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sparse.jl
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sparse.jl
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# check sparse matrix construction
@test isequal(full(sparse(complex(ones(5,5),ones(5,5)))), complex(ones(5,5),ones(5,5)))
# check matrix operations
se33 = speye(3)
do33 = ones(3)
@test isequal(se33 * se33, se33)
# check sparse binary op
@test all(full(se33 + convert(SparseMatrixCSC{Float32,Int32}, se33)) == 2*eye(3))
@test all(full(se33 * convert(SparseMatrixCSC{Float32,Int32}, se33)) == eye(3))
# check horiz concatenation
@test all([se33 se33] == sparse([1, 2, 3, 1, 2, 3], [1, 2, 3, 4, 5, 6], ones(6)))
# check vert concatenation
@test all([se33; se33] == sparse([1, 4, 2, 5, 3, 6], [1, 1, 2, 2, 3, 3], ones(6)))
# check h+v concatenation
se44 = speye(4)
sz42 = spzeros(4, 2)
sz41 = spzeros(4, 1)
sz34 = spzeros(3, 4)
se77 = speye(7)
@test all([se44 sz42 sz41; sz34 se33] == se77)
# check blkdiag concatenation
@test all(blkdiag(se33, se33) == sparse([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], ones(6)))
# check concatenation promotion
sz41_f32 = spzeros(Float32, 4, 1)
se33_i32 = speye(Int32, 3, 3)
@test all([se44 sz42 sz41_f32; sz34 se33_i32] == se77)
# check mixed sparse-dense concatenation
sz33 = spzeros(3, 3)
de33 = eye(3)
@test all([se33 de33; sz33 se33] == full([se33 se33; sz33 se33 ]))
# check splicing + concatenation on
# random instances, with nested vcat
# also side-checks sparse ref
for i = 1 : 10
a = sprand(5, 4, 0.5)
@test all([a[1:2,1:2] a[1:2,3:4]; a[3:5,1] [a[3:4,2:4]; a[5,2:4]]] == a)
end
# sparse ref
a116 = reshape(1:16, 4, 4)
s116 = sparse(a116)
p = [4, 1, 2, 3, 2]
@test full(s116[p,:]) == a116[p,:]
@test full(s116[:,p]) == a116[:,p]
@test full(s116[p,p]) == a116[p,p]
# sparse assign
p = [4, 1, 3]
a116[p, p] = -1
s116[p, p] = -1
@test a116 == s116
p = [2, 1, 4]
a116[p, p] = reshape(1:9, 3, 3)
s116[p, p] = reshape(1:9, 3, 3)
@test a116 == s116
# matrix-vector multiplication (non-square)
for i = 1:5
a = sprand(10, 5, 0.5)
b = rand(5)
@test maximum(abs(a*b - full(a)*b)) < 100*eps()
end
# complex matrix-vector multiplication and left-division
for i = 1:5
a = speye(5) + 0.1*sprandn(5, 5, 0.2)
b = randn(5,3) + im*randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + 0.1*sprandn(5, 5, 0.2) + 0.1*im*sprandn(5, 5, 0.2)
b = randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + tril(0.1*sprandn(5, 5, 0.2))
b = randn(5,3) + im*randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + tril(0.1*sprandn(5, 5, 0.2) + 0.1*im*sprandn(5, 5, 0.2))
b = randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + triu(0.1*sprandn(5, 5, 0.2))
b = randn(5,3) + im*randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + triu(0.1*sprandn(5, 5, 0.2) + 0.1*im*sprandn(5, 5, 0.2))
b = randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = speye(5) + triu(0.1*sprandn(5, 5, 0.2))
b = randn(5,3) + im*randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
a = spdiagm(randn(5)) + im*spdiagm(randn(5))
b = randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
b = randn(5,3) + im*randn(5,3)
@test (maximum(abs(a*b - full(a)*b)) < 100*eps())
@test (maximum(abs(a'b - full(a)'b)) < 100*eps())
@test (maximum(abs(a\b - full(a)\b)) < 1000*eps())
@test (maximum(abs(a'\b - full(a')\b)) < 1000*eps())
@test (maximum(abs(a.'\b - full(a.')\b)) < 1000*eps())
end
# matrix multiplication and kron
for i = 1:5
a = sprand(10, 5, 0.7)
b = sprand(5, 10, 0.3)
@test maximum(abs(a*b - full(a)*full(b))) < 100*eps()
@test full(kron(a,b)) == kron(full(a), full(b))
