├── .gitignore ├── Makefile ├── README.md ├── activation_func.go ├── activation_function_test.go ├── engine └── engine.go ├── enter.go ├── evaluation └── evaluation.go ├── examples ├── README.md ├── sonar │ ├── README.md │ ├── data.csv │ └── main.go └── wine-quality │ ├── README.md │ ├── main.go │ ├── winequality-red.csv │ └── winequality-white.csv ├── gopher_neural.go ├── layer.go ├── layer_test.go ├── learn ├── learn.go ├── learn_test.go └── samples.go ├── network.go ├── network_test.go ├── neuron.go ├── neuron_test.go ├── persist ├── persist.go └── persist_test.go └── synapse.go /.gitignore: -------------------------------------------------------------------------------- 1 | *.test 2 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | test: test_neural test_persist test_learn test_engine test_evaluation 2 | 3 | test_persist: 4 | @( go test ./persist/ ) 5 | 6 | test_neural: 7 | @( go test ) 8 | 9 | test_learn: 10 | @( go test ./learn/ ) 11 | 12 | test_engine: 13 | @( go test ./engine/ ) 14 | 15 | test_engine: 16 | @( go test ./evaluation/ ) 17 | 18 | goget: 19 | @( \ 20 | go get github.com/breskos/gopher-neural; \ 21 | go get github.com/breskos/gopher-neural/persist; \ 22 | go get github.com/breskos/gopher-neural/learn; \ 23 | go get github.com/breskos/gopher-neural/engine; \ 24 | go get github.com/breskos/gopher-neural/evaluation; \ 25 | ) 26 | 27 | gogetu: 28 | @( \ 29 | go get github.com/breskos/gopher-neural; \ 30 | go get github.com/breskos/gopher-neural/persist; \ 31 | go get github.com/breskos/gopher-neural/learn; \ 32 | go get github.com/breskos/gopher-neural/engine; \ 33 | go get github.com/breskos/gopher-neural/evaluation; \ 34 | ) 35 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # gopher-neural 2 | 3 | This project is deprecated. 4 | Development continues under the new name __gopher-learn__ here: https://github.com/breskos/gopher-learn 5 | 6 | # Gopher-Learn 7 | 8 | What is Gopher-Learn: 9 | - Artificial neural network written in Golang with training / testing framework 10 | - Rich measurement mechanisms to control the training 11 | - Examples for fast understanding 12 | - Can also be used for iterative online learning (using online module) for autonomous agents 13 | 14 | 15 | -------------------------------------------------------------------------------- /activation_func.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import "math" 4 | 5 | type ActivationFunction func(float64) float64 6 | 7 | func NewLogisticFunc(a float64) ActivationFunction { 8 | return func(x float64) float64 { 9 | return LogisticFunc(x, a) 10 | } 11 | } 12 | func LogisticFunc(x, a float64) float64 { 13 | return 1 / (1 + math.Exp(-a*x)) 14 | } 15 | -------------------------------------------------------------------------------- /activation_function_test.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import ( 4 | . "launchpad.net/gocheck" 5 | ) 6 | 7 | func (s *SuiteT) TestLogisticFunc(c *C) { 8 | f := NewLogisticFunc(1) 9 | 10 | c.Assert(f(0), Equals, 0.5) 11 | c.Assert(1-f(6) > 0, Equals, true) 12 | c.Assert(1-f(6) < 0.1, Equals, true) 13 | } 14 | -------------------------------------------------------------------------------- /engine/engine.go: -------------------------------------------------------------------------------- 1 | package engine 2 | 3 | import ( 4 | "fmt" 5 | "math/rand" 6 | 7 | neural "github.com/breskos/gopher-neural" 8 | "github.com/breskos/gopher-neural/evaluation" 9 | "github.com/breskos/gopher-neural/learn" 10 | "github.com/breskos/gopher-neural/persist" 11 | ) 12 | 13 | const ( 14 | // CriterionAccuracy decides evaluation by accuracy 15 | CriterionAccuracy = 0 16 | // CriterionBalancedAccuracy decides evaluation by balanced accuracy 17 | CriterionBalancedAccuracy = 1 18 | // CriterionFMeasure decides evaluation by f-measure 19 | CriterionFMeasure = 2 20 | // CriterionSimple decides on simple wrong/correct ratio 21 | CriterionSimple = 3 22 | // CriterionDistance decieds evaluation by distance to ideal output 23 | CriterionDistance = 4 24 | // some output tokens 25 | runToken = "," 26 | epochToken = "." 27 | tryToken = "*" 28 | ) 29 | 30 | // Engine contains every necessary for starting the engine 31 | type Engine struct { 32 | NetworkInput int 33 | NetworkLayer []int 34 | NetworkOutput int 35 | Data *learn.Set 36 | WinnerNetwork *neural.Network 37 | WinnerEvaluation evaluation.Evaluation 38 | Verbose bool 39 | Usage int 40 | RegressionThreshold float64 41 | } 42 | 43 | // NewEngine creates a new Engine object 44 | func NewEngine(usage int, hiddenLayer []int, data *learn.Set) *Engine { 45 | var outputLength int 46 | if neural.Regression == usage { 47 | outputLength = 1 48 | } else { 49 | outputLength = len(data.Samples[0].Output) 50 | } 51 | return &Engine{ 52 | NetworkInput: len(data.Samples[0].Vector), 53 | NetworkOutput: outputLength, 54 | NetworkLayer: hiddenLayer, 55 | Data: data, 56 | WinnerNetwork: build(usage, len(data.Samples[0].Vector), hiddenLayer, data.ClassToLabel), 57 | WinnerEvaluation: *evaluation.NewEvaluation(usage, data.GetClasses()), 58 | Verbose: false, 59 | Usage: usage, 60 | RegressionThreshold: 0.0, 61 | } 62 | } 63 | 64 | // SetVerbose set verbose mode default = false 65 | func (e *Engine) SetVerbose(verbose bool) { 66 | e.Verbose = verbose 67 | } 68 | 69 | // SetRegressionThreshold sets the evaluation threshold for the regression 70 | func (e *Engine) SetRegressionThreshold(threshold float64) { 71 | e.RegressionThreshold = threshold 72 | } 73 | 74 | // GetWinner returns the winner network from training 75 | func (e *Engine) GetWinner() (*neural.Network, *evaluation.Evaluation) { 76 | return e.WinnerNetwork, &e.WinnerEvaluation 77 | } 78 | 79 | // Start takes the paramter to start the engine and run it 80 | func (e *Engine) Start(criterion, tries, epochs int, trainingSplit, startLearning, decay float64) { 81 | network := build(e.Usage, e.NetworkInput, e.NetworkLayer, e.Data.ClassToLabel) 82 | training, validation := split(e.Usage, e.Data, trainingSplit) 83 | for try := 0; try < tries; try++ { 84 | learning := startLearning 85 | if e.Verbose { 86 | fmt.Printf("\n> start try %v. training / test: %v / %v (%v)\n", (try + 1), len(training.Samples), len(validation.Samples), trainingSplit) 87 | } 88 | for ; learning > 0.0; learning -= decay { 89 | train(e.Usage, network, training, learning, epochs) 90 | evaluation := evaluate(e.Usage, network, validation, training, e.RegressionThreshold) 91 | if compare(e.Usage, criterion, &e.WinnerEvaluation, evaluation) { 92 | e.WinnerNetwork = copy(network) 93 | e.WinnerEvaluation = *evaluation 94 | if e.Verbose { 95 | print(&e.WinnerEvaluation) 96 | } 97 | } 98 | } 99 | if e.Verbose { 100 | fmt.Print(tryToken + "\n") 101 | } 102 | } 103 | } 104 | 105 | func print(e *evaluation.Evaluation) { 106 | fmt.Printf("\n [Best] acc: %v / bacc: %v / f1: %v / correct: %v / distance: %v\n", e.GetOverallAccuracy(), e.GetOverallBalancedAccuracy(), e.GetOverallFMeasure(), e.GetCorrectRatio(), e.GetDistance()) 107 | } 108 | 109 | func build(usage int, input int, hidden []int, labels map[int]string) *neural.Network { 110 | hidden = append(hidden, len(labels)) 111 | network := neural.NewNetwork(input, hidden, labels) 112 | network.RandomizeSynapses() 113 | return network 114 | } 115 | 116 | func split(usage int, set *learn.Set, ratio float64) (*learn.Set, *learn.Set) { 117 | multiplier := 100 118 | normalizedRatio := int(ratio * float64(multiplier)) 119 | var training, evaluation learn.Set 120 | training.ClassToLabel = set.ClassToLabel 121 | evaluation.ClassToLabel = set.ClassToLabel 122 | for i := range set.Samples { 123 | if rand.Intn(multiplier) <= normalizedRatio { 124 | training.Samples = append(training.Samples, set.Samples[i]) 125 | } else { 126 | evaluation.Samples = append(evaluation.Samples, set.Samples[i]) 127 | } 128 | } 129 | return &training, &evaluation 130 | } 131 | 132 | func train(usage int, network *neural.Network, data *learn.Set, learning float64, epochs int) { 133 | for e := 0; e < epochs; e++ { 134 | for sample := range data.Samples { 135 | learn.Learn(network, data.Samples[sample].Vector, data.Samples[sample].Output, learning) 136 | } 137 | fmt.Print(epochToken) 138 | } 139 | fmt.Print(runToken) 140 | } 141 | 142 | func evaluate(usage int, network *neural.Network, test *learn.Set, train *learn.Set, regressionThreshold float64) *evaluation.Evaluation { 143 | evaluation := evaluation.NewEvaluation(usage, train.GetClasses()) 144 | evaluation.SetRegressionThreshold(regressionThreshold) 145 | for sample := range test.Samples { 146 | evaluation.AddDistance(network, test.Samples[sample].Vector, test.Samples[sample].Output) 147 | if neural.Classification == usage { 148 | winner := network.CalculateWinnerLabel(test.Samples[sample].Vector) 149 | evaluation.Add(test.Samples[sample].Label, winner) 150 | } else { 151 | prediction := network.Calculate(test.Samples[sample].Vector) 152 | evaluation.AddRegression(test.Samples[sample].Value, prediction[0]) 153 | } 154 | } 155 | return evaluation 156 | } 157 | 158 | func compare(usage int, criterion int, current *evaluation.Evaluation, try *evaluation.Evaluation) bool { 159 | if current.Correct+current.Wrong == 0 { 160 | return true 161 | } 162 | switch criterion { 163 | case CriterionAccuracy: 164 | if current.GetOverallAccuracy() < try.GetOverallAccuracy() { 165 | return true 166 | } 167 | case CriterionBalancedAccuracy: 168 | if current.GetOverallBalancedAccuracy() < try.GetOverallBalancedAccuracy() { 169 | return true 170 | } 171 | case CriterionFMeasure: 172 | if current.GetOverallFMeasure() < try.GetOverallFMeasure() { 173 | return true 174 | } 175 | case CriterionSimple: 176 | if current.GetCorrectRatio() < try.GetCorrectRatio() { 177 | return true 178 | } 179 | case CriterionDistance: 180 | if current.GetDistance() > try.GetDistance() { 181 | return true 182 | } 183 | } 184 | return false 185 | } 186 | 187 | func copy(from *neural.Network) *neural.Network { 188 | return persist.FromDump(persist.ToDump(from)) 189 | } 190 | -------------------------------------------------------------------------------- /enter.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | type Enter struct { 4 | OutSynapses []*Synapse 5 | Input float64 `json:"-"` 6 | } 7 | 8 | func NewEnter() *Enter { 9 | return &Enter{} 10 | } 11 | 12 | func (e *Enter) SynapseTo(nTo *Neuron, weight float64) { 13 | syn := NewSynapse(weight) 14 | 15 | e.OutSynapses = append(e.OutSynapses, syn) 16 | nTo.InSynapses = append(nTo.InSynapses, syn) 17 | } 18 | 19 | func (e *Enter) SetInput(val float64) { 20 | e.Input = val 21 | } 22 | 23 | func (e *Enter) ConnectTo(layer *Layer) { 24 | for _, n := range layer.Neurons { 25 | e.SynapseTo(n, 0) 26 | } 27 | } 28 | 29 | func (e *Enter) Signal() { 30 | for _, s := range e.OutSynapses { 31 | s.Signal(e.Input) 32 | } 33 | } 34 | -------------------------------------------------------------------------------- /evaluation/evaluation.go: -------------------------------------------------------------------------------- 1 | package evaluation 2 | 3 | import ( 4 | "fmt" 5 | "math" 6 | 7 | neural "github.com/breskos/gopher-neural" 8 | ) 9 | 10 | // TODO (abresk) write tests for evaluation 11 | 12 | // Evaluation contains all the structures necessary for the evaluation 13 | type Evaluation struct { 14 | Confusion map[string]map[string]int 15 | Correct int 16 | Wrong int 17 | OverallDistance float64 18 | Usage int 19 | Threshold float64 20 | } 21 | 22 | // NewEvaluation creates a new evaluation object 23 | func NewEvaluation(usage int, classes []string) *Evaluation { 24 | evaluation := &Evaluation{ 25 | Usage: usage, 26 | Confusion: make(map[string]map[string]int), 27 | } 28 | for i := range classes { 29 | evaluation.Confusion[classes[i]] = make(map[string]int) 30 | for j := range classes { 31 | evaluation.Confusion[classes[i]][classes[j]] = 0 32 | } 33 | } 34 | return evaluation 35 | } 36 | 37 | // SetRegressionThreshold sets the threshold if you are trying to do Pos / Neg with a regressor 38 | func (e *Evaluation) SetRegressionThreshold(threshold float64) { 39 | e.Threshold = threshold 40 | } 41 | 42 | // Add adds a new data point to the evaluation 43 | func (e *Evaluation) Add(labeledClass, predictedClass string) { 44 | if _, ok := e.Confusion[labeledClass][predictedClass]; ok { 45 | e.Confusion[labeledClass][predictedClass]++ 46 | } else { 47 | e.Confusion[labeledClass][predictedClass] = 1 48 | } 49 | if labeledClass == predictedClass { 50 | e.Correct++ 51 | } else { 52 | e.Wrong++ 53 | } 54 | } 55 | 56 | // AddRegression add a predicted regresssion value to tht set 57 | func (e *Evaluation) AddRegression(label, predicted float64) { 58 | if math.Abs(label-predicted) >= e.Threshold { 59 | e.Wrong++ 60 | } else { 61 | e.Correct++ 62 | } 63 | } 64 | 65 | // GetTruePositives returns TP 66 | func (e *Evaluation) GetTruePositives(label string) int { 67 | return e.Confusion[label][label] 68 | } 69 | 70 | // GetFalsePositives returns FP 71 | func (e *Evaluation) GetFalsePositives(label string) int { 72 | s := 0 73 | for l := range e.Confusion { 74 | if l != label { 75 | s += e.Confusion[l][label] 76 | } 77 | } 78 | return s 79 | } 80 | 81 | // GetTrueNegatives returns TN 82 | func (e *Evaluation) GetTrueNegatives(label string) int { 83 | s := 0 84 | for la := range e.Confusion { 85 | if la != label { 86 | for l := range e.Confusion[la] { 87 | if l != label { 88 | s += e.Confusion[la][l] 89 | } 90 | } 91 | } 92 | } 93 | return s 94 | } 95 | 96 | // GetFalseNegatives returns FNs 97 | func (e *Evaluation) GetFalseNegatives(label string) int { 98 | s := 0 99 | for la := range e.Confusion[label] { 100 | for l := range e.Confusion[la] { 101 | if l != label && la == label { 102 | s += e.Confusion[la][l] 103 | } 104 | } 105 | } 106 | return s 107 | } 108 | 109 | // GetPositives TP + FN 110 | func (e *Evaluation) GetPositives(label string) int { 111 | return e.GetTruePositives(label) + e.GetFalseNegatives(label) 112 | } 113 | 114 | // GetNegatives FP + TN 115 | func (e *Evaluation) GetNegatives(label string) int { 116 | return e.GetFalsePositives(label) + e.GetTrueNegatives(label) 117 | } 118 | 119 | // GetAccuracy (TP+TN) / (P+N) 120 | func (e *Evaluation) GetAccuracy(label string) float64 { 121 | if float64(e.GetPositives(label)+e.GetNegatives(label)) == 0.0 { 122 | return 0.0 123 | } 124 | return float64(e.GetTruePositives(label)+e.GetTrueNegatives(label)) / float64(e.GetPositives(label)+e.GetNegatives(label)) 125 | } 126 | 127 | // GetRecall TP/P, TP/(TP + FN) 128 | func (e *Evaluation) GetRecall(label string) float64 { 129 | if float64(e.GetPositives(label)) == 0.0 { 130 | return 0.0 131 | } 132 | return float64(e.GetTruePositives(label)) / float64(e.GetPositives(label)) 133 | } 134 | 135 | // GetSensitivity like recall 136 | func (e *Evaluation) GetSensitivity(label string) float64 { 137 | return e.GetRecall(label) 138 | } 139 | 140 | // GetSpecificity TN / N, TN/(FP+TN) 141 | func (e *Evaluation) GetSpecificity(label string) float64 { 142 | if float64(e.GetNegatives(label)) == 0.0 { 143 | return 0.0 144 | } 145 | return float64(e.GetTrueNegatives(label)) / float64(e.GetNegatives(label)) 146 | } 147 | 148 | // GetPrecision TP/(TP+FP) 149 | func (e *Evaluation) GetPrecision(label string) float64 { 150 | if float64(e.GetTruePositives(label)+e.GetFalsePositives(label)) == 0.0 { 151 | return 0.0 152 | } 153 | return float64(e.GetTruePositives(label)) / float64(e.GetTruePositives(label)+e.GetFalsePositives(label)) 154 | } 155 | 156 | // GetFallout FP / N 157 | func (e *Evaluation) GetFallout(label string) float64 { 158 | if float64(e.GetNegatives(label)) == 0.0 { 159 | return 0.0 160 | } 161 | return float64(e.GetFalsePositives(label)) / float64(e.GetNegatives(label)) 162 | } 163 | 164 | // GetFalsePositiveRate same as fallout 165 | func (e *Evaluation) GetFalsePositiveRate(label string) float64 { 166 | return e.GetFallout(label) 167 | } 168 | 169 | // GetFalseDiscoveryRate FP / (FP+TP) 170 | func (e *Evaluation) GetFalseDiscoveryRate(label string) float64 { 171 | if float64(e.GetFalsePositives(label)+e.GetTruePositives(label)) == 0.0 { 172 | return 0.0 173 | } 174 | return float64(e.GetFalsePositives(label)) / float64(e.GetFalsePositives(label)+e.GetTruePositives(label)) 175 | } 176 | 177 | // GetNegativePredictionValue TN/(TN+FN) 178 | func (e *Evaluation) GetNegativePredictionValue(label string) float64 { 179 | if float64(e.GetTrueNegatives(label)+e.GetFalseNegatives(label)) == 0.0 { 180 | return 0.0 181 | } 182 | return float64(e.GetTrueNegatives(label)) / float64(e.GetTrueNegatives(label)+e.GetFalseNegatives(label)) 183 | } 184 | 185 | // GetFMeasure 2TP/(2TP+FP+FN) 186 | func (e *Evaluation) GetFMeasure(label string) float64 { 187 | if float64(2*e.GetTruePositives(label)+e.GetFalsePositives(label)+e.GetFalseNegatives(label)) == 0.0 { 188 | return 0.0 189 | } 190 | return 2.0 * float64(e.GetTruePositives(label)) / float64(2*e.GetTruePositives(label)+e.GetFalsePositives(label)+e.GetFalseNegatives(label)) 191 | } 192 | 193 | // GetBalancedAccuracy (TP/P + TN/N) / 2 194 | func (e *Evaluation) GetBalancedAccuracy(label string) float64 { 195 | var positives, negatives float64 196 | if float64(e.GetPositives(label)) == 0.0 { 197 | positives = 0.0 198 | } else { 199 | positives = float64(e.GetTruePositives(label)) / float64(e.GetPositives(label)) 200 | } 201 | if float64(e.GetNegatives(label)) == 0.0 { 202 | negatives = 0.0 203 | } else { 204 | negatives = float64(e.GetTrueNegatives(label)) / float64(e.GetNegatives(label)) 205 | } 206 | return (positives + negatives) / 2.0 207 | } 208 | 209 | // GetOverallBalancedAccuracy calculates for the training evaluation 210 | func (e *Evaluation) GetOverallBalancedAccuracy() float64 { 211 | classes := float64(len(e.Confusion)) 212 | sum := 0.0 213 | for k := range e.Confusion { 214 | sum += e.GetBalancedAccuracy(k) 215 | } 216 | return sum / classes 217 | } 218 | 219 | // GetOverallAccuracy calculates for the training evaluation 220 | func (e *Evaluation) GetOverallAccuracy() float64 { 221 | classes := float64(len(e.Confusion)) 222 | sum := 0.0 223 | for k := range e.Confusion { 224 | sum += e.GetAccuracy(k) 225 | } 226 | return sum / classes 227 | } 228 | 229 | // GetOverallFMeasure calculates for the training evaluation 230 | func (e *Evaluation) GetOverallFMeasure() float64 { 231 | classes := float64(len(e.Confusion)) 232 | sum := 0.0 233 | for k := range e.Confusion { 234 | sum += e.GetFMeasure(k) 235 | } 236 | return sum / classes 237 | } 238 | 239 | // GetInformedness = Sensitivity + Specificity − 1 240 | func (e *Evaluation) GetInformedness(label string) float64 { 241 | return e.GetSensitivity(label) + e.GetSpecificity(label) - 1.0 242 | } 243 | 244 | // GetMarkedness = Precision + NegativePredictionValue − 1 245 | func (e *Evaluation) GetMarkedness(label string) float64 { 246 | return e.GetPrecision(label) + e.GetNegativePredictionValue(label) - 1 247 | } 248 | 249 | // AddDistance adds distance between ideal output and output of the network 250 | func (e *Evaluation) AddDistance(n *neural.Network, in, ideal []float64) float64 { 251 | // This function was part of the former go-neural and moved to this package. 