data_test.go

package CloudForest

import (
	"math/rand"
	"strings"
	"testing"
	"time"
)

//A toy feature matrix where either of the first
//two variables should be easilly predictible
//by the other by a single greedy tree.
var fm = `.	0	1	2	3	4	5	6	7
N:NumTarget	.0	.0	.0	.0	.0	.9	.9	.9
C:CatTarget	0	0	0	0	0	1	1	1
C:QuadVar	0	0	1	2	3	4	3	0
C:BoolVar	0 	0	1	1	1	1	1	1
N:FloatVar	.9	.9	.9	.9	.2	.9	.9	.9`

var fmissing = `.	0	1	2	3	4	5	6	7
N:NumTarget	.0	.0	.0	.0	.0	.9	.9	.9
C:CatTarget	0	0	0	0	0	1	1	1
C:QuadVar	NA	0	1	2	3	4	3	0
C:BoolVar	0 	0	1	1	NA	1	1	1
N:FloatVar	.9	NA	.9	.9	.2	.9	.9	.9`

//Note: Iris and Boston Housing Data in string literals at end of File.

func GetAllClassificationTargets(f CatFeature) []Target {
	costs := make(map[string]float64)

	for _, cat := range f.(*DenseCatFeature).Back {
		costs[cat] = 1.0
	}
	//regret := NewRegretTarget(f)
	//regret.SetCosts(costs)
	return []Target{f,
		NewEntropyTarget(f),
		NewAdaBoostTarget(f.Copy().(CatFeature)),
		NewWRFTarget(f, costs),
		//regret,
	}

}

func GetAllRegressionTargets(f NumFeature) []Target {
	return []Target{f,
		&L1Target{f},
		//NewOrdinalTarget(f),
		//&GradBoostTarget{f.Copy().(NumFeature), .1},
		//NewNumAdaBoostTarget(f.Copy().(NumFeature)),
	}
}

func TestWierdTargets(t *testing.T) {
	//wierd targets that don't meat the performance standards
	//so we check to make sure they at least grow trees
	fmReader := strings.NewReader(fm)

	fm1 := ParseAFM(fmReader)
	fm2 := ParseAFM(strings.NewReader(fmissing))

	for _, fm := range []*FeatureMatrix{fm1, fm2} {

		cases := []int{0, 1, 2, 3, 4, 5, 6, 7}
		canidates := []int{2, 3, 4}

		numtarget := fm.Data[0].(*DenseNumFeature)
		cattarget := fm.Data[1].(*DenseCatFeature)

		costs := make(map[string]float64)

		for _, cat := range cattarget.Back {
			costs[cat] = 1.0
		}
		regret := NewRegretTarget(cattarget)
		regret.SetCosts(costs)

		targets := []Target{
			//&DensityTarget{&fm.Data, fm.Data[0].Length()},
			regret,
			NewOrdinalTarget(numtarget),
			&GradBoostTarget{numtarget.Copy().(NumFeature), .1},
			NewNumAdaBoostTarget(numtarget.Copy().(NumFeature)),
		}

		for _, target := range targets {
			forest := GrowRandomForest(fm, target, canidates, fm.Data[0].Length(), 3, 10, 1, false, false, false, false, nil)
			if len(forest.Trees) != 10 {
				t.Errorf("%T didn't grow 10 trees.", target)
			}

			count := 0
			forest.Trees[0].Root.Recurse(func(*Node, []int, int) { count++ }, fm, cases, 0)
			if count < 1 {
				t.Errorf("Tree grown with %T has only %v nodes.", target, count)
			}
		}

		target := &DensityTarget{&fm.Data, fm.Data[0].Length()}
		tree := NewTree()
		allocs := NewBestSplitAllocs(len(cases), cattarget)
		tree.Grow(fm, target, cases, canidates, nil, 3, 1, false, false, false, false, false, nil, nil, allocs)
		count := 0
		tree.Root.Recurse(func(*Node, []int, int) { count++ }, fm, cases, 0)
		if count < 1 {
			t.Errorf("Denisty tree has only %v nodes", count)
		}
	}
}

func TestTreeTargets(t *testing.T) {
	fmReader := strings.NewReader(fm)

