Make .from-matrix enable to accept unshaped @x

titsuki · Aug 27, 2023 · fd8a695 · fd8a695
1 parent 87b3608
commit fd8a695
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Showing 4 changed files with 20 additions and 3 deletions.
diff --git a/README.md b/README.md
@@ -21,7 +21,7 @@ say $dmat.num-col; # 127
 my $model = Algorithm::XGBoost.train($dmat, 10);
 $model.num-feature.say; # 127
 
-my @test[2;2] = [[0e0,0e0],[0e0,1e0]];
+my @test = [[0e0,0e0],[0e0,1e0]];
 my $test = Algorithm::XGBoost::DMatrix.from-matrix(@test);
 say $model.predict($test); # (0.9858561754226685 0.9858561754226685)
 ```

diff --git a/lib/Algorithm/XGBoost.rakumod b/lib/Algorithm/XGBoost.rakumod
@@ -72,7 +72,7 @@ say $dmat.num-col; # 127
 my $model = Algorithm::XGBoost.train($dmat, 10);
 $model.num-feature.say; # 127
 
-my @test[2;2] = [[0e0,0e0],[0e0,1e0]];
+my @test = [[0e0,0e0],[0e0,1e0]];
 my $test = Algorithm::XGBoost::DMatrix.from-matrix(@test);
 say $model.predict($test); # (0.9858561754226685 0.9858561754226685)
 

diff --git a/lib/Algorithm/XGBoost/DMatrix.rakumod b/lib/Algorithm/XGBoost/DMatrix.rakumod
@@ -18,7 +18,12 @@ method from-file(::?CLASS:U: Str $path --> ::?CLASS:D) {
     nativecast(Algorithm::XGBoost::DMatrix, $h);
 }
 
-method from-matrix(::?CLASS:U: @x where { $_.shape ~~ ($,$) }, @y?, Num :$missing = NaN --> ::?CLASS:D) {
+multi method from-matrix(::?CLASS:U: @x, @y?, Num :$missing = NaN --> ::?CLASS:D) {
+    my @shaped-x[+@x;@x[0].elems] = @x.clone;
+    ::?CLASS.from-matrix(@shaped-x, @y);
+}
+
+multi method from-matrix(::?CLASS:U: @x where { $_.shape ~~ ($,$) }, @y?, Num :$missing = NaN --> ::?CLASS:D) is default {
     my $h = Pointer.new;
     my $data = CArray[num32].new(@x.flat);
     my ($nr, $nc) = @x.shape;

diff --git a/t/01-basic.rakutest b/t/01-basic.rakutest
@@ -20,6 +20,18 @@ subtest {
     is $model.predict($test), (0.9858561754226685, 0.9858561754226685);
 }, "Make sure SYNOPSIS works fine";
 
+subtest {
+    my $dmat = Algorithm::XGBoost::DMatrix.from-file("{$*PROGRAM.parent.absolute}/../misc/agaricus.txt.train");
+    is $dmat.num-row, 6513;
+    is $dmat.num-col, 127;
+    my $model = Algorithm::XGBoost.train($dmat, 10);
+    is $model.num-feature, 127;
+
+    my @test = [[0e0,0e0],[0e0,1e0]];
+    my $test = Algorithm::XGBoost::DMatrix.from-matrix(@test);
+    is $model.predict($test), (0.9858561754226685, 0.9858561754226685);
+}, "Make sure SYNOPSIS (unshaped version) works fine";
+
 subtest {
     my @train[3;2] = [[0e0,0e0],[0e0,1e0],[1e0,0e0]];
     my @y = [1e0, 0e0, 1e0];