Reintroduce nnz and nonzeros as discussed in #6769

Deprecate nfilled Deprecate nonzeros for StridedArray and BitArray find()/findnz() over sparse matrices no longer filters out stored zeros
JuliaLang · May 25, 2014 · 4fa5457 · 4fa5457
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commit 4fa5457
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Showing 12 changed files with 59 additions and 88 deletions.
diff --git a/base/abstractarray.jl b/base/abstractarray.jl
@@ -19,7 +19,6 @@ isreal{T<:Real,n}(x::AbstractArray{T,n}) = true
 ndims{T,n}(::AbstractArray{T,n}) = n
 ndims{T,n}(::Type{AbstractArray{T,n}}) = n
 ndims{T<:AbstractArray}(::Type{T}) = ndims(super(T))
-nfilled(t::AbstractArray) = length(t)
 length(t::AbstractArray) = prod(size(t))::Int
 endof(a::AbstractArray) = length(a)
 first(a::AbstractArray) = a[1]

diff --git a/base/array.jl b/base/array.jl
@@ -1102,24 +1102,6 @@ function findnz{T}(A::StridedMatrix{T})
     return (I, J, NZs)
 end
 
-function nonzeros{T}(A::StridedArray{T})
-    nnzA = countnz(A)
-    V = similar(A, T, nnzA)
-    count = 1
-    if nnzA > 0
-        for i=1:length(A)
-            Ai = A[i]
-            if Ai != 0
-                V[count] = Ai
-                count += 1
-            end
-        end
-    end
-    return V
-end
-
-nonzeros(x::Number) = x == 0 ? Array(typeof(x),0) : [x]
-
 function findmax(a)
     if isempty(a)
         error("array must be non-empty")

diff --git a/base/bitarray.jl b/base/bitarray.jl
@@ -1385,8 +1385,6 @@ function findnz(B::BitMatrix)
     return I, J, trues(length(I))
 end
 
-nonzeros(B::BitArray) = trues(countnz(B))
-
 ## Reductions ##
 
 sum(A::BitArray, region) = reducedim(+, A, region, 0, Array(Int,reduced_dims(A,region)))

diff --git a/base/deprecated.jl b/base/deprecated.jl
@@ -380,6 +380,16 @@ end
 export mmread
 
 # 0.3 deprecations
+
+function nfilled(X)
+    depwarn("nfilled has been renamed to nnz", :nfilled)
+    nnz(X)
+end
+export nfilled
+
+@deprecate nonzeros(A::StridedArray) A[find(A)]
+@deprecate nonzeros(B::BitArray) trues(countnz(B))
+
 @deprecate dense  full
 
 export Stat
@@ -448,12 +458,6 @@ Set{T<:Number}(xs::T...) = Set{T}(xs)
 
 # 0.3 discontinued functions
 
-function nnz(X)
-    depwarn("nnz has been renamed to countnz and is no longer computed in constant time for sparse matrices. Instead, use nfilled() for the number of elements in a sparse matrix.", :nnz)
-    countnz(X)
-end
-export nnz
-
 scale!{T<:Base.LinAlg.BlasReal}(X::Array{T}, s::Complex) = error("scale!: Cannot scale a real array by a complex value in-place.  Use scale(X::Array{Real}, s::Complex) instead.")
 
 @deprecate which(f::Callable, args...) @which f(args...)
diff --git a/base/exports.jl b/base/exports.jl
@@ -536,7 +536,7 @@ export
     minimum,
     minmax,
     ndims,
-    nfilled,
+    nnz,
     nonzeros,
     nthperm!,
     nthperm,

diff --git a/base/linalg/cholmod.jl b/base/linalg/cholmod.jl
@@ -14,7 +14,7 @@ export                                  # types
 using Base.LinAlg.UMFPACK               # for decrement, increment, etc.
 
 import Base: (*), convert, copy, ctranspose, eltype, findnz, getindex, hcat,
-             isvalid, nfilled, show, size, sort!, transpose, vcat
+             isvalid, nnz, show, size, sort!, transpose, vcat
 
 import ..LinAlg: (\), A_mul_Bc, A_mul_Bt, Ac_ldiv_B, Ac_mul_B, At_ldiv_B, At_mul_B,
                  cholfact, cholfact!, copy, det, diag,
@@ -778,7 +778,7 @@ for Ti in (:Int32,:Int64)
                   (Ptr{c_CholmodSparse{Tv,$Ti}},Ptr{c_CholmodSparse{Tv,$Ti}},Cint,Ptr{Uint8}),
                   &A.c,&B.c,true,cmn($Ti))
         end
-        function nfilled{Tv<:CHMVTypes}(A::CholmodSparse{Tv,$Ti})
+        function nnz{Tv<:CHMVTypes}(A::CholmodSparse{Tv,$Ti})
             ccall((@chm_nm "nnz" $Ti
                    ,:libcholmod), Int, (Ptr{c_CholmodSparse{Tv,$Ti}},Ptr{Uint8}),&A.c,cmn($Ti))
         end

diff --git a/base/linalg/sparse.jl b/base/linalg/sparse.jl
@@ -347,7 +347,7 @@ function sparse_diff1{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti})
     m,n = size(S)
     m > 1 || return SparseMatrixCSC{Tv,Ti}(0, n, ones(n+1), Ti[], Tv[])
     colptr = Array(Ti, n+1)
-    numnz = 2 * nfilled(S) # upper bound; will shrink later
+    numnz = 2 * nnz(S) # upper bound; will shrink later
     rowval = Array(Ti, numnz)
     nzval = Array(Tv, numnz)
     numnz = 0
@@ -387,7 +387,7 @@ function sparse_diff2{Tv,Ti}(a::SparseMatrixCSC{Tv,Ti})
 
     m,n = size(a)
     colptr = Array(Ti, max(n,1))
-    numnz = 2 * nfilled(a) # upper bound; will shrink later
+    numnz = 2 * nnz(a) # upper bound; will shrink later
     rowval = Array(Ti, numnz)
     nzval = Array(Tv, numnz)
 