252 | out := n.Calculate(in) 253 | var d float64 254 | for i := range out { 255 | d += math.Pow(out[i]-ideal[i], 2) 256 | } 257 | e.OverallDistance += d / 2.0 258 | return d / 2.0 259 | } 260 | 261 | // GetDistance returns the distance from the evaluation 262 | func (e *Evaluation) GetDistance() float64 { 263 | return e.OverallDistance / float64(e.Wrong+e.Correct) 264 | } 265 | 266 | // GetCorrectRatio returns correct classified samples ratio 267 | func (e *Evaluation) GetCorrectRatio() float64 { 268 | return float64(e.Correct) / float64(e.Wrong+e.Correct) 269 | } 270 | 271 | // PrintConfusionMatrix prints the confusion matrix of the evaluation 272 | func (e *Evaluation) PrintConfusionMatrix() { 273 | fmt.Printf("\t|") 274 | for k := range e.Confusion { 275 | fmt.Printf("%v\t|", k) 276 | } 277 | fmt.Print("\n") 278 | for cl := range e.Confusion { 279 | fmt.Printf("%v\t|", cl) 280 | for c := range e.Confusion[cl] { 281 | fmt.Printf("%v\t|", e.Confusion[cl][c]) 282 | } 283 | fmt.Printf("\n") 284 | } 285 | 286 | } 287 | 288 | // GetRegressionSummary returns a summary of the evaluated regression 289 | func (e *Evaluation) GetRegressionSummary() { 290 | fmt.Println("summary") 291 | fmt.Printf("correct: %v\n", e.Correct) 292 | fmt.Printf("wrong: %v\n", e.Wrong) 293 | fmt.Printf("ratio: %v\n", float64(e.Correct)/float64(e.Correct+e.Wrong)) 294 | } 295 | 296 | // GetSummary returns a summary 297 | func (e *Evaluation) GetSummary(label string) { 298 | fmt.Printf("summary for class %v\n", label) 299 | fmt.Printf(" * TP: %v TN: %v FP: %v FN: %v\n", e.GetTruePositives(label), e.GetTrueNegatives(label), e.GetFalsePositives(label), e.GetFalseNegatives(label)) 300 | fmt.Printf(" * Recall/Sensitivity: %v\n", e.GetRecall(label)) 301 | fmt.Printf(" * Precision: %v\n", e.GetPrecision(label)) 302 | fmt.Printf(" * Fallout/FalsePosRate: %v\n", e.GetFallout(label)) 303 | fmt.Printf(" * False Discovey Rate: %v\n", e.GetFalseDiscoveryRate(label)) 304 | fmt.Printf(" * Negative Prediction Rate: %v\n", e.GetNegativePredictionValue(label)) 305 | fmt.Println("--") 306 | fmt.Printf(" * Accuracy: %v\n", e.GetAccuracy(label)) 307 | fmt.Printf(" * F-Measure: %v\n", e.GetFMeasure(label)) 308 | fmt.Printf(" * Balanced Accuracy: %v\n", e.GetBalancedAccuracy(label)) 309 | fmt.Printf(" * Informedness: %v\n", e.GetInformedness(label)) 310 | fmt.Printf(" * Markedness: %v\n", e.GetMarkedness(label)) 311 | 312 | } 313 | -------------------------------------------------------------------------------- /examples/README.md: -------------------------------------------------------------------------------- 1 | # Examples for gopher-neural 2 | 3 | ![gopher-neural-logo](http://alexander.bre.sk/x/gopher-neural-small.png " The Gopher Neural logo ") 4 | 5 | This repository provides examples to gopher-neural. You can find the code here: https://github.com/breskos/gopher-neural 6 | 7 | ## Sonar 8 | 9 | Basic example that uses the engine of gopher-neural to train a small neural network with basic parameters. More information in sonar ReadMe file. Data from: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+%28Sonar%2C+Mines+vs.+Rocks%29 10 | 11 | ## Wine quality 12 | 13 | In this example we train a regressor (we don't have classes but real value to determine). The quality of the wine is very important because wine is tasty (or should be). So here we have some criteria we can use to determine good from bad wine. Data from: http://archive.ics.uci.edu/ml/datasets/Wine+Quality 14 | 15 | ## More to come 16 | 17 | stay tuned. 18 | -------------------------------------------------------------------------------- /examples/sonar/README.md: -------------------------------------------------------------------------------- 1 | # Sonar Data Set 2 | 3 | Abstract: The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock. 4 | 5 | Connectionist Bench (Sonar, Mines vs. Rocks) Data Set 6 | 7 | Found here: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+%28Sonar%2C+Mines+vs.+Rocks%29 8 | 9 | ## Example 10 | 11 | In this example a data set was used to demonstrate 12 | * a MLP with 100 hidden neurons 13 | * that uses CriterionDistance to decide for the best model 14 | * gives a summary of the training 15 | * and persists the file 16 | 17 | Below the command line output can be seen. 18 | ``` 19 | > go run main.go 20 | 21 | ... 22 | 23 | summary for class R 24 | * TP: 23 TN: 30 FP: 0 FN: 8 25 | * Recall/Sensitivity: 0.7419354838709677 26 | * Precision: 1 27 | * Fallout/FalsePosRate: 0 28 | * False Discovey Rate: 0 29 | * Negative Prediction Rate: 0.7894736842105263 30 | -- 31 | * Accuracy: 0.8688524590163934 32 | * F-Measure: 0.8518518518518519 33 | * Balanced Accuracy: 0.8709677419354839 34 | * Informedness: 0.7419354838709677 35 | * Markedness: 0.7894736842105263 36 | 37 | summary for class M 38 | * TP: 30 TN: 23 FP: 8 FN: 0 39 | * Recall/Sensitivity: 1 40 | * Precision: 0.7894736842105263 41 | * Fallout/FalsePosRate: 0.25806451612903225 42 | * False Discovey Rate: 0.21052631578947367 43 | * Negative Prediction Rate: 1 44 | -- 45 | * Accuracy: 0.8688524590163934 46 | * F-Measure: 0.8823529411764706 47 | * Balanced Accuracy: 0.8709677419354839 48 | * Informedness: 0.7419354838709677 49 | * Markedness: 0.7894736842105263 50 | ``` 51 | -------------------------------------------------------------------------------- /examples/sonar/data.csv: -------------------------------------------------------------------------------- 1 | 0.0200,0.0371,0.0428,0.0207,0.0954,0.0986,0.1539,0.1601,0.3109,0.2111,0.1609,0.1582,0.2238,0.0645,0.0660,0.2273,0.3100,0.2999,0.5078,0.4797,0.5783,0.5071,0.4328,0.5550,0.6711,0.6415,0.7104,0.8080,0.6791,0.3857,0.1307,0.2604,0.5121,0.7547,0.8537,0.8507,0.6692,0.6097,0.4943,0.2744,0.0510,0.2834,0.2825,0.4256,0.2641,0.1386,0.1051,0.1343,0.0383,0.0324,0.0232,0.0027,0.0065,0.0159,0.0072,0.0167,0.0180,0.0084,0.0090,0.0032,R 2 | 0.0453,0.0523,0.0843,0.0689,0.1183,0.2583,0.2156,0.3481,0.3337,0.2872,0.4918,0.6552,0.6919,0.7797,0.7464,0.9444,1.0000,0.8874,0.8024,0.7818,0.5212,0.4052,0.3957,0.3914,0.3250,0.3200,0.3271,0.2767,0.4423,0.2028,0.3788,0.2947,0.1984,0.2341,0.1306,0.4182,0.3835,0.1057,0.1840,0.1970,0.1674,0.0583,0.1401,0.1628,0.0621,0.0203,0.0530,0.0742,0.0409,0.0061,0.0125,0.0084,0.0089,0.0048,0.0094,0.0191,0.0140,0.0049,0.0052,0.0044,R 3 | 0.0262,0.0582,0.1099,0.1083,0.0974,0.2280,0.2431,0.3771,0.5598,0.6194,0.6333,0.7060,0.5544,0.5320,0.6479,0.6931,0.6759,0.7551,0.8929,0.8619,0.7974,0.6737,0.4293,0.3648,0.5331,0.2413,0.5070,0.8533,0.6036,0.8514,0.8512,0.5045,0.1862,0.2709,0.4232,0.3043,0.6116,0.6756,0.5375,0.4719,0.4647,0.2587,0.2129,0.2222,0.2111,0.0176,0.1348,0.0744,0.0130,0.0106,0.0033,0.0232,0.0166,0.0095,0.0180,0.0244,0.0316,0.0164,0.0095,0.0078,R 4 | 0.0100,0.0171,0.0623,0.0205,0.0205,0.0368,0.1098,0.1276,0.0598,0.1264,0.0881,0.1992,0.0184,0.2261,0.1729,0.2131,0.0693,0.2281,0.4060,0.3973,0.2741,0.3690,0.5556,0.4846,0.3140,0.5334,0.5256,0.2520,0.2090,0.3559,0.6260,0.7340,0.6120,0.3497,0.3953,0.3012,0.5408,0.8814,0.9857,0.9167,0.6121,0.5006,0.3210,0.3202,0.4295,0.3654,0.2655,0.1576,0.0681,0.0294,0.0241,0.0121,0.0036,0.0150,0.0085,0.0073,0.0050,0.0044,0.0040,0.0117,R 5 | 0.0762,0.0666,0.0481,0.0394,0.0590,0.0649,0.1209,0.2467,0.3564,0.4459,0.4152,0.3952,0.4256,0.4135,0.4528,0.5326,0.7306,0.6193,0.2032,0.4636,0.4148,0.4292,0.5730,0.5399,0.3161,0.2285,0.6995,1.0000,0.7262,0.4724,0.5103,0.5459,0.2881,0.0981,0.1951,0.4181,0.4604,0.3217,0.2828,0.2430,0.1979,0.2444,0.1847,0.0841,0.0692,0.0528,0.0357,0.0085,0.0230,0.0046,0.0156,0.0031,0.0054,0.0105,0.0110,0.0015,0.0072,0.0048,0.0107,0.0094,R 6 | 0.0286,0.0453,0.0277,0.0174,0.0384,0.0990,0.1201,0.1833,0.2105,0.3039,0.2988,0.4250,0.6343,0.8198,1.0000,0.9988,0.9508,0.9025,0.7234,0.5122,0.2074,0.3985,0.5890,0.2872,0.2043,0.5782,0.5389,0.3750,0.3411,0.5067,0.5580,0.4778,0.3299,0.2198,0.1407,0.2856,0.3807,0.4158,0.4054,0.3296,0.2707,0.2650,0.0723,0.1238,0.1192,0.1089,0.0623,0.0494,0.0264,0.0081,0.0104,0.0045,0.0014,0.0038,0.0013,0.0089,0.0057,0.0027,0.0051,0.0062,R 7 | 0.0317,0.0956,0.1321,0.1408,0.1674,0.1710,0.0731,0.1401,0.2083,0.3513,0.1786,0.0658,0.0513,0.3752,0.5419,0.5440,0.5150,0.4262,0.2024,0.4233,0.7723,0.9735,0.9390,0.5559,0.5268,0.6826,0.5713,0.5429,0.2177,0.2149,0.5811,0.6323,0.2965,0.1873,0.2969,0.5163,0.6153,0.4283,0.5479,0.6133,0.5017,0.2377,0.1957,0.1749,0.1304,0.0597,0.1124,0.1047,0.0507,0.0159,0.0195,0.0201,0.0248,0.0131,0.0070,0.0138,0.0092,0.0143,0.0036,0.0103,R 8 | 0.0519,0.0548,0.0842,0.0319,0.1158,0.0922,0.1027,0.0613,0.1465,0.2838,0.2802,0.3086,0.2657,0.3801,0.5626,0.4376,0.2617,0.1199,0.6676,0.9402,0.7832,0.5352,0.6809,0.9174,0.7613,0.8220,0.8872,0.6091,0.2967,0.1103,0.1318,0.0624,0.0990,0.4006,0.3666,0.1050,0.1915,0.3930,0.4288,0.2546,0.1151,0.2196,0.1879,0.1437,0.2146,0.2360,0.1125,0.0254,0.0285,0.0178,0.0052,0.0081,0.0120,0.0045,0.0121,0.0097,0.0085,0.0047,0.0048,0.0053,R 9 | 0.0223,0.0375,0.0484,0.0475,0.0647,0.0591,0.0753,0.0098,0.0684,0.1487,0.1156,0.1654,0.3833,0.3598,0.1713,0.1136,0.0349,0.3796,0.7401,0.9925,0.9802,0.8890,0.6712,0.4286,0.3374,0.7366,0.9611,0.7353,0.4856,0.1594,0.3007,0.4096,0.3170,0.3305,0.3408,0.2186,0.2463,0.2726,0.1680,0.2792,0.2558,0.1740,0.2121,0.1099,0.0985,0.1271,0.1459,0.1164,0.0777,0.0439,0.0061,0.0145,0.0128,0.0145,0.0058,0.0049,0.0065,0.0093,0.0059,0.0022,R 10 | 0.0164,0.0173,0.0347,0.0070,0.0187,0.0671,0.1056,0.0697,0.0962,0.0251,0.0801,0.1056,0.1266,0.0890,0.0198,0.1133,0.2826,0.3234,0.3238,0.4333,0.6068,0.7652,0.9203,0.9719,0.9207,0.7545,0.8289,0.8907,0.7309,0.6896,0.5829,0.4935,0.3101,0.0306,0.0244,0.1108,0.1594,0.1371,0.0696,0.0452,0.0620,0.1421,0.1597,0.1384,0.0372,0.0688,0.0867,0.0513,0.0092,0.0198,0.0118,0.0090,0.0223,0.0179,0.0084,0.0068,0.0032,0.0035,0.0056,0.0040,R 11 | 0.0039,0.0063,0.0152,0.0336,0.0310,0.0284,0.0396,0.0272,0.0323,0.0452,0.0492,0.0996,0.1424,0.1194,0.0628,0.0907,0.1177,0.1429,0.1223,0.1104,0.1847,0.3715,0.4382,0.5707,0.6654,0.7476,0.7654,0.8555,0.9720,0.9221,0.7502,0.7209,0.7757,0.6055,0.5021,0.4499,0.3947,0.4281,0.4427,0.3749,0.1972,0.0511,0.0793,0.1269,0.1533,0.0690,0.0402,0.0534,0.0228,0.0073,0.0062,0.0062,0.0120,0.0052,0.0056,0.0093,0.0042,0.0003,0.0053,0.0036,R 12 | 0.0123,0.0309,0.0169,0.0313,0.0358,0.0102,0.0182,0.0579,0.1122,0.0835,0.0548,0.0847,0.2026,0.2557,0.1870,0.2032,0.1463,0.2849,0.5824,0.7728,0.7852,0.8515,0.5312,0.3653,0.5973,0.8275,1.0000,0.8673,0.6301,0.4591,0.3940,0.2576,0.2817,0.2641,0.2757,0.2698,0.3994,0.4576,0.3940,0.2522,0.1782,0.1354,0.0516,0.0337,0.0894,0.0861,0.0872,0.0445,0.0134,0.0217,0.0188,0.0133,0.0265,0.0224,0.0074,0.0118,0.0026,0.0092,0.0009,0.0044,R 13 | 0.0079,0.0086,0.0055,0.0250,0.0344,0.0546,0.0528,0.0958,0.1009,0.1240,0.1097,0.1215,0.1874,0.3383,0.3227,0.2723,0.3943,0.6432,0.7271,0.8673,0.9674,0.9847,0.9480,0.8036,0.6833,0.5136,0.3090,0.0832,0.4019,0.2344,0.1905,0.1235,0.1717,0.2351,0.2489,0.3649,0.3382,0.1589,0.0989,0.1089,0.1043,0.0839,0.1391,0.0819,0.0678,0.0663,0.1202,0.0692,0.0152,0.0266,0.0174,0.0176,0.0127,0.0088,0.0098,0.0019,0.0059,0.0058,0.0059,0.0032,R 14 | 0.0090,0.0062,0.0253,0.0489,0.1197,0.1589,0.1392,0.0987,0.0955,0.1895,0.1896,0.2547,0.4073,0.2988,0.2901,0.5326,0.4022,0.1571,0.3024,0.3907,0.3542,0.4438,0.6414,0.4601,0.6009,0.8690,0.8345,0.7669,0.5081,0.4620,0.5380,0.5375,0.3844,0.3601,0.7402,0.7761,0.3858,0.0667,0.3684,0.6114,0.3510,0.2312,0.2195,0.3051,0.1937,0.1570,0.0479,0.0538,0.0146,0.0068,0.0187,0.0059,0.0095,0.0194,0.0080,0.0152,0.0158,0.0053,0.0189,0.0102,R 15 | 0.0124,0.0433,0.0604,0.0449,0.0597,0.0355,0.0531,0.0343,0.1052,0.2120,0.1640,0.1901,0.3026,0.2019,0.0592,0.2390,0.3657,0.3809,0.5929,0.6299,0.5801,0.4574,0.4449,0.3691,0.6446,0.8940,0.8978,0.4980,0.3333,0.2350,0.1553,0.3666,0.4340,0.3082,0.3024,0.4109,0.5501,0.4129,0.5499,0.5018,0.3132,0.2802,0.2351,0.2298,0.1155,0.0724,0.0621,0.0318,0.0450,0.0167,0.0078,0.0083,0.0057,0.0174,0.0188,0.0054,0.0114,0.0196,0.0147,0.0062,R 16 | 0.0298,0.0615,0.0650,0.0921,0.1615,0.2294,0.2176,0.2033,0.1459,0.0852,0.2476,0.3645,0.2777,0.2826,0.3237,0.4335,0.5638,0.4555,0.4348,0.6433,0.3932,0.1989,0.3540,0.9165,0.9371,0.4620,0.2771,0.6613,0.8028,0.4200,0.5192,0.6962,0.5792,0.8889,0.7863,0.7133,0.7615,0.4401,0.3009,0.3163,0.2809,0.2898,0.0526,0.1867,0.1553,0.1633,0.1252,0.0748,0.0452,0.0064,0.0154,0.0031,0.0153,0.0071,0.0212,0.0076,0.0152,0.0049,0.0200,0.0073,R 17 | 0.0352,0.0116,0.0191,0.0469,0.0737,0.1185,0.1683,0.1541,0.1466,0.2912,0.2328,0.2237,0.2470,0.1560,0.3491,0.3308,0.2299,0.2203,0.2493,0.4128,0.3158,0.6191,0.5854,0.3395,0.2561,0.5599,0.8145,0.6941,0.6985,0.8660,0.5930,0.3664,0.6750,0.8697,0.7837,0.7552,0.5789,0.4713,0.1252,0.6087,0.7322,0.5977,0.3431,0.1803,0.2378,0.3424,0.2303,0.0689,0.0216,0.0469,0.0426,0.0346,0.0158,0.0154,0.0109,0.0048,0.0095,0.0015,0.0073,0.0067,R 18 | 0.0192,0.0607,0.0378,0.0774,0.1388,0.0809,0.0568,0.0219,0.1037,0.1186,0.1237,0.1601,0.3520,0.4479,0.3769,0.5761,0.6426,0.6790,0.7157,0.5466,0.5399,0.6362,0.7849,0.7756,0.5780,0.4862,0.4181,0.2457,0.0716,0.0613,0.1816,0.4493,0.5976,0.3785,0.2495,0.5771,0.8852,0.8409,0.3570,0.3133,0.6096,0.6378,0.2709,0.1419,0.1260,0.1288,0.0790,0.0829,0.0520,0.0216,0.0360,0.0331,0.0131,0.0120,0.0108,0.0024,0.0045,0.0037,0.0112,0.0075,R 19 | 0.0270,0.0092,0.0145,0.0278,0.0412,0.0757,0.1026,0.1138,0.0794,0.1520,0.1675,0.1370,0.1361,0.1345,0.2144,0.5354,0.6830,0.5600,0.3093,0.3226,0.4430,0.5573,0.5782,0.6173,0.8132,0.9819,0.9823,0.9166,0.7423,0.7736,0.8473,0.7352,0.6671,0.6083,0.6239,0.5972,0.5715,0.5242,0.2924,0.1536,0.2003,0.2031,0.2207,0.1778,0.1353,0.1373,0.0749,0.0472,0.0325,0.0179,0.0045,0.0084,0.0010,0.0018,0.0068,0.0039,0.0120,0.0132,0.0070,0.0088,R 20 | 0.0126,0.0149,0.0641,0.1732,0.2565,0.2559,0.2947,0.4110,0.4983,0.5920,0.5832,0.5419,0.5472,0.5314,0.4981,0.6985,0.8292,0.7839,0.8215,0.9363,1.0000,0.9224,0.7839,0.5470,0.4562,0.5922,0.5448,0.3971,0.0882,0.2385,0.2005,0.0587,0.2544,0.2009,0.0329,0.1547,0.1212,0.2446,0.3171,0.3195,0.3051,0.0836,0.1266,0.1381,0.1136,0.0516,0.0073,0.0278,0.0372,0.0121,0.0153,0.0092,0.0035,0.0098,0.0121,0.0006,0.0181,0.0094,0.0116,0.0063,R 21 | 0.0473,0.0509,0.0819,0.1252,0.1783,0.3070,0.3008,0.2362,0.3830,0.3759,0.3021,0.2909,0.2301,0.1411,0.1582,0.2430,0.4474,0.5964,0.6744,0.7969,0.8319,0.7813,0.8626,0.7369,0.4122,0.2596,0.3392,0.3788,0.4488,0.6281,0.7449,0.7328,0.7704,0.7870,0.6048,0.5860,0.6385,0.7279,0.6286,0.5316,0.4069,0.1791,0.1625,0.2527,0.1903,0.1643,0.0604,0.0209,0.0436,0.0175,0.0107,0.0193,0.0118,0.0064,0.0042,0.0054,0.0049,0.0082,0.0028,0.0027,R 22 | 0.0664,0.0575,0.0842,0.0372,0.0458,0.0771,0.0771,0.1130,0.2353,0.1838,0.2869,0.4129,0.3647,0.1984,0.2840,0.4039,0.5837,0.6792,0.6086,0.4858,0.3246,0.2013,0.2082,0.1686,0.2484,0.2736,0.2984,0.4655,0.6990,0.7474,0.7956,0.7981,0.6715,0.6942,0.7440,0.8169,0.8912,1.0000,0.8753,0.7061,0.6803,0.5898,0.4618,0.3639,0.1492,0.1216,0.1306,0.1198,0.0578,0.0235,0.0135,0.0141,0.0190,0.0043,0.0036,0.0026,0.0024,0.0162,0.0109,0.0079,R 23 | 0.0099,0.0484,0.0299,0.0297,0.0652,0.1077,0.2363,0.2385,0.0075,0.1882,0.1456,0.1892,0.3176,0.1340,0.2169,0.2458,0.2589,0.2786,0.2298,0.0656,0.1441,0.1179,0.1668,0.1783,0.2476,0.2570,0.1036,0.5356,0.7124,0.6291,0.4756,0.6015,0.7208,0.6234,0.5725,0.7523,0.8712,0.9252,0.9709,0.9297,0.8995,0.7911,0.5600,0.2838,0.4407,0.5507,0.4331,0.2905,0.1981,0.0779,0.0396,0.0173,0.0149,0.0115,0.0202,0.0139,0.0029,0.0160,0.0106,0.0134,R 24 | 0.0115,0.0150,0.0136,0.0076,0.0211,0.1058,0.1023,0.0440,0.0931,0.0734,0.0740,0.0622,0.1055,0.1183,0.1721,0.2584,0.3232,0.3817,0.4243,0.4217,0.4449,0.4075,0.3306,0.4012,0.4466,0.5218,0.7552,0.9503,1.0000,0.9084,0.8283,0.7571,0.7262,0.6152,0.5680,0.5757,0.5324,0.3672,0.1669,0.0866,0.0646,0.1891,0.2683,0.2887,0.2341,0.1668,0.1015,0.1195,0.0704,0.0167,0.0107,0.0091,0.0016,0.0084,0.0064,0.0026,0.0029,0.0037,0.0070,0.0041,R 25 | 0.0293,0.0644,0.0390,0.0173,0.0476,0.0816,0.0993,0.0315,0.0736,0.0860,0.0414,0.0472,0.0835,0.0938,0.1466,0.0809,0.1179,0.2179,0.3326,0.3258,0.2111,0.2302,0.3361,0.4259,0.4609,0.2606,0.0874,0.2862,0.5606,0.8344,0.8096,0.7250,0.8048,0.9435,1.0000,0.8960,0.5516,0.3037,0.2338,0.2382,0.3318,0.3821,0.1575,0.2228,0.1582,0.1433,0.1634,0.1133,0.0567,0.0133,0.0170,0.0035,0.0052,0.0083,0.0078,0.0075,0.0105,0.0160,0.0095,0.0011,R 26 | 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0.0177,0.0300,0.0288,0.0394,0.0630,0.0526,0.0688,0.0633,0.0624,0.0613,0.1680,0.3476,0.4561,0.5188,0.6308,0.7201,0.5153,0.3818,0.2644,0.3345,0.4865,0.6628,0.7389,0.9213,1.0000,0.7750,0.5593,0.6172,0.8635,0.6592,0.4770,0.4983,0.3330,0.3076,0.2876,0.2226,0.0794,0.0603,0.1049,0.0606,0.1530,0.0983,0.1643,0.1901,0.1107,0.1917,0.1467,0.0392,0.0356,0.0270,0.0168,0.0102,0.0122,0.0044,0.0075,0.0124,0.0099,0.0057,0.0032,0.0019,R 