	fm := ParseAFM(fmReader)

	if len(fm.Data) != 5 {
		t.Errorf("Simple feature matrix has %v features not 5", len(fm.Data))
	}

	cases := []int{0, 1, 2, 3, 4, 5, 6, 7}
	canidates := []int{2, 3, 4}

	//regression
	numtarget := fm.Data[0]
	regressiontargets := GetAllRegressionTargets(numtarget.(*DenseNumFeature))
	//regressiontargets = append(regressiontargets, &OrdinalTarget{numtarget.(NumFeature), 2, .9})
	for _, target := range regressiontargets {
		tree := NewTree()
		allocs := NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, 3, 1, false, false, false, false, false, nil, nil, allocs)

		count := 0
		tree.Root.Recurse(func(*Node, []int, int) { count++ }, fm, cases, 0)
		if count != 9 {
			t.Errorf("Regression tree grown with %T has  has %v nodes not 9", target, count)
		}

		votes := NewNumBallotBox(numtarget.Length())

		tree.Vote(fm, votes)

		err := votes.TallyError(numtarget)
		if err != 0.0 {
			t.Errorf("Single tree regression on simple case using %T had nonzero  error: %v", target, err)
		}
	}

	//clasification
	cattarget := fm.Data[1]
	classtargets := GetAllClassificationTargets(cattarget.(*DenseCatFeature))
	for _, target := range classtargets {
		tree := NewTree()

		allocs := NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, 3, 1, false, false, false, false, false, nil, nil, allocs)

		count := 0
		tree.Root.Recurse(func(*Node, []int, int) { count++ }, fm, cases, 0)
		switch target.(type) {
		case *DenseCatFeature:
			caseFeatureCounts := new(SparseCounter)
			leaves := tree.GetLeaves(fm, caseFeatureCounts)

			if caseFeatureCounts.Map[0][2] == 0 || caseFeatureCounts.Map[4][4] == 0 {
				t.Error("Sparse feature importance result bad.")
			}

			casecount := 0
			for _, leaf := range leaves {
				casecount += len(leaf.Cases)

			}
			if casecount != len(cases) {
				t.Error("Leaf did not parition cases.")
			}
			if count != 9 {
				t.Errorf("Classification tree grown with %T has  has %v nodes not 9", target, count)
			}

		case *EntropyTarget:
			if count != 7 {
				t.Errorf("Classification tree grown with %T has  has %v nodes not 3", target, count)
			}
			continue
		case *RegretTarget:
			if count != 5 {
				t.Errorf("Classification tree grown with %T has  has %v nodes not 3", target, count)
			}
			continue

		default:
			if count != 9 {
				t.Errorf("Classification tree grown with %T has  has %v nodes not 9", target, count)
			}

		}

		catvotes := NewCatBallotBox(cattarget.Length())

		tree.Vote(fm, catvotes)

		err := catvotes.TallyError(cattarget)
		if err != 0.0 {
			t.Errorf("Sinlge tree clasification on simple case using %T had nonzero error: %v", target, err)
		}
	}

}

func TestMissing(t *testing.T) {
	fmimputed := ParseAFM(strings.NewReader(fm))
	fm := ParseAFM(strings.NewReader(fm))

	fmimputed.ImputeMissing()

	if len(fm.Data) != 5 {
		t.Errorf("Simple feature matrix has %v features not 5", len(fm.Data))
	}

	cases := []int{0, 1, 2, 3, 4, 5, 6, 7}
	canidates := []int{2, 3, 4}

	//regression
	numtarget := fm.Data[0]
	regressiontargets := GetAllRegressionTargets(numtarget.(*DenseNumFeature))
	for _, target := range regressiontargets {
		tree := NewTree()
		allocs := NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, len(canidates), 1, true, false, false, false, false, nil, nil, allocs)

		votes := NewNumBallotBox(numtarget.Length())

		tree.Vote(fm, votes)

		err := votes.TallyError(numtarget)
		if err != 0.0 {
			t.Errorf("Error: Single tree regression  with split missing using %T had nonzero  error: %v", target, err)
		}

		tree = NewTree()
		allocs = NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, len(canidates), 1, false, false, false, false, false, nil, nil, allocs)