end
# scale and scale!
sA = sprandn(3, 7, 0.5)
dA = full(sA)
b = randn(7)
@test scale(dA, b) == scale(sA, b)
@test scale(dA, b) == scale!(copy(sA), b)
b = randn(3)
@test scale(b, dA) == scale(b, sA)
@test scale(b, dA) == scale!(b, copy(sA))
# reductions
@test sum(se33)[1] == 3.0
@test sum(se33, 1) == [1.0 1.0 1.0]
@test sum(se33, 2) == [1.0 1.0 1.0]'
@test prod(se33)[1] == 0.0
@test prod(se33, 1) == [0.0 0.0 0.0]
@test prod(se33, 2) == [0.0 0.0 0.0]'
# spdiagm
@test full(spdiagm((ones(2), ones(2)), (0, -1), 3, 3)) ==
[1.0 0.0 0.0; 1.0 1.0 0.0; 0.0 1.0 0.0]
# elimination tree
## upper triangle of the pattern test matrix from Figure 4.2 of
## "Direct Methods for Sparse Linear Systems" by Tim Davis, SIAM, 2006
rowval = int32([1,2,2,3,4,5,1,4,6,1,7,2,5,8,6,9,3,4,6,8,10,3,5,7,8,10,11])
colval = int32([1,2,3,3,4,5,6,6,6,7,7,8,8,8,9,9,10,10,10,10,10,11,11,11,11,11,11])
A = sparse(rowval, colval, ones(length(rowval)))
P,post = Base.LinAlg.etree(A, true)
@test P == int32([6,3,8,6,8,7,9,10,10,11,0])
@test post == int32([2,3,5,8,1,4,6,7,9,10,11])
# issue #4986, reinterpret
sfe22 = speye(Float64, 2)
mfe22 = eye(Float64, 2)
@test reinterpret(Int64, sfe22) == reinterpret(Int64, mfe22)
# issue #5190
@test_throws DimensionMismatch sparsevec([3,5,7],[0.1,0.0,3.2],4)
# issue #5169
@test nnz(sparse([1,1],[1,2],[0.0,-0.0])) == 0
# issue #5386
K,J,V = findnz(SparseMatrixCSC(2,1,[1,3],[1,2],[1.0,0.0]))
@test length(K) == length(J) == length(V) == 1
# https://groups.google.com/d/msg/julia-users/Yq4dh8NOWBQ/GU57L90FZ3EJ
A = speye(Bool, 5)
@test find(A) == find(x -> x == true, A) == find(full(A))
# issue #5437
@test nnz(sparse([1,2,3],[1,2,3],[0.0,1.0,2.0])) == 2
# issue #5824
@test sprand(4,5,0.5).^0 == sparse(ones(4,5))
# issue #5985
@test sprandbool(4, 5, 0.0) == sparse(zeros(Bool, 4, 5))
@test sprandbool(4, 5, 1.00) == sparse(ones(Bool, 4, 5))
sprb45nnzs = zeros(5)
for i=1:5
sprb45 = sprandbool(4, 5, 0.5)
@test length(sprb45) == 20
sprb45nnzs[i] = sum(sprb45)[1]
end
@test 4 <= mean(sprb45nnzs) <= 16
# issue #5853, sparse diff
for i=1:2, a={[1 2 3], [1 2 3]', eye(3)}
@test all(diff(sparse(a),i) == diff(a,i))
end
# test for "access to undefined error" types that initially allocate elements as #undef
@test all(sparse(1:2, 1:2, Number[1,2])^2 == sparse(1:2, 1:2, [1,4]))
sd1 = diff(sparse([1,1,1], [1,2,3], Number[1,2,3]), 1)
# issue #6036
P = spzeros(Float64, 3, 3)
for i = 1:3
P[i,i] = i
end
@test minimum(P) === 0.0
@test maximum(P) === 3.0
@test minimum(-P) === -3.0
@test maximum(-P) === 0.0
@test maximum(P, (1,)) == [1.0 2.0 3.0]
@test maximum(P, (2,)) == reshape([1.0,2.0,3.0],3,1)
@test maximum(P, (1,2)) == reshape([3.0],1,1)
@test maximum(sparse(-ones(3,3))) == -1
@test minimum(sparse(ones(3,3))) == 1