29 | 0.0100,0.0275,0.0190,0.0371,0.0416,0.0201,0.0314,0.0651,0.1896,0.2668,0.3376,0.3282,0.2432,0.1268,0.1278,0.4441,0.6795,0.7051,0.7966,0.9401,0.9857,0.8193,0.5789,0.6394,0.7043,0.6875,0.4081,0.1811,0.2064,0.3917,0.3791,0.2042,0.2227,0.3341,0.3984,0.5077,0.5534,0.3352,0.2723,0.2278,0.2044,0.1986,0.0835,0.0908,0.1380,0.1948,0.1211,0.0843,0.0589,0.0247,0.0118,0.0088,0.0104,0.0036,0.0088,0.0047,0.0117,0.0020,0.0091,0.0058,R 30 | 0.0189,0.0308,0.0197,0.0622,0.0080,0.0789,0.1440,0.1451,0.1789,0.2522,0.2607,0.3710,0.3906,0.2672,0.2716,0.4183,0.6988,0.5733,0.2226,0.2631,0.7473,0.7263,0.3393,0.2824,0.6053,0.5897,0.4967,0.8616,0.8339,0.4084,0.2268,0.1745,0.0507,0.1588,0.3040,0.1369,0.1605,0.2061,0.0734,0.0202,0.1638,0.1583,0.1830,0.1886,0.1008,0.0663,0.0183,0.0404,0.0108,0.0143,0.0091,0.0038,0.0096,0.0142,0.0190,0.0140,0.0099,0.0092,0.0052,0.0075,R 31 | 0.0240,0.0218,0.0324,0.0569,0.0330,0.0513,0.0897,0.0713,0.0569,0.0389,0.1934,0.2434,0.2906,0.2606,0.3811,0.4997,0.3015,0.3655,0.6791,0.7307,0.5053,0.4441,0.6987,0.8133,0.7781,0.8943,0.8929,0.8913,0.8610,0.8063,0.5540,0.2446,0.3459,0.1615,0.2467,0.5564,0.4681,0.0979,0.1582,0.0751,0.3321,0.3745,0.2666,0.1078,0.1418,0.1687,0.0738,0.0634,0.0144,0.0226,0.0061,0.0162,0.0146,0.0093,0.0112,0.0094,0.0054,0.0019,0.0066,0.0023,R 32 | 0.0084,0.0153,0.0291,0.0432,0.0951,0.0752,0.0414,0.0259,0.0692,0.1753,0.1970,0.1167,0.1683,0.0814,0.2179,0.5121,0.7231,0.7776,0.6222,0.3501,0.3733,0.2622,0.3776,0.7361,0.8673,0.8223,0.7772,0.7862,0.5652,0.3635,0.3534,0.3865,0.3370,0.1693,0.2627,0.3195,0.1388,0.1048,0.1681,0.1910,0.1174,0.0933,0.0856,0.0951,0.0986,0.0956,0.0426,0.0407,0.0106,0.0179,0.0056,0.0236,0.0114,0.0136,0.0117,0.0060,0.0058,0.0031,0.0072,0.0045,R 33 | 0.0195,0.0213,0.0058,0.0190,0.0319,0.0571,0.1004,0.0668,0.0691,0.0242,0.0728,0.0639,0.3002,0.3854,0.4767,0.4602,0.3175,0.4160,0.6428,1.0000,0.8631,0.5212,0.3156,0.5952,0.7732,0.6042,0.4375,0.5487,0.4720,0.6235,0.3851,0.1590,0.3891,0.5294,0.3504,0.4480,0.4041,0.5031,0.6475,0.5493,0.3548,0.2028,0.1882,0.0845,0.1315,0.1590,0.0562,0.0617,0.0343,0.0370,0.0261,0.0157,0.0074,0.0271,0.0203,0.0089,0.0095,0.0095,0.0021,0.0053,R 34 | 0.0442,0.0477,0.0049,0.0581,0.0278,0.0678,0.1664,0.1490,0.0974,0.1268,0.1109,0.2375,0.2007,0.2140,0.1109,0.2036,0.2468,0.6682,0.8345,0.8252,0.8017,0.8982,0.9664,0.8515,0.6626,0.3241,0.2054,0.5669,0.5726,0.4877,0.7532,0.7600,0.5185,0.4120,0.5560,0.5569,0.1336,0.3831,0.4611,0.4330,0.2556,0.1466,0.3489,0.2659,0.0944,0.1370,0.1344,0.0416,0.0719,0.0637,0.0210,0.0204,0.0216,0.0135,0.0055,0.0073,0.0080,0.0105,0.0059,0.0105,R 35 | 0.0311,0.0491,0.0692,0.0831,0.0079,0.0200,0.0981,0.1016,0.2025,0.0767,0.1767,0.2555,0.2812,0.2722,0.3227,0.3463,0.5395,0.7911,0.9064,0.8701,0.7672,0.2957,0.4148,0.6043,0.3178,0.3482,0.6158,0.8049,0.6289,0.4999,0.5830,0.6660,0.4124,0.1260,0.2487,0.4676,0.5382,0.3150,0.2139,0.1848,0.1679,0.2328,0.1015,0.0713,0.0615,0.0779,0.0761,0.0845,0.0592,0.0068,0.0089,0.0087,0.0032,0.0130,0.0188,0.0101,0.0229,0.0182,0.0046,0.0038,R 36 | 0.0206,0.0132,0.0533,0.0569,0.0647,0.1432,0.1344,0.2041,0.1571,0.1573,0.2327,0.1785,0.1507,0.1916,0.2061,0.2307,0.2360,0.1299,0.3812,0.5858,0.4497,0.4876,1.0000,0.8675,0.4718,0.5341,0.6197,0.7143,0.5605,0.3728,0.2481,0.1921,0.1386,0.3325,0.2883,0.3228,0.2607,0.2040,0.2396,0.1319,0.0683,0.0334,0.0716,0.0976,0.0787,0.0522,0.0500,0.0231,0.0221,0.0144,0.0307,0.0386,0.0147,0.0018,0.0100,0.0096,0.0077,0.0180,0.0109,0.0070,R 37 | 0.0094,0.0166,0.0398,0.0359,0.0681,0.0706,0.1020,0.0893,0.0381,0.1328,0.1303,0.0273,0.0644,0.0712,0.1204,0.0717,0.1224,0.2349,0.3684,0.3918,0.4925,0.8793,0.9606,0.8786,0.6905,0.6937,0.5674,0.6540,0.7802,0.7575,0.5836,0.6316,0.8108,0.9039,0.8647,0.6695,0.4027,0.2370,0.2685,0.3662,0.3267,0.2200,0.2996,0.2205,0.1163,0.0635,0.0465,0.0422,0.0174,0.0172,0.0134,0.0141,0.0191,0.0145,0.0065,0.0129,0.0217,0.0087,0.0077,0.0122,R 38 | 0.0333,0.0221,0.0270,0.0481,0.0679,0.0981,0.0843,0.1172,0.0759,0.0920,0.1475,0.0522,0.1119,0.0970,0.1174,0.1678,0.1642,0.1205,0.0494,0.1544,0.3485,0.6146,0.9146,0.9364,0.8677,0.8772,0.8553,0.8833,1.0000,0.8296,0.6601,0.5499,0.5716,0.6859,0.6825,0.5142,0.2750,0.1358,0.1551,0.2646,0.1994,0.1883,0.2746,0.1651,0.0575,0.0695,0.0598,0.0456,0.0021,0.0068,0.0036,0.0022,0.0032,0.0060,0.0054,0.0063,0.0143,0.0132,0.0051,0.0041,R 39 | 0.0123,0.0022,0.0196,0.0206,0.0180,0.0492,0.0033,0.0398,0.0791,0.0475,0.1152,0.0520,0.1192,0.1943,0.1840,0.2077,0.1956,0.1630,0.1218,0.1017,0.1354,0.3157,0.4645,0.5906,0.6776,0.8119,0.8594,0.9228,0.8387,0.7238,0.6292,0.5181,0.4629,0.5255,0.5147,0.3929,0.1279,0.0411,0.0859,0.1131,0.1306,0.1757,0.2648,0.1955,0.0656,0.0580,0.0319,0.0301,0.0272,0.0074,0.0149,0.0125,0.0134,0.0026,0.0038,0.0018,0.0113,0.0058,0.0047,0.0071,R 40 | 0.0091,0.0213,0.0206,0.0505,0.0657,0.0795,0.0970,0.0872,0.0743,0.0837,0.1579,0.0898,0.0309,0.1856,0.2969,0.2032,0.1264,0.1655,0.1661,0.2091,0.2310,0.4460,0.6634,0.6933,0.7663,0.8206,0.7049,0.7560,0.7466,0.6387,0.4846,0.3328,0.5356,0.8741,0.8573,0.6718,0.3446,0.3150,0.2702,0.2598,0.2742,0.3594,0.4382,0.2460,0.0758,0.0187,0.0797,0.0748,0.0367,0.0155,0.0300,0.0112,0.0112,0.0102,0.0026,0.0097,0.0098,0.0043,0.0071,0.0108,R 41 | 0.0068,0.0232,0.0513,0.0444,0.0249,0.0637,0.0422,0.1130,0.1911,0.2475,0.1606,0.0922,0.2398,0.3220,0.4295,0.2652,0.0666,0.1442,0.2373,0.2595,0.2493,0.3903,0.6384,0.8037,0.7026,0.6874,0.6997,0.8558,1.0000,0.9621,0.8996,0.7575,0.6902,0.5686,0.4396,0.4546,0.2959,0.1587,0.1681,0.0842,0.1173,0.1754,0.2728,0.1705,0.0194,0.0213,0.0354,0.0420,0.0093,0.0204,0.0199,0.0173,0.0163,0.0055,0.0045,0.0068,0.0041,0.0052,0.0194,0.0105,R 42 | 0.0093,0.0185,0.0056,0.0064,0.0260,0.0458,0.0470,0.0057,0.0425,0.0640,0.0888,0.1599,0.1541,0.2768,0.2176,0.2799,0.3491,0.2824,0.2479,0.3005,0.4300,0.4684,0.4520,0.5026,0.6217,0.6571,0.6632,0.7321,0.8534,1.0000,0.8448,0.6354,0.6308,0.6211,0.6976,0.5868,0.4889,0.3683,0.2043,0.1469,0.2220,0.1449,0.1490,0.1211,0.1144,0.0791,0.0365,0.0152,0.0085,0.0120,0.0022,0.0069,0.0064,0.0129,0.0114,0.0054,0.0089,0.0050,0.0058,0.0025,R 43 | 0.0211,0.0319,0.0415,0.0286,0.0121,0.0438,0.1299,0.1390,0.0695,0.0568,0.0869,0.1935,0.1478,0.1871,0.1994,0.3283,0.6861,0.5814,0.2500,0.1734,0.3363,0.5588,0.6592,0.7012,0.8099,0.8901,0.8745,0.7887,0.8725,0.9376,0.8920,0.7508,0.6832,0.7610,0.9017,1.0000,0.9123,0.7388,0.5915,0.4057,0.3019,0.2331,0.2931,0.2298,0.2391,0.1910,0.1096,0.0300,0.0171,0.0383,0.0053,0.0090,0.0042,0.0153,0.0106,0.0020,0.0105,0.0049,0.0070,0.0080,R 44 | 0.0093,0.0269,0.0217,0.0339,0.0305,0.1172,0.1450,0.0638,0.0740,0.1360,0.2132,0.3738,0.3738,0.2673,0.2333,0.5367,0.7312,0.7659,0.6271,0.4395,0.4330,0.4326,0.5544,0.7360,0.8589,0.8989,0.9420,0.9401,0.9379,0.8575,0.7284,0.6700,0.7547,0.8773,0.9919,0.9922,0.9419,0.8388,0.6605,0.4816,0.2917,0.1769,0.1136,0.0701,0.1578,0.1938,0.1106,0.0693,0.0176,0.0205,0.0309,0.0212,0.0091,0.0056,0.0086,0.0092,0.0070,0.0116,0.0060,0.0110,R 45 | 0.0257,0.0447,0.0388,0.0239,0.1315,0.1323,0.1608,0.2145,0.0847,0.0561,0.0891,0.0861,0.1531,0.1524,0.1849,0.2871,0.2009,0.2748,0.5017,0.2172,0.4978,0.5265,0.3647,0.5768,0.5161,0.5715,0.4006,0.3650,0.6685,0.8659,0.8052,0.4082,0.3379,0.5092,0.6776,0.7313,0.6062,0.7040,0.8849,0.8979,0.7751,0.7247,0.7733,0.7762,0.6009,0.4514,0.3096,0.1859,0.0956,0.0206,0.0206,0.0096,0.0153,0.0096,0.0131,0.0198,0.0025,0.0199,0.0255,0.0180,R 46 | 0.0408,0.0653,0.0397,0.0604,0.0496,0.1817,0.1178,0.1024,0.0583,0.2176,0.2459,0.3332,0.3087,0.2613,0.3232,0.3731,0.4203,0.5364,0.7062,0.8196,0.8835,0.8299,0.7609,0.7605,0.8367,0.8905,0.7652,0.5897,0.3037,0.0823,0.2787,0.7241,0.8032,0.8050,0.7676,0.7468,0.6253,0.1730,0.2916,0.5003,0.5220,0.4824,0.4004,0.3877,0.1651,0.0442,0.0663,0.0418,0.0475,0.0235,0.0066,0.0062,0.0129,0.0184,0.0069,0.0198,0.0199,0.0102,0.0070,0.0055,R 47 | 0.0308,0.0339,0.0202,0.0889,0.1570,0.1750,0.0920,0.1353,0.1593,0.2795,0.3336,0.2940,0.1608,0.3335,0.4985,0.7295,0.7350,0.8253,0.8793,0.9657,1.0000,0.8707,0.6471,0.5973,0.8218,0.7755,0.6111,0.4195,0.2990,0.1354,0.2438,0.5624,0.5555,0.6963,0.7298,0.7022,0.5468,0.1421,0.4738,0.6410,0.4375,0.3178,0.2377,0.2808,0.1374,0.1136,0.1034,0.0688,0.0422,0.0117,0.0070,0.0167,0.0127,0.0138,0.0090,0.0051,0.0029,0.0122,0.0056,0.0020,R 48 | 0.0373,0.0281,0.0232,0.0225,0.0179,0.0733,0.0841,0.1031,0.0993,0.0802,0.1564,0.2565,0.2624,0.1179,0.0597,0.1563,0.2241,0.3586,0.1792,0.3256,0.6079,0.6988,0.8391,0.8553,0.7710,0.6215,0.5736,0.4402,0.4056,0.4411,0.5130,0.5965,0.7272,0.6539,0.5902,0.5393,0.4897,0.4081,0.4145,0.6003,0.7196,0.6633,0.6287,0.4087,0.3212,0.2518,0.1482,0.0988,0.0317,0.0269,0.0066,0.0008,0.0045,0.0024,0.0006,0.0073,0.0096,0.0054,0.0085,0.0060,R 49 | 0.0190,0.0038,0.0642,0.0452,0.0333,0.0690,0.0901,0.1454,0.0740,0.0349,0.1459,0.3473,0.3197,0.2823,0.0166,0.0572,0.2164,0.4563,0.3819,0.5627,0.6484,0.7235,0.8242,0.8766,1.0000,0.8582,0.6563,0.5087,0.4817,0.4530,0.4521,0.4532,0.5385,0.5308,0.5356,0.5271,0.4260,0.2436,0.1205,0.3845,0.4107,0.5067,0.4216,0.2479,0.1586,0.1124,0.0651,0.0789,0.0325,0.0070,0.0026,0.0093,0.0118,0.0112,0.0094,0.0140,0.0072,0.0022,0.0055,0.0122,R 50 | 0.0119,0.0582,0.0623,0.0600,0.1397,0.1883,0.1422,0.1447,0.0487,0.0864,0.2143,0.3720,0.2665,0.2113,0.1103,0.1136,0.1934,0.4142,0.3279,0.6222,0.7468,0.7676,0.7867,0.8253,1.0000,0.9481,0.7539,0.6008,0.5437,0.5387,0.5619,0.5141,0.6084,0.5621,0.5956,0.6078,0.5025,0.2829,0.0477,0.2811,0.3422,0.5147,0.4372,0.2470,0.1708,0.1343,0.0838,0.0755,0.0304,0.0074,0.0069,0.0025,0.0103,0.0074,0.0123,0.0069,0.0076,0.0073,0.0030,0.0138,R 51 | 0.0353,0.0713,0.0326,0.0272,0.0370,0.0792,0.1083,0.0687,0.0298,0.0880,0.1078,0.0979,0.2250,0.2819,0.2099,0.1240,0.1699,0.0939,0.1091,0.1410,0.1268,0.3151,0.1430,0.2264,0.5756,0.7876,0.7158,0.5998,0.5583,0.6295,0.7659,0.8940,0.8436,0.6807,0.8380,1.0000,0.9497,0.7866,0.5647,0.3480,0.2585,0.2304,0.2948,0.3363,0.3017,0.2193,0.1316,0.1078,0.0559,0.0035,0.0098,0.0163,0.0242,0.0043,0.0202,0.0108,0.0037,0.0096,0.0093,0.0053,R 52 | 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0.0216,0.0124,0.0174,0.0152,0.0608,0.1026,0.1139,0.0877,0.1160,0.0866,0.1564,0.0780,0.0997,0.0915,0.0662,0.1134,0.1740,0.2573,0.3294,0.3910,0.5438,0.6115,0.7022,0.7610,0.7973,0.9105,0.8807,0.7949,0.7990,0.7180,0.6407,0.6312,0.5929,0.6168,0.6498,0.6764,0.6253,0.5117,0.3890,0.3273,0.2509,0.1530,0.1323,0.1657,0.1215,0.0978,0.0452,0.0273,0.0179,0.0092,0.0018,0.0052,0.0049,0.0096,0.0134,0.0122,0.0047,0.0018,0.0006,0.0023,R 59 | 0.0225,0.0019,0.0075,0.0097,0.0445,0.0906,0.0889,0.0655,0.1624,0.1452,0.1442,0.0948,0.0618,0.1641,0.0708,0.0844,0.2590,0.2679,0.3094,0.4678,0.5958,0.7245,0.8773,0.9214,0.9282,0.9942,1.0000,0.9071,0.8545,0.7293,0.6499,0.6071,0.5588,0.5967,0.6275,0.5459,0.4786,0.3965,0.2087,0.1651,0.1836,0.0652,0.0758,0.0486,0.0353,0.0297,0.0241,0.0379,0.0119,0.0073,0.0051,0.0034,0.0129,0.0100,0.0044,0.0057,0.0030,0.0035,0.0021,0.0027,R 60 | 0.0125,0.0152,0.0218,0.0175,0.0362,0.0696,0.0873,0.0616,0.1252,0.1302,0.0888,0.0500,0.0628,0.1274,0.0801,0.0742,0.2048,0.2950,0.3193,0.4567,0.5959,0.7101,0.8225,0.8425,0.9065,0.9802,1.0000,0.8752,0.7583,0.6616,0.5786,0.5128,0.4776,0.4994,0.5197,0.5071,0.4577,0.3505,0.1845,0.1890,0.1967,0.1041,0.0550,0.0492,0.0622,0.0505,0.0247,0.0219,0.0102,0.0047,0.0019,0.0041,0.0074,0.0030,0.0050,0.0048,0.0017,0.0041,0.0086,0.0058,R 61 | 0.0130,0.0006,0.0088,0.0456,0.0525,0.0778,0.0931,0.0941,0.1711,0.1483,0.1532,0.1100,0.0890,0.1236,0.1197,0.1145,0.2137,0.2838,0.3640,0.5430,0.6673,0.7979,0.9273,0.9027,0.9192,1.0000,0.9821,0.9092,0.8184,0.6962,0.5900,0.5447,0.5142,0.5389,0.5531,0.5318,0.4826,0.3790,0.1831,0.1750,0.1679,0.0674,0.0609,0.0375,0.0533,0.0278,0.0179,0.0114,0.0073,0.0116,0.0092,0.0078,0.0041,0.0013,0.0011,0.0045,0.0039,0.0022,0.0023,0.0016,R 62 | 0.0135,0.0045,0.0051,0.0289,0.0561,0.0929,0.1031,0.0883,0.1596,0.1908,0.1576,0.1112,0.1197,0.1174,0.1415,0.2215,0.2658,0.2713,0.3862,0.5717,0.6797,0.8747,1.0000,0.8948,0.8420,0.9174,0.9307,0.9050,0.8228,0.6986,0.5831,0.4924,0.4563,0.5159,0.5670,0.5284,0.5144,0.3742,0.2282,0.1193,0.1088,0.0431,0.1070,0.0583,0.0046,0.0473,0.0408,0.0290,0.0192,0.0094,0.0025,0.0037,0.0084,0.0102,0.0096,0.0024,0.0037,0.0028,0.0030,0.0030,R 63 | 0.0086,0.0215,0.0242,0.0445,0.0667,0.0771,0.0499,0.0906,0.1229,0.1185,0.0775,0.1101,0.1042,0.0853,0.0456,0.1304,0.2690,0.2947,0.3669,0.4948,0.6275,0.8162,0.9237,0.8710,0.8052,0.8756,1.0000,0.9858,0.9427,0.8114,0.6987,0.6810,0.6591,0.6954,0.7290,0.6680,0.5917,0.4899,0.3439,0.2366,0.1716,0.1013,0.0766,0.0845,0.0260,0.0333,0.0205,0.0309,0.0101,0.0095,0.0047,0.0072,0.0054,0.0022,0.0016,0.0029,0.0058,0.0050,0.0024,0.0030,R 64 | 0.0067,0.0096,0.0024,0.0058,0.0197,0.0618,0.0432,0.0951,0.0836,0.1180,0.0978,0.0909,0.0656,0.0593,0.0832,0.1297,0.2038,0.3811,0.4451,0.5224,0.5911,0.6566,0.6308,0.5998,0.4958,0.5647,0.6906,0.8513,1.0000,0.9166,0.7676,0.6177,0.5468,0.5516,0.5463,0.5515,0.4561,0.3466,0.3384,0.2853,0.2502,0.1641,0.1605,0.1491,0.1326,0.0687,0.0602,0.0561,0.0306,0.0154,0.0029,0.0048,0.0023,0.0020,0.0040,0.0019,0.0034,0.0034,0.0051,0.0031,R 65 | 0.0071,0.0103,0.0135,0.0494,0.0253,0.0806,0.0701,0.0738,0.0117,0.0898,0.0289,0.1554,0.1437,0.1035,0.1424,0.1227,0.0892,0.2047,0.0827,0.1524,0.3031,0.1608,0.0667,0.1426,0.0395,0.1653,0.3399,0.4855,0.5206,0.5508,0.6102,0.5989,0.6764,0.8897,1.0000,0.9517,0.8459,0.7073,0.6697,0.6326,0.5102,0.4161,0.2816,0.1705,0.1421,0.0971,0.0879,0.0863,0.0355,0.0233,0.0252,0.0043,0.0048,0.0076,0.0124,0.0105,0.0054,0.0032,0.0073,0.0063,R 66 | 0.0176,0.0172,0.0501,0.0285,0.0262,0.0351,0.0362,0.0535,0.0258,0.0474,0.0526,0.1854,0.1040,0.0948,0.0912,0.1688,0.1568,0.0375,0.1316,0.2086,0.1976,0.0946,0.1965,0.1242,0.0616,0.2141,0.4642,0.6471,0.6340,0.6107,0.7046,0.5376,0.5934,0.8443,0.9481,0.9705,0.7766,0.6313,0.5760,0.6148,0.5450,0.4813,0.3406,0.1916,0.1134,0.0640,0.0911,0.0980,0.0563,0.0187,0.0088,0.0042,0.0175,0.0171,0.0079,0.0050,0.0112,0.0179,0.0294,0.0063,R 67 | 0.0265,0.0440,0.0137,0.0084,0.0305,0.0438,0.0341,0.0780,0.0844,0.0779,0.0327,0.2060,0.1908,0.1065,0.1457,0.2232,0.2070,0.1105,0.1078,0.1165,0.2224,0.0689,0.2060,0.2384,0.0904,0.2278,0.5872,0.8457,0.8467,0.7679,0.8055,0.6260,0.6545,0.8747,0.9885,0.9348,0.6960,0.5733,0.5872,0.6663,0.5651,0.5247,0.3684,0.1997,0.1512,0.0508,0.0931,0.0982,0.0524,0.0188,0.0100,0.0038,0.0187,0.0156,0.0068,0.0097,0.0073,0.0081,0.0086,0.0095,R 68 | 0.0368,0.0403,0.0317,0.0293,0.0820,0.1342,0.1161,0.0663,0.0155,0.0506,0.0906,0.2545,0.1464,0.1272,0.1223,0.1669,0.1424,0.1285,0.1857,0.1136,0.2069,0.0219,0.2400,0.2547,0.0240,0.1923,0.4753,0.7003,0.6825,0.6443,0.7063,0.5373,0.6601,0.8708,0.9518,0.9605,0.7712,0.6772,0.6431,0.6720,0.6035,0.5155,0.3802,0.2278,0.1522,0.0801,0.0804,0.0752,0.0566,0.0175,0.0058,0.0091,0.0160,0.0160,0.0081,0.0070,0.0135,0.0067,0.0078,0.0068,R 69 | 0.0195,0.0142,0.0181,0.0406,0.0391,0.0249,0.0892,0.0973,0.0840,0.1191,0.1522,0.1322,0.1434,0.1244,0.0653,0.0890,0.1226,0.1846,0.3880,0.3658,0.2297,0.2610,0.4193,0.5848,0.5643,0.5448,0.4772,0.6897,0.9797,1.0000,0.9546,0.8835,0.7662,0.6547,0.5447,0.4593,0.4679,0.1987,0.0699,0.1493,0.1713,0.1654,0.2600,0.3846,0.3754,0.2414,0.1077,0.0224,0.0155,0.0187,0.0125,0.0028,0.0067,0.0120,0.0012,0.0022,0.0058,0.0042,0.0067,0.0012,R 70 | 0.0216,0.0215,0.0273,0.0139,0.0357,0.0785,0.0906,0.0908,0.1151,0.0973,0.1203,0.1102,0.1192,0.1762,0.2390,0.2138,0.1929,0.1765,0.0746,0.1265,0.2005,0.1571,0.2605,0.5386,0.8440,1.0000,0.8684,0.6742,0.5537,0.4638,0.3609,0.2055,0.1620,0.2092,0.3100,0.2344,0.1058,0.0383,0.0528,0.1291,0.2241,0.1915,0.1587,0.0942,0.0840,0.0670,0.0342,0.0469,0.0357,0.0136,0.0082,0.0140,0.0044,0.0052,0.0073,0.0021,0.0047,0.0024,0.0009,0.0017,R 71 | 0.0065,0.0122,0.0068,0.0108,0.0217,0.0284,0.0527,0.0575,0.1054,0.1109,0.0937,0.0827,0.0920,0.0911,0.1487,0.1666,0.1268,0.1374,0.1095,0.1286,0.2146,0.2889,0.4238,0.6168,0.8167,0.9622,0.8280,0.5816,0.4667,0.3539,0.2727,0.1410,0.1863,0.2176,0.2360,0.1725,0.0589,0.0621,0.1847,0.2452,0.2984,0.3041,0.2275,0.1480,0.1102,0.1178,0.0608,0.0333,0.0276,0.0100,0.0023,0.0069,0.0025,0.0027,0.0052,0.0036,0.0026,0.0036,0.0006,0.0035,R 72 | 0.0036,0.0078,0.0092,0.0387,0.0530,0.1197,0.1243,0.1026,0.1239,0.0888,0.0937,0.1245,0.1599,0.1542,0.1846,0.1732,0.1477,0.1748,0.1455,0.1579,0.2257,0.1975,0.3368,0.5828,0.8505,1.0000,0.8457,0.6624,0.5564,0.3925,0.3233,0.2054,0.1920,0.2227,0.3147,0.2268,0.0795,0.0748,0.1166,0.1969,0.2619,0.2507,0.1983,0.0948,0.0931,0.0965,0.0381,0.0435,0.0336,0.0055,0.0079,0.0119,0.0055,0.0035,0.0036,0.0004,0.0018,0.0049,0.0024,0.0016,R 73 | 0.0208,0.0186,0.0131,0.0211,0.0610,0.0613,0.0612,0.0506,0.0989,0.1093,0.1063,0.1179,0.1291,0.1591,0.1680,0.1918,0.1615,0.1647,0.1397,0.1426,0.2429,0.2816,0.4290,0.6443,0.9061,1.0000,0.8087,0.6119,0.5260,0.3677,0.2746,0.1020,0.1339,0.1582,0.1952,0.1787,0.0429,0.1096,0.1762,0.2481,0.3150,0.2920,0.1902,0.0696,0.0758,0.0910,0.0441,0.0244,0.0265,0.0095,0.0140,0.0074,0.0063,0.0081,0.0087,0.0044,0.0028,0.0019,0.0049,0.0023,R 74 | 0.0139,0.0222,0.0089,0.0108,0.0215,0.0136,0.0659,0.0954,0.0786,0.1015,0.1261,0.0828,0.0493,0.0848,0.1514,0.1396,0.1066,0.1923,0.2991,0.3247,0.3797,0.5658,0.7483,0.8757,0.9048,0.7511,0.6858,0.7043,0.5864,0.3773,0.2206,0.2628,0.2672,0.2907,0.1982,0.2288,0.3186,0.2871,0.2921,0.2806,0.2682,0.2112,0.1513,0.1789,0.1850,0.1717,0.0898,0.0656,0.0445,0.0110,0.0024,0.0062,0.0072,0.0113,0.0012,0.0022,0.0025,0.0059,0.0039,0.0048,R 75 | 0.0109,0.0093,0.0121,0.0378,0.0679,0.0863,0.1004,0.0664,0.0941,0.1036,0.0972,0.0501,0.1546,0.3404,0.4804,0.6570,0.7738,0.7827,0.8152,0.8129,0.8297,0.8535,0.8870,0.8894,0.8980,0.9667,1.0000,0.9134,0.6762,0.4659,0.2895,0.2959,0.1746,0.2112,0.2569,0.2276,0.2149,0.1601,0.0371,0.0117,0.0488,0.0288,0.0597,0.0431,0.0369,0.0025,0.0327,0.0257,0.0182,0.0108,0.0124,0.0077,0.0023,0.0117,0.0053,0.0077,0.0076,0.0056,0.0055,0.0039,R 76 | 0.0202,0.0104,0.0325,0.0239,0.0807,0.1529,0.1154,0.0608,0.1317,0.1370,0.0843,0.0269,0.1254,0.3046,0.5584,0.7973,0.8341,0.8057,0.8616,0.8769,0.9413,0.9403,0.9409,1.0000,0.9725,0.9309,0.9351,0.7317,0.4421,0.3244,0.4161,0.4611,0.4031,0.3000,0.2459,0.1348,0.2541,0.2255,0.1598,0.1485,0.0845,0.0569,0.0855,0.1262,0.1153,0.0570,0.0426,0.0425,0.0235,0.0006,0.0188,0.0127,0.0081,0.0067,0.0043,0.0065,0.0049,0.0054,0.0073,0.0054,R 