		votes = NewNumBallotBox(numtarget.Length())

		tree.Vote(fm, votes)

		err = votes.TallyError(numtarget)
		if err != 0.0 {
			t.Errorf("Error: Single tree regression with penalized missing %T had nonzero  error: %v", target, err)
		}

		tree = NewTree()
		allocs = NewBestSplitAllocs(len(cases), target)
		tree.Grow(fmimputed, target, cases, canidates, nil, len(canidates), 1, false, false, false, false, false, nil, nil, allocs)

		votes = NewNumBallotBox(numtarget.Length())

		tree.Vote(fmimputed, votes)

		err = votes.TallyError(numtarget)
		if err != 0.0 {
			t.Errorf("Error: Single tree regression with imputed missing %T had nonzero  error: %v", target, err)
		}

	}

	//clasification
	cattarget := fm.Data[1]
	classtargets := GetAllClassificationTargets(cattarget.(*DenseCatFeature))
	for _, target := range classtargets {
		tree := NewTree()

		allocs := NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, len(canidates), 1, true, false, false, false, false, nil, nil, allocs)

		catvotes := NewCatBallotBox(cattarget.Length())

		tree.Vote(fm, catvotes)

		err := catvotes.TallyError(cattarget)
		if err > 0.0 {
			t.Errorf("Error: Sinlge tree clasification on simple case with split missing using %T had nonzero error: %v", target, err)
		}

		tree = NewTree()

		allocs = NewBestSplitAllocs(len(cases), target)
		tree.Grow(fm, target, cases, canidates, nil, len(canidates), 1, false, false, false, false, false, nil, nil, allocs)

		catvotes = NewCatBallotBox(cattarget.Length())

		tree.Vote(fm, catvotes)

		err = catvotes.TallyError(cattarget)
		if err > 0.0 {
			t.Errorf("Error: Sinlge tree clasification on simple case with penalized missing %T had nonzero error: %v", target, err)
		}

		tree = NewTree()

		allocs = NewBestSplitAllocs(len(cases), target)
		tree.Grow(fmimputed, target, cases, canidates, nil, len(canidates), 1, false, false, false, false, false, nil, nil, allocs)

		catvotes = NewCatBallotBox(cattarget.Length())

		tree.Vote(fmimputed, catvotes)

		err = catvotes.TallyError(cattarget)
		if err > 0.0 {
			t.Errorf("Error: Sinlge tree clasification on simple case with imputed missing %T had nonzero error: %v", target, err)
		}

	}

}

//Test classification target typs on iris data set. Also test arff loading.
func TestIris(t *testing.T) {
	if testing.Short() {
		t.Skip("Skipping classification tests on iris data set.")
	}
	candidates := []int{1, 2, 3, 4}
	// irisreader := strings.NewReader(irisarff)
	// fm := ParseARFF(irisreader)
	// targeti := 4

	irisreader := strings.NewReader(irislibsvm)
	fm := ParseLibSVM(irisreader)
	targeti := 0

	if len(fm.CaseLabels) != 150 || fm.Data[0].Length() != 150 {
		t.Errorf("Iris feature matrix has %v case labels, %v values not 150", len(fm.CaseLabels), fm.Data[0].Length())
	}
	if len(fm.Data) != 5 {
		t.Errorf("Iris feature matrix has %v features not 5", len(fm.Data))
	}

	if fm.Data[targeti].NCats() != 3 {
		t.Errorf("Iris target has %v cats not 3", fm.Data[0].NCats())
	}

	for i, f := range fm.Data {
		if f.Length() != 150 {
			t.Errorf("Iris feature %v has %v cases not 150", i, f.Length())
		}
	}

	ncat := 0
	nnum := 0
	for _, feature := range fm.Data {
		if feature.NCats() == 0 {
			nnum++
		} else {
			ncat++
		}
	}

	if nnum != 4 && ncat != 1 {
		t.Errorf("Iris feature matrix has %v num features and %v cat not 4 and 1", nnum, ncat)
	}