@@ -516,8 +516,8 @@ end
 # kron
 
 function kron{Tv,Ti}(a::SparseMatrixCSC{Tv,Ti}, b::SparseMatrixCSC{Tv,Ti})
-    numnzA = nfilled(a)
-    numnzB = nfilled(b)
+    numnzA = nnz(a)
+    numnzB = nnz(b)
 
     numnz = numnzA * numnzB
 
@@ -593,7 +593,7 @@ inv(A::SparseMatrixCSC) = error("The inverse of a sparse matrix can often be den
 function scale!{Tv,Ti}(C::SparseMatrixCSC{Tv,Ti}, A::SparseMatrixCSC, b::Vector)
     m, n = size(A)
     (n==length(b) && size(A)==size(C)) || throw(DimensionMismatch(""))
-    numnz = nfilled(A)
+    numnz = nnz(A)
     C.colptr = convert(Array{Ti}, A.colptr)
     C.rowval = convert(Array{Ti}, A.rowval)
     C.nzval = Array(Tv, numnz)
@@ -606,7 +606,7 @@ end
 function scale!{Tv,Ti}(C::SparseMatrixCSC{Tv,Ti}, b::Vector, A::SparseMatrixCSC)
     m, n = size(A)
     (n==length(b) && size(A)==size(C)) || throw(DimensionMismatch(""))
-    numnz = nfilled(A)
+    numnz = nnz(A)
     C.colptr = convert(Array{Ti}, A.colptr)
     C.rowval = convert(Array{Ti}, A.rowval)
     C.nzval = Array(Tv, numnz)

diff --git a/base/sparse.jl b/base/sparse.jl
@@ -7,7 +7,7 @@ import Base.NonTupleType, Base.float
 
 export SparseMatrixCSC, 
        blkdiag, dense, diag, diagm, droptol!, dropzeros!, etree, full, 
-       getindex, ishermitian, issparse, issym, istril, istriu, 
+       getindex, ishermitian, issparse, issym, istril, istriu, nnz,
        setindex!, sparse, sparsevec, spdiagm, speye, spones, 
        sprand, sprandbool, sprandn, spzeros, symperm, trace, tril, tril!, 
        triu, triu!

diff --git a/base/sparse/csparse.jl b/base/sparse/csparse.jl
@@ -128,7 +128,7 @@ function transpose!{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti}, T::SparseMatrixCSC{Tv,Ti})
     rowval_T = T.rowval
     nzval_T = T.nzval
 
-    nnzS = nfilled(S)
+    nnzS = nnz(S)
     colptr_S = S.colptr
     rowval_S = S.rowval
     nzval_S = S.nzval
@@ -147,15 +147,15 @@ end
 
 function transpose{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti})
     (nT, mT) = size(S)
-    nnzS = nfilled(S)    
+    nnzS = nnz(S)    
     rowval_S = S.rowval
 
     rowval_T = Array(Ti, nnzS)
     nzval_T = Array(Tv, nnzS)
 
     colptr_T = zeros(Ti, nT+1)
     colptr_T[1] = 1
-    @inbounds for i=1:nfilled(S)
+    @inbounds for i=1:nnz(S)
         colptr_T[rowval_S[i]+1] += 1
     end
     colptr_T = cumsum(colptr_T)
@@ -172,7 +172,7 @@ function ctranspose!{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti}, T::SparseMatrixCSC{Tv,Ti}
     rowval_T = T.rowval
     nzval_T = T.nzval
 
-    nnzS = nfilled(S)
+    nnzS = nnz(S)
     colptr_S = S.colptr
     rowval_S = S.rowval
     nzval_S = S.nzval
@@ -191,15 +191,15 @@ end
 
 function ctranspose{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti}) 
     (nT, mT) = size(S)
-    nnzS = nfilled(S)
+    nnzS = nnz(S)
     rowval_S = S.rowval
 
     rowval_T = Array(Ti, nnzS)
     nzval_T = Array(Tv, nnzS)
 
     colptr_T = zeros(Ti, nT+1)
     colptr_T[1] = 1
-    @inbounds for i=1:nfilled(S)
+    @inbounds for i=1:nnz(S)
         colptr_T[rowval_S[i]+1] += 1
     end
     colptr_T = cumsum(colptr_T)
@@ -335,7 +335,7 @@ end
 # Section 2.7: Removing entries from a matrix
 # http://www.cise.ufl.edu/research/sparse/CSparse/
 function fkeep!{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti}, f, other)
-    nzorig = nfilled(A)
+    nzorig = nnz(A)
     nz = 1
     for j = 1:A.n
         p = A.colptr[j]                 # record current position