77 | 0.0239,0.0189,0.0466,0.0440,0.0657,0.0742,0.1380,0.1099,0.1384,0.1376,0.0938,0.0259,0.1499,0.2851,0.5743,0.8278,0.8669,0.8131,0.9045,0.9046,1.0000,0.9976,0.9872,0.9761,0.9009,0.9724,0.9675,0.7633,0.4434,0.3822,0.4727,0.4007,0.3381,0.3172,0.2222,0.0733,0.2692,0.1888,0.0712,0.1062,0.0694,0.0300,0.0893,0.1459,0.1348,0.0391,0.0546,0.0469,0.0201,0.0095,0.0155,0.0091,0.0151,0.0080,0.0018,0.0078,0.0045,0.0026,0.0036,0.0024,R 78 | 0.0336,0.0294,0.0476,0.0539,0.0794,0.0804,0.1136,0.1228,0.1235,0.0842,0.0357,0.0689,0.1705,0.3257,0.4602,0.6225,0.7327,0.7843,0.7988,0.8261,1.0000,0.9814,0.9620,0.9601,0.9118,0.9086,0.7931,0.5877,0.3474,0.4235,0.4633,0.3410,0.2849,0.2847,0.1742,0.0549,0.1192,0.1154,0.0855,0.1811,0.1264,0.0799,0.0378,0.1268,0.1125,0.0505,0.0949,0.0677,0.0259,0.0170,0.0033,0.0150,0.0111,0.0032,0.0035,0.0169,0.0137,0.0015,0.0069,0.0051,R 79 | 0.0231,0.0351,0.0030,0.0304,0.0339,0.0860,0.1738,0.1351,0.1063,0.0347,0.0575,0.1382,0.2274,0.4038,0.5223,0.6847,0.7521,0.7760,0.7708,0.8627,1.0000,0.8873,0.8057,0.8760,0.9066,0.9430,0.8846,0.6500,0.2970,0.2423,0.2992,0.2285,0.2277,0.1529,0.1037,0.0352,0.1073,0.1373,0.1331,0.1454,0.1115,0.0440,0.0762,0.1381,0.0831,0.0654,0.0844,0.0595,0.0497,0.0313,0.0154,0.0106,0.0097,0.0022,0.0052,0.0072,0.0056,0.0038,0.0043,0.0030,R 80 | 0.0108,0.0086,0.0058,0.0460,0.0752,0.0887,0.1015,0.0494,0.0472,0.0393,0.1106,0.1412,0.2202,0.2976,0.4116,0.4754,0.5390,0.6279,0.7060,0.7918,0.9493,1.0000,0.9645,0.9432,0.8658,0.7895,0.6501,0.4492,0.4739,0.6153,0.4929,0.3195,0.3735,0.3336,0.1052,0.0671,0.0379,0.0461,0.1694,0.2169,0.1677,0.0644,0.0159,0.0778,0.0653,0.0210,0.0509,0.0387,0.0262,0.0101,0.0161,0.0029,0.0078,0.0114,0.0083,0.0058,0.0003,0.0023,0.0026,0.0027,R 81 | 0.0229,0.0369,0.0040,0.0375,0.0455,0.1452,0.2211,0.1188,0.0750,0.1631,0.2709,0.3358,0.4091,0.4400,0.5485,0.7213,0.8137,0.9185,1.0000,0.9418,0.9116,0.9349,0.7484,0.5146,0.4106,0.3443,0.6981,0.8713,0.9013,0.8014,0.4380,0.1319,0.1709,0.2484,0.3044,0.2312,0.1338,0.2056,0.2474,0.2790,0.1610,0.0056,0.0351,0.1148,0.1331,0.0276,0.0763,0.0631,0.0309,0.0240,0.0115,0.0064,0.0022,0.0122,0.0151,0.0056,0.0026,0.0029,0.0104,0.0163,R 82 | 0.0100,0.0194,0.0155,0.0489,0.0839,0.1009,0.1627,0.2071,0.2696,0.2990,0.3242,0.3565,0.3951,0.5201,0.6953,0.8468,1.0000,0.9278,0.8510,0.8010,0.8142,0.8825,0.7302,0.6107,0.7159,0.8458,0.6319,0.4808,0.6291,0.7152,0.6005,0.4235,0.4106,0.3992,0.1730,0.1975,0.2370,0.1339,0.1583,0.3151,0.1968,0.2054,0.1272,0.1129,0.1946,0.2195,0.1930,0.1498,0.0773,0.0196,0.0122,0.0130,0.0073,0.0077,0.0075,0.0060,0.0080,0.0019,0.0053,0.0019,R 83 | 0.0409,0.0421,0.0573,0.0130,0.0183,0.1019,0.1054,0.1070,0.2302,0.2259,0.2373,0.3323,0.3827,0.4840,0.6812,0.7555,0.9522,0.9826,0.8871,0.8268,0.7561,0.8217,0.6967,0.6444,0.6948,0.8014,0.6053,0.6084,0.8877,0.8557,0.5563,0.2897,0.3638,0.4786,0.2908,0.0899,0.2043,0.1707,0.0407,0.1286,0.1581,0.2191,0.1701,0.0971,0.2217,0.2732,0.1874,0.1062,0.0665,0.0405,0.0113,0.0028,0.0036,0.0105,0.0120,0.0087,0.0061,0.0061,0.0030,0.0078,R 84 | 0.0217,0.0340,0.0392,0.0236,0.1081,0.1164,0.1398,0.1009,0.1147,0.1777,0.4079,0.4113,0.3973,0.5078,0.6509,0.8073,0.9819,1.0000,0.9407,0.8452,0.8106,0.8460,0.6212,0.5815,0.7745,0.8204,0.5601,0.2989,0.5009,0.6628,0.5753,0.4055,0.3746,0.3481,0.1580,0.1422,0.2130,0.1866,0.1003,0.2396,0.2241,0.2029,0.0710,0.1606,0.1669,0.1700,0.1829,0.1403,0.0506,0.0224,0.0095,0.0031,0.0103,0.0078,0.0077,0.0094,0.0031,0.0030,0.0013,0.0069,R 85 | 0.0378,0.0318,0.0423,0.0350,0.1787,0.1635,0.0887,0.0817,0.1779,0.2053,0.3135,0.3118,0.3686,0.3885,0.5850,0.7868,0.9739,1.0000,0.9843,0.8610,0.8443,0.9061,0.5847,0.4033,0.5946,0.6793,0.6389,0.5002,0.5578,0.4831,0.4729,0.3318,0.3969,0.3894,0.2314,0.1036,0.1312,0.0864,0.2569,0.3179,0.2649,0.2714,0.1713,0.0584,0.1230,0.2200,0.2198,0.1074,0.0423,0.0162,0.0093,0.0046,0.0044,0.0078,0.0102,0.0065,0.0061,0.0062,0.0043,0.0053,R 86 | 0.0365,0.1632,0.1636,0.1421,0.1130,0.1306,0.2112,0.2268,0.2992,0.3735,0.3042,0.0387,0.2679,0.5397,0.6204,0.7257,0.8350,0.6888,0.4450,0.3921,0.5605,0.7545,0.8311,1.0000,0.8762,0.7092,0.7009,0.5014,0.3942,0.4456,0.4072,0.0773,0.1423,0.0401,0.3597,0.6847,0.7076,0.3597,0.0612,0.3027,0.3966,0.3868,0.2380,0.2059,0.2288,0.1704,0.1587,0.1792,0.1022,0.0151,0.0223,0.0110,0.0071,0.0205,0.0164,0.0063,0.0078,0.0094,0.0110,0.0068,R 87 | 0.0188,0.0370,0.0953,0.0824,0.0249,0.0488,0.1424,0.1972,0.1873,0.1806,0.2139,0.1523,0.1975,0.4844,0.7298,0.7807,0.7906,0.6122,0.4200,0.2807,0.5148,0.7569,0.8596,1.0000,0.8457,0.6797,0.6971,0.5843,0.4772,0.5201,0.4241,0.1592,0.1668,0.0588,0.3967,0.7147,0.7319,0.3509,0.0589,0.2690,0.4200,0.3874,0.2440,0.2000,0.2307,0.1886,0.1960,0.1701,0.1366,0.0398,0.0143,0.0093,0.0033,0.0113,0.0030,0.0057,0.0090,0.0057,0.0068,0.0024,R 88 | 0.0856,0.0454,0.0382,0.0203,0.0385,0.0534,0.2140,0.3110,0.2837,0.2751,0.2707,0.0946,0.1020,0.4519,0.6737,0.6699,0.7066,0.5632,0.3785,0.2721,0.5297,0.7697,0.8643,0.9304,0.9372,0.6247,0.6024,0.6810,0.5047,0.5775,0.4754,0.2400,0.2779,0.1997,0.5305,0.7409,0.7775,0.4424,0.1416,0.3508,0.4482,0.4208,0.3054,0.2235,0.2611,0.2798,0.2392,0.2021,0.1326,0.0358,0.0128,0.0172,0.0138,0.0079,0.0037,0.0051,0.0258,0.0102,0.0037,0.0037,R 89 | 0.0274,0.0242,0.0621,0.0560,0.1129,0.0973,0.1823,0.1745,0.1440,0.1808,0.2366,0.0906,0.1749,0.4012,0.5187,0.7312,0.9062,0.9260,0.7434,0.4463,0.5103,0.6952,0.7755,0.8364,0.7283,0.6399,0.5759,0.4146,0.3495,0.4437,0.2665,0.2024,0.1942,0.0765,0.3725,0.5843,0.4827,0.2347,0.0999,0.3244,0.3990,0.2975,0.1684,0.1761,0.1683,0.0729,0.1190,0.1297,0.0748,0.0067,0.0255,0.0113,0.0108,0.0085,0.0047,0.0074,0.0104,0.0161,0.0220,0.0173,R 90 | 0.0235,0.0291,0.0749,0.0519,0.0227,0.0834,0.0677,0.2002,0.2876,0.3674,0.2974,0.0837,0.1912,0.5040,0.6352,0.6804,0.7505,0.6595,0.4509,0.2964,0.4019,0.6794,0.8297,1.0000,0.8240,0.7115,0.7726,0.6124,0.4936,0.5648,0.4906,0.1820,0.1811,0.1107,0.4603,0.6650,0.6423,0.2166,0.1951,0.4947,0.4925,0.4041,0.2402,0.1392,0.1779,0.1946,0.1723,0.1522,0.0929,0.0179,0.0242,0.0083,0.0037,0.0095,0.0105,0.0030,0.0132,0.0068,0.0108,0.0090,R 91 | 0.0126,0.0519,0.0621,0.0518,0.1072,0.2587,0.2304,0.2067,0.3416,0.4284,0.3015,0.1207,0.3299,0.5707,0.6962,0.9751,1.0000,0.9293,0.6210,0.4586,0.5001,0.5032,0.7082,0.8420,0.8109,0.7690,0.8105,0.6203,0.2356,0.2595,0.6299,0.6762,0.2903,0.4393,0.8529,0.7180,0.4801,0.5856,0.4993,0.2866,0.0601,0.1167,0.2737,0.2812,0.2078,0.0660,0.0491,0.0345,0.0172,0.0287,0.0027,0.0208,0.0048,0.0199,0.0126,0.0022,0.0037,0.0034,0.0114,0.0077,R 92 | 0.0253,0.0808,0.0507,0.0244,0.1724,0.3823,0.3729,0.3583,0.3429,0.2197,0.2653,0.3223,0.5582,0.6916,0.7943,0.7152,0.3512,0.2008,0.2676,0.4299,0.5280,0.3489,0.1430,0.5453,0.6338,0.7712,0.6838,0.8015,0.8073,0.8310,0.7792,0.5049,0.1413,0.2767,0.5084,0.4787,0.1356,0.2299,0.2789,0.3833,0.2933,0.1155,0.1705,0.1294,0.0909,0.0800,0.0567,0.0198,0.0114,0.0151,0.0085,0.0178,0.0073,0.0079,0.0038,0.0116,0.0033,0.0039,0.0081,0.0053,R 93 | 0.0260,0.0192,0.0254,0.0061,0.0352,0.0701,0.1263,0.1080,0.1523,0.1630,0.1030,0.2187,0.1542,0.2630,0.2940,0.2978,0.0699,0.1401,0.2990,0.3915,0.3598,0.2403,0.4208,0.5675,0.6094,0.6323,0.6549,0.7673,1.0000,0.8463,0.5509,0.4444,0.5169,0.4268,0.1802,0.0791,0.0535,0.1906,0.2561,0.2153,0.2769,0.2841,0.1733,0.0815,0.0335,0.0933,0.1018,0.0309,0.0208,0.0318,0.0132,0.0118,0.0120,0.0051,0.0070,0.0015,0.0035,0.0008,0.0044,0.0077,R 94 | 0.0459,0.0437,0.0347,0.0456,0.0067,0.0890,0.1798,0.1741,0.1598,0.1408,0.2693,0.3259,0.4545,0.5785,0.4471,0.2231,0.2164,0.3201,0.2915,0.4235,0.4460,0.2380,0.6415,0.8966,0.8918,0.7529,0.6838,0.8390,1.0000,0.8362,0.5427,0.4577,0.8067,0.6973,0.3915,0.1558,0.1598,0.2161,0.5178,0.4782,0.2344,0.3599,0.2785,0.1807,0.0352,0.0473,0.0322,0.0408,0.0163,0.0088,0.0121,0.0067,0.0032,0.0109,0.0164,0.0151,0.0070,0.0085,0.0117,0.0056,R 95 | 0.0025,0.0309,0.0171,0.0228,0.0434,0.1224,0.1947,0.1661,0.1368,0.1430,0.0994,0.2250,0.2444,0.3239,0.3039,0.2410,0.0367,0.1672,0.3038,0.4069,0.3613,0.1994,0.4611,0.6849,0.7272,0.7152,0.7102,0.8516,1.0000,0.7690,0.4841,0.3717,0.6096,0.5110,0.2586,0.0916,0.0947,0.2287,0.3480,0.2095,0.1901,0.2941,0.2211,0.1524,0.0746,0.0606,0.0692,0.0446,0.0344,0.0082,0.0108,0.0149,0.0077,0.0036,0.0114,0.0085,0.0101,0.0016,0.0028,0.0014,R 96 | 0.0291,0.0400,0.0771,0.0809,0.0521,0.1051,0.0145,0.0674,0.1294,0.1146,0.0942,0.0794,0.0252,0.1191,0.1045,0.2050,0.1556,0.2690,0.3784,0.4024,0.3470,0.1395,0.1208,0.2827,0.1500,0.2626,0.4468,0.7520,0.9036,0.7812,0.4766,0.2483,0.5372,0.6279,0.3647,0.4572,0.6359,0.6474,0.5520,0.3253,0.2292,0.0653,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0056,0.0237,0.0204,0.0050,0.0137,0.0164,0.0081,0.0139,0.0111,R 97 | 0.0181,0.0146,0.0026,0.0141,0.0421,0.0473,0.0361,0.0741,0.1398,0.1045,0.0904,0.0671,0.0997,0.1056,0.0346,0.1231,0.1626,0.3652,0.3262,0.2995,0.2109,0.2104,0.2085,0.2282,0.0747,0.1969,0.4086,0.6385,0.7970,0.7508,0.5517,0.2214,0.4672,0.4479,0.2297,0.3235,0.4480,0.5581,0.6520,0.5354,0.2478,0.2268,0.1788,0.0898,0.0536,0.0374,0.0990,0.0956,0.0317,0.0142,0.0076,0.0223,0.0255,0.0145,0.0233,0.0041,0.0018,0.0048,0.0089,0.0085,R 98 | 0.0491,0.0279,0.0592,0.1270,0.1772,0.1908,0.2217,0.0768,0.1246,0.2028,0.0947,0.2497,0.2209,0.3195,0.3340,0.3323,0.2780,0.2975,0.2948,0.1729,0.3264,0.3834,0.3523,0.5410,0.5228,0.4475,0.5340,0.5323,0.3907,0.3456,0.4091,0.4639,0.5580,0.5727,0.6355,0.7563,0.6903,0.6176,0.5379,0.5622,0.6508,0.4797,0.3736,0.2804,0.1982,0.2438,0.1789,0.1706,0.0762,0.0238,0.0268,0.0081,0.0129,0.0161,0.0063,0.0119,0.0194,0.0140,0.0332,0.0439,M 99 | 0.1313,0.2339,0.3059,0.4264,0.4010,0.1791,0.1853,0.0055,0.1929,0.2231,0.2907,0.2259,0.3136,0.3302,0.3660,0.3956,0.4386,0.4670,0.5255,0.3735,0.2243,0.1973,0.4337,0.6532,0.5070,0.2796,0.4163,0.5950,0.5242,0.4178,0.3714,0.2375,0.0863,0.1437,0.2896,0.4577,0.3725,0.3372,0.3803,0.4181,0.3603,0.2711,0.1653,0.1951,0.2811,0.2246,0.1921,0.1500,0.0665,0.0193,0.0156,0.0362,0.0210,0.0154,0.0180,0.0013,0.0106,0.0127,0.0178,0.0231,M 100 | 0.0201,0.0423,0.0554,0.0783,0.0620,0.0871,0.1201,0.2707,0.1206,0.0279,0.2251,0.2615,0.1770,0.3709,0.4533,0.5553,0.4616,0.3797,0.3450,0.2665,0.2395,0.1127,0.2556,0.5169,0.3779,0.4082,0.5353,0.5116,0.4544,0.4258,0.3869,0.3939,0.4661,0.3974,0.2194,0.1816,0.1023,0.2108,0.3253,0.3697,0.2912,0.3010,0.2563,0.1927,0.2062,0.1751,0.0841,0.1035,0.0641,0.0153,0.0081,0.0191,0.0182,0.0160,0.0290,0.0090,0.0242,0.0224,0.0190,0.0096,M 101 | 0.0629,0.1065,0.1526,0.1229,0.1437,0.1190,0.0884,0.0907,0.2107,0.3597,0.5466,0.5205,0.5127,0.5395,0.6558,0.8705,0.9786,0.9335,0.7917,0.7383,0.6908,0.3850,0.0671,0.0502,0.2717,0.2839,0.2234,0.1911,0.0408,0.2531,0.1979,0.1891,0.2433,0.1956,0.2667,0.1340,0.1073,0.2023,0.1794,0.0227,0.1313,0.1775,0.1549,0.1626,0.0708,0.0129,0.0795,0.0762,0.0117,0.0061,0.0257,0.0089,0.0262,0.0108,0.0138,0.0187,0.0230,0.0057,0.0113,0.0131,M 102 | 0.0335,0.0134,0.0696,0.1180,0.0348,0.1180,0.1948,0.1607,0.3036,0.4372,0.5533,0.5771,0.7022,0.7067,0.7367,0.7391,0.8622,0.9458,0.8782,0.7913,0.5760,0.3061,0.0563,0.0239,0.2554,0.4862,0.5027,0.4402,0.2847,0.1797,0.3560,0.3522,0.3321,0.3112,0.3638,0.0754,0.1834,0.1820,0.1815,0.1593,0.0576,0.0954,0.1086,0.0812,0.0784,0.0487,0.0439,0.0586,0.0370,0.0185,0.0302,0.0244,0.0232,0.0093,0.0159,0.0193,0.0032,0.0377,0.0126,0.0156,M 103 | 0.0587,0.1210,0.1268,0.1498,0.1436,0.0561,0.0832,0.0672,0.1372,0.2352,0.3208,0.4257,0.5201,0.4914,0.5950,0.7221,0.9039,0.9111,0.8723,0.7686,0.7326,0.5222,0.3097,0.3172,0.2270,0.1640,0.1746,0.1835,0.2048,0.1674,0.2767,0.3104,0.3399,0.4441,0.5046,0.2814,0.1681,0.2633,0.3198,0.1933,0.0934,0.0443,0.0780,0.0722,0.0405,0.0553,0.1081,0.1139,0.0767,0.0265,0.0215,0.0331,0.0111,0.0088,0.0158,0.0122,0.0038,0.0101,0.0228,0.0124,M 104 | 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0.0116,0.0179,0.0449,0.1096,0.1913,0.0924,0.0761,0.1092,0.0757,0.1006,0.2500,0.3988,0.3809,0.4753,0.6165,0.6464,0.8024,0.9208,0.9832,0.9634,0.8646,0.8325,0.8276,0.8007,0.6102,0.4853,0.4355,0.4307,0.4399,0.3833,0.3032,0.3035,0.3197,0.2292,0.2131,0.2347,0.3201,0.4455,0.3655,0.2715,0.1747,0.1781,0.2199,0.1056,0.0573,0.0307,0.0237,0.0470,0.0102,0.0057,0.0031,0.0163,0.0099,0.0084,0.0270,0.0277,0.0097,0.0054,0.0148,0.0092,M 107 | 0.0331,0.0423,0.0474,0.0818,0.0835,0.0756,0.0374,0.0961,0.0548,0.0193,0.0897,0.1734,0.1936,0.2803,0.3313,0.5020,0.6360,0.7096,0.8333,0.8730,0.8073,0.7507,0.7526,0.7298,0.6177,0.4946,0.4531,0.4099,0.4540,0.4124,0.3139,0.3194,0.3692,0.3776,0.4469,0.4777,0.4716,0.4664,0.3893,0.4255,0.4064,0.3712,0.3863,0.2802,0.1283,0.1117,0.1303,0.0787,0.0436,0.0224,0.0133,0.0078,0.0174,0.0176,0.0038,0.0129,0.0066,0.0044,0.0134,0.0092,M 108 | 0.0428,0.0555,0.0708,0.0618,0.1215,0.1524,0.1543,0.0391,0.0610,0.0113,0.1255,0.2473,0.3011,0.3747,0.4520,0.5392,0.6588,0.7113,0.7602,0.8672,0.8416,0.7974,0.8385,0.9317,0.8555,0.6162,0.4139,0.3269,0.3108,0.2554,0.3367,0.4465,0.5000,0.5111,0.5194,0.4619,0.4234,0.4372,0.4277,0.4433,0.3700,0.3324,0.2564,0.2527,0.2137,0.1789,0.1010,0.0528,0.0453,0.0118,0.0009,0.0142,0.0179,0.0079,0.0060,0.0131,0.0089,0.0084,0.0113,0.0049,M 109 | 0.0599,0.0474,0.0498,0.0387,0.1026,0.0773,0.0853,0.0447,0.1094,0.0351,0.1582,0.2023,0.2268,0.2829,0.3819,0.4665,0.6687,0.8647,0.9361,0.9367,0.9144,0.9162,0.9311,0.8604,0.7327,0.5763,0.4162,0.4113,0.4146,0.3149,0.2936,0.3169,0.3149,0.4132,0.3994,0.4195,0.4532,0.4419,0.4737,0.3431,0.3194,0.3370,0.2493,0.2650,0.1748,0.0932,0.0530,0.0081,0.0342,0.0137,0.0028,0.0013,0.0005,0.0227,0.0209,0.0081,0.0117,0.0114,0.0112,0.0100,M 110 | 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0.0335,0.0258,0.0398,0.0570,0.0529,0.1091,0.1709,0.1684,0.1865,0.2660,0.3188,0.3553,0.3116,0.1965,0.1780,0.2794,0.2870,0.3969,0.5599,0.6936,0.7969,0.7452,0.8203,0.9261,0.8810,0.8814,0.9301,0.9955,0.8576,0.6069,0.3934,0.2464,0.1645,0.1140,0.0956,0.0080,0.0702,0.0936,0.0894,0.1127,0.0873,0.1020,0.1964,0.2256,0.1814,0.2012,0.1688,0.1037,0.0501,0.0136,0.0130,0.0120,0.0039,0.0053,0.0062,0.0046,0.0045,0.0022,0.0005,0.0031,M 203 | 0.0272,0.0378,0.0488,0.0848,0.1127,0.1103,0.1349,0.2337,0.3113,0.3997,0.3941,0.3309,0.2926,0.1760,0.1739,0.2043,0.2088,0.2678,0.2434,0.1839,0.2802,0.6172,0.8015,0.8313,0.8440,0.8494,0.9168,1.0000,0.7896,0.5371,0.6472,0.6505,0.4959,0.2175,0.0990,0.0434,0.1708,0.1979,0.1880,0.1108,0.1702,0.0585,0.0638,0.1391,0.0638,0.0581,0.0641,0.1044,0.0732,0.0275,0.0146,0.0091,0.0045,0.0043,0.0043,0.0098,0.0054,0.0051,0.0065,0.0103,M 204 | 0.0187,0.0346,0.0168,0.0177,0.0393,0.1630,0.2028,0.1694,0.2328,0.2684,0.3108,0.2933,0.2275,0.0994,0.1801,0.2200,0.2732,0.2862,0.2034,0.1740,0.4130,0.6879,0.8120,0.8453,0.8919,0.9300,0.9987,1.0000,0.8104,0.6199,0.6041,0.5547,0.4160,0.1472,0.0849,0.0608,0.0969,0.1411,0.1676,0.1200,0.1201,0.1036,0.1977,0.1339,0.0902,0.1085,0.1521,0.1363,0.0858,0.0290,0.0203,0.0116,0.0098,0.0199,0.0033,0.0101,0.0065,0.0115,0.0193,0.0157,M 205 | 0.0323,0.0101,0.0298,0.0564,0.0760,0.0958,0.0990,0.1018,0.1030,0.2154,0.3085,0.3425,0.2990,0.1402,0.1235,0.1534,0.1901,0.2429,0.2120,0.2395,0.3272,0.5949,0.8302,0.9045,0.9888,0.9912,0.9448,1.0000,0.9092,0.7412,0.7691,0.7117,0.5304,0.2131,0.0928,0.1297,0.1159,0.1226,0.1768,0.0345,0.1562,0.0824,0.1149,0.1694,0.0954,0.0080,0.0790,0.1255,0.0647,0.0179,0.0051,0.0061,0.0093,0.0135,0.0063,0.0063,0.0034,0.0032,0.0062,0.0067,M 206 | 0.0522,0.0437,0.0180,0.0292,0.0351,0.1171,0.1257,0.1178,0.1258,0.2529,0.2716,0.2374,0.1878,0.0983,0.0683,0.1503,0.1723,0.2339,0.1962,0.1395,0.3164,0.5888,0.7631,0.8473,0.9424,0.9986,0.9699,1.0000,0.8630,0.6979,0.7717,0.7305,0.5197,0.1786,0.1098,0.1446,0.1066,0.1440,0.1929,0.0325,0.1490,0.0328,0.0537,0.1309,0.0910,0.0757,0.1059,0.1005,0.0535,0.0235,0.0155,0.0160,0.0029,0.0051,0.0062,0.0089,0.0140,0.0138,0.0077,0.0031,M 207 | 0.0303,0.0353,0.0490,0.0608,0.0167,0.1354,0.1465,0.1123,0.1945,0.2354,0.2898,0.2812,0.1578,0.0273,0.0673,0.1444,0.2070,0.2645,0.2828,0.4293,0.5685,0.6990,0.7246,0.7622,0.9242,1.0000,0.9979,0.8297,0.7032,0.7141,0.6893,0.4961,0.2584,0.0969,0.0776,0.0364,0.1572,0.1823,0.1349,0.0849,0.0492,0.1367,0.1552,0.1548,0.1319,0.0985,0.1258,0.0954,0.0489,0.0241,0.0042,0.0086,0.0046,0.0126,0.0036,0.0035,0.0034,0.0079,0.0036,0.0048,M 208 | 0.0260,0.0363,0.0136,0.0272,0.0214,0.0338,0.0655,0.1400,0.1843,0.2354,0.2720,0.2442,0.1665,0.0336,0.1302,0.1708,0.2177,0.3175,0.3714,0.4552,0.5700,0.7397,0.8062,0.8837,0.9432,1.0000,0.9375,0.7603,0.7123,0.8358,0.7622,0.4567,0.1715,0.1549,0.1641,0.1869,0.2655,0.1713,0.0959,0.0768,0.0847,0.2076,0.2505,0.1862,0.1439,0.1470,0.0991,0.0041,0.0154,0.0116,0.0181,0.0146,0.0129,0.0047,0.0039,0.0061,0.0040,0.0036,0.0061,0.0115,M 209 | -------------------------------------------------------------------------------- /examples/sonar/main.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "fmt" 5 | 6 | neural "github.com/breskos/gopher-neural" 7 | "github.com/breskos/gopher-neural/engine" 8 | "github.com/breskos/gopher-neural/learn" 9 | "github.com/breskos/gopher-neural/persist" 10 | ) 11 | 12 | const ( 13 | dataFile = "data.csv" 14 | networkFile = "network.json" 15 | tries = 1 16 | epochs = 100 17 | trainingSplit = 0.7 18 | learningRate = 0.4 19 | decay = 0.005 20 | ) 21 | 22 | func main() { 23 | data := learn.NewSet(neural.Classification) 24 | ok, err := data.LoadFromCSV(dataFile) 25 | if !ok || nil != err { 26 | fmt.Printf("something went wrong -> %v", err) 27 | } 28 | e := engine.NewEngine(neural.Classification, []int{100}, data) 29 | e.SetVerbose(true) 30 | e.Start(engine.CriterionDistance, tries, epochs, trainingSplit, learningRate, decay) 31 | network, evaluation := e.GetWinner() 32 | 33 | evaluation.GetSummary("R") 34 | fmt.Println() 35 | evaluation.GetSummary("M") 36 | 37 | err = persist.ToFile(networkFile, network) 38 | if err != nil { 39 | fmt.Printf("error while saving network: %v\n", err) 40 | } 41 | 42 | network2, err := persist.FromFile(networkFile) 43 | if err != nil { 44 | fmt.Printf("error while loading network: %v\n", err) 45 | } 46 | 47 | w := network2.CalculateWinnerLabel(data.Samples[0].Vector) 48 | fmt.Printf("%v -> %v\n", data.Samples[0].Label, w) 49 | w = network2.CalculateWinnerLabel(data.Samples[70].Vector) 50 | fmt.Printf("%v -> %v\n", data.Samples[70].Label, w) 51 | w = network2.CalculateWinnerLabel(data.Samples[120].Vector) 52 | fmt.Printf("%v -> %v\n", data.Samples[120].Label, w) 53 | 54 | // print confusion matrix 55 | fmt.Println(" * Confusion Matrix *") 56 | evaluation.PrintConfusionMatrix() 57 | } 58 | -------------------------------------------------------------------------------- /examples/wine-quality/README.md: -------------------------------------------------------------------------------- 1 | # Wine quality 2 | 3 | Abstract: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009]). 