	cattarget := fm.Data[targeti]
	classtargets := GetAllClassificationTargets(cattarget.(*DenseCatFeature))
	for _, target := range classtargets {
		trainingStart := time.Now()
		forest := GrowRandomForest(fm, target.(Feature), candidates, fm.Data[0].Length(), 3, 10, 1, false, false, false, false, nil)
		trainingEnd := time.Now()
		catvotes := NewCatBallotBox(cattarget.Length())

		for _, tree := range forest.Trees {
			tree.Vote(fm, catvotes)
		}

		err := catvotes.TallyError(cattarget)

		switch cattarget.(type) {
		case *AdaBoostTarget:
			//TODO ...ada boost target needs configuration to work better...limit on tree size?
			if err > 0.05 {
				t.Errorf("Error: Classification of iris using %T had error: %v", target, err)
			}

		default:
			if err > 0.05 {
				t.Errorf("Error: Classification of iris using %T had error: %v", target, err)
			}
		}
		t.Logf("Log: 10 tree classification of iris using %T had error: %v took: %v", target, err, trainingEnd.Sub(trainingStart))

	}

	//put some missing values in
	for j, Feature := range fm.Data {
		if j != targeti {
			for i := 0; i < Feature.Length(); i++ {
				if rand.Float64() < .05 {
					Feature.PutMissing(i)
				}
			}
		}
	}

	//need to get again for boosting target
	classtargets = GetAllClassificationTargets(cattarget.(*DenseCatFeature))

	for _, target := range classtargets {
		trainingStart := time.Now()
		forest := GrowRandomForest(fm, target.(Feature), candidates, fm.Data[0].Length(), 3, 20, 1, false, false, false, false, nil)
		trainingEnd := time.Now()
		catvotes := NewCatBallotBox(cattarget.Length())

		for _, tree := range forest.Trees {
			tree.Vote(fm, catvotes)
		}

		err := catvotes.TallyError(cattarget)
		if err > 0.1 {
			t.Errorf("Error: Classification of iris with .05 missing using %T had error: %v", target, err)
		}
		t.Logf("Log: 10 tree classification of iris with .05 missing using %T had error: %v took: %v", target, err, trainingEnd.Sub(trainingStart))

	}

}

//Test classification target typs on iris data set. Also test arff loading.
func TestBoston(t *testing.T) {
	if testing.Short() {
		t.Skip("Skipping regression tests on boston data set.")
	}
	boston := strings.NewReader(boston_housing)

	fm := ParseARFF(boston)

	if len(fm.Data) != 14 {
		t.Errorf("Boston feature matrix has %v features not 14", len(fm.Data))
	}

	if len(fm.CaseLabels) != 506 || fm.Data[0].Length() != 506 {
		t.Errorf("Boston feature matrix has %v case labels, %v values not 506", len(fm.CaseLabels), fm.Data[0].Length())
	}

	candidates := []int{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}

	numtarget := fm.Data[fm.Map["class"]]
	targets := GetAllRegressionTargets(numtarget.(*DenseNumFeature))
	for _, target := range targets {
		trainingStart := time.Now()
		forest := GrowRandomForest(fm, target.(Feature), candidates, fm.Data[0].Length(), 4, 20, 1, false, false, false, false, nil)
		trainingEnd := time.Now()
		numvotes := NewNumBallotBox(numtarget.Length())

		for _, tree := range forest.Trees {
			tree.Vote(fm, numvotes)
		}

		err := numvotes.TallyR2Score(numtarget)
		if err < 0.95 {
			t.Errorf("Error: Regression of boston housing prices using %T had low R2 score: %v", target, err)
		}
		t.Logf("Log: Regression of boston housing prices %T had R2 score: %v took: %v", target, err, trainingEnd.Sub(trainingStart))
	}

	//put some missing values in
	for _, Feature := range fm.Data[:13] {
		for i := 0; i < Feature.Length(); i++ {
			if rand.Float64() < .01 {
				Feature.PutStr(i, "NA")
			}
		}
	}