# Unary functions
a = sprand(5,15, 0.5)
afull = full(a)
for op in (:sin, :cos, :tan, :iceil, :ifloor, :ceil, :floor, :abs, :abs2)
@eval begin
@test ($op)(afull) == full($(op)(a))
end
end
# getindex tests
ni = 23
nj = 32
a116 = reshape(1:(ni*nj), ni, nj)
s116 = sparse(a116)
ad116 = diagm(diag(a116))
sd116 = sparse(ad116)
for (aa116, ss116) in [(a116, s116), (ad116, sd116)]
ij=11; i=3; j=2
@test ss116[ij] == aa116[ij]
@test ss116[(i,j)] == aa116[i,j]
@test ss116[i,j] == aa116[i,j]
@test ss116[i-1,j] == aa116[i-1,j]
ss116[i,j] = 0
@test ss116[i,j] == 0
ss116 = sparse(aa116)
# range indexing
@test full(ss116[i,:]) == aa116[i,:]
@test full(ss116[:,j]) == aa116[:,j]'' # sparse matrices/vectors always have ndims==2:
@test full(ss116[i,1:2:end]) == aa116[i,1:2:end]
@test full(ss116[1:2:end,j]) == aa116[1:2:end,j]''
@test full(ss116[i,end:-2:1]) == aa116[i,end:-2:1]
@test full(ss116[end:-2:1,j]) == aa116[end:-2:1,j]''
# float-range indexing is not supported
# sorted vector indexing
@test full(ss116[i,[3:2:end-3]]) == aa116[i,[3:2:end-3]]
@test full(ss116[[3:2:end-3],j]) == aa116[[3:2:end-3],j]''
@test full(ss116[i,[end-3:-2:1]]) == aa116[i,[end-3:-2:1]]
@test full(ss116[[end-3:-2:1],j]) == aa116[[end-3:-2:1],j]''
# unsorted vector indexing with repetition
p = [4, 1, 2, 3, 2, 6]
@test full(ss116[p,:]) == aa116[p,:]
@test full(ss116[:,p]) == aa116[:,p]
@test full(ss116[p,p]) == aa116[p,p]
# bool indexing
li = randbool(size(aa116,1))
lj = randbool(size(aa116,2))
@test full(ss116[li,j]) == aa116[li,j]''
@test full(ss116[li,:]) == aa116[li,:]
@test full(ss116[i,lj]) == aa116[i,lj]
@test full(ss116[:,lj]) == aa116[:,lj]
@test full(ss116[li,lj]) == aa116[li,lj]
end
# workaround issue #7197: comment out let-block
#let S = SparseMatrixCSC(3, 3, Uint8[1,1,1,1], Uint8[], Int64[])
S1290 = SparseMatrixCSC(3, 3, Uint8[1,1,1,1], Uint8[], Int64[])
S1290[1,1] = 1
S1290[5] = 2
S1290[end] = 3
@test S1290[end] == (S1290[1] + S1290[2,2])
@test 6 == sum(diag(S1290))
@test (full(S1290)[[3,1],1])'' == full(S1290[[3,1],1])
# end
# setindex tests
let a = spzeros(Int, 10, 10)
@test countnz(a) == 0
a[1,:] = 1
@test countnz(a) == 10
@test a[1,:] == sparse(ones(Int,1,10))
a[:,2] = 2
@test countnz(a) == 19
@test a[:,2] == 2*sparse(ones(Int,10,1))
a[1,:] = 1:10
@test a[1,:] == sparse([1:10]')
a[:,2] = 1:10
@test a[:,2] == sparse([1:10])
end
let A = spzeros(Int, 10, 20)
A[1:5,1:10] = 10
A[1:5,1:10] = 10
@test countnz(A) == 50
@test A[1:5,1:10] == 10 * ones(Int, 5, 10)
A[6:10,11:20] = 0
@test countnz(A) == 50
A[6:10,11:20] = 20
@test countnz(A) == 100
@test A[6:10,11:20] == 20 * ones(Int, 5, 10)
A[4:8,8:16] = 15
@test countnz(A) == 121
@test A[4:8,8:16] == 15 * ones(Int, 5, 9)
end
let ASZ = 1000, TSZ = 800