4 | http://archive.ics.uci.edu/ml/datasets/Wine+Quality 5 | 6 | ## Data 7 | |fixed acidity|volatile acidity|citric acid|residual sugar|chlorides|free sulfur dioxide|total sulfur dioxide|density|pH|sulphates|alcohol|__quality__| 8 | |-------------|----------------|-----------|--------------|---------|------------------|-------------|------|--------|--|---------|-------|-------| 9 | |7|0.27|0.36|20.7|0.045|45|170|1.001|3|0.45|8.8|6| 10 | |6.3|0.3|0.34|1.6|0.049|14|132|0.994|3.3|0.49|9.5|6| 11 | |8.1|0.28|0.4|6.9|0.05|30|97|0.9951|3.26|0.44|10.1|6| 12 | 13 | 14 | 15 | ## Example 16 | This example demonstrate the application of a regressor: 17 | * with 100 epochs, 70 percent training data, 0.9 learning with 0.001 decay 18 | * Regression threshold of 0.2 19 | * 100 hidden neurons 20 | 21 | ## Learning 22 | The learning here is that the regressor decides between correct vs. wrong classified using a threshold. You can change this threshold to bring more tolerance to the system. 23 | 24 | ## Source 25 | Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez 26 | A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal 27 | @2009 28 | -------------------------------------------------------------------------------- /examples/wine-quality/main.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "fmt" 5 | 6 | neural "github.com/breskos/gopher-neural" 7 | "github.com/breskos/gopher-neural/engine" 8 | "github.com/breskos/gopher-neural/learn" 9 | "github.com/breskos/gopher-neural/persist" 10 | ) 11 | 12 | const ( 13 | dataFile = "winequality-white.csv" 14 | networkFile = "network.json" 15 | tries = 1 16 | epochs = 100 17 | trainingSplit = 0.7 18 | learningRate = 0.9 19 | decay = 0.001 20 | hiddenNeurons = 100 21 | regressionThreshold = 0.005 // helps evaluation to define between wrong or right 22 | ) 23 | 24 | func main() { 25 | data := learn.NewSet(neural.Regression) 26 | ok, err := data.LoadFromCSV(dataFile) 27 | if !ok || nil != err { 28 | fmt.Printf("something went wrong -> %v", err) 29 | } 30 | e := engine.NewEngine(neural.Regression, []int{hiddenNeurons}, data) 31 | e.SetRegressionThreshold(regressionThreshold) 32 | e.SetVerbose(true) 33 | // here we ware choosing CriterionSimple because we want the regressor that produces the best examples 34 | e.Start(engine.CriterionSimple, tries, epochs, trainingSplit, learningRate, decay) 35 | network, evaluation := e.GetWinner() 36 | 37 | // regression evaluation 38 | evaluation.GetRegressionSummary() 39 | 40 | err = persist.ToFile(networkFile, network) 41 | if err != nil { 42 | fmt.Printf("error while saving network: %v\n", err) 43 | } 44 | // persisted network 45 | network2, err := persist.FromFile(networkFile) 46 | if err != nil { 47 | fmt.Printf("error while loading network: %v\n", err) 48 | } 49 | 50 | // some examples 51 | w := network2.Calculate(data.Samples[0].Vector) 52 | fmt.Printf("%v -> %v\n", data.Samples[0].Value, w) 53 | w = network2.Calculate(data.Samples[52].Vector) 54 | fmt.Printf("%v -> %v\n", data.Samples[52].Value, w) 55 | w = network2.Calculate(data.Samples[180].Vector) 56 | fmt.Printf("%v -> %v\n", data.Samples[189].Value, w) 57 | } 58 | -------------------------------------------------------------------------------- /examples/wine-quality/winequality-red.csv: -------------------------------------------------------------------------------- 1 | 7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,0.5 2 | 7.8,0.88,0,2.6,0.098,25,67,0.9968,3.2,0.68,9.8,0.5 3 | 7.8,0.76,0.04,2.3,0.092,15,54,0.997,3.26,0.65,9.8,0.5 4 | 11.2,0.28,0.56,1.9,0.075,17,60,0.998,3.16,0.58,9.8,0.6 5 | 7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,0.5 6 | 7.4,0.66,0,1.8,0.075,13,40,0.9978,3.51,0.56,9.4,0.5 7 | 7.9,0.6,0.06,1.6,0.069,15,59,0.9964,3.3,0.46,9.4,0.5 8 | 7.3,0.65,0,1.2,0.065,15,21,0.9946,3.39,0.47,10,0.7 9 | 7.8,0.58,0.02,2,0.073,9,18,0.9968,3.36,0.57,9.5,0.7 10 | 7.5,0.5,0.36,6.1,0.071,17,102,0.9978,3.35,0.8,10.5,0.5 11 | 6.7,0.58,0.08,1.8,0.097,15,65,0.9959,3.28,0.54,9.2,0.5 12 | 7.5,0.5,0.36,6.1,0.071,17,102,0.9978,3.35,0.8,10.5,0.5 13 | 5.6,0.615,0,1.6,0.089,16,59,0.9943,3.58,0.52,9.9,0.5 14 | 7.8,0.61,0.29,1.6,0.114,9,29,0.9974,3.26,1.56,9.1,0.5 15 | 8.9,0.62,0.18,3.8,0.176,52,145,0.9986,3.16,0.88,9.2,0.5 16 | 8.9,0.62,0.19,3.9,0.17,51,148,0.9986,3.17,0.93,9.2,0.5 17 | 8.5,0.28,0.56,1.8,0.092,35,103,0.9969,3.3,0.75,10.5,0.7 18 | 8.1,0.56,0.28,1.7,0.368,16,56,0.9968,3.11,1.28,9.3,0.5 19 | 7.4,0.59,0.08,4.4,0.086,6,29,0.9974,3.38,0.5,9,0.4 20 | 7.9,0.32,0.51,1.8,0.341,17,56,0.9969,3.04,1.08,9.2,0.6 21 | 8.9,0.22,0.48,1.8,0.077,29,60,0.9968,3.39,0.53,9.4,0.6 22 | 7.6,0.39,0.31,2.3,0.082,23,71,0.9982,3.52,0.65,9.7,0.5 23 | 7.9,0.43,0.21,1.6,0.106,10,37,0.9966,3.17,0.91,9.5,0.5 24 | 8.5,0.49,0.11,2.3,0.084,9,67,0.9968,3.17,0.53,9.4,0.5 25 | 6.9,0.4,0.14,2.4,0.085,21,40,0.9968,3.43,0.63,9.7,0.6 26 | 6.3,0.39,0.16,1.4,0.08,11,23,0.9955,3.34,0.56,9.3,0.5 27 | 7.6,0.41,0.24,1.8,0.08,4,11,0.9962,3.28,0.59,9.5,0.5 28 | 7.9,0.43,0.21,1.6,0.106,10,37,0.9966,3.17,0.91,9.5,0.5 29 | 7.1,0.71,0,1.9,0.08,14,35,0.9972,3.47,0.55,9.4,0.5 30 | 7.8,0.645,0,2,0.082,8,16,0.9964,3.38,0.59,9.8,0.6 31 | 6.7,0.675,0.07,2.4,0.089,17,82,0.9958,3.35,0.54,10.1,0.5 32 | 6.9,0.685,0,2.5,0.105,22,37,0.9966,3.46,0.57,10.6,0.6 33 | 8.3,0.655,0.12,2.3,0.083,15,113,0.9966,3.17,0.66,9.8,0.5 34 | 6.9,0.605,0.12,10.7,0.073,40,83,0.9993,3.45,0.52,9.4,0.6 35 | 5.2,0.32,0.25,1.8,0.103,13,50,0.9957,3.38,0.55,9.2,0.5 36 | 7.8,0.645,0,5.5,0.086,5,18,0.9986,3.4,0.55,9.6,0.6 37 | 7.8,0.6,0.14,2.4,0.086,3,15,0.9975,3.42,0.6,10.8,0.6 38 | 8.1,0.38,0.28,2.1,0.066,13,30,0.9968,3.23,0.73,9.7,0.7 39 | 5.7,1.13,0.09,1.5,0.172,7,19,0.994,3.5,0.48,9.8,0.4 40 | 7.3,0.45,0.36,5.9,0.074,12,87,0.9978,3.33,0.83,10.5,0.5 41 | 7.3,0.45,0.36,5.9,0.074,12,87,0.9978,3.33,0.83,10.5,0.5 42 | 8.8,0.61,0.3,2.8,0.088,17,46,0.9976,3.26,0.51,9.3,0.4 43 | 7.5,0.49,0.2,2.6,0.332,8,14,0.9968,3.21,0.9,10.5,0.6 44 | 8.1,0.66,0.22,2.2,0.069,9,23,0.9968,3.3,1.2,10.3,0.5 45 | 6.8,0.67,0.02,1.8,0.05,5,11,0.9962,3.48,0.52,9.5,0.5 46 | 4.6,0.52,0.15,2.1,0.054,8,65,0.9934,3.9,0.56,13.1,0.4 47 | 7.7,0.935,0.43,2.2,0.114,22,114,0.997,3.25,0.73,9.2,0.5 48 | 8.7,0.29,0.52,1.6,0.113,12,37,0.9969,3.25,0.58,9.5,0.5 49 | 6.4,0.4,0.23,1.6,0.066,5,12,0.9958,3.34,0.56,9.2,0.5 50 | 5.6,0.31,0.37,1.4,0.074,12,96,0.9954,3.32,0.58,9.2,0.5 51 | 8.8,0.66,0.26,1.7,0.074,4,23,0.9971,3.15,0.74,9.2,0.5 52 | 6.6,0.52,0.04,2.2,0.069,8,15,0.9956,3.4,0.63,9.4,0.6 53 | 6.6,0.5,0.04,2.1,0.068,6,14,0.9955,3.39,0.64,9.4,0.6 54 | 8.6,0.38,0.36,3,0.081,30,119,0.997,3.2,0.56,9.4,0.5 55 | 7.6,0.51,0.15,2.8,0.11,33,73,0.9955,3.17,0.63,10.2,0.6 56 | 7.7,0.62,0.04,3.8,0.084,25,45,0.9978,3.34,0.53,9.5,0.5 57 | 10.2,0.42,0.57,3.4,0.07,4,10,0.9971,3.04,0.63,9.6,0.5 58 | 7.5,0.63,0.12,5.1,0.111,50,110,0.9983,3.26,0.77,9.4,0.5 59 | 7.8,0.59,0.18,2.3,0.076,17,54,0.9975,3.43,0.59,10,0.5 60 | 7.3,0.39,0.31,2.4,0.074,9,46,0.9962,3.41,0.54,9.4,0.6 61 | 8.8,0.4,0.4,2.2,0.079,19,52,0.998,3.44,0.64,9.2,0.5 62 | 7.7,0.69,0.49,1.8,0.115,20,112,0.9968,3.21,0.71,9.3,0.5 63 | 7.5,0.52,0.16,1.9,0.085,12,35,0.9968,3.38,0.62,9.5,0.7 64 | 7,0.735,0.05,2,0.081,13,54,0.9966,3.39,0.57,9.8,0.5 65 | 7.2,0.725,0.05,4.65,0.086,4,11,0.9962,3.41,0.39,10.9,0.5 66 | 7.2,0.725,0.05,4.65,0.086,4,11,0.9962,3.41,0.39,10.9,0.5 67 | 7.5,0.52,0.11,1.5,0.079,11,39,0.9968,3.42,0.58,9.6,0.5 68 | 6.6,0.705,0.07,1.6,0.076,6,15,0.9962,3.44,0.58,10.7,0.5 69 | 9.3,0.32,0.57,2,0.074,27,65,0.9969,3.28,0.79,10.7,0.5 70 | 8,0.705,0.05,1.9,0.074,8,19,0.9962,3.34,0.95,10.5,0.6 71 | 7.7,0.63,0.08,1.9,0.076,15,27,0.9967,3.32,0.54,9.5,0.6 72 | 7.7,0.67,0.23,2.1,0.088,17,96,0.9962,3.32,0.48,9.5,0.5 73 | 7.7,0.69,0.22,1.9,0.084,18,94,0.9961,3.31,0.48,9.5,0.5 74 | 8.3,0.675,0.26,2.1,0.084,11,43,0.9976,3.31,0.53,9.2,0.4 75 | 9.7,0.32,0.54,2.5,0.094,28,83,0.9984,3.28,0.82,9.6,0.5 76 | 8.8,0.41,0.64,2.2,0.093,9,42,0.9986,3.54,0.66,10.5,0.5 77 | 8.8,0.41,0.64,2.2,0.093,9,42,0.9986,3.54,0.66,10.5,0.5 78 | 6.8,0.785,0,2.4,0.104,14,30,0.9966,3.52,0.55,10.7,0.6 79 | 6.7,0.75,0.12,2,0.086,12,80,0.9958,3.38,0.52,10.1,0.5 80 | 8.3,0.625,0.2,1.5,0.08,27,119,0.9972,3.16,1.12,9.1,0.4 81 | 6.2,0.45,0.2,1.6,0.069,3,15,0.9958,3.41,0.56,9.2,0.5 82 | 7.8,0.43,0.7,1.9,0.464,22,67,0.9974,3.13,1.28,9.4,0.5 83 | 7.4,0.5,0.47,2,0.086,21,73,0.997,3.36,0.57,9.1,0.5 84 | 7.3,0.67,0.26,1.8,0.401,16,51,0.9969,3.16,1.14,9.4,0.5 85 | 6.3,0.3,0.48,1.8,0.069,18,61,0.9959,3.44,0.78,10.3,0.6 86 | 6.9,0.55,0.15,2.2,0.076,19,40,0.9961,3.41,0.59,10.1,0.5 87 | 8.6,0.49,0.28,1.9,0.11,20,136,0.9972,2.93,1.95,9.9,0.6 88 | 7.7,0.49,0.26,1.9,0.062,9,31,0.9966,3.39,0.64,9.6,0.5 89 | 9.3,0.39,0.44,2.1,0.107,34,125,0.9978,3.14,1.22,9.5,0.5 90 | 7,0.62,0.08,1.8,0.076,8,24,0.9978,3.48,0.53,9,0.5 91 | 7.9,0.52,0.26,1.9,0.079,42,140,0.9964,3.23,0.54,9.5,0.5 92 | 8.6,0.49,0.28,1.9,0.11,20,136,0.9972,2.93,1.95,9.9,0.6 93 | 8.6,0.49,0.29,2,0.11,19,133,0.9972,2.93,1.98,9.8,0.5 94 | 7.7,0.49,0.26,1.9,0.062,9,31,0.9966,3.39,0.64,9.6,0.5 95 | 5,1.02,0.04,1.4,0.045,41,85,0.9938,3.75,0.48,10.5,0.4 96 | 4.7,0.6,0.17,2.3,0.058,17,106,0.9932,3.85,0.6,12.9,0.6 97 | 6.8,0.775,0,3,0.102,8,23,0.9965,3.45,0.56,10.7,0.5 98 | 7,0.5,0.25,2,0.07,3,22,0.9963,3.25,0.63,9.2,0.5 99 | 7.6,0.9,0.06,2.5,0.079,5,10,0.9967,3.39,0.56,9.8,0.5 100 | 8.1,0.545,0.18,1.9,0.08,13,35,0.9972,3.3,0.59,9,0.6 101 | 8.3,0.61,0.3,2.1,0.084,11,50,0.9972,3.4,0.61,10.2,0.6 102 | 7.8,0.5,0.3,1.9,0.075,8,22,0.9959,3.31,0.56,10.4,0.6 103 | 8.1,0.545,0.18,1.9,0.08,13,35,0.9972,3.3,0.59,9,0.6 104 | 8.1,0.575,0.22,2.1,0.077,12,65,0.9967,3.29,0.51,9.2,0.5 105 | 7.2,0.49,0.24,2.2,0.07,5,36,0.996,3.33,0.48,9.4,0.5 106 | 8.1,0.575,0.22,2.1,0.077,12,65,0.9967,3.29,0.51,9.2,0.5 107 | 7.8,0.41,0.68,1.7,0.467,18,69,0.9973,3.08,1.31,9.3,0.5 108 | 6.2,0.63,0.31,1.7,0.088,15,64,0.9969,3.46,0.79,9.3,0.5 109 | 8,0.33,0.53,2.5,0.091,18,80,0.9976,3.37,0.8,9.6,0.6 110 | 8.1,0.785,0.52,2,0.122,37,153,0.9969,3.21,0.69,9.3,0.5 111 | 7.8,0.56,0.19,1.8,0.104,12,47,0.9964,3.19,0.93,9.5,0.5 112 | 8.4,0.62,0.09,2.2,0.084,11,108,0.9964,3.15,0.66,9.8,0.5 113 | 8.4,0.6,0.1,2.2,0.085,14,111,0.9964,3.15,0.66,9.8,0.5 114 | 10.1,0.31,0.44,2.3,0.08,22,46,0.9988,3.32,0.67,9.7,0.6 115 | 7.8,0.56,0.19,1.8,0.104,12,47,0.9964,3.19,0.93,9.5,0.5 116 | 9.4,0.4,0.31,2.2,0.09,13,62,0.9966,3.07,0.63,10.5,0.6 117 | 8.3,0.54,0.28,1.9,0.077,11,40,0.9978,3.39,0.61,10,0.6 118 | 7.8,0.56,0.12,2,0.082,7,28,0.997,3.37,0.5,9.4,0.6 119 | 8.8,0.55,0.04,2.2,0.119,14,56,0.9962,3.21,0.6,10.9,0.6 120 | 7,0.69,0.08,1.8,0.097,22,89,0.9959,3.34,0.54,9.2,0.6 121 | 7.3,1.07,0.09,1.7,0.178,10,89,0.9962,3.3,0.57,9,0.5 122 | 8.8,0.55,0.04,2.2,0.119,14,56,0.9962,3.21,0.6,10.9,0.6 123 | 7.3,0.695,0,2.5,0.075,3,13,0.998,3.49,0.52,9.2,0.5 124 | 8,0.71,0,2.6,0.08,11,34,0.9976,3.44,0.53,9.5,0.5 125 | 7.8,0.5,0.17,1.6,0.082,21,102,0.996,3.39,0.48,9.5,0.5 126 | 9,0.62,0.04,1.9,0.146,27,90,0.9984,3.16,0.7,9.4,0.5 127 | 8.2,1.33,0,1.7,0.081,3,12,0.9964,3.53,0.49,10.9,0.5 128 | 8.1,1.33,0,1.8,0.082,3,12,0.9964,3.54,0.48,10.9,0.5 129 | 8,0.59,0.16,1.8,0.065,3,16,0.9962,3.42,0.92,10.5,0.7 130 | 6.1,0.38,0.15,1.8,0.072,6,19,0.9955,3.42,0.57,9.4,0.5 131 | 8,0.745,0.56,2,0.118,30,134,0.9968,3.24,0.66,9.4,0.5 132 | 5.6,0.5,0.09,2.3,0.049,17,99,0.9937,3.63,0.63,13,0.5 133 | 5.6,0.5,0.09,2.3,0.049,17,99,0.9937,3.63,0.63,13,0.5 134 | 6.6,0.5,0.01,1.5,0.06,17,26,0.9952,3.4,0.58,9.8,0.6 135 | 7.9,1.04,0.05,2.2,0.084,13,29,0.9959,3.22,0.55,9.9,0.6 136 | 8.4,0.745,0.11,1.9,0.09,16,63,0.9965,3.19,0.82,9.6,0.5 137 | 8.3,0.715,0.15,1.8,0.089,10,52,0.9968,3.23,0.77,9.5,0.5 138 | 7.2,0.415,0.36,2,0.081,13,45,0.9972,3.48,0.64,9.2,0.5 139 | 7.8,0.56,0.19,2.1,0.081,15,105,0.9962,3.33,0.54,9.5,0.5 140 | 7.8,0.56,0.19,2,0.081,17,108,0.9962,3.32,0.54,9.5,0.5 141 | 8.4,0.745,0.11,1.9,0.09,16,63,0.9965,3.19,0.82,9.6,0.5 142 | 8.3,0.715,0.15,1.8,0.089,10,52,0.9968,3.23,0.77,9.5,0.5 143 | 5.2,0.34,0,1.8,0.05,27,63,0.9916,3.68,0.79,14,0.6 144 | 6.3,0.39,0.08,1.7,0.066,3,20,0.9954,3.34,0.58,9.4,0.5 145 | 5.2,0.34,0,1.8,0.05,27,63,0.9916,3.68,0.79,14,0.6 146 | 8.1,0.67,0.55,1.8,0.117,32,141,0.9968,3.17,0.62,9.4,0.5 147 | 5.8,0.68,0.02,1.8,0.087,21,94,0.9944,3.54,0.52,10,0.5 148 | 7.6,0.49,0.26,1.6,0.236,10,88,0.9968,3.11,0.8,9.3,0.5 149 | 6.9,0.49,0.1,2.3,0.074,12,30,0.9959,3.42,0.58,10.2,0.6 150 | 8.2,0.4,0.44,2.8,0.089,11,43,0.9975,3.53,0.61,10.5,0.6 151 | 7.3,0.33,0.47,2.1,0.077,5,11,0.9958,3.33,0.53,10.3,0.6 152 | 9.2,0.52,1,3.4,0.61,32,69,0.9996,2.74,2.0,9.4,0.4 153 | 7.5,0.6,0.03,1.8,0.095,25,99,0.995,3.35,0.54,10.1,0.5 154 | 7.5,0.6,0.03,1.8,0.095,25,99,0.995,3.35,0.54,10.1,0.5 155 | 7.1,0.43,0.42,5.5,0.07,29,129,0.9973,3.42,0.72,10.5,0.5 156 | 7.1,0.43,0.42,5.5,0.071,28,128,0.9973,3.42,0.71,10.5,0.5 157 | 7.1,0.43,0.42,5.5,0.07,29,129,0.9973,3.42,0.72,10.5,0.5 158 | 7.1,0.43,0.42,5.5,0.071,28,128,0.9973,3.42,0.71,10.5,0.5 159 | 7.1,0.68,0,2.2,0.073,12,22,0.9969,3.48,0.5,9.3,0.5 160 | 6.8,0.6,0.18,1.9,0.079,18,86,0.9968,3.59,0.57,9.3,0.6 161 | 7.6,0.95,0.03,2,0.09,7,20,0.9959,3.2,0.56,9.6,0.5 162 | 7.6,0.68,0.02,1.3,0.072,9,20,0.9965,3.17,1.08,9.2,0.4 163 | 7.8,0.53,0.04,1.7,0.076,17,31,0.9964,3.33,0.56,10,0.6 164 | 7.4,0.6,0.26,7.3,0.07,36,121,0.9982,3.37,0.49,9.4,0.5 165 | 7.3,0.59,0.26,7.2,0.07,35,121,0.9981,3.37,0.49,9.4,0.5 166 | 7.8,0.63,0.48,1.7,0.1,14,96,0.9961,3.19,0.62,9.5,0.5 167 | 6.8,0.64,0.1,2.1,0.085,18,101,0.9956,3.34,0.52,10.2,0.5 168 | 7.3,0.55,0.03,1.6,0.072,17,42,0.9956,3.37,0.48,9,0.4 169 | 6.8,0.63,0.07,2.1,0.089,11,44,0.9953,3.47,0.55,10.4,0.6 170 | 7.5,0.705,0.24,1.8,0.36,15,63,0.9964,3,1.59,9.5,0.5 171 | 7.9,0.885,0.03,1.8,0.058,4,8,0.9972,3.36,0.33,9.1,0.4 172 | 8,0.42,0.17,2,0.073,6,18,0.9972,3.29,0.61,9.2,0.6 173 | 8,0.42,0.17,2,0.073,6,18,0.9972,3.29,0.61,9.2,0.6 174 | 7.4,0.62,0.05,1.9,0.068,24,42,0.9961,3.42,0.57,11.5,0.6 175 | 7.3,0.38,0.21,2,0.08,7,35,0.9961,3.33,0.47,9.5,0.5 176 | 6.9,0.5,0.04,1.5,0.085,19,49,0.9958,3.35,0.78,9.5,0.5 177 | 7.3,0.38,0.21,2,0.08,7,35,0.9961,3.33,0.47,9.5,0.5 178 | 7.5,0.52,0.42,2.3,0.087,8,38,0.9972,3.58,0.61,10.5,0.6 179 | 7,0.805,0,2.5,0.068,7,20,0.9969,3.48,0.56,9.6,0.5 180 | 8.8,0.61,0.14,2.4,0.067,10,42,0.9969,3.19,0.59,9.5,0.5 181 | 8.8,0.61,0.14,2.4,0.067,10,42,0.9969,3.19,0.59,9.5,0.5 182 | 8.9,0.61,0.49,2,0.27,23,110,0.9972,3.12,1.02,9.3,0.5 183 | 7.2,0.73,0.02,2.5,0.076,16,42,0.9972,3.44,0.52,9.3,0.5 184 | 6.8,0.61,0.2,1.8,0.077,11,65,0.9971,3.54,0.58,9.3,0.5 185 | 6.7,0.62,0.21,1.9,0.079,8,62,0.997,3.52,0.58,9.3,0.6 186 | 8.9,0.31,0.57,2,0.111,26,85,0.9971,3.26,0.53,9.7,0.5 187 | 7.4,0.39,0.48,2,0.082,14,67,0.9972,3.34,0.55,9.2,0.5 188 | 7.7,0.705,0.1,2.6,0.084,9,26,0.9976,3.39,0.49,9.7,0.5 189 | 7.9,0.5,0.33,2,0.084,15,143,0.9968,3.2,0.55,9.5,0.5 190 | 7.9,0.49,0.32,1.9,0.082,17,144,0.9968,3.2,0.55,9.5,0.5 191 | 8.2,0.5,0.35,2.9,0.077,21,127,0.9976,3.23,0.62,9.4,0.5 192 | 6.4,0.37,0.25,1.9,0.074,21,49,0.9974,3.57,0.62,9.8,0.6 193 | 6.8,0.63,0.12,3.8,0.099,16,126,0.9969,3.28,0.61,9.5,0.5 194 | 7.6,0.55,0.21,2.2,0.071,7,28,0.9964,3.28,0.55,9.7,0.5 195 | 7.6,0.55,0.21,2.2,0.071,7,28,0.9964,3.28,0.55,9.7,0.5 196 | 7.8,0.59,0.33,2,0.074,24,120,0.9968,3.25,0.54,9.4,0.5 197 | 7.3,0.58,0.3,2.4,0.074,15,55,0.9968,3.46,0.59,10.2,0.5 198 | 11.5,0.3,0.6,2,0.067,12,27,0.9981,3.11,0.97,10.1,0.6 199 | 5.4,0.835,0.08,1.2,0.046,13,93,0.9924,3.57,0.85,13,0.7 200 | 6.9,1.09,0.06,2.1,0.061,12,31,0.9948,3.51,0.43,11.4,0.4 201 | 9.6,0.32,0.47,1.4,0.056,9,24,0.99695,3.22,0.82,10.3,0.7 202 | 8.8,0.37,0.48,2.1,0.097,39,145,0.9975,3.04,1.03,9.3,0.5 203 | 6.8,0.5,0.11,1.5,0.075,16,49,0.99545,3.36,0.79,9.5,0.5 204 | 7,0.42,0.35,1.6,0.088,16,39,0.9961,3.34,0.55,9.2,0.5 205 | 7,0.43,0.36,1.6,0.089,14,37,0.99615,3.34,0.56,9.2,0.6 206 | 12.8,0.3,0.74,2.6,0.095,9,28,0.9994,3.2,0.77,10.8,0.7 207 | 12.8,0.3,0.74,2.6,0.095,9,28,0.9994,3.2,0.77,10.8,0.7 208 | 7.8,0.57,0.31,1.8,0.069,26,120,0.99625,3.29,0.53,9.3,0.5 209 | 7.8,0.44,0.28,2.7,0.1,18,95,0.9966,3.22,0.67,9.4,0.5 210 | 11,0.3,0.58,2.1,0.054,7,19,0.998,3.31,0.88,10.5,0.7 211 | 9.7,0.53,0.6,2,0.039,5,19,0.99585,3.3,0.86,12.4,0.6 212 | 8,0.725,0.24,2.8,0.083,10,62,0.99685,3.35,0.56,10,0.6 213 | 11.6,0.44,0.64,2.1,0.059,5,15,0.998,3.21,0.67,10.2,0.6 214 | 8.2,0.57,0.26,2.2,0.06,28,65,0.9959,3.3,0.43,10.1,0.5 215 | 7.8,0.735,0.08,2.4,0.092,10,41,0.9974,3.24,0.71,9.8,0.6 216 | 7,0.49,0.49,5.6,0.06,26,121,0.9974,3.34,0.76,10.5,0.5 217 | 8.7,0.625,0.16,2,0.101,13,49,0.9962,3.14,0.57,11,0.5 218 | 8.1,0.725,0.22,2.2,0.072,11,41,0.9967,3.36,0.55,9.1,0.5 219 | 7.5,0.49,0.19,1.9,0.076,10,44,0.9957,3.39,0.54,9.7,0.5 220 | 7.8,0.53,0.33,2.4,0.08,24,144,0.99655,3.3,0.6,9.5,0.5 221 | 7.8,0.34,0.37,2,0.082,24,58,0.9964,3.34,0.59,9.4,0.6 222 | 7.4,0.53,0.26,2,0.101,16,72,0.9957,3.15,0.57,9.4,0.5 223 | 6.8,0.61,0.04,1.5,0.057,5,10,0.99525,3.42,0.6,9.5,0.5 224 | 8.6,0.645,0.25,2,0.083,8,28,0.99815,3.28,0.6,10,0.6 225 | 8.4,0.635,0.36,2,0.089,15,55,0.99745,3.31,0.57,10.4,0.4 226 | 7.7,0.43,0.25,2.6,0.073,29,63,0.99615,3.37,0.58,10.5,0.6 227 | 8.9,0.59,0.5,2,0.337,27,81,0.9964,3.04,1.61,9.5,0.6 228 | 9,0.82,0.14,2.6,0.089,9,23,0.9984,3.39,0.63,9.8,0.5 229 | 7.7,0.43,0.25,2.6,0.073,29,63,0.99615,3.37,0.58,10.5,0.6 230 | 6.9,0.52,0.25,2.6,0.081,10,37,0.99685,3.46,0.5,11,0.5 231 | 5.2,0.48,0.04,1.6,0.054,19,106,0.9927,3.54,0.62,12.2,0.7 232 | 8,0.38,0.06,1.8,0.078,12,49,0.99625,3.37,0.52,9.9,0.6 