	//need to get again for boosting target
	targets = GetAllRegressionTargets(numtarget.(*DenseNumFeature))

	for _, target := range targets {
		trainingStart := time.Now()
		forest := GrowRandomForest(fm, target.(Feature), candidates, fm.Data[0].Length(), 4, 20, 1, false, false, false, false, nil)
		trainingEnd := time.Now()
		numvotes := NewNumBallotBox(numtarget.Length())

		for _, tree := range forest.Trees {
			tree.Vote(fm, numvotes)
		}

		err := numvotes.TallyR2Score(numtarget)
		if err < 0.90 {
			t.Errorf("Error: Regression of boston housing prices with .01 missing using %T had low R2 score: %v", target, err)
		}
		t.Logf("Log: Regression of boston housing prices with .01 missing with %T had R2 score: %v took: %v", target, err, trainingEnd.Sub(trainingStart))
	}

}

var irislibsvm = `1 1:-0.555556 2:0.25 3:-0.864407 4:-0.916667 
1 1:-0.666667 2:-0.166667 3:-0.864407 4:-0.916667 
1 1:-0.777778 3:-0.898305 4:-0.916667 
1 1:-0.833333 2:-0.0833334 3:-0.830508 4:-0.916667 
1 1:-0.611111 2:0.333333 3:-0.864407 4:-0.916667 
1 1:-0.388889 2:0.583333 3:-0.762712 4:-0.75 
1 1:-0.833333 2:0.166667 3:-0.864407 4:-0.833333 
1 1:-0.611111 2:0.166667 3:-0.830508 4:-0.916667 
1 1:-0.944444 2:-0.25 3:-0.864407 4:-0.916667 
1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1 
1 1:-0.388889 2:0.416667 3:-0.830508 4:-0.916667 
1 1:-0.722222 2:0.166667 3:-0.79661 4:-0.916667 
1 1:-0.722222 2:-0.166667 3:-0.864407 4:-1 
1 1:-1 2:-0.166667 3:-0.966102 4:-1 
1 1:-0.166667 2:0.666667 3:-0.932203 4:-0.916667 
1 1:-0.222222 2:1 3:-0.830508 4:-0.75 
1 1:-0.388889 2:0.583333 3:-0.898305 4:-0.75 
1 1:-0.555556 2:0.25 3:-0.864407 4:-0.833333 
1 1:-0.222222 2:0.5 3:-0.762712 4:-0.833333 
1 1:-0.555556 2:0.5 3:-0.830508 4:-0.833333 
1 1:-0.388889 2:0.166667 3:-0.762712 4:-0.916667 
1 1:-0.555556 2:0.416667 3:-0.830508 4:-0.75 
1 1:-0.833333 2:0.333333 3:-1 4:-0.916667 
1 1:-0.555556 2:0.0833333 3:-0.762712 4:-0.666667 
1 1:-0.722222 2:0.166667 3:-0.694915 4:-0.916667 
1 1:-0.611111 2:-0.166667 3:-0.79661 4:-0.916667 
1 1:-0.611111 2:0.166667 3:-0.79661 4:-0.75 
1 1:-0.5 2:0.25 3:-0.830508 4:-0.916667 
1 1:-0.5 2:0.166667 3:-0.864407 4:-0.916667 
1 1:-0.777778 3:-0.79661 4:-0.916667 
1 1:-0.722222 2:-0.0833334 3:-0.79661 4:-0.916667 
1 1:-0.388889 2:0.166667 3:-0.830508 4:-0.75 
1 1:-0.5 2:0.75 3:-0.830508 4:-1 
1 1:-0.333333 2:0.833333 3:-0.864407 4:-0.916667 
1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1 
1 1:-0.611111 3:-0.932203 4:-0.916667 
1 1:-0.333333 2:0.25 3:-0.898305 4:-0.916667 
1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1 
1 1:-0.944444 2:-0.166667 3:-0.898305 4:-0.916667 
1 1:-0.555556 2:0.166667 3:-0.830508 4:-0.916667 
1 1:-0.611111 2:0.25 3:-0.898305 4:-0.833333 
1 1:-0.888889 2:-0.75 3:-0.898305 4:-0.833333 
1 1:-0.944444 3:-0.898305 4:-0.916667 
1 1:-0.611111 2:0.25 3:-0.79661 4:-0.583333 
1 1:-0.555556 2:0.5 3:-0.694915 4:-0.75 
1 1:-0.722222 2:-0.166667 3:-0.864407 4:-0.833333 
1 1:-0.555556 2:0.5 3:-0.79661 4:-0.916667 
1 1:-0.833333 3:-0.864407 4:-0.916667 
1 1:-0.444444 2:0.416667 3:-0.830508 4:-0.916667 
1 1:-0.611111 2:0.0833333 3:-0.864407 4:-0.916667 
2 1:0.5 3:0.254237 4:0.0833333 
2 1:0.166667 3:0.186441 4:0.166667 
2 1:0.444444 2:-0.0833334 3:0.322034 4:0.166667 
2 1:-0.333333 2:-0.75 3:0.0169491 4:-4.03573e-08 
2 1:0.222222 2:-0.333333 3:0.220339 4:0.166667 
2 1:-0.222222 2:-0.333333 3:0.186441 4:-4.03573e-08 
2 1:0.111111 2:0.0833333 3:0.254237 4:0.25 
2 1:-0.666667 2:-0.666667 3:-0.220339 4:-0.25 
2 1:0.277778 2:-0.25 3:0.220339 4:-4.03573e-08 
2 1:-0.5 2:-0.416667 3:-0.0169491 4:0.0833333 
2 1:-0.611111 2:-1 3:-0.152542 4:-0.25 
2 1:-0.111111 2:-0.166667 3:0.0847457 4:0.166667 
2 1:-0.0555556 2:-0.833333 3:0.0169491 4:-0.25 
2 1:-1.32455e-07 2:-0.25 3:0.254237 4:0.0833333 
2 1:-0.277778 2:-0.25 3:-0.118644 4:-4.03573e-08 
2 1:0.333333 2:-0.0833334 3:0.152542 4:0.0833333 
