] = cycle_weight
reservoir_matrix[uidx + 1, uidx] = T(cycle_weight)
end
for lidx in (first(axes(reservoir_matrix, 1)) + 1):last(axes(reservoir_matrix, 1))
reservoir_matrix[lidx - 1, lidx] = second_cycle_weight
reservoir_matrix[lidx - 1, lidx] = T(second_cycle_weight)
end

reservoir_matrix[1, dims[1]] = second_cycle_weight
reservoir_matrix[dims[1], 1] = cycle_weight
reservoir_matrix[1, dims[1]] = T(second_cycle_weight)
reservoir_matrix[dims[1], 1] = T(cycle_weight)
return return_init_as(Val(return_sparse), reservoir_matrix)
end

Expand Down Expand Up @@ -1347,7 +1347,7 @@ function selfloop_cycle(rng::AbstractRNG, ::Type{T}, dims::Integer...;
return_sparse::Bool=false) where {T <: Number}
throw_sparse_error(return_sparse)
reservoir_matrix = simple_cycle(rng, T, dims...;
weight=cycle_weight, return_sparse=false)
weight=T(cycle_weight), return_sparse=false)
reservoir_matrix += T(selfloop_weight) .* I(dims[1])
return return_init_as(Val(return_sparse), reservoir_matrix)
end
Expand Down
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