233 | 8.5,0.37,0.2,2.8,0.09,18,58,0.998,3.34,0.7,9.6,0.6 234 | 6.9,0.52,0.25,2.6,0.081,10,37,0.99685,3.46,0.5,11,0.5 235 | 8.2,1,0.09,2.3,0.065,7,37,0.99685,3.32,0.55,9,0.6 236 | 7.2,0.63,0,1.9,0.097,14,38,0.99675,3.37,0.58,9,0.6 237 | 7.2,0.63,0,1.9,0.097,14,38,0.99675,3.37,0.58,9,0.6 238 | 7.2,0.645,0,1.9,0.097,15,39,0.99675,3.37,0.58,9.2,0.6 239 | 7.2,0.63,0,1.9,0.097,14,38,0.99675,3.37,0.58,9,0.6 240 | 8.2,1,0.09,2.3,0.065,7,37,0.99685,3.32,0.55,9,0.6 241 | 8.9,0.635,0.37,1.7,0.263,5,62,0.9971,3,1.09,9.3,0.5 242 | 12,0.38,0.56,2.1,0.093,6,24,0.99925,3.14,0.71,10.9,0.6 243 | 7.7,0.58,0.1,1.8,0.102,28,109,0.99565,3.08,0.49,9.8,0.6 244 | 15,0.21,0.44,2.2,0.075,10,24,1.00005,3.07,0.84,9.2,0.7 245 | 15,0.21,0.44,2.2,0.075,10,24,1.00005,3.07,0.84,9.2,0.7 246 | 7.3,0.66,0,2,0.084,6,23,0.9983,3.61,0.96,9.9,0.6 247 | 7.1,0.68,0.07,1.9,0.075,16,51,0.99685,3.38,0.52,9.5,0.5 248 | 8.2,0.6,0.17,2.3,0.072,11,73,0.9963,3.2,0.45,9.3,0.5 249 | 7.7,0.53,0.06,1.7,0.074,9,39,0.99615,3.35,0.48,9.8,0.6 250 | 7.3,0.66,0,2,0.084,6,23,0.9983,3.61,0.96,9.9,0.6 251 | 10.8,0.32,0.44,1.6,0.063,16,37,0.9985,3.22,0.78,10,0.6 252 | 7.1,0.6,0,1.8,0.074,16,34,0.9972,3.47,0.7,9.9,0.6 253 | 11.1,0.35,0.48,3.1,0.09,5,21,0.9986,3.17,0.53,10.5,0.5 254 | 7.7,0.775,0.42,1.9,0.092,8,86,0.9959,3.23,0.59,9.5,0.5 255 | 7.1,0.6,0,1.8,0.074,16,34,0.9972,3.47,0.7,9.9,0.6 256 | 8,0.57,0.23,3.2,0.073,17,119,0.99675,3.26,0.57,9.3,0.5 257 | 9.4,0.34,0.37,2.2,0.075,5,13,0.998,3.22,0.62,9.2,0.5 258 | 6.6,0.695,0,2.1,0.075,12,56,0.9968,3.49,0.67,9.2,0.5 259 | 7.7,0.41,0.76,1.8,0.611,8,45,0.9968,3.06,1.26,9.4,0.5 260 | 10,0.31,0.47,2.6,0.085,14,33,0.99965,3.36,0.8,10.5,0.7 261 | 7.9,0.33,0.23,1.7,0.077,18,45,0.99625,3.29,0.65,9.3,0.5 262 | 7,0.975,0.04,2,0.087,12,67,0.99565,3.35,0.6,9.4,0.4 263 | 8,0.52,0.03,1.7,0.07,10,35,0.99575,3.34,0.57,10,0.5 264 | 7.9,0.37,0.23,1.8,0.077,23,49,0.9963,3.28,0.67,9.3,0.5 265 | 12.5,0.56,0.49,2.4,0.064,5,27,0.9999,3.08,0.87,10.9,0.5 266 | 11.8,0.26,0.52,1.8,0.071,6,10,0.9968,3.2,0.72,10.2,0.7 267 | 8.1,0.87,0,3.3,0.096,26,61,1.00025,3.6,0.72,9.8,0.4 268 | 7.9,0.35,0.46,3.6,0.078,15,37,0.9973,3.35,0.86,12.8,0.8 269 | 6.9,0.54,0.04,3,0.077,7,27,0.9987,3.69,0.91,9.4,0.6 270 | 11.5,0.18,0.51,4,0.104,4,23,0.9996,3.28,0.97,10.1,0.6 271 | 7.9,0.545,0.06,4,0.087,27,61,0.9965,3.36,0.67,10.7,0.6 272 | 11.5,0.18,0.51,4,0.104,4,23,0.9996,3.28,0.97,10.1,0.6 273 | 10.9,0.37,0.58,4,0.071,17,65,0.99935,3.22,0.78,10.1,0.5 274 | 8.4,0.715,0.2,2.4,0.076,10,38,0.99735,3.31,0.64,9.4,0.5 275 | 7.5,0.65,0.18,7,0.088,27,94,0.99915,3.38,0.77,9.4,0.5 276 | 7.9,0.545,0.06,4,0.087,27,61,0.9965,3.36,0.67,10.7,0.6 277 | 6.9,0.54,0.04,3,0.077,7,27,0.9987,3.69,0.91,9.4,0.6 278 | 11.5,0.18,0.51,4,0.104,4,23,0.9996,3.28,0.97,10.1,0.6 279 | 10.3,0.32,0.45,6.4,0.073,5,13,0.9976,3.23,0.82,12.6,0.8 280 | 8.9,0.4,0.32,5.6,0.087,10,47,0.9991,3.38,0.77,10.5,0.7 281 | 11.4,0.26,0.44,3.6,0.071,6,19,0.9986,3.12,0.82,9.3,0.6 282 | 7.7,0.27,0.68,3.5,0.358,5,10,0.9972,3.25,1.08,9.9,0.7 283 | 7.6,0.52,0.12,3,0.067,12,53,0.9971,3.36,0.57,9.1,0.5 284 | 8.9,0.4,0.32,5.6,0.087,10,47,0.9991,3.38,0.77,10.5,0.7 285 | 9.9,0.59,0.07,3.4,0.102,32,71,1.00015,3.31,0.71,9.8,0.5 286 | 9.9,0.59,0.07,3.4,0.102,32,71,1.00015,3.31,0.71,9.8,0.5 287 | 12,0.45,0.55,2,0.073,25,49,0.9997,3.1,0.76,10.3,0.6 288 | 7.5,0.4,0.12,3,0.092,29,53,0.9967,3.37,0.7,10.3,0.6 289 | 8.7,0.52,0.09,2.5,0.091,20,49,0.9976,3.34,0.86,10.6,0.7 290 | 11.6,0.42,0.53,3.3,0.105,33,98,1.001,3.2,0.95,9.2,0.5 291 | 8.7,0.52,0.09,2.5,0.091,20,49,0.9976,3.34,0.86,10.6,0.7 292 | 11,0.2,0.48,2,0.343,6,18,0.9979,3.3,0.71,10.5,0.5 293 | 10.4,0.55,0.23,2.7,0.091,18,48,0.9994,3.22,0.64,10.3,0.6 294 | 6.9,0.36,0.25,2.4,0.098,5,16,0.9964,3.41,0.6,10.1,0.6 295 | 13.3,0.34,0.52,3.2,0.094,17,53,1.0014,3.05,0.81,9.5,0.6 296 | 10.8,0.5,0.46,2.5,0.073,5,27,1.0001,3.05,0.64,9.5,0.5 297 | 10.6,0.83,0.37,2.6,0.086,26,70,0.9981,3.16,0.52,9.9,0.5 298 | 7.1,0.63,0.06,2,0.083,8,29,0.99855,3.67,0.73,9.6,0.5 299 | 7.2,0.65,0.02,2.3,0.094,5,31,0.9993,3.67,0.8,9.7,0.5 300 | 6.9,0.67,0.06,2.1,0.08,8,33,0.99845,3.68,0.71,9.6,0.5 301 | 7.5,0.53,0.06,2.6,0.086,20,44,0.9965,3.38,0.59,10.7,0.6 302 | 11.1,0.18,0.48,1.5,0.068,7,15,0.9973,3.22,0.64,10.1,0.6 303 | 8.3,0.705,0.12,2.6,0.092,12,28,0.9994,3.51,0.72,10,0.5 304 | 7.4,0.67,0.12,1.6,0.186,5,21,0.996,3.39,0.54,9.5,0.5 305 | 8.4,0.65,0.6,2.1,0.112,12,90,0.9973,3.2,0.52,9.2,0.5 306 | 10.3,0.53,0.48,2.5,0.063,6,25,0.9998,3.12,0.59,9.3,0.6 307 | 7.6,0.62,0.32,2.2,0.082,7,54,0.9966,3.36,0.52,9.4,0.5 308 | 10.3,0.41,0.42,2.4,0.213,6,14,0.9994,3.19,0.62,9.5,0.6 309 | 10.3,0.43,0.44,2.4,0.214,5,12,0.9994,3.19,0.63,9.5,0.6 310 | 7.4,0.29,0.38,1.7,0.062,9,30,0.9968,3.41,0.53,9.5,0.6 311 | 10.3,0.53,0.48,2.5,0.063,6,25,0.9998,3.12,0.59,9.3,0.6 312 | 7.9,0.53,0.24,2,0.072,15,105,0.996,3.27,0.54,9.4,0.6 313 | 9,0.46,0.31,2.8,0.093,19,98,0.99815,3.32,0.63,9.5,0.6 314 | 8.6,0.47,0.3,3,0.076,30,135,0.9976,3.3,0.53,9.4,0.5 315 | 7.4,0.36,0.29,2.6,0.087,26,72,0.99645,3.39,0.68,11,0.5 316 | 7.1,0.35,0.29,2.5,0.096,20,53,0.9962,3.42,0.65,11,0.6 317 | 9.6,0.56,0.23,3.4,0.102,37,92,0.9996,3.3,0.65,10.1,0.5 318 | 9.6,0.77,0.12,2.9,0.082,30,74,0.99865,3.3,0.64,10.4,0.6 319 | 9.8,0.66,0.39,3.2,0.083,21,59,0.9989,3.37,0.71,11.5,0.7 320 | 9.6,0.77,0.12,2.9,0.082,30,74,0.99865,3.3,0.64,10.4,0.6 321 | 9.8,0.66,0.39,3.2,0.083,21,59,0.9989,3.37,0.71,11.5,0.7 322 | 9.3,0.61,0.26,3.4,0.09,25,87,0.99975,3.24,0.62,9.7,0.5 323 | 7.8,0.62,0.05,2.3,0.079,6,18,0.99735,3.29,0.63,9.3,0.5 324 | 10.3,0.59,0.42,2.8,0.09,35,73,0.999,3.28,0.7,9.5,0.6 325 | 10,0.49,0.2,11,0.071,13,50,1.0015,3.16,0.69,9.2,0.6 326 | 10,0.49,0.2,11,0.071,13,50,1.0015,3.16,0.69,9.2,0.6 327 | 11.6,0.53,0.66,3.65,0.121,6,14,0.9978,3.05,0.74,11.5,0.7 328 | 10.3,0.44,0.5,4.5,0.107,5,13,0.998,3.28,0.83,11.5,0.5 329 | 13.4,0.27,0.62,2.6,0.082,6,21,1.0002,3.16,0.67,9.7,0.6 330 | 10.7,0.46,0.39,2,0.061,7,15,0.9981,3.18,0.62,9.5,0.5 331 | 10.2,0.36,0.64,2.9,0.122,10,41,0.998,3.23,0.66,12.5,0.6 332 | 10.2,0.36,0.64,2.9,0.122,10,41,0.998,3.23,0.66,12.5,0.6 333 | 8,0.58,0.28,3.2,0.066,21,114,0.9973,3.22,0.54,9.4,0.6 334 | 8.4,0.56,0.08,2.1,0.105,16,44,0.9958,3.13,0.52,11,0.5 335 | 7.9,0.65,0.01,2.5,0.078,17,38,0.9963,3.34,0.74,11.7,0.7 336 | 11.9,0.695,0.53,3.4,0.128,7,21,0.9992,3.17,0.84,12.2,0.7 337 | 8.9,0.43,0.45,1.9,0.052,6,16,0.9948,3.35,0.7,12.5,0.6 338 | 7.8,0.43,0.32,2.8,0.08,29,58,0.9974,3.31,0.64,10.3,0.5 339 | 12.4,0.49,0.58,3,0.103,28,99,1.0008,3.16,1,11.5,0.6 340 | 12.5,0.28,0.54,2.3,0.082,12,29,0.9997,3.11,1.36,9.8,0.7 341 | 12.2,0.34,0.5,2.4,0.066,10,21,1,3.12,1.18,9.2,0.6 342 | 10.6,0.42,0.48,2.7,0.065,5,18,0.9972,3.21,0.87,11.3,0.6 343 | 10.9,0.39,0.47,1.8,0.118,6,14,0.9982,3.3,0.75,9.8,0.6 344 | 10.9,0.39,0.47,1.8,0.118,6,14,0.9982,3.3,0.75,9.8,0.6 345 | 11.9,0.57,0.5,2.6,0.082,6,32,1.0006,3.12,0.78,10.7,0.6 346 | 7,0.685,0,1.9,0.067,40,63,0.9979,3.6,0.81,9.9,0.5 347 | 6.6,0.815,0.02,2.7,0.072,17,34,0.9955,3.58,0.89,12.3,0.7 348 | 13.8,0.49,0.67,3,0.093,6,15,0.9986,3.02,0.93,12,0.6 349 | 9.6,0.56,0.31,2.8,0.089,15,46,0.9979,3.11,0.92,10,0.6 350 | 9.1,0.785,0,2.6,0.093,11,28,0.9994,3.36,0.86,9.4,0.6 351 | 10.7,0.67,0.22,2.7,0.107,17,34,1.0004,3.28,0.98,9.9,0.6 352 | 9.1,0.795,0,2.6,0.096,11,26,0.9994,3.35,0.83,9.4,0.6 353 | 7.7,0.665,0,2.4,0.09,8,19,0.9974,3.27,0.73,9.3,0.5 354 | 13.5,0.53,0.79,4.8,0.12,23,77,1.0018,3.18,0.77,13,0.5 355 | 6.1,0.21,0.4,1.4,0.066,40.5,165,0.9912,3.25,0.59,11.9,0.6 356 | 6.7,0.75,0.01,2.4,0.078,17,32,0.9955,3.55,0.61,12.8,0.6 357 | 11.5,0.41,0.52,3,0.08,29,55,1.0001,3.26,0.88,11,0.5 358 | 10.5,0.42,0.66,2.95,0.116,12,29,0.997,3.24,0.75,11.7,0.7 359 | 11.9,0.43,0.66,3.1,0.109,10,23,1,3.15,0.85,10.4,0.7 360 | 12.6,0.38,0.66,2.6,0.088,10,41,1.001,3.17,0.68,9.8,0.6 361 | 8.2,0.7,0.23,2,0.099,14,81,0.9973,3.19,0.7,9.4,0.5 362 | 8.6,0.45,0.31,2.6,0.086,21,50,0.9982,3.37,0.91,9.9,0.6 363 | 11.9,0.58,0.66,2.5,0.072,6,37,0.9992,3.05,0.56,10,0.5 364 | 12.5,0.46,0.63,2,0.071,6,15,0.9988,2.99,0.87,10.2,0.5 365 | 12.8,0.615,0.66,5.8,0.083,7,42,1.0022,3.07,0.73,10,0.7 366 | 10,0.42,0.5,3.4,0.107,7,21,0.9979,3.26,0.93,11.8,0.6 367 | 12.8,0.615,0.66,5.8,0.083,7,42,1.0022,3.07,0.73,10,0.7 368 | 10.4,0.575,0.61,2.6,0.076,11,24,1,3.16,0.69,9,0.5 369 | 10.3,0.34,0.52,2.8,0.159,15,75,0.9998,3.18,0.64,9.4,0.5 370 | 9.4,0.27,0.53,2.4,0.074,6,18,0.9962,3.2,1.13,12,0.7 371 | 6.9,0.765,0.02,2.3,0.063,35,63,0.9975,3.57,0.78,9.9,0.5 372 | 7.9,0.24,0.4,1.6,0.056,11,25,0.9967,3.32,0.87,8.7,0.6 373 | 9.1,0.28,0.48,1.8,0.067,26,46,0.9967,3.32,1.04,10.6,0.6 374 | 7.4,0.55,0.22,2.2,0.106,12,72,0.9959,3.05,0.63,9.2,0.5 375 | 14,0.41,0.63,3.8,0.089,6,47,1.0014,3.01,0.81,10.8,0.6 376 | 11.5,0.54,0.71,4.4,0.124,6,15,0.9984,3.01,0.83,11.8,0.7 377 | 11.5,0.45,0.5,3,0.078,19,47,1.0003,3.26,1.11,11,0.6 378 | 9.4,0.27,0.53,2.4,0.074,6,18,0.9962,3.2,1.13,12,0.7 379 | 11.4,0.625,0.66,6.2,0.088,6,24,0.9988,3.11,0.99,13.3,0.6 380 | 8.3,0.42,0.38,2.5,0.094,24,60,0.9979,3.31,0.7,10.8,0.6 381 | 8.3,0.26,0.42,2,0.08,11,27,0.9974,3.21,0.8,9.4,0.6 382 | 13.7,0.415,0.68,2.9,0.085,17,43,1.0014,3.06,0.8,10,0.6 383 | 8.3,0.26,0.42,2,0.08,11,27,0.9974,3.21,0.8,9.4,0.6 384 | 8.3,0.26,0.42,2,0.08,11,27,0.9974,3.21,0.8,9.4,0.6 385 | 7.7,0.51,0.28,2.1,0.087,23,54,0.998,3.42,0.74,9.2,0.5 386 | 7.4,0.63,0.07,2.4,0.09,11,37,0.9979,3.43,0.76,9.7,0.6 387 | 7.8,0.54,0.26,2,0.088,23,48,0.9981,3.41,0.74,9.2,0.6 388 | 8.3,0.66,0.15,1.9,0.079,17,42,0.9972,3.31,0.54,9.6,0.6 389 | 7.8,0.46,0.26,1.9,0.088,23,53,0.9981,3.43,0.74,9.2,0.6 390 | 9.6,0.38,0.31,2.5,0.096,16,49,0.9982,3.19,0.7,10,0.7 391 | 5.6,0.85,0.05,1.4,0.045,12,88,0.9924,3.56,0.82,12.9,0.8 392 | 13.7,0.415,0.68,2.9,0.085,17,43,1.0014,3.06,0.8,10,0.6 393 | 9.5,0.37,0.52,2,0.082,6,26,0.998,3.18,0.51,9.5,0.5 394 | 8.4,0.665,0.61,2,0.112,13,95,0.997,3.16,0.54,9.1,0.5 395 | 12.7,0.6,0.65,2.3,0.063,6,25,0.9997,3.03,0.57,9.9,0.5 396 | 12,0.37,0.76,4.2,0.066,7,38,1.0004,3.22,0.6,13,0.7 397 | 6.6,0.735,0.02,7.9,0.122,68,124,0.9994,3.47,0.53,9.9,0.5 398 | 11.5,0.59,0.59,2.6,0.087,13,49,0.9988,3.18,0.65,11,0.6 399 | 11.5,0.59,0.59,2.6,0.087,13,49,0.9988,3.18,0.65,11,0.6 400 | 8.7,0.765,0.22,2.3,0.064,9,42,0.9963,3.1,0.55,9.4,0.5 401 | 6.6,0.735,0.02,7.9,0.122,68,124,0.9994,3.47,0.53,9.9,0.5 402 | 7.7,0.26,0.3,1.7,0.059,20,38,0.9949,3.29,0.47,10.8,0.6 403 | 12.2,0.48,0.54,2.6,0.085,19,64,1,3.1,0.61,10.5,0.6 404 | 11.4,0.6,0.49,2.7,0.085,10,41,0.9994,3.15,0.63,10.5,0.6 405 | 7.7,0.69,0.05,2.7,0.075,15,27,0.9974,3.26,0.61,9.1,0.5 406 | 8.7,0.31,0.46,1.4,0.059,11,25,0.9966,3.36,0.76,10.1,0.6 407 | 9.8,0.44,0.47,2.5,0.063,9,28,0.9981,3.24,0.65,10.8,0.6 408 | 12,0.39,0.66,3,0.093,12,30,0.9996,3.18,0.63,10.8,0.7 409 | 10.4,0.34,0.58,3.7,0.174,6,16,0.997,3.19,0.7,11.3,0.6 410 | 12.5,0.46,0.49,4.5,0.07,26,49,0.9981,3.05,0.57,9.6,0.4 411 | 9,0.43,0.34,2.5,0.08,26,86,0.9987,3.38,0.62,9.5,0.6 412 | 9.1,0.45,0.35,2.4,0.08,23,78,0.9987,3.38,0.62,9.5,0.5 413 | 7.1,0.735,0.16,1.9,0.1,15,77,0.9966,3.27,0.64,9.3,0.5 414 | 9.9,0.4,0.53,6.7,0.097,6,19,0.9986,3.27,0.82,11.7,0.7 415 | 8.8,0.52,0.34,2.7,0.087,24,122,0.9982,3.26,0.61,9.5,0.5 416 | 8.6,0.725,0.24,6.6,0.117,31,134,1.0014,3.32,1.07,9.3,0.5 417 | 10.6,0.48,0.64,2.2,0.111,6,20,0.997,3.26,0.66,11.7,0.6 418 | 7,0.58,0.12,1.9,0.091,34,124,0.9956,3.44,0.48,10.5,0.5 419 | 11.9,0.38,0.51,2,0.121,7,20,0.9996,3.24,0.76,10.4,0.6 420 | 6.8,0.77,0,1.8,0.066,34,52,0.9976,3.62,0.68,9.9,0.5 421 | 9.5,0.56,0.33,2.4,0.089,35,67,0.9972,3.28,0.73,11.8,0.7 422 | 6.6,0.84,0.03,2.3,0.059,32,48,0.9952,3.52,0.56,12.3,0.7 423 | 7.7,0.96,0.2,2,0.047,15,60,0.9955,3.36,0.44,10.9,0.5 424 | 10.5,0.24,0.47,2.1,0.066,6,24,0.9978,3.15,0.9,11,0.7 425 | 7.7,0.96,0.2,2,0.047,15,60,0.9955,3.36,0.44,10.9,0.5 426 | 6.6,0.84,0.03,2.3,0.059,32,48,0.9952,3.52,0.56,12.3,0.7 427 | 6.4,0.67,0.08,2.1,0.045,19,48,0.9949,3.49,0.49,11.4,0.6 428 | 9.5,0.78,0.22,1.9,0.077,6,32,0.9988,3.26,0.56,10.6,0.6 429 | 9.1,0.52,0.33,1.3,0.07,9,30,0.9978,3.24,0.6,9.3,0.5 430 | 12.8,0.84,0.63,2.4,0.088,13,35,0.9997,3.1,0.6,10.4,0.6 431 | 10.5,0.24,0.47,2.1,0.066,6,24,0.9978,3.15,0.9,11,0.7 432 | 7.8,0.55,0.35,2.2,0.074,21,66,0.9974,3.25,0.56,9.2,0.5 433 | 11.9,0.37,0.69,2.3,0.078,12,24,0.9958,3,0.65,12.8,0.6 434 | 12.3,0.39,0.63,2.3,0.091,6,18,1.0004,3.16,0.49,9.5,0.5 435 | 10.4,0.41,0.55,3.2,0.076,22,54,0.9996,3.15,0.89,9.9,0.6 436 | 12.3,0.39,0.63,2.3,0.091,6,18,1.0004,3.16,0.49,9.5,0.5 437 | 8,0.67,0.3,2,0.06,38,62,0.9958,3.26,0.56,10.2,0.6 438 | 11.1,0.45,0.73,3.2,0.066,6,22,0.9986,3.17,0.66,11.2,0.6 439 | 10.4,0.41,0.55,3.2,0.076,22,54,0.9996,3.15,0.89,9.9,0.6 440 | 7,0.62,0.18,1.5,0.062,7,50,0.9951,3.08,0.6,9.3,0.5 441 | 12.6,0.31,0.72,2.2,0.072,6,29,0.9987,2.88,0.82,9.8,0.8 442 | 11.9,0.4,0.65,2.15,0.068,7,27,0.9988,3.06,0.68,11.3,0.6 443 | 15.6,0.685,0.76,3.7,0.1,6,43,1.0032,2.95,0.68,11.2,0.7 444 | 10,0.44,0.49,2.7,0.077,11,19,0.9963,3.23,0.63,11.6,0.7 445 | 5.3,0.57,0.01,1.7,0.054,5,27,0.9934,3.57,0.84,12.5,0.7 446 | 9.5,0.735,0.1,2.1,0.079,6,31,0.9986,3.23,0.56,10.1,0.6 447 | 12.5,0.38,0.6,2.6,0.081,31,72,0.9996,3.1,0.73,10.5,0.5 448 | 9.3,0.48,0.29,2.1,0.127,6,16,0.9968,3.22,0.72,11.2,0.5 449 | 8.6,0.53,0.22,2,0.1,7,27,0.9967,3.2,0.56,10.2,0.6 450 | 11.9,0.39,0.69,2.8,0.095,17,35,0.9994,3.1,0.61,10.8,0.6 451 | 11.9,0.39,0.69,2.8,0.095,17,35,0.9994,3.1,0.61,10.8,0.6 452 | 8.4,0.37,0.53,1.8,0.413,9,26,0.9979,3.06,1.06,9.1,0.6 453 | 6.8,0.56,0.03,1.7,0.084,18,35,0.9968,3.44,0.63,10,0.6 454 | 10.4,0.33,0.63,2.8,0.084,5,22,0.9998,3.26,0.74,11.2,0.7 455 | 7,0.23,0.4,1.6,0.063,21,67,0.9952,3.5,0.63,11.1,0.5 456 | 11.3,0.62,0.67,5.2,0.086,6,19,0.9988,3.22,0.69,13.4,0.8 457 | 8.9,0.59,0.39,2.3,0.095,5,22,0.9986,3.37,0.58,10.3,0.5 458 | 9.2,0.63,0.21,2.7,0.097,29,65,0.9988,3.28,0.58,9.6,0.5 459 | 10.4,0.33,0.63,2.8,0.084,5,22,0.9998,3.26,0.74,11.2,0.7 460 | 11.6,0.58,0.66,2.2,0.074,10,47,1.0008,3.25,0.57,9,0.3 461 | 9.2,0.43,0.52,2.3,0.083,14,23,0.9976,3.35,0.61,11.3,0.6 462 | 8.3,0.615,0.22,2.6,0.087,6,19,0.9982,3.26,0.61,9.3,0.5 463 | 11,0.26,0.68,2.55,0.085,10,25,0.997,3.18,0.61,11.8,0.5 464 | 8.1,0.66,0.7,2.2,0.098,25,129,0.9972,3.08,0.53,9,0.5 465 | 11.5,0.315,0.54,2.1,0.084,5,15,0.9987,2.98,0.7,9.2,0.6 466 | 10,0.29,0.4,2.9,0.098,10,26,1.0006,3.48,0.91,9.7,0.5 467 | 10.3,0.5,0.42,2,0.069,21,51,0.9982,3.16,0.72,11.5,0.6 468 | 8.8,0.46,0.45,2.6,0.065,7,18,0.9947,3.32,0.79,14,0.6 469 | 11.4,0.36,0.69,2.1,0.09,6,21,1,3.17,0.62,9.2,0.6 470 | 8.7,0.82,0.02,1.2,0.07,36,48,0.9952,3.2,0.58,9.8,0.5 471 | 13,0.32,0.65,2.6,0.093,15,47,0.9996,3.05,0.61,10.6,0.5 472 | 9.6,0.54,0.42,2.4,0.081,25,52,0.997,3.2,0.71,11.4,0.6 473 | 12.5,0.37,0.55,2.6,0.083,25,68,0.9995,3.15,0.82,10.4,0.6 474 | 9.9,0.35,0.55,2.1,0.062,5,14,0.9971,3.26,0.79,10.6,0.5 475 | 10.5,0.28,0.51,1.7,0.08,10,24,0.9982,3.2,0.89,9.4,0.6 476 | 9.6,0.68,0.24,2.2,0.087,5,28,0.9988,3.14,0.6,10.2,0.5 477 | 9.3,0.27,0.41,2,0.091,6,16,0.998,3.28,0.7,9.7,0.5 478 | 10.4,0.24,0.49,1.8,0.075,6,20,0.9977,3.18,1.06,11,0.6 479 | 9.6,0.68,0.24,2.2,0.087,5,28,0.9988,3.14,0.6,10.2,0.5 480 | 9.4,0.685,0.11,2.7,0.077,6,31,0.9984,3.19,0.7,10.1,0.6 481 | 10.6,0.28,0.39,15.5,0.069,6,23,1.0026,3.12,0.66,9.2,0.5 482 | 9.4,0.3,0.56,2.8,0.08,6,17,0.9964,3.15,0.92,11.7,0.8 483 | 10.6,0.36,0.59,2.2,0.152,6,18,0.9986,3.04,1.05,9.4,0.5 484 | 10.6,0.36,0.6,2.2,0.152,7,18,0.9986,3.04,1.06,9.4,0.5 485 | 10.6,0.44,0.68,4.1,0.114,6,24,0.997,3.06,0.66,13.4,0.6 486 | 10.2,0.67,0.39,1.9,0.054,6,17,0.9976,3.17,0.47,10,0.5 487 | 10.2,0.67,0.39,1.9,0.054,6,17,0.9976,3.17,0.47,10,0.5 488 | 10.2,0.645,0.36,1.8,0.053,5,14,0.9982,3.17,0.42,10,0.6 489 | 11.6,0.32,0.55,2.8,0.081,35,67,1.0002,3.32,0.92,10.8,0.7 490 | 9.3,0.39,0.4,2.6,0.073,10,26,0.9984,3.34,0.75,10.2,0.6 491 | 9.3,0.775,0.27,2.8,0.078,24,56,0.9984,3.31,0.67,10.6,0.6 492 | 9.2,0.41,0.5,2.5,0.055,12,25,0.9952,3.34,0.79,13.3,0.7 493 | 8.9,0.4,0.51,2.6,0.052,13,27,0.995,3.32,0.9,13.4,0.7 494 | 8.7,0.69,0.31,3,0.086,23,81,1.0002,3.48,0.74,11.6,0.6 495 | 6.5,0.39,0.23,8.3,0.051,28,91,0.9952,3.44,0.55,12.1,0.6 496 | 10.7,0.35,0.53,2.6,0.07,5,16,0.9972,3.15,0.65,11,0.8 497 | 7.8,0.52,0.25,1.9,0.081,14,38,0.9984,3.43,0.65,9,0.6 498 | 7.2,0.34,0.32,2.5,0.09,43,113,0.9966,3.32,0.79,11.1,0.5 499 | 10.7,0.35,0.53,2.6,0.07,5,16,0.9972,3.15,0.65,11,0.8 500 | 8.7,0.69,0.31,3,0.086,23,81,1.0002,3.48,0.74,11.6,0.6 501 | 7.8,0.52,0.25,1.9,0.081,14,38,0.9984,3.43,0.65,9,0.6 502 | 10.4,0.44,0.73,6.55,0.074,38,76,0.999,3.17,0.85,12,0.7 503 | 10.4,0.44,0.73,6.55,0.074,38,76,0.999,3.17,0.85,12,0.7 504 | 10.5,0.26,0.47,1.9,0.078,6,24,0.9976,3.18,1.04,10.9,0.7 505 | 10.5,0.24,0.42,1.8,0.077,6,22,0.9976,3.21,1.05,10.8,0.7 506 | 10.2,0.49,0.63,2.9,0.072,10,26,0.9968,3.16,0.78,12.5,0.7 507 | 10.4,0.24,0.46,1.8,0.075,6,21,0.9976,3.25,1.02,10.8,0.7 508 | 11.2,0.67,0.55,2.3,0.084,6,13,1,3.17,0.71,9.5,0.6 509 | 10,0.59,0.31,2.2,0.09,26,62,0.9994,3.18,0.63,10.2,0.6 510 | 13.3,0.29,0.75,2.8,0.084,23,43,0.9986,3.04,0.68,11.4,0.7 511 | 12.4,0.42,0.49,4.6,0.073,19,43,0.9978,3.02,0.61,9.5,0.5 512 | 10,0.59,0.31,2.2,0.09,26,62,0.9994,3.18,0.63,10.2,0.6 513 | 10.7,0.4,0.48,2.1,0.125,15,49,0.998,3.03,0.81,9.7,0.6 514 | 10.5,0.51,0.64,2.4,0.107,6,15,0.9973,3.09,0.66,11.8,0.7 515 | 10.5,0.51,0.64,2.4,0.107,6,15,0.9973,3.09,0.66,11.8,0.7 516 | 8.5,0.655,0.49,6.1,0.122,34,151,1.001,3.31,1.14,9.3,0.5 517 | 12.5,0.6,0.49,4.3,0.1,5,14,1.001,3.25,0.74,11.9,0.6 518 | 10.4,0.61,0.49,2.1,0.2,5,16,0.9994,3.16,0.63,8.4,0.3 519 | 10.9,0.21,0.49,2.8,0.088,11,32,0.9972,3.22,0.68,11.7,0.6 520 | 7.3,0.365,0.49,2.5,0.088,39,106,0.9966,3.36,0.78,11,0.5 521 | 9.8,0.25,0.49,2.7,0.088,15,33,0.9982,3.42,0.9,10,0.6 522 | 7.6,0.41,0.49,2,0.088,16,43,0.998,3.48,0.64,9.1,0.5 523 | 8.2,0.39,0.49,2.3,0.099,47,133,0.9979,3.38,0.99,9.8,0.5 524 | 9.3,0.4,0.49,2.5,0.085,38,142,0.9978,3.22,0.55,9.4,0.5 525 | 9.2,0.43,0.49,2.4,0.086,23,116,0.9976,3.23,0.64,9.5,0.5 526 | 10.4,0.64,0.24,2.8,0.105,29,53,0.9998,3.24,0.67,9.9,0.5 527 | 7.3,0.365,0.49,2.5,0.088,39,106,0.9966,3.36,0.78,11,0.5 528 | 7,0.38,0.49,2.5,0.097,33,85,0.9962,3.39,0.77,11.4,0.6 529 | 