2 1:-0.277778 2:-0.166667 3:0.186441 4:0.166667 
2 1:-0.166667 2:-0.416667 3:0.0508474 4:-0.25 
2 1:0.0555554 2:-0.833333 3:0.186441 4:0.166667 
2 1:-0.277778 2:-0.583333 3:-0.0169491 4:-0.166667 
2 1:-0.111111 3:0.288136 4:0.416667 
2 1:-1.32455e-07 2:-0.333333 3:0.0169491 4:-4.03573e-08 
2 1:0.111111 2:-0.583333 3:0.322034 4:0.166667 
2 1:-1.32455e-07 2:-0.333333 3:0.254237 4:-0.0833333 
2 1:0.166667 2:-0.25 3:0.118644 4:-4.03573e-08 
2 1:0.277778 2:-0.166667 3:0.152542 4:0.0833333 
2 1:0.388889 2:-0.333333 3:0.288136 4:0.0833333 
2 1:0.333333 2:-0.166667 3:0.355932 4:0.333333 
2 1:-0.0555556 2:-0.25 3:0.186441 4:0.166667 
2 1:-0.222222 2:-0.5 3:-0.152542 4:-0.25 
2 1:-0.333333 2:-0.666667 3:-0.0508475 4:-0.166667 
2 1:-0.333333 2:-0.666667 3:-0.0847458 4:-0.25 
2 1:-0.166667 2:-0.416667 3:-0.0169491 4:-0.0833333 
2 1:-0.0555556 2:-0.416667 3:0.38983 4:0.25 
2 1:-0.388889 2:-0.166667 3:0.186441 4:0.166667 
2 1:-0.0555556 2:0.166667 3:0.186441 4:0.25 
2 1:0.333333 2:-0.0833334 3:0.254237 4:0.166667 
2 1:0.111111 2:-0.75 3:0.152542 4:-4.03573e-08 
2 1:-0.277778 2:-0.166667 3:0.0508474 4:-4.03573e-08 
2 1:-0.333333 2:-0.583333 3:0.0169491 4:-4.03573e-08 
2 1:-0.333333 2:-0.5 3:0.152542 4:-0.0833333 
2 1:-1.32455e-07 2:-0.166667 3:0.220339 4:0.0833333 
2 1:-0.166667 2:-0.5 3:0.0169491 4:-0.0833333 
2 1:-0.611111 2:-0.75 3:-0.220339 4:-0.25 
2 1:-0.277778 2:-0.416667 3:0.0847457 4:-4.03573e-08 
2 1:-0.222222 2:-0.166667 3:0.0847457 4:-0.0833333 
2 1:-0.222222 2:-0.25 3:0.0847457 4:-4.03573e-08 
2 1:0.0555554 2:-0.25 3:0.118644 4:-4.03573e-08 
2 1:-0.555556 2:-0.583333 3:-0.322034 4:-0.166667 
2 1:-0.222222 2:-0.333333 3:0.0508474 4:-4.03573e-08 
3 1:0.111111 2:0.0833333 3:0.694915 4:1 
3 1:-0.166667 2:-0.416667 3:0.38983 4:0.5 
3 1:0.555555 2:-0.166667 3:0.661017 4:0.666667 
3 1:0.111111 2:-0.25 3:0.559322 4:0.416667 
3 1:0.222222 2:-0.166667 3:0.627119 4:0.75 
3 1:0.833333 2:-0.166667 3:0.898305 4:0.666667 
3 1:-0.666667 2:-0.583333 3:0.186441 4:0.333333 
3 1:0.666667 2:-0.25 3:0.79661 4:0.416667 
3 1:0.333333 2:-0.583333 3:0.627119 4:0.416667 
3 1:0.611111 2:0.333333 3:0.728813 4:1 
3 1:0.222222 3:0.38983 4:0.583333 
3 1:0.166667 2:-0.416667 3:0.457627 4:0.5 
3 1:0.388889 2:-0.166667 3:0.525424 4:0.666667 
3 1:-0.222222 2:-0.583333 3:0.355932 4:0.583333 
3 1:-0.166667 2:-0.333333 3:0.38983 4:0.916667 
3 1:0.166667 3:0.457627 4:0.833333 
3 1:0.222222 2:-0.166667 3:0.525424 4:0.416667 
3 1:0.888889 2:0.5 3:0.932203 4:0.75 
3 1:0.888889 2:-0.5 3:1 4:0.833333 
3 1:-0.0555556 2:-0.833333 3:0.355932 4:0.166667 
3 1:0.444444 3:0.59322 4:0.833333 
3 1:-0.277778 2:-0.333333 3:0.322034 4:0.583333 
3 1:0.888889 2:-0.333333 3:0.932203 4:0.583333 
3 1:0.111111 2:-0.416667 3:0.322034 4:0.416667 
3 1:0.333333 2:0.0833333 3:0.59322 4:0.666667 
3 1:0.611111 3:0.694915 4:0.416667 
3 1:0.0555554 2:-0.333333 3:0.288136 4:0.416667 
3 1:-1.32455e-07 2:-0.166667 3:0.322034 4:0.416667 
3 1:0.166667 2:-0.333333 3:0.559322 4:0.666667 
3 1:0.611111 2:-0.166667 3:0.627119 4:0.25 
3 1:0.722222 2:-0.333333 3:0.728813 4:0.5 
3 1:1 2:0.5 3:0.830508 4:0.583333 
3 1:0.166667 2:-0.333333 3:0.559322 4:0.75 
3 1:0.111111 2:-0.333333 3:0.38983 4:0.166667 
3 1:-1.32455e-07 2:-0.5 3:0.559322 4:0.0833333 
3 1:0.888889 2:-0.166667 3:0.728813 4:0.833333 
3 1:0.111111 2:0.166667 3:0.559322 4:0.916667 
3 1:0.166667 2:-0.0833334 3:0.525424 4:0.416667 
3 1:-0.0555556 2:-0.166667 3:0.288136 4:0.416667 
3 1:0.444444 2:-0.0833334 3:0.491525 4:0.666667 
3 1:0.333333 2:-0.0833334 3:0.559322 4:0.916667 
3 1:0.444444 2:-0.0833334 3:0.38983 4:0.833333 
3 1:-0.166667 2:-0.416667 3:0.38983 4:0.5 
3 1:0.388889 3:0.661017 4:0.833333 
3 1:0.333333 2:0.0833333 3:0.59322 4:1 
3 1:0.333333 2:-0.166667 3:0.423729 4:0.833333 
3 1:0.111111 2:-0.583333 3:0.355932 4:0.5 
3 1:0.222222 2:-0.166667 3:0.423729 4:0.583333 
3 1:0.0555554 2:0.166667 3:0.491525 4:0.833333 
3 1:-0.111111 2:-0.166667 3:0.38983 4:0.416667 
`