A = sprand(ASZ, 2*ASZ, 0.0001)
B = copy(A)
nA = countnz(A)
x = A[1:TSZ, 1:(2*TSZ)]
nx = countnz(x)
A[1:TSZ, 1:(2*TSZ)] = 0
nB = countnz(A)
@test nB == (nA - nx)
A[1:TSZ, 1:(2*TSZ)] = x
@test countnz(A) == nA
@test A == B
A[1:TSZ, 1:(2*TSZ)] = 10
@test countnz(A) == nB + 2*TSZ*TSZ
A[1:TSZ, 1:(2*TSZ)] = x
@test countnz(A) == nA
@test A == B
end
let A = speye(Int, 5), I=[1:10], X=reshape([trues(10), falses(15)],5,5)
@test A[I] == A[X] == reshape([1,0,0,0,0,0,1,0,0,0], 10, 1)
A[I] = [1:10]
@test A[I] == A[X] == reshape([1:10], 10, 1)
end
let S = sprand(50, 30, 0.5, x->int(rand(x)*100)), I = sprandbool(50, 30, 0.2)
FS = full(S)
FI = full(I)
@test sparse(FS[FI]) == S[I] == S[FI]
@test sum(S[FI]) + sum(S[!FI]) == sum(S)
sumS1 = sum(S)
sumFI = sum(S[FI])
S[FI] = 0
@test sum(S[FI]) == 0
sumS2 = sum(S)
@test (sum(S) + sumFI) == sumS1
S[FI] = 10
@test sum(S) == sumS2 + 10*sum(FI)
S[FI] = 0
@test sum(S) == sumS2
S[FI] = [1:sum(FI)]
@test sum(S) == sumS2 + sum(1:sum(FI))
end
let S = sprand(50, 30, 0.5, x->int(rand(x)*100))
N = length(S) >> 2
I = randperm(N) .* 4
J = randperm(N)
sumS1 = sum(S)
sumS2 = sum(S[I])
S[I] = 0
@test sum(S) == (sumS1 - sumS2)
S[I] = J
@test sum(S) == (sumS1 - sumS2 + sum(J))
end
#Issue 7507
@test (i7507=sparsevec(Dict{Int64, Float64}(), 10))==spzeros(10,1)
#Issue 7650
let S = spzeros(3, 3)
@test size(reshape(S, 9, 1)) == (9,1)
end
let X = eye(5), M = rand(5,4), C = spzeros(3,3)
SX = sparse(X); SM = sparse(M)
VX = vec(X); VSX = vec(SX)
VM = vec(M); VSM1 = vec(SM); VSM2 = sparsevec(M)
VC = vec(C)
@test reshape(VX, (25,1)) == VSX
@test reshape(VM, (20,1)) == VSM1 == VSM2
@test size(VC) == (9,1)
@test nnz(VC) == 0
@test nnz(VSX) == 5
end
#Issue 7677
let A = sprand(5,5,0.5,(n)->rand(Float64,n)), ACPY = copy(A)
B = reshape(A,25,1)
@test A == ACPY
C = reinterpret(Int64, A, (25, 1))
@test A == ACPY
D = reinterpret(Int64, B)
@test C == D
end
# issue #8225
@test_throws BoundsError sparse([0],[-1],[1.0],2,2)
# issue #8363
@test_throws BoundsError sparsevec([-1=>1,1=>2])
# issue #8976
@test conj(sparse([1im])) == sparse(conj([1im]))
@test conj!(sparse([1im])) == sparse(conj!([1im]))
# issue #9525
@test_throws BoundsError sparse([3], [5], 1.0, 3, 3)
#findn
b = findn( speye(4) )
@test (length(b[1]) == 4)
@test (length(b[2]) == 4)
#rotations
a = sparse( [1,1,2,3], [1,3,4,1], [1,2,3,4] )
@test rot180(a,2) == a
@test rot180(a,1) == sparse( [3,3,2,1], [4,2,1,4], [1,2,3,4] )
@test rotr90(a,1) == sparse( [1,3,4,1], [3,3,2,1], [1,2,3,4] )
@test rotl90(a,1) == sparse( [4,2,1,4], [1,1,2,3], [1,2,3,4] )
@test rotl90(a,2) == rot180(a)
@test rotr90(a,2) == rot180(a)
@test rotl90(a,3) == rotr90(a)
@test rotr90(a,3) == rotl90(a)
#ensure we have preserved the correct dimensions!