8.2,0.42,0.49,2.6,0.084,32,55,0.9988,3.34,0.75,8.7,0.6 530 | 9.9,0.63,0.24,2.4,0.077,6,33,0.9974,3.09,0.57,9.4,0.5 531 | 9.1,0.22,0.24,2.1,0.078,1,28,0.999,3.41,0.87,10.3,0.6 532 | 11.9,0.38,0.49,2.7,0.098,12,42,1.0004,3.16,0.61,10.3,0.5 533 | 11.9,0.38,0.49,2.7,0.098,12,42,1.0004,3.16,0.61,10.3,0.5 534 | 10.3,0.27,0.24,2.1,0.072,15,33,0.9956,3.22,0.66,12.8,0.6 535 | 10,0.48,0.24,2.7,0.102,13,32,1,3.28,0.56,10,0.6 536 | 9.1,0.22,0.24,2.1,0.078,1,28,0.999,3.41,0.87,10.3,0.6 537 | 9.9,0.63,0.24,2.4,0.077,6,33,0.9974,3.09,0.57,9.4,0.5 538 | 8.1,0.825,0.24,2.1,0.084,5,13,0.9972,3.37,0.77,10.7,0.6 539 | 12.9,0.35,0.49,5.8,0.066,5,35,1.0014,3.2,0.66,12,0.7 540 | 11.2,0.5,0.74,5.15,0.1,5,17,0.9996,3.22,0.62,11.2,0.5 541 | 9.2,0.59,0.24,3.3,0.101,20,47,0.9988,3.26,0.67,9.6,0.5 542 | 9.5,0.46,0.49,6.3,0.064,5,17,0.9988,3.21,0.73,11,0.6 543 | 9.3,0.715,0.24,2.1,0.07,5,20,0.9966,3.12,0.59,9.9,0.5 544 | 11.2,0.66,0.24,2.5,0.085,16,53,0.9993,3.06,0.72,11,0.6 545 | 14.3,0.31,0.74,1.8,0.075,6,15,1.0008,2.86,0.79,8.4,0.6 546 | 9.1,0.47,0.49,2.6,0.094,38,106,0.9982,3.08,0.59,9.1,0.5 547 | 7.5,0.55,0.24,2,0.078,10,28,0.9983,3.45,0.78,9.5,0.6 548 | 10.6,0.31,0.49,2.5,0.067,6,21,0.9987,3.26,0.86,10.7,0.6 549 | 12.4,0.35,0.49,2.6,0.079,27,69,0.9994,3.12,0.75,10.4,0.6 550 | 9,0.53,0.49,1.9,0.171,6,25,0.9975,3.27,0.61,9.4,0.6 551 | 6.8,0.51,0.01,2.1,0.074,9,25,0.9958,3.33,0.56,9.5,0.6 552 | 9.4,0.43,0.24,2.8,0.092,14,45,0.998,3.19,0.73,10,0.6 553 | 9.5,0.46,0.24,2.7,0.092,14,44,0.998,3.12,0.74,10,0.6 554 | 5,1.04,0.24,1.6,0.05,32,96,0.9934,3.74,0.62,11.5,0.5 555 | 15.5,0.645,0.49,4.2,0.095,10,23,1.00315,2.92,0.74,11.1,0.5 556 | 15.5,0.645,0.49,4.2,0.095,10,23,1.00315,2.92,0.74,11.1,0.5 557 | 10.9,0.53,0.49,4.6,0.118,10,17,1.0002,3.07,0.56,11.7,0.6 558 | 15.6,0.645,0.49,4.2,0.095,10,23,1.00315,2.92,0.74,11.1,0.5 559 | 10.9,0.53,0.49,4.6,0.118,10,17,1.0002,3.07,0.56,11.7,0.6 560 | 13,0.47,0.49,4.3,0.085,6,47,1.0021,3.3,0.68,12.7,0.6 561 | 12.7,0.6,0.49,2.8,0.075,5,19,0.9994,3.14,0.57,11.4,0.5 562 | 9,0.44,0.49,2.4,0.078,26,121,0.9978,3.23,0.58,9.2,0.5 563 | 9,0.54,0.49,2.9,0.094,41,110,0.9982,3.08,0.61,9.2,0.5 564 | 7.6,0.29,0.49,2.7,0.092,25,60,0.9971,3.31,0.61,10.1,0.6 565 | 13,0.47,0.49,4.3,0.085,6,47,1.0021,3.3,0.68,12.7,0.6 566 | 12.7,0.6,0.49,2.8,0.075,5,19,0.9994,3.14,0.57,11.4,0.5 567 | 8.7,0.7,0.24,2.5,0.226,5,15,0.9991,3.32,0.6,9,0.6 568 | 8.7,0.7,0.24,2.5,0.226,5,15,0.9991,3.32,0.6,9,0.6 569 | 9.8,0.5,0.49,2.6,0.25,5,20,0.999,3.31,0.79,10.7,0.6 570 | 6.2,0.36,0.24,2.2,0.095,19,42,0.9946,3.57,0.57,11.7,0.6 571 | 11.5,0.35,0.49,3.3,0.07,10,37,1.0003,3.32,0.91,11,0.6 572 | 6.2,0.36,0.24,2.2,0.095,19,42,0.9946,3.57,0.57,11.7,0.6 573 | 10.2,0.24,0.49,2.4,0.075,10,28,0.9978,3.14,0.61,10.4,0.5 574 | 10.5,0.59,0.49,2.1,0.07,14,47,0.9991,3.3,0.56,9.6,0.4 575 | 10.6,0.34,0.49,3.2,0.078,20,78,0.9992,3.19,0.7,10,0.6 576 | 12.3,0.27,0.49,3.1,0.079,28,46,0.9993,3.2,0.8,10.2,0.6 577 | 9.9,0.5,0.24,2.3,0.103,6,14,0.9978,3.34,0.52,10,0.4 578 | 8.8,0.44,0.49,2.8,0.083,18,111,0.9982,3.3,0.6,9.5,0.5 579 | 8.8,0.47,0.49,2.9,0.085,17,110,0.9982,3.29,0.6,9.8,0.5 580 | 10.6,0.31,0.49,2.2,0.063,18,40,0.9976,3.14,0.51,9.8,0.6 581 | 12.3,0.5,0.49,2.2,0.089,5,14,1.0002,3.19,0.44,9.6,0.5 582 | 12.3,0.5,0.49,2.2,0.089,5,14,1.0002,3.19,0.44,9.6,0.5 583 | 11.7,0.49,0.49,2.2,0.083,5,15,1,3.19,0.43,9.2,0.5 584 | 12,0.28,0.49,1.9,0.074,10,21,0.9976,2.98,0.66,9.9,0.7 585 | 11.8,0.33,0.49,3.4,0.093,54,80,1.0002,3.3,0.76,10.7,0.7 586 | 7.6,0.51,0.24,2.4,0.091,8,38,0.998,3.47,0.66,9.6,0.6 587 | 11.1,0.31,0.49,2.7,0.094,16,47,0.9986,3.12,1.02,10.6,0.7 588 | 7.3,0.73,0.24,1.9,0.108,18,102,0.9967,3.26,0.59,9.3,0.5 589 | 5,0.42,0.24,2,0.06,19,50,0.9917,3.72,0.74,14,0.8 590 | 10.2,0.29,0.49,2.6,0.059,5,13,0.9976,3.05,0.74,10.5,0.7 591 | 9,0.45,0.49,2.6,0.084,21,75,0.9987,3.35,0.57,9.7,0.5 592 | 6.6,0.39,0.49,1.7,0.07,23,149,0.9922,3.12,0.5,11.5,0.6 593 | 9,0.45,0.49,2.6,0.084,21,75,0.9987,3.35,0.57,9.7,0.5 594 | 9.9,0.49,0.58,3.5,0.094,9,43,1.0004,3.29,0.58,9,0.5 595 | 7.9,0.72,0.17,2.6,0.096,20,38,0.9978,3.4,0.53,9.5,0.5 596 | 8.9,0.595,0.41,7.9,0.086,30,109,0.9998,3.27,0.57,9.3,0.5 597 | 12.4,0.4,0.51,2,0.059,6,24,0.9994,3.04,0.6,9.3,0.6 598 | 11.9,0.58,0.58,1.9,0.071,5,18,0.998,3.09,0.63,10,0.6 599 | 8.5,0.585,0.18,2.1,0.078,5,30,0.9967,3.2,0.48,9.8,0.6 600 | 12.7,0.59,0.45,2.3,0.082,11,22,1,3,0.7,9.3,0.6 601 | 8.2,0.915,0.27,2.1,0.088,7,23,0.9962,3.26,0.47,10,0.4 602 | 13.2,0.46,0.52,2.2,0.071,12,35,1.0006,3.1,0.56,9,0.6 603 | 7.7,0.835,0,2.6,0.081,6,14,0.9975,3.3,0.52,9.3,0.5 604 | 13.2,0.46,0.52,2.2,0.071,12,35,1.0006,3.1,0.56,9,0.6 605 | 8.3,0.58,0.13,2.9,0.096,14,63,0.9984,3.17,0.62,9.1,0.6 606 | 8.3,0.6,0.13,2.6,0.085,6,24,0.9984,3.31,0.59,9.2,0.6 607 | 9.4,0.41,0.48,4.6,0.072,10,20,0.9973,3.34,0.79,12.2,0.7 608 | 8.8,0.48,0.41,3.3,0.092,26,52,0.9982,3.31,0.53,10.5,0.6 609 | 10.1,0.65,0.37,5.1,0.11,11,65,1.0026,3.32,0.64,10.4,0.6 610 | 6.3,0.36,0.19,3.2,0.075,15,39,0.9956,3.56,0.52,12.7,0.6 611 | 8.8,0.24,0.54,2.5,0.083,25,57,0.9983,3.39,0.54,9.2,0.5 612 | 13.2,0.38,0.55,2.7,0.081,5,16,1.0006,2.98,0.54,9.4,0.5 613 | 7.5,0.64,0,2.4,0.077,18,29,0.9965,3.32,0.6,10,0.6 614 | 8.2,0.39,0.38,1.5,0.058,10,29,0.9962,3.26,0.74,9.8,0.5 615 | 9.2,0.755,0.18,2.2,0.148,10,103,0.9969,2.87,1.36,10.2,0.6 616 | 9.6,0.6,0.5,2.3,0.079,28,71,0.9997,3.5,0.57,9.7,0.5 617 | 9.6,0.6,0.5,2.3,0.079,28,71,0.9997,3.5,0.57,9.7,0.5 618 | 11.5,0.31,0.51,2.2,0.079,14,28,0.9982,3.03,0.93,9.8,0.6 619 | 11.4,0.46,0.5,2.7,0.122,4,17,1.0006,3.13,0.7,10.2,0.5 620 | 11.3,0.37,0.41,2.3,0.088,6,16,0.9988,3.09,0.8,9.3,0.5 621 | 8.3,0.54,0.24,3.4,0.076,16,112,0.9976,3.27,0.61,9.4,0.5 622 | 8.2,0.56,0.23,3.4,0.078,14,104,0.9976,3.28,0.62,9.4,0.5 623 | 10,0.58,0.22,1.9,0.08,9,32,0.9974,3.13,0.55,9.5,0.5 624 | 7.9,0.51,0.25,2.9,0.077,21,45,0.9974,3.49,0.96,12.1,0.6 625 | 6.8,0.69,0,5.6,0.124,21,58,0.9997,3.46,0.72,10.2,0.5 626 | 6.8,0.69,0,5.6,0.124,21,58,0.9997,3.46,0.72,10.2,0.5 627 | 8.8,0.6,0.29,2.2,0.098,5,15,0.9988,3.36,0.49,9.1,0.5 628 | 8.8,0.6,0.29,2.2,0.098,5,15,0.9988,3.36,0.49,9.1,0.5 629 | 8.7,0.54,0.26,2.5,0.097,7,31,0.9976,3.27,0.6,9.3,0.6 630 | 7.6,0.685,0.23,2.3,0.111,20,84,0.9964,3.21,0.61,9.3,0.5 631 | 8.7,0.54,0.26,2.5,0.097,7,31,0.9976,3.27,0.6,9.3,0.6 632 | 10.4,0.28,0.54,2.7,0.105,5,19,0.9988,3.25,0.63,9.5,0.5 633 | 7.6,0.41,0.14,3,0.087,21,43,0.9964,3.32,0.57,10.5,0.6 634 | 10.1,0.935,0.22,3.4,0.105,11,86,1.001,3.43,0.64,11.3,0.4 635 | 7.9,0.35,0.21,1.9,0.073,46,102,0.9964,3.27,0.58,9.5,0.5 636 | 8.7,0.84,0,1.4,0.065,24,33,0.9954,3.27,0.55,9.7,0.5 637 | 9.6,0.88,0.28,2.4,0.086,30,147,0.9979,3.24,0.53,9.4,0.5 638 | 9.5,0.885,0.27,2.3,0.084,31,145,0.9978,3.24,0.53,9.4,0.5 639 | 7.7,0.915,0.12,2.2,0.143,7,23,0.9964,3.35,0.65,10.2,0.7 640 | 8.9,0.29,0.35,1.9,0.067,25,57,0.997,3.18,1.36,10.3,0.6 641 | 9.9,0.54,0.45,2.3,0.071,16,40,0.9991,3.39,0.62,9.4,0.5 642 | 9.5,0.59,0.44,2.3,0.071,21,68,0.9992,3.46,0.63,9.5,0.5 643 | 9.9,0.54,0.45,2.3,0.071,16,40,0.9991,3.39,0.62,9.4,0.5 644 | 9.5,0.59,0.44,2.3,0.071,21,68,0.9992,3.46,0.63,9.5,0.5 645 | 9.9,0.54,0.45,2.3,0.071,16,40,0.9991,3.39,0.62,9.4,0.5 646 | 7.8,0.64,0.1,6,0.115,5,11,0.9984,3.37,0.69,10.1,0.7 647 | 7.3,0.67,0.05,3.6,0.107,6,20,0.9972,3.4,0.63,10.1,0.5 648 | 8.3,0.845,0.01,2.2,0.07,5,14,0.9967,3.32,0.58,11,0.4 649 | 8.7,0.48,0.3,2.8,0.066,10,28,0.9964,3.33,0.67,11.2,0.7 650 | 6.7,0.42,0.27,8.6,0.068,24,148,0.9948,3.16,0.57,11.3,0.6 651 | 10.7,0.43,0.39,2.2,0.106,8,32,0.9986,2.89,0.5,9.6,0.5 652 | 9.8,0.88,0.25,2.5,0.104,35,155,1.001,3.41,0.67,11.2,0.5 653 | 15.9,0.36,0.65,7.5,0.096,22,71,0.9976,2.98,0.84,14.9,0.5 654 | 9.4,0.33,0.59,2.8,0.079,9,30,0.9976,3.12,0.54,12,0.6 655 | 8.6,0.47,0.47,2.4,0.074,7,29,0.9979,3.08,0.46,9.5,0.5 656 | 9.7,0.55,0.17,2.9,0.087,20,53,1.0004,3.14,0.61,9.4,0.5 657 | 10.7,0.43,0.39,2.2,0.106,8,32,0.9986,2.89,0.5,9.6,0.5 658 | 12,0.5,0.59,1.4,0.073,23,42,0.998,2.92,0.68,10.5,0.7 659 | 7.2,0.52,0.07,1.4,0.074,5,20,0.9973,3.32,0.81,9.6,0.6 660 | 7.1,0.84,0.02,4.4,0.096,5,13,0.997,3.41,0.57,11,0.4 661 | 7.2,0.52,0.07,1.4,0.074,5,20,0.9973,3.32,0.81,9.6,0.6 662 | 7.5,0.42,0.31,1.6,0.08,15,42,0.9978,3.31,0.64,9,0.5 663 | 7.2,0.57,0.06,1.6,0.076,9,27,0.9972,3.36,0.7,9.6,0.6 664 | 10.1,0.28,0.46,1.8,0.05,5,13,0.9974,3.04,0.79,10.2,0.6 665 | 12.1,0.4,0.52,2,0.092,15,54,1,3.03,0.66,10.2,0.5 666 | 9.4,0.59,0.14,2,0.084,25,48,0.9981,3.14,0.56,9.7,0.5 667 | 8.3,0.49,0.36,1.8,0.222,6,16,0.998,3.18,0.6,9.5,0.6 668 | 11.3,0.34,0.45,2,0.082,6,15,0.9988,2.94,0.66,9.2,0.6 669 | 10,0.73,0.43,2.3,0.059,15,31,0.9966,3.15,0.57,11,0.5 670 | 11.3,0.34,0.45,2,0.082,6,15,0.9988,2.94,0.66,9.2,0.6 671 | 6.9,0.4,0.24,2.5,0.083,30,45,0.9959,3.26,0.58,10,0.5 672 | 8.2,0.73,0.21,1.7,0.074,5,13,0.9968,3.2,0.52,9.5,0.5 673 | 9.8,1.24,0.34,2,0.079,32,151,0.998,3.15,0.53,9.5,0.5 674 | 8.2,0.73,0.21,1.7,0.074,5,13,0.9968,3.2,0.52,9.5,0.5 675 | 10.8,0.4,0.41,2.2,0.084,7,17,0.9984,3.08,0.67,9.3,0.6 676 | 9.3,0.41,0.39,2.2,0.064,12,31,0.9984,3.26,0.65,10.2,0.5 677 | 10.8,0.4,0.41,2.2,0.084,7,17,0.9984,3.08,0.67,9.3,0.6 678 | 8.6,0.8,0.11,2.3,0.084,12,31,0.9979,3.4,0.48,9.9,0.5 679 | 8.3,0.78,0.1,2.6,0.081,45,87,0.9983,3.48,0.53,10,0.5 680 | 10.8,0.26,0.45,3.3,0.06,20,49,0.9972,3.13,0.54,9.6,0.5 681 | 13.3,0.43,0.58,1.9,0.07,15,40,1.0004,3.06,0.49,9,0.5 682 | 8,0.45,0.23,2.2,0.094,16,29,0.9962,3.21,0.49,10.2,0.6 683 | 8.5,0.46,0.31,2.25,0.078,32,58,0.998,3.33,0.54,9.8,0.5 684 | 8.1,0.78,0.23,2.6,0.059,5,15,0.997,3.37,0.56,11.3,0.5 685 | 9.8,0.98,0.32,2.3,0.078,35,152,0.998,3.25,0.48,9.4,0.5 686 | 8.1,0.78,0.23,2.6,0.059,5,15,0.997,3.37,0.56,11.3,0.5 687 | 7.1,0.65,0.18,1.8,0.07,13,40,0.997,3.44,0.6,9.1,0.5 688 | 9.1,0.64,0.23,3.1,0.095,13,38,0.9998,3.28,0.59,9.7,0.5 689 | 7.7,0.66,0.04,1.6,0.039,4,9,0.9962,3.4,0.47,9.4,0.5 690 | 8.1,0.38,0.48,1.8,0.157,5,17,0.9976,3.3,1.05,9.4,0.5 691 | 7.4,1.185,0,4.25,0.097,5,14,0.9966,3.63,0.54,10.7,0.3 692 | 9.2,0.92,0.24,2.6,0.087,12,93,0.9998,3.48,0.54,9.8,0.5 693 | 8.6,0.49,0.51,2,0.422,16,62,0.9979,3.03,1.17,9,0.5 694 | 9,0.48,0.32,2.8,0.084,21,122,0.9984,3.32,0.62,9.4,0.5 695 | 9,0.47,0.31,2.7,0.084,24,125,0.9984,3.31,0.61,9.4,0.5 696 | 5.1,0.47,0.02,1.3,0.034,18,44,0.9921,3.9,0.62,12.8,0.6 697 | 7,0.65,0.02,2.1,0.066,8,25,0.9972,3.47,0.67,9.5,0.6 698 | 7,0.65,0.02,2.1,0.066,8,25,0.9972,3.47,0.67,9.5,0.6 699 | 9.4,0.615,0.28,3.2,0.087,18,72,1.0001,3.31,0.53,9.7,0.5 700 | 11.8,0.38,0.55,2.1,0.071,5,19,0.9986,3.11,0.62,10.8,0.6 701 | 10.6,1.02,0.43,2.9,0.076,26,88,0.9984,3.08,0.57,10.1,0.6 702 | 7,0.65,0.02,2.1,0.066,8,25,0.9972,3.47,0.67,9.5,0.6 703 | 7,0.64,0.02,2.1,0.067,9,23,0.997,3.47,0.67,9.4,0.6 704 | 7.5,0.38,0.48,2.6,0.073,22,84,0.9972,3.32,0.7,9.6,0.4 705 | 9.1,0.765,0.04,1.6,0.078,4,14,0.998,3.29,0.54,9.7,0.4 706 | 8.4,1.035,0.15,6,0.073,11,54,0.999,3.37,0.49,9.9,0.5 707 | 7,0.78,0.08,2,0.093,10,19,0.9956,3.4,0.47,10,0.5 708 | 7.4,0.49,0.19,3,0.077,16,37,0.9966,3.37,0.51,10.5,0.5 709 | 7.8,0.545,0.12,2.5,0.068,11,35,0.996,3.34,0.61,11.6,0.6 710 | 9.7,0.31,0.47,1.6,0.062,13,33,0.9983,3.27,0.66,10,0.6 711 | 10.6,1.025,0.43,2.8,0.08,21,84,0.9985,3.06,0.57,10.1,0.5 712 | 8.9,0.565,0.34,3,0.093,16,112,0.9998,3.38,0.61,9.5,0.5 713 | 8.7,0.69,0,3.2,0.084,13,33,0.9992,3.36,0.45,9.4,0.5 714 | 8,0.43,0.36,2.3,0.075,10,48,0.9976,3.34,0.46,9.4,0.5 715 | 9.9,0.74,0.28,2.6,0.078,21,77,0.998,3.28,0.51,9.8,0.5 716 | 7.2,0.49,0.18,2.7,0.069,13,34,0.9967,3.29,0.48,9.2,0.6 717 | 8,0.43,0.36,2.3,0.075,10,48,0.9976,3.34,0.46,9.4,0.5 718 | 7.6,0.46,0.11,2.6,0.079,12,49,0.9968,3.21,0.57,10,0.5 719 | 8.4,0.56,0.04,2,0.082,10,22,0.9976,3.22,0.44,9.6,0.5 720 | 7.1,0.66,0,3.9,0.086,17,45,0.9976,3.46,0.54,9.5,0.5 721 | 8.4,0.56,0.04,2,0.082,10,22,0.9976,3.22,0.44,9.6,0.5 722 | 8.9,0.48,0.24,2.85,0.094,35,106,0.9982,3.1,0.53,9.2,0.5 723 | 7.6,0.42,0.08,2.7,0.084,15,48,0.9968,3.21,0.59,10,0.5 724 | 7.1,0.31,0.3,2.2,0.053,36,127,0.9965,2.94,1.62,9.5,0.5 725 | 7.5,1.115,0.1,3.1,0.086,5,12,0.9958,3.54,0.6,11.2,0.4 726 | 9,0.66,0.17,3,0.077,5,13,0.9976,3.29,0.55,10.4,0.5 727 | 8.1,0.72,0.09,2.8,0.084,18,49,0.9994,3.43,0.72,11.1,0.6 728 | 6.4,0.57,0.02,1.8,0.067,4,11,0.997,3.46,0.68,9.5,0.5 729 | 6.4,0.57,0.02,1.8,0.067,4,11,0.997,3.46,0.68,9.5,0.5 730 | 6.4,0.865,0.03,3.2,0.071,27,58,0.995,3.61,0.49,12.7,0.6 731 | 9.5,0.55,0.66,2.3,0.387,12,37,0.9982,3.17,0.67,9.6,0.5 732 | 8.9,0.875,0.13,3.45,0.088,4,14,0.9994,3.44,0.52,11.5,0.5 733 | 7.3,0.835,0.03,2.1,0.092,10,19,0.9966,3.39,0.47,9.6,0.5 734 | 7,0.45,0.34,2.7,0.082,16,72,0.998,3.55,0.6,9.5,0.5 735 | 7.7,0.56,0.2,2,0.075,9,39,0.9987,3.48,0.62,9.3,0.5 736 | 7.7,0.965,0.1,2.1,0.112,11,22,0.9963,3.26,0.5,9.5,0.5 737 | 7.7,0.965,0.1,2.1,0.112,11,22,0.9963,3.26,0.5,9.5,0.5 738 | 8.2,0.59,0,2.5,0.093,19,58,1.0002,3.5,0.65,9.3,0.6 739 | 9,0.46,0.23,2.8,0.092,28,104,0.9983,3.1,0.56,9.2,0.5 740 | 9,0.69,0,2.4,0.088,19,38,0.999,3.35,0.6,9.3,0.5 741 | 8.3,0.76,0.29,4.2,0.075,12,16,0.9965,3.45,0.68,11.5,0.6 742 | 9.2,0.53,0.24,2.6,0.078,28,139,0.99788,3.21,0.57,9.5,0.5 743 | 6.5,0.615,0,1.9,0.065,9,18,0.9972,3.46,0.65,9.2,0.5 744 | 11.6,0.41,0.58,2.8,0.096,25,101,1.00024,3.13,0.53,10,0.5 745 | 11.1,0.39,0.54,2.7,0.095,21,101,1.0001,3.13,0.51,9.5,0.5 746 | 7.3,0.51,0.18,2.1,0.07,12,28,0.99768,3.52,0.73,9.5,0.6 747 | 8.2,0.34,0.38,2.5,0.08,12,57,0.9978,3.3,0.47,9,0.6 748 | 8.6,0.33,0.4,2.6,0.083,16,68,0.99782,3.3,0.48,9.4,0.5 749 | 7.2,0.5,0.18,2.1,0.071,12,31,0.99761,3.52,0.72,9.6,0.6 750 | 7.3,0.51,0.18,2.1,0.07,12,28,0.99768,3.52,0.73,9.5,0.6 751 | 8.3,0.65,0.1,2.9,0.089,17,40,0.99803,3.29,0.55,9.5,0.5 752 | 8.3,0.65,0.1,2.9,0.089,17,40,0.99803,3.29,0.55,9.5,0.5 753 | 7.6,0.54,0.13,2.5,0.097,24,66,0.99785,3.39,0.61,9.4,0.5 754 | 8.3,0.65,0.1,2.9,0.089,17,40,0.99803,3.29,0.55,9.5,0.5 755 | 7.8,0.48,0.68,1.7,0.415,14,32,0.99656,3.09,1.06,9.1,0.6 756 | 7.8,0.91,0.07,1.9,0.058,22,47,0.99525,3.51,0.43,10.7,0.6 757 | 6.3,0.98,0.01,2,0.057,15,33,0.99488,3.6,0.46,11.2,0.6 758 | 8.1,0.87,0,2.2,0.084,10,31,0.99656,3.25,0.5,9.8,0.5 759 | 8.1,0.87,0,2.2,0.084,10,31,0.99656,3.25,0.5,9.8,0.5 760 | 8.8,0.42,0.21,2.5,0.092,33,88,0.99823,3.19,0.52,9.2,0.5 761 | 9,0.58,0.25,2.8,0.075,9,104,0.99779,3.23,0.57,9.7,0.5 762 | 9.3,0.655,0.26,2,0.096,5,35,0.99738,3.25,0.42,9.6,0.5 763 | 8.8,0.7,0,1.7,0.069,8,19,0.99701,3.31,0.53,10,0.6 764 | 9.3,0.655,0.26,2,0.096,5,35,0.99738,3.25,0.42,9.6,0.5 765 | 9.1,0.68,0.11,2.8,0.093,11,44,0.99888,3.31,0.55,9.5,0.6 766 | 9.2,0.67,0.1,3,0.091,12,48,0.99888,3.31,0.54,9.5,0.6 767 | 8.8,0.59,0.18,2.9,0.089,12,74,0.99738,3.14,0.54,9.4,0.5 768 | 7.5,0.6,0.32,2.7,0.103,13,98,0.99938,3.45,0.62,9.5,0.5 769 | 7.1,0.59,0.02,2.3,0.082,24,94,0.99744,3.55,0.53,9.7,0.6 770 | 7.9,0.72,0.01,1.9,0.076,7,32,0.99668,3.39,0.54,9.6,0.5 771 | 7.1,0.59,0.02,2.3,0.082,24,94,0.99744,3.55,0.53,9.7,0.6 772 | 9.4,0.685,0.26,2.4,0.082,23,143,0.9978,3.28,0.55,9.4,0.5 773 | 9.5,0.57,0.27,2.3,0.082,23,144,0.99782,3.27,0.55,9.4,0.5 774 | 7.9,0.4,0.29,1.8,0.157,1,44,0.9973,3.3,0.92,9.5,0.6 775 | 7.9,0.4,0.3,1.8,0.157,2,45,0.99727,3.31,0.91,9.5,0.6 776 | 7.2,1,0,3,0.102,7,16,0.99586,3.43,0.46,10,0.5 777 | 6.9,0.765,0.18,2.4,0.243,5.5,48,0.99612,3.4,0.6,10.3,0.6 778 | 6.9,0.635,0.17,2.4,0.241,6,18,0.9961,3.4,0.59,10.3,0.6 779 | 8.3,0.43,0.3,3.4,0.079,7,34,0.99788,3.36,0.61,10.5,0.5 780 | 7.1,0.52,0.03,2.6,0.076,21,92,0.99745,3.5,0.6,9.8,0.5 781 | 7,0.57,0,2,0.19,12,45,0.99676,3.31,0.6,9.4,0.6 782 | 6.5,0.46,0.14,2.4,0.114,9,37,0.99732,3.66,0.65,9.8,0.5 783 | 9,0.82,0.05,2.4,0.081,26,96,0.99814,3.36,0.53,10,0.5 784 | 6.5,0.46,0.14,2.4,0.114,9,37,0.99732,3.66,0.65,9.8,0.5 785 | 7.1,0.59,0.01,2.5,0.077,20,85,0.99746,3.55,0.59,9.8,0.5 786 | 9.9,0.35,0.41,2.3,0.083,11,61,0.9982,3.21,0.5,9.5,0.5 787 | 9.9,0.35,0.41,2.3,0.083,11,61,0.9982,3.21,0.5,9.5,0.5 788 | 10,0.56,0.24,2.2,0.079,19,58,0.9991,3.18,0.56,10.1,0.6 789 | 10,0.56,0.24,2.2,0.079,19,58,0.9991,3.18,0.56,10.1,0.6 790 | 8.6,0.63,0.17,2.9,0.099,21,119,0.998,3.09,0.52,9.3,0.5 791 | 7.4,0.37,0.43,2.6,0.082,18,82,0.99708,3.33,0.68,9.7,0.6 792 | 8.8,0.64,0.17,2.9,0.084,25,130,0.99818,3.23,0.54,9.6,0.5 793 | 7.1,0.61,0.02,2.5,0.081,17,87,0.99745,3.48,0.6,9.7,0.6 794 | 7.7,0.6,0,2.6,0.055,7,13,0.99639,3.38,0.56,10.8,0.5 795 | 10.1,0.27,0.54,2.3,0.065,7,26,0.99531,3.17,0.53,12.5,0.6 796 | 10.8,0.89,0.3,2.6,0.132,7,60,0.99786,2.99,1.18,10.2,0.5 797 | 8.7,0.46,0.31,2.5,0.126,24,64,0.99746,3.1,0.74,9.6,0.5 798 | 9.3,0.37,0.44,1.6,0.038,21,42,0.99526,3.24,0.81,10.8,0.7 799 | 9.4,0.5,0.34,3.6,0.082,5,14,0.9987,3.29,0.52,10.7,0.6 800 | 9.4,0.5,0.34,3.6,0.082,5,14,0.9987,3.29,0.52,10.7,0.6 801 | 7.2,0.61,0.08,4,0.082,26,108,0.99641,3.25,0.51,9.4,0.5 802 | 8.6,0.55,0.09,3.3,0.068,8,17,0.99735,3.23,0.44,10,0.5 803 | 5.1,0.585,0,1.7,0.044,14,86,0.99264,3.56,0.94,12.9,0.7 804 | 7.7,0.56,0.08,2.5,0.114,14,46,0.9971,3.24,0.66,9.6,0.6 805 | 8.4,0.52,0.22,2.7,0.084,4,18,0.99682,3.26,0.57,9.9,0.6 806 | 8.2,0.28,0.4,2.4,0.052,4,10,0.99356,3.33,0.7,12.8,0.7 807 | 8.4,0.25,0.39,2,0.041,4,10,0.99386,3.27,0.71,12.5,0.7 808 | 8.2,0.28,0.4,2.4,0.052,4,10,0.99356,3.33,0.7,12.8,0.7 809 | 7.4,0.53,0.12,1.9,0.165,4,12,0.99702,3.26,0.86,9.2,0.5 810 | 7.6,0.48,0.31,2.8,0.07,4,15,0.99693,3.22,0.55,10.3,0.6 811 | 7.3,0.49,0.1,2.6,0.068,4,14,0.99562,3.3,0.47,10.5,0.5 812 | 12.9,0.5,0.55,2.8,0.072,7,24,1.00012,3.09,0.68,10.9,0.6 813 | 10.8,0.45,0.33,2.5,0.099,20,38,0.99818,3.24,0.71,10.8,0.5 814 | 6.9,0.39,0.24,2.1,0.102,4,7,0.99462,3.44,0.58,11.4,0.4 815 | 12.6,0.41,0.54,2.8,0.103,19,41,0.99939,3.21,0.76,11.3,0.6 816 | 10.8,0.45,0.33,2.5,0.099,20,38,0.99818,3.24,0.71,10.8,0.5 817 | 9.8,0.51,0.19,3.2,0.081,8,30,0.9984,3.23,0.58,10.5,0.6 818 | 10.8,0.29,0.42,1.6,0.084,19,27,0.99545,3.28,0.73,11.9,0.6 819 | 7.1,0.715,0,2.35,0.071,21,47,0.99632,3.29,0.45,9.4,0.5 820 | 9.1,0.66,0.15,3.2,0.097,9,59,0.99976,3.28,0.54,9.6,0.5 821 | 7,0.685,0,1.9,0.099,9,22,0.99606,3.34,0.6,9.7,0.5 822 | 4.9,0.42,0,2.1,0.048,16,42,0.99154,3.71,0.74,14,0.7 823 | 6.7,0.54,0.13,2,0.076,15,36,0.9973,3.61,0.64,9.8,0.5 824 | 6.7,0.54,0.13,2,0.076,15,36,0.9973,3.61,0.64,9.8,0.5 825 | 7.1,0.48,0.28,2.8,0.068,6,16,0.99682,3.24,0.53,10.3,0.5 826 | 