var boston_housing = `% 1. Title: Boston Housing Data
% 
% 2. Sources:
%    (a) Origin:  This dataset was taken from the StatLib library which is
%                 maintained at Carnegie Mellon University.
%    (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 
%                  demand for clean air', J. Environ. Economics & Management,
%                  vol.5, 81-102, 1978.
%    (c) Date: July 7, 1993
% 
% 3. Past Usage:
%    -   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 
%        1980.   N.B. Various transformations are used in the table on
%        pages 244-261.
%     -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
%        In Proceedings on the Tenth International Conference of Machine 
%        Learning, 236-243, University of Massachusetts, Amherst. Morgan
%        Kaufmann.
% 
% 4. Relevant Information:
% 
%    Concerns housing values in suburbs of Boston.
% 
% 5. Number of Instances: 506
% 
% 6. Number of Attributes: 13 continuous attributes (including "class"
%                          attribute "MEDV"), 1 binary-valued attribute.
% 
% 7. Attribute Information:
% 
%     1. CRIM      per capita crime rate by town
%     2. ZN        proportion of residential land zoned for lots over 
%                  25,000 sq.ft.
%     3. INDUS     proportion of non-retail business acres per town
%     4. CHAS      Charles River dummy variable (= 1 if tract bounds 
%                  river; 0 otherwise)
%     5. NOX       nitric oxides concentration (parts per 10 million)
%     6. RM        average number of rooms per dwelling
%     7. AGE       proportion of owner-occupied units built prior to 1940
%     8. DIS       weighted distances to five Boston employment centres
%     9. RAD       index of accessibility to radial highways
%     10. TAX      full-value property-tax rate per $10,000
%     11. PTRATIO  pupil-teacher ratio by town
%     12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
%                  by town
%     13. LSTAT    % lower status of the population
%     14. MEDV     Median value of owner-occupied homes in $1000's
% 
% 8. Missing Attribute Values:  None.
% 

