Use countnz instead of nnz

[chezou]

In Base.show function, there is deprecated function nnz.
It is preffered to use countnz instead of nnz.

[johnmyleswhite]

I think this deprecation was already reversed: JuliaLang/julia@8879b88

[chezou]

hmm, I'm using following version of Julia, but still getting some warning.

julia> versioninfo()
Julia Version 0.3.0-prerelease+3551
Commit 547facf* (2014-06-07 20:57 UTC)
Platform Info:
  System: Darwin (x86_64-apple-darwin12.5.0)
  CPU: Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
  WORD_SIZE: 64
  BLAS: libgfortblas
  LAPACK: liblapack
  LIBM: libopenlibm

julia> model = svm(X[:,train], Y[train])
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
 in show at /Users/mic/.julia/v0.3/SVM/src/SVM.jl:15
 in anonymous at show.jl:1045
 in with_output_limit at show.jl:1022
 in showlimited at show.jl:1044
 in writemime at replutil.jl:2
 in display at REPL.jl:114
 in display at REPL.jl:117
 in display at multimedia.jl:149
 in print_response at REPL.jl:133
 in print_response at REPL.jl:121
 in anonymous at REPL.jl:521
 in anonymous at REPL.jl:687
 in anonymous at LineEdit.jl:1121
 * Non-zero weights: 4
 * Accuracy: 1.000000
 * Iterations: 100
 * Converged: true

[paulanalyst]

I have the same ,
and
iris = dataset("datasets", "iris") or iris = data("datasets", "iris") id corectly ?
_
_ _ ()_ | A fresh approach to technical computing
() | () () | Documentation: http://docs.julialang.org
_ _ | | __ _ | Type "help()" to list help topics
| | | | | | |/ ` | |
| | || | | | (| | | Version 0.3.0-prerelease+3687 (2014-06-16 00:19 UTC)
/ |_'|||__'| | Commit 9381e34 (1 day old master)
|__/ | x86_64-w64-mingw32

julia> Pkg.update()
INFO: Updating METADATA...
INFO: Computing changes...
INFO: No packages to install, update or remove

julia> using SVM

julia> using RDatasets

julia> iris = data("datasets", "iris")
ERROR: no method data(ASCIIString, ASCIIString)

julia> iris = datasets("datasets", "iris")
ERROR: datasets not defined

julia> iris = dataset("datasets", "iris")
150x5 DataFrame
|-------|-------------|------------|-------------|------------|-------------|
| Row # | SepalLength | SepalWidth | PetalLength | PetalWidth | Species |
| 1 | 5.1 | 3.5 | 1.4 | 0.2 | "setosa" |
| 2 | 4.9 | 3.0 | 1.4 | 0.2 | "setosa" |
| 3 | 4.7 | 3.2 | 1.3 | 0.2 | "setosa" |
| 4 | 4.6 | 3.1 | 1.5 | 0.2 | "setosa" |
| 5 | 5.0 | 3.6 | 1.4 | 0.2 | "setosa" |
| 6 | 5.4 | 3.9 | 1.7 | 0.4 | "setosa" |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | "setosa" |
| 8 | 5.0 | 3.4 | 1.5 | 0.2 | "setosa" |
| 9 | 4.4 | 2.9 | 1.4 | 0.2 | "setosa" |
?
| 141 | 6.7 | 3.1 | 5.6 | 2.4 | "virginica" |
| 142 | 6.9 | 3.1 | 5.1 | 2.3 | "virginica" |
| 143 | 5.8 | 2.7 | 5.1 | 1.9 | "virginica" |
| 144 | 6.8 | 3.2 | 5.9 | 2.3 | "virginica" |
| 145 | 6.7 | 3.3 | 5.7 | 2.5 | "virginica" |
| 146 | 6.7 | 3.0 | 5.2 | 2.3 | "virginica" |
| 147 | 6.3 | 2.5 | 5.0 | 1.9 | "virginica" |
| 148 | 6.5 | 3.0 | 5.2 | 2.0 | "virginica" |
| 149 | 6.2 | 3.4 | 5.4 | 2.3 | "virginica" |
| 150 | 5.9 | 3.0 | 5.1 | 1.8 | "virginica" |