a = speye(3,5)
@test size(rot180(a)) == (3,5)
@test size(rotr90(a)) == (5,3)
@test size(rotl90(a)) == (5,3)
function test_getindex_algs{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::AbstractVector, alg::Int)
# Sorted vectors for indexing rows.
# Similar to getindex_general but without the transpose trick.
(m, n) = size(A)
!isempty(I) && ((I[1] < 1) || (I[end] > m)) && BoundsError()
if !isempty(J)
minj, maxj = extrema(J)
((minj < 1) || (maxj > n)) && BoundsError()
end
(alg == 0) ? Base.SparseMatrix.getindex_I_sorted_bsearch_A(A, I, J) :
(alg == 1) ? Base.SparseMatrix.getindex_I_sorted_bsearch_I(A, I, J) :
Base.SparseMatrix.getindex_I_sorted_linear(A, I, J)
end
let M=2^14, N=2^4
Irand = randperm(M);
Jrand = randperm(N);
SA = [sprand(M, N, d) for d in [1., 0.1, 0.01, 0.001, 0.0001, 0.]];
IA = [sort(Irand[1:int(n)]) for n in [M, M*0.1, M*0.01, M*0.001, M*0.0001, 0.]];
debug = false
if debug
println("row sizes: $([int(nnz(S)/S.n) for S in SA])");
println("I sizes: $([length(I) for I in IA])");
@printf(" S | I | binary S | binary I | linear | best\n")
end
J = Jrand;
for I in IA
for S in SA
res = Any[1,2,3]
times = Float64[0,0,0]
best = [typemax(Float64), 0]
for searchtype in [0, 1, 2]
tres = @timed test_getindex_algs(S, I, J, searchtype)
res[searchtype+1] = tres[1]
times[searchtype+1] = tres[2]
if best[1] > tres[2]
best[1] = tres[2]
best[2] = searchtype
end
end
if debug
@printf(" %7d | %7d | %4.2e | %4.2e | %4.2e | %s\n", int(nnz(S)/S.n), length(I), times[1], times[2], times[3],
(0 == best[2]) ? "binary S" : (1 == best[2]) ? "binary I" : "linear")
end
if res[1] != res[2]
println("1 and 2")
elseif res[2] != res[3]
println("2, 3")
end
@assert res[1] == res[2] == res[3]
end
end
end
let M = 2^8, N=2^3
Irand = randperm(M)
Jrand = randperm(N)
I = sort([Irand, Irand, Irand])
J = [Jrand, Jrand]
SA = [sprand(M, N, d) for d in [1., 0.1, 0.01, 0.001, 0.0001, 0.]];
for S in SA
res = Any[1,2,3]
for searchtype in [0, 1, 2]
res[searchtype+1] = test_getindex_algs(S, I, J, searchtype)
end
@assert res[1] == res[2] == res[3]
end
end
# issue #9917
@test full(sparse([])) == zeros(0, 1)
@test_throws BoundsError sparse([])[1]
x = speye(100)
@test_throws BoundsError x[-10:10]
# issue #10407
@test maximum(spzeros(5, 5)) == 0.0
@test minimum(spzeros(5, 5)) == 0.0
# issue #10411
for (m,n) in ((2,-2),(-2,2),(-2,-2))
@test_throws ArgumentError spzeros(m,n)
@test_throws ArgumentError speye(m,n)
@test_throws ArgumentError sprand(m,n,0.2)
end
# promotion in spdiagm
@test spdiagm(([1,2],[3.5],[4+5im]), (0,1,-1), 2,2) == [1 3.5; 4+5im 2]