@relation 'housing'
@attribute CRIM real
@attribute ZN real
@attribute INDUS real
@attribute CHAS { 0, 1}
@attribute NOX real
@attribute RM real
@attribute AGE real
@attribute DIS real
@attribute RAD real
@attribute TAX real
@attribute PTRATIO real
@attribute B real
@attribute LSTAT real
@attribute class real
@data
0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3
0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20
0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6
0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4
0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4
0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7
0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5
0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25
0.04981,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4
0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9
0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4
0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7
0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6
0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3
0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6
0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7
0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16
0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2
0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25
0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33
0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5
0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4
0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22
0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4
0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9
0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2
0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7
0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8
0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4
0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1
0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4
0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20
0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8
0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2
0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3
0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28
0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9
0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8
0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9
0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9
0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6
0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5
0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2
0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6
0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7
0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6
0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22
0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9
0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25
0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6
0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4
0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4
0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7
0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8
0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2
0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5
0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5
0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6
0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3
0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1
0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5
0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5
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0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4
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0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5
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0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2
0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2
0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4
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0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3
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0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3
0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8
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0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18
0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3
0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2
1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6
0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23
0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4
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0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1
0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4
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0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3
0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8
0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14
1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4
3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4
4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6
2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8
2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8
2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6
2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6
2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8
2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4
1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5
1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6
1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3
2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4
1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17
3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6
2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1
1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3
1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3
1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3
1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27
1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50
1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50
1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50
2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7
2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25
2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50
1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8
2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8
2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3
1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4
2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1
0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1
0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6
0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6
0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4
0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2
0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6
0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9
0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2
0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8
0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2
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0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1
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0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3
0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6
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0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5
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0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1
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0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5
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0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9
0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44
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0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36
0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1
0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8
0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1
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0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31
0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5
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37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9
7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11
9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5
8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5
10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7
11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7
13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5
9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1
5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4
9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4
12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
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%
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