julia> X = array(iris[:, 1:4])'
4x150 Array{Float64,2}:
5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 6.8 6.7 6.7 6.3 6.5 6.2 5.9
3.5 3.0 3.2 3.1 3.6 3.9 3.4 3.4 3.2 3.3 3.0 2.5 3.0 3.4 3.0
1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 5.9 5.7 5.2 5.0 5.2 5.4 5.1
0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 2.3 2.5 2.3 1.9 2.0 2.3 1.8

julia> p, n = size(X)
(4,150)

julia> Y = [species == "setosa" ? 1.0 : -1.0 for species in iris[:, "Species"]]
WARNING: indexing DataFrames with strings is deprecated; use symbols instead
in getindex at C:\Users\SAMSUNG2.julia\v0.3\DataFrames\src\deprecated.jl:180
in anonymous at no file
150-element Array{Float64,1}:
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
?
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0

julia> train = randbool(n)
150-element BitArray{1}:
true
true
false
false
false
true
false
false
true
true
?
false
false
true
true
true
false
false
true
false
false

julia> model = svm(X[:,train], Y[train])
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
in show at C:\Users\SAMSUNG2.julia\v0.3\SVM\src\SVM.jl:15

• Non-zero weights: 4
• Accuracy: 1.000000
• Iterations: 100
• Converged: true

julia> accuracy = nnz(predict(model, X[:,~train]) .== Y[~train])/nnz(~train)
ERROR: no method predict(SVMFit, Array{Float64,2})

julia> model = svm(X, Y, k = 150)
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
in show at C:\Users\SAMSUNG2.julia\v0.3\SVM\src\SVM.jl:15

• Non-zero weights: 4
• Accuracy: 1.000000
• Iterations: 100
• Converged: true

julia> model = svm(X, Y, lambda = 0.1)
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
in show at C:\Users\SAMSUNG2.julia\v0.3\SVM\src\SVM.jl:15
in anonymous at show.jl:1067
in stream_wait at stream.jl:255
in with_output_limit at show.jl:1044
in stream_wait at stream.jl:252

• Non-zero weights: 4
• Accuracy: 1.000000
• Iterations: 100
• Converged: true

julia> model = svm(X, Y, T = 1000)
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
in show at C:\Users\SAMSUNG2.julia\v0.3\SVM\src\SVM.jl:15
in anonymous at show.jl:1067
in stream_wait at stream.jl:255
in with_output_limit at show.jl:1044
in stream_wait at stream.jl:252

• Non-zero weights: 4
• Accuracy: 1.000000
• Iterations: 1000
• Converged: true

julia>

Paul

[chezou]

@paulanalyst I've pull requested about Readme's example. see also #10

[chezou]

I reproduce same warning on following version.

julia> versioninfo()
Julia Version 0.3.0-prerelease+3687
Commit 9381e34* (2014-06-16 00:19 UTC)
Platform Info:
  System: Darwin (x86_64-apple-darwin13.2.0)
  CPU: Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
  WORD_SIZE: 64
  BLAS: libopenblas (USE64BITINT NO_AFFINITY)
  LAPACK: libopenblas
  LIBM: libopenlibm

julia> model = svm(X[:,train], Y[train])
Fitted linear SVM
WARNING: nnz(A::StridedArray) is deprecated, use countnz(A) instead.
 in show at /Users/mic/.julia/v0.3/SVM/src/SVM.jl:15
 in anonymous at show.jl:1067
 in showlimited at show.jl:1066
 in writemime at replutil.jl:2
 in display at REPL.jl:117
 in display at REPL.jl:120
 in display at multimedia.jl:149
 in print_response at REPL.jl:139
 in print_response at REPL.jl:124
 in anonymous at REPL.jl:541
 * Non-zero weights: 4
 * Accuracy: 1.000000
 * Iterations: 100
 * Converged: true