7.1,0.46,0.14,2.8,0.076,15,37,0.99624,3.36,0.49,10.7,0.5 827 | 7.5,0.27,0.34,2.3,0.05,4,8,0.9951,3.4,0.64,11,0.7 828 | 7.1,0.46,0.14,2.8,0.076,15,37,0.99624,3.36,0.49,10.7,0.5 829 | 7.8,0.57,0.09,2.3,0.065,34,45,0.99417,3.46,0.74,12.7,0.8 830 | 5.9,0.61,0.08,2.1,0.071,16,24,0.99376,3.56,0.77,11.1,0.6 831 | 7.5,0.685,0.07,2.5,0.058,5,9,0.99632,3.38,0.55,10.9,0.4 832 | 5.9,0.61,0.08,2.1,0.071,16,24,0.99376,3.56,0.77,11.1,0.6 833 | 10.4,0.44,0.42,1.5,0.145,34,48,0.99832,3.38,0.86,9.9,0.3 834 | 11.6,0.47,0.44,1.6,0.147,36,51,0.99836,3.38,0.86,9.9,0.4 835 | 8.8,0.685,0.26,1.6,0.088,16,23,0.99694,3.32,0.47,9.4,0.5 836 | 7.6,0.665,0.1,1.5,0.066,27,55,0.99655,3.39,0.51,9.3,0.5 837 | 6.7,0.28,0.28,2.4,0.012,36,100,0.99064,3.26,0.39,11.7,0.7 838 | 6.7,0.28,0.28,2.4,0.012,36,100,0.99064,3.26,0.39,11.7,0.7 839 | 10.1,0.31,0.35,1.6,0.075,9,28,0.99672,3.24,0.83,11.2,0.7 840 | 6,0.5,0.04,2.2,0.092,13,26,0.99647,3.46,0.47,10,0.5 841 | 11.1,0.42,0.47,2.65,0.085,9,34,0.99736,3.24,0.77,12.1,0.7 842 | 6.6,0.66,0,3,0.115,21,31,0.99629,3.45,0.63,10.3,0.5 843 | 10.6,0.5,0.45,2.6,0.119,34,68,0.99708,3.23,0.72,10.9,0.6 844 | 7.1,0.685,0.35,2,0.088,9,92,0.9963,3.28,0.62,9.4,0.5 845 | 9.9,0.25,0.46,1.7,0.062,26,42,0.9959,3.18,0.83,10.6,0.6 846 | 6.4,0.64,0.21,1.8,0.081,14,31,0.99689,3.59,0.66,9.8,0.5 847 | 6.4,0.64,0.21,1.8,0.081,14,31,0.99689,3.59,0.66,9.8,0.5 848 | 7.4,0.68,0.16,1.8,0.078,12,39,0.9977,3.5,0.7,9.9,0.6 849 | 6.4,0.64,0.21,1.8,0.081,14,31,0.99689,3.59,0.66,9.8,0.5 850 | 6.4,0.63,0.21,1.6,0.08,12,32,0.99689,3.58,0.66,9.8,0.5 851 | 9.3,0.43,0.44,1.9,0.085,9,22,0.99708,3.28,0.55,9.5,0.5 852 | 9.3,0.43,0.44,1.9,0.085,9,22,0.99708,3.28,0.55,9.5,0.5 853 | 8,0.42,0.32,2.5,0.08,26,122,0.99801,3.22,1.07,9.7,0.5 854 | 9.3,0.36,0.39,1.5,0.08,41,55,0.99652,3.47,0.73,10.9,0.6 855 | 9.3,0.36,0.39,1.5,0.08,41,55,0.99652,3.47,0.73,10.9,0.6 856 | 7.6,0.735,0.02,2.5,0.071,10,14,0.99538,3.51,0.71,11.7,0.7 857 | 9.3,0.36,0.39,1.5,0.08,41,55,0.99652,3.47,0.73,10.9,0.6 858 | 8.2,0.26,0.34,2.5,0.073,16,47,0.99594,3.4,0.78,11.3,0.7 859 | 11.7,0.28,0.47,1.7,0.054,17,32,0.99686,3.15,0.67,10.6,0.7 860 | 6.8,0.56,0.22,1.8,0.074,15,24,0.99438,3.4,0.82,11.2,0.6 861 | 7.2,0.62,0.06,2.7,0.077,15,85,0.99746,3.51,0.54,9.5,0.5 862 | 5.8,1.01,0.66,2,0.039,15,88,0.99357,3.66,0.6,11.5,0.6 863 | 7.5,0.42,0.32,2.7,0.067,7,25,0.99628,3.24,0.44,10.4,0.5 864 | 7.2,0.62,0.06,2.5,0.078,17,84,0.99746,3.51,0.53,9.7,0.5 865 | 7.2,0.62,0.06,2.7,0.077,15,85,0.99746,3.51,0.54,9.5,0.5 866 | 7.2,0.635,0.07,2.6,0.077,16,86,0.99748,3.51,0.54,9.7,0.5 867 | 6.8,0.49,0.22,2.3,0.071,13,24,0.99438,3.41,0.83,11.3,0.6 868 | 6.9,0.51,0.23,2,0.072,13,22,0.99438,3.4,0.84,11.2,0.6 869 | 6.8,0.56,0.22,1.8,0.074,15,24,0.99438,3.4,0.82,11.2,0.6 870 | 7.6,0.63,0.03,2,0.08,27,43,0.99578,3.44,0.64,10.9,0.6 871 | 7.7,0.715,0.01,2.1,0.064,31,43,0.99371,3.41,0.57,11.8,0.6 872 | 6.9,0.56,0.03,1.5,0.086,36,46,0.99522,3.53,0.57,10.6,0.5 873 | 7.3,0.35,0.24,2,0.067,28,48,0.99576,3.43,0.54,10,0.4 874 | 9.1,0.21,0.37,1.6,0.067,6,10,0.99552,3.23,0.58,11.1,0.7 875 | 10.4,0.38,0.46,2.1,0.104,6,10,0.99664,3.12,0.65,11.8,0.7 876 | 8.8,0.31,0.4,2.8,0.109,7,16,0.99614,3.31,0.79,11.8,0.7 877 | 7.1,0.47,0,2.2,0.067,7,14,0.99517,3.4,0.58,10.9,0.4 878 | 7.7,0.715,0.01,2.1,0.064,31,43,0.99371,3.41,0.57,11.8,0.6 879 | 8.8,0.61,0.19,4,0.094,30,69,0.99787,3.22,0.5,10,0.6 880 | 7.2,0.6,0.04,2.5,0.076,18,88,0.99745,3.53,0.55,9.5,0.5 881 | 9.2,0.56,0.18,1.6,0.078,10,21,0.99576,3.15,0.49,9.9,0.5 882 | 7.6,0.715,0,2.1,0.068,30,35,0.99533,3.48,0.65,11.4,0.6 883 | 8.4,0.31,0.29,3.1,0.194,14,26,0.99536,3.22,0.78,12,0.6 884 | 7.2,0.6,0.04,2.5,0.076,18,88,0.99745,3.53,0.55,9.5,0.5 885 | 8.8,0.61,0.19,4,0.094,30,69,0.99787,3.22,0.5,10,0.6 886 | 8.9,0.75,0.14,2.5,0.086,9,30,0.99824,3.34,0.64,10.5,0.5 887 | 9,0.8,0.12,2.4,0.083,8,28,0.99836,3.33,0.65,10.4,0.6 888 | 10.7,0.52,0.38,2.6,0.066,29,56,0.99577,3.15,0.79,12.1,0.7 889 | 6.8,0.57,0,2.5,0.072,32,64,0.99491,3.43,0.56,11.2,0.6 890 | 10.7,0.9,0.34,6.6,0.112,23,99,1.00289,3.22,0.68,9.3,0.5 891 | 7.2,0.34,0.24,2,0.071,30,52,0.99576,3.44,0.58,10.1,0.5 892 | 7.2,0.66,0.03,2.3,0.078,16,86,0.99743,3.53,0.57,9.7,0.5 893 | 10.1,0.45,0.23,1.9,0.082,10,18,0.99774,3.22,0.65,9.3,0.6 894 | 7.2,0.66,0.03,2.3,0.078,16,86,0.99743,3.53,0.57,9.7,0.5 895 | 7.2,0.63,0.03,2.2,0.08,17,88,0.99745,3.53,0.58,9.8,0.6 896 | 7.1,0.59,0.01,2.3,0.08,27,43,0.9955,3.42,0.58,10.7,0.6 897 | 8.3,0.31,0.39,2.4,0.078,17,43,0.99444,3.31,0.77,12.5,0.7 898 | 7.1,0.59,0.01,2.3,0.08,27,43,0.9955,3.42,0.58,10.7,0.6 899 | 8.3,0.31,0.39,2.4,0.078,17,43,0.99444,3.31,0.77,12.5,0.7 900 | 8.3,1.02,0.02,3.4,0.084,6,11,0.99892,3.48,0.49,11,0.3 901 | 8.9,0.31,0.36,2.6,0.056,10,39,0.99562,3.4,0.69,11.8,0.5 902 | 7.4,0.635,0.1,2.4,0.08,16,33,0.99736,3.58,0.69,10.8,0.7 903 | 7.4,0.635,0.1,2.4,0.08,16,33,0.99736,3.58,0.69,10.8,0.7 904 | 6.8,0.59,0.06,6,0.06,11,18,0.9962,3.41,0.59,10.8,0.7 905 | 6.8,0.59,0.06,6,0.06,11,18,0.9962,3.41,0.59,10.8,0.7 906 | 9.2,0.58,0.2,3,0.081,15,115,0.998,3.23,0.59,9.5,0.5 907 | 7.2,0.54,0.27,2.6,0.084,12,78,0.9964,3.39,0.71,11,0.5 908 | 6.1,0.56,0,2.2,0.079,6,9,0.9948,3.59,0.54,11.5,0.6 909 | 7.4,0.52,0.13,2.4,0.078,34,61,0.99528,3.43,0.59,10.8,0.6 910 | 7.3,0.305,0.39,1.2,0.059,7,11,0.99331,3.29,0.52,11.5,0.6 911 | 9.3,0.38,0.48,3.8,0.132,3,11,0.99577,3.23,0.57,13.2,0.6 912 | 9.1,0.28,0.46,9,0.114,3,9,0.99901,3.18,0.6,10.9,0.6 913 | 10,0.46,0.44,2.9,0.065,4,8,0.99674,3.33,0.62,12.2,0.6 914 | 9.4,0.395,0.46,4.6,0.094,3,10,0.99639,3.27,0.64,12.2,0.7 915 | 7.3,0.305,0.39,1.2,0.059,7,11,0.99331,3.29,0.52,11.5,0.6 916 | 8.6,0.315,0.4,2.2,0.079,3,6,0.99512,3.27,0.67,11.9,0.6 917 | 5.3,0.715,0.19,1.5,0.161,7,62,0.99395,3.62,0.61,11,0.5 918 | 6.8,0.41,0.31,8.8,0.084,26,45,0.99824,3.38,0.64,10.1,0.6 919 | 8.4,0.36,0.32,2.2,0.081,32,79,0.9964,3.3,0.72,11,0.6 920 | 8.4,0.62,0.12,1.8,0.072,38,46,0.99504,3.38,0.89,11.8,0.6 921 | 9.6,0.41,0.37,2.3,0.091,10,23,0.99786,3.24,0.56,10.5,0.5 922 | 8.4,0.36,0.32,2.2,0.081,32,79,0.9964,3.3,0.72,11,0.6 923 | 8.4,0.62,0.12,1.8,0.072,38,46,0.99504,3.38,0.89,11.8,0.6 924 | 6.8,0.41,0.31,8.8,0.084,26,45,0.99824,3.38,0.64,10.1,0.6 925 | 8.6,0.47,0.27,2.3,0.055,14,28,0.99516,3.18,0.8,11.2,0.5 926 | 8.6,0.22,0.36,1.9,0.064,53,77,0.99604,3.47,0.87,11,0.7 927 | 9.4,0.24,0.33,2.3,0.061,52,73,0.99786,3.47,0.9,10.2,0.6 928 | 8.4,0.67,0.19,2.2,0.093,11,75,0.99736,3.2,0.59,9.2,0.4 929 | 8.6,0.47,0.27,2.3,0.055,14,28,0.99516,3.18,0.8,11.2,0.5 930 | 8.7,0.33,0.38,3.3,0.063,10,19,0.99468,3.3,0.73,12,0.7 931 | 6.6,0.61,0.01,1.9,0.08,8,25,0.99746,3.69,0.73,10.5,0.5 932 | 7.4,0.61,0.01,2,0.074,13,38,0.99748,3.48,0.65,9.8,0.5 933 | 7.6,0.4,0.29,1.9,0.078,29,66,0.9971,3.45,0.59,9.5,0.6 934 | 7.4,0.61,0.01,2,0.074,13,38,0.99748,3.48,0.65,9.8,0.5 935 | 6.6,0.61,0.01,1.9,0.08,8,25,0.99746,3.69,0.73,10.5,0.5 936 | 8.8,0.3,0.38,2.3,0.06,19,72,0.99543,3.39,0.72,11.8,0.6 937 | 8.8,0.3,0.38,2.3,0.06,19,72,0.99543,3.39,0.72,11.8,0.6 938 | 12,0.63,0.5,1.4,0.071,6,26,0.99791,3.07,0.6,10.4,0.4 939 | 7.2,0.38,0.38,2.8,0.068,23,42,0.99356,3.34,0.72,12.9,0.7 940 | 6.2,0.46,0.17,1.6,0.073,7,11,0.99425,3.61,0.54,11.4,0.5 941 | 9.6,0.33,0.52,2.2,0.074,13,25,0.99509,3.36,0.76,12.4,0.7 942 | 9.9,0.27,0.49,5,0.082,9,17,0.99484,3.19,0.52,12.5,0.7 943 | 10.1,0.43,0.4,2.6,0.092,13,52,0.99834,3.22,0.64,10,0.7 944 | 9.8,0.5,0.34,2.3,0.094,10,45,0.99864,3.24,0.6,9.7,0.7 945 | 8.3,0.3,0.49,3.8,0.09,11,24,0.99498,3.27,0.64,12.1,0.7 946 | 10.2,0.44,0.42,2,0.071,7,20,0.99566,3.14,0.79,11.1,0.7 947 | 10.2,0.44,0.58,4.1,0.092,11,24,0.99745,3.29,0.99,12,0.7 948 | 8.3,0.28,0.48,2.1,0.093,6,12,0.99408,3.26,0.62,12.4,0.7 949 | 8.9,0.12,0.45,1.8,0.075,10,21,0.99552,3.41,0.76,11.9,0.7 950 | 8.9,0.12,0.45,1.8,0.075,10,21,0.99552,3.41,0.76,11.9,0.7 951 | 8.9,0.12,0.45,1.8,0.075,10,21,0.99552,3.41,0.76,11.9,0.7 952 | 8.3,0.28,0.48,2.1,0.093,6,12,0.99408,3.26,0.62,12.4,0.7 953 | 8.2,0.31,0.4,2.2,0.058,6,10,0.99536,3.31,0.68,11.2,0.7 954 | 10.2,0.34,0.48,2.1,0.052,5,9,0.99458,3.2,0.69,12.1,0.7 955 | 7.6,0.43,0.4,2.7,0.082,6,11,0.99538,3.44,0.54,12.2,0.6 956 | 8.5,0.21,0.52,1.9,0.09,9,23,0.99648,3.36,0.67,10.4,0.5 957 | 9,0.36,0.52,2.1,0.111,5,10,0.99568,3.31,0.62,11.3,0.6 958 | 9.5,0.37,0.52,2,0.088,12,51,0.99613,3.29,0.58,11.1,0.6 959 | 6.4,0.57,0.12,2.3,0.12,25,36,0.99519,3.47,0.71,11.3,0.7 960 | 8,0.59,0.05,2,0.089,12,32,0.99735,3.36,0.61,10,0.5 961 | 8.5,0.47,0.27,1.9,0.058,18,38,0.99518,3.16,0.85,11.1,0.6 962 | 7.1,0.56,0.14,1.6,0.078,7,18,0.99592,3.27,0.62,9.3,0.5 963 | 6.6,0.57,0.02,2.1,0.115,6,16,0.99654,3.38,0.69,9.5,0.5 964 | 8.8,0.27,0.39,2,0.1,20,27,0.99546,3.15,0.69,11.2,0.6 965 | 8.5,0.47,0.27,1.9,0.058,18,38,0.99518,3.16,0.85,11.1,0.6 966 | 8.3,0.34,0.4,2.4,0.065,24,48,0.99554,3.34,0.86,11,0.6 967 | 9,0.38,0.41,2.4,0.103,6,10,0.99604,3.13,0.58,11.9,0.7 968 | 8.5,0.66,0.2,2.1,0.097,23,113,0.99733,3.13,0.48,9.2,0.5 969 | 9,0.4,0.43,2.4,0.068,29,46,0.9943,3.2,0.6,12.2,0.6 970 | 6.7,0.56,0.09,2.9,0.079,7,22,0.99669,3.46,0.61,10.2,0.5 971 | 10.4,0.26,0.48,1.9,0.066,6,10,0.99724,3.33,0.87,10.9,0.6 972 | 10.4,0.26,0.48,1.9,0.066,6,10,0.99724,3.33,0.87,10.9,0.6 973 | 10.1,0.38,0.5,2.4,0.104,6,13,0.99643,3.22,0.65,11.6,0.7 974 | 8.5,0.34,0.44,1.7,0.079,6,12,0.99605,3.52,0.63,10.7,0.5 975 | 8.8,0.33,0.41,5.9,0.073,7,13,0.99658,3.3,0.62,12.1,0.7 976 | 7.2,0.41,0.3,2.1,0.083,35,72,0.997,3.44,0.52,9.4,0.5 977 | 7.2,0.41,0.3,2.1,0.083,35,72,0.997,3.44,0.52,9.4,0.5 978 | 8.4,0.59,0.29,2.6,0.109,31,119,0.99801,3.15,0.5,9.1,0.5 979 | 7,0.4,0.32,3.6,0.061,9,29,0.99416,3.28,0.49,11.3,0.7 980 | 12.2,0.45,0.49,1.4,0.075,3,6,0.9969,3.13,0.63,10.4,0.5 981 | 9.1,0.5,0.3,1.9,0.065,8,17,0.99774,3.32,0.71,10.5,0.6 982 | 9.5,0.86,0.26,1.9,0.079,13,28,0.99712,3.25,0.62,10,0.5 983 | 7.3,0.52,0.32,2.1,0.07,51,70,0.99418,3.34,0.82,12.9,0.6 984 | 9.1,0.5,0.3,1.9,0.065,8,17,0.99774,3.32,0.71,10.5,0.6 985 | 12.2,0.45,0.49,1.4,0.075,3,6,0.9969,3.13,0.63,10.4,0.5 986 | 7.4,0.58,0,2,0.064,7,11,0.99562,3.45,0.58,11.3,0.6 987 | 9.8,0.34,0.39,1.4,0.066,3,7,0.9947,3.19,0.55,11.4,0.7 988 | 7.1,0.36,0.3,1.6,0.08,35,70,0.99693,3.44,0.5,9.4,0.5 989 | 7.7,0.39,0.12,1.7,0.097,19,27,0.99596,3.16,0.49,9.4,0.5 990 | 9.7,0.295,0.4,1.5,0.073,14,21,0.99556,3.14,0.51,10.9,0.6 991 | 7.7,0.39,0.12,1.7,0.097,19,27,0.99596,3.16,0.49,9.4,0.5 992 | 7.1,0.34,0.28,2,0.082,31,68,0.99694,3.45,0.48,9.4,0.5 993 | 6.5,0.4,0.1,2,0.076,30,47,0.99554,3.36,0.48,9.4,0.6 994 | 7.1,0.34,0.28,2,0.082,31,68,0.99694,3.45,0.48,9.4,0.5 995 | 10,0.35,0.45,2.5,0.092,20,88,0.99918,3.15,0.43,9.4,0.5 996 | 7.7,0.6,0.06,2,0.079,19,41,0.99697,3.39,0.62,10.1,0.6 997 | 5.6,0.66,0,2.2,0.087,3,11,0.99378,3.71,0.63,12.8,0.7 998 | 5.6,0.66,0,2.2,0.087,3,11,0.99378,3.71,0.63,12.8,0.7 999 | 8.9,0.84,0.34,1.4,0.05,4,10,0.99554,3.12,0.48,9.1,0.6 1000 | 6.4,0.69,0,1.65,0.055,7,12,0.99162,3.47,0.53,12.9,0.6 1001 | 7.5,0.43,0.3,2.2,0.062,6,12,0.99495,3.44,0.72,11.5,0.7 1002 | 9.9,0.35,0.38,1.5,0.058,31,47,0.99676,3.26,0.82,10.6,0.7 1003 | 9.1,0.29,0.33,2.05,0.063,13,27,0.99516,3.26,0.84,11.7,0.7 1004 | 6.8,0.36,0.32,1.8,0.067,4,8,0.9928,3.36,0.55,12.8,0.7 1005 | 8.2,0.43,0.29,1.6,0.081,27,45,0.99603,3.25,0.54,10.3,0.5 1006 | 6.8,0.36,0.32,1.8,0.067,4,8,0.9928,3.36,0.55,12.8,0.7 1007 | 9.1,0.29,0.33,2.05,0.063,13,27,0.99516,3.26,0.84,11.7,0.7 1008 | 9.1,0.3,0.34,2,0.064,12,25,0.99516,3.26,0.84,11.7,0.7 1009 | 8.9,0.35,0.4,3.6,0.11,12,24,0.99549,3.23,0.7,12,0.7 1010 | 9.6,0.5,0.36,2.8,0.116,26,55,0.99722,3.18,0.68,10.9,0.5 1011 | 8.9,0.28,0.45,1.7,0.067,7,12,0.99354,3.25,0.55,12.3,0.7 1012 | 8.9,0.32,0.31,2,0.088,12,19,0.9957,3.17,0.55,10.4,0.6 1013 | 7.7,1.005,0.15,2.1,0.102,11,32,0.99604,3.23,0.48,10,0.5 1014 | 7.5,0.71,0,1.6,0.092,22,31,0.99635,3.38,0.58,10,0.6 1015 | 8,0.58,0.16,2,0.12,3,7,0.99454,3.22,0.58,11.2,0.6 1016 | 10.5,0.39,0.46,2.2,0.075,14,27,0.99598,3.06,0.84,11.4,0.6 1017 | 8.9,0.38,0.4,2.2,0.068,12,28,0.99486,3.27,0.75,12.6,0.7 1018 | 8,0.18,0.37,0.9,0.049,36,109,0.99007,2.89,0.44,12.7,0.6 1019 | 8,0.18,0.37,0.9,0.049,36,109,0.99007,2.89,0.44,12.7,0.6 1020 | 7,0.5,0.14,1.8,0.078,10,23,0.99636,3.53,0.61,10.4,0.5 1021 | 11.3,0.36,0.66,2.4,0.123,3,8,0.99642,3.2,0.53,11.9,0.6 1022 | 11.3,0.36,0.66,2.4,0.123,3,8,0.99642,3.2,0.53,11.9,0.6 1023 | 7,0.51,0.09,2.1,0.062,4,9,0.99584,3.35,0.54,10.5,0.5 1024 | 8.2,0.32,0.42,2.3,0.098,3,9,0.99506,3.27,0.55,12.3,0.6 1025 | 7.7,0.58,0.01,1.8,0.088,12,18,0.99568,3.32,0.56,10.5,0.7 1026 | 8.6,0.83,0,2.8,0.095,17,43,0.99822,3.33,0.6,10.4,0.6 1027 | 7.9,0.31,0.32,1.9,0.066,14,36,0.99364,3.41,0.56,12.6,0.6 1028 | 6.4,0.795,0,2.2,0.065,28,52,0.99378,3.49,0.52,11.6,0.5 1029 | 7.2,0.34,0.21,2.5,0.075,41,68,0.99586,3.37,0.54,10.1,0.6 1030 | 7.7,0.58,0.01,1.8,0.088,12,18,0.99568,3.32,0.56,10.5,0.7 1031 | 7.1,0.59,0,2.1,0.091,9,14,0.99488,3.42,0.55,11.5,0.7 1032 | 7.3,0.55,0.01,1.8,0.093,9,15,0.99514,3.35,0.58,11,0.7 1033 | 8.1,0.82,0,4.1,0.095,5,14,0.99854,3.36,0.53,9.6,0.5 1034 | 7.5,0.57,0.08,2.6,0.089,14,27,0.99592,3.3,0.59,10.4,0.6 1035 | 8.9,0.745,0.18,2.5,0.077,15,48,0.99739,3.2,0.47,9.7,0.6 1036 | 10.1,0.37,0.34,2.4,0.085,5,17,0.99683,3.17,0.65,10.6,0.7 1037 | 7.6,0.31,0.34,2.5,0.082,26,35,0.99356,3.22,0.59,12.5,0.7 1038 | 7.3,0.91,0.1,1.8,0.074,20,56,0.99672,3.35,0.56,9.2,0.5 1039 | 8.7,0.41,0.41,6.2,0.078,25,42,0.9953,3.24,0.77,12.6,0.7 1040 | 8.9,0.5,0.21,2.2,0.088,21,39,0.99692,3.33,0.83,11.1,0.6 1041 | 7.4,0.965,0,2.2,0.088,16,32,0.99756,3.58,0.67,10.2,0.5 1042 | 6.9,0.49,0.19,1.7,0.079,13,26,0.99547,3.38,0.64,9.8,0.6 1043 | 8.9,0.5,0.21,2.2,0.088,21,39,0.99692,3.33,0.83,11.1,0.6 1044 | 9.5,0.39,0.41,8.9,0.069,18,39,0.99859,3.29,0.81,10.9,0.7 1045 | 6.4,0.39,0.33,3.3,0.046,12,53,0.99294,3.36,0.62,12.2,0.6 1046 | 6.9,0.44,0,1.4,0.07,32,38,0.99438,3.32,0.58,11.4,0.6 1047 | 7.6,0.78,0,1.7,0.076,33,45,0.99612,3.31,0.62,10.7,0.6 1048 | 7.1,0.43,0.17,1.8,0.082,27,51,0.99634,3.49,0.64,10.4,0.5 1049 | 9.3,0.49,0.36,1.7,0.081,3,14,0.99702,3.27,0.78,10.9,0.6 1050 | 9.3,0.5,0.36,1.8,0.084,6,17,0.99704,3.27,0.77,10.8,0.6 1051 | 7.1,0.43,0.17,1.8,0.082,27,51,0.99634,3.49,0.64,10.4,0.5 1052 | 8.5,0.46,0.59,1.4,0.414,16,45,0.99702,3.03,1.34,9.2,0.5 1053 | 5.6,0.605,0.05,2.4,0.073,19,25,0.99258,3.56,0.55,12.9,0.5 1054 | 8.3,0.33,0.42,2.3,0.07,9,20,0.99426,3.38,0.77,12.7,0.7 1055 | 8.2,0.64,0.27,2,0.095,5,77,0.99747,3.13,0.62,9.1,0.6 1056 | 8.2,0.64,0.27,2,0.095,5,77,0.99747,3.13,0.62,9.1,0.6 1057 | 8.9,0.48,0.53,4,0.101,3,10,0.99586,3.21,0.59,12.1,0.7 1058 | 7.6,0.42,0.25,3.9,0.104,28,90,0.99784,3.15,0.57,9.1,0.5 1059 | 9.9,0.53,0.57,2.4,0.093,30,52,0.9971,3.19,0.76,11.6,0.7 1060 | 8.9,0.48,0.53,4,0.101,3,10,0.99586,3.21,0.59,12.1,0.7 1061 | 11.6,0.23,0.57,1.8,0.074,3,8,0.9981,3.14,0.7,9.9,0.6 1062 | 9.1,0.4,0.5,1.8,0.071,7,16,0.99462,3.21,0.69,12.5,0.8 1063 | 8,0.38,0.44,1.9,0.098,6,15,0.9956,3.3,0.64,11.4,0.6 1064 | 10.2,0.29,0.65,2.4,0.075,6,17,0.99565,3.22,0.63,11.8,0.6 1065 | 8.2,0.74,0.09,2,0.067,5,10,0.99418,3.28,0.57,11.8,0.6 1066 | 7.7,0.61,0.18,2.4,0.083,6,20,0.9963,3.29,0.6,10.2,0.6 1067 | 6.6,0.52,0.08,2.4,0.07,13,26,0.99358,3.4,0.72,12.5,0.7 1068 | 11.1,0.31,0.53,2.2,0.06,3,10,0.99572,3.02,0.83,10.9,0.7 1069 | 11.1,0.31,0.53,2.2,0.06,3,10,0.99572,3.02,0.83,10.9,0.7 1070 | 8,0.62,0.35,2.8,0.086,28,52,0.997,3.31,0.62,10.8,0.5 1071 | 9.3,0.33,0.45,1.5,0.057,19,37,0.99498,3.18,0.89,11.1,0.7 1072 | 7.5,0.77,0.2,8.1,0.098,30,92,0.99892,3.2,0.58,9.2,0.5 1073 | 7.2,0.35,0.26,1.8,0.083,33,75,0.9968,3.4,0.58,9.5,0.6 1074 | 8,0.62,0.33,2.7,0.088,16,37,0.9972,3.31,0.58,10.7,0.6 1075 | 7.5,0.77,0.2,8.1,0.098,30,92,0.99892,3.2,0.58,9.2,0.5 1076 | 9.1,0.25,0.34,2,0.071,45,67,0.99769,3.44,0.86,10.2,0.7 1077 | 9.9,0.32,0.56,2,0.073,3,8,0.99534,3.15,0.73,11.4,0.6 1078 | 8.6,0.37,0.65,6.4,0.08,3,8,0.99817,3.27,0.58,11,0.5 1079 | 8.6,0.37,0.65,6.4,0.08,3,8,0.99817,3.27,0.58,11,0.5 1080 | 7.9,0.3,0.68,8.3,0.05,37.5,278,0.99316,3.01,0.51,12.3,0.7 1081 | 10.3,0.27,0.56,1.4,0.047,3,8,0.99471,3.16,0.51,11.8,0.6 1082 | 7.9,0.3,0.68,8.3,0.05,37.5,289,0.99316,3.01,0.51,12.3,0.7 1083 | 7.2,0.38,0.3,1.8,0.073,31,70,0.99685,3.42,0.59,9.5,0.6 1084 | 8.7,0.42,0.45,2.4,0.072,32,59,0.99617,3.33,0.77,12,0.6 1085 | 7.2,0.38,0.3,1.8,0.073,31,70,0.99685,3.42,0.59,9.5,0.6 1086 | 6.8,0.48,0.08,1.8,0.074,40,64,0.99529,3.12,0.49,9.6,0.5 1087 | 8.5,0.34,0.4,4.7,0.055,3,9,0.99738,3.38,0.66,11.6,0.7 1088 | 7.9,0.19,0.42,1.6,0.057,18,30,0.994,3.29,0.69,11.2,0.6 1089 | 11.6,0.41,0.54,1.5,0.095,22,41,0.99735,3.02,0.76,9.9,0.7 1090 | 11.6,0.41,0.54,1.5,0.095,22,41,0.99735,3.02,0.76,9.9,0.7 1091 | 10,0.26,0.54,1.9,0.083,42,74,0.99451,2.98,0.63,11.8,0.8 1092 | 7.9,0.34,0.42,2,0.086,8,19,0.99546,3.35,0.6,11.4,0.6 1093 | 7,0.54,0.09,2,0.081,10,16,0.99479,3.43,0.59,11.5,0.6 1094 | 9.2,0.31,0.36,2.2,0.079,11,31,0.99615,3.33,0.86,12,0.7 1095 | 6.6,0.725,0.09,5.5,0.117,9,17,0.99655,3.35,0.49,10.8,0.6 1096 | 9.4,0.4,0.47,2.5,0.087,6,20,0.99772,3.15,0.5,10.5,0.5 1097 | 6.6,0.725,0.09,5.5,0.117,9,17,0.99655,3.35,0.49,10.8,0.6 1098 | 8.6,0.52,0.38,1.5,0.096,5,18,0.99666,3.2,0.52,9.4,0.5 1099 | 8,0.31,0.45,2.1,0.216,5,16,0.99358,3.15,0.81,12.5,0.7 1100 | 8.6,0.52,0.38,1.5,0.096,5,18,0.99666,3.2,0.52,9.4,0.5 1101 | 8.4,0.34,0.42,2.1,0.072,23,36,0.99392,3.11,0.78,12.4,0.6 1102 | 7.4,0.49,0.27,2.1,0.071,14,25,0.99388,3.35,0.63,12,0.6 1103 | 6.1,0.48,0.09,1.7,0.078,18,30,0.99402,3.45,0.54,11.2,0.6 1104 | 7.4,0.49,0.27,2.1,0.071,14,25,0.99388,3.35,0.63,12,0.6 1105 | 8,0.48,0.34,2.2,0.073,16,25,0.9936,3.28,0.66,12.4,0.6 1106 | 6.3,0.57,0.28,2.1,0.048,13,49,0.99374,3.41,0.6,12.8,0.5 1107 | 8.2,0.23,0.42,1.9,0.069,9,17,0.99376,3.21,0.54,12.3,0.6 1108 | 9.1,0.3,0.41,2,0.068,10,24,0.99523,3.27,0.85,11.7,0.7 1109 | 8.1,0.78,0.1,3.3,0.09,4,13,0.99855,3.36,0.49,9.5,0.5 1110 | 10.8,0.47,0.43,2.1,0.171,27,66,0.9982,3.17,0.76,10.8,0.6 1111 | 8.3,0.53,0,1.4,0.07,6,14,0.99593,3.25,0.64,10,0.6 1112 | 5.4,0.42,0.27,2,0.092,23,55,0.99471,3.78,0.64,12.3,0.7 1113 | 7.9,0.33,0.41,1.5,0.056,6,35,0.99396,3.29,0.71,11,0.6 1114 | 8.9,0.24,0.39,1.6,0.074,3,10,0.99698,3.12,0.59,9.5,0.6 1115 | 5,0.4,0.5,4.3,0.046,29,80,0.9902,3.49,0.66,13.6,0.6 1116 | 7,0.69,0.07,2.5,0.091,15,21,0.99572,3.38,0.6,11.3,0.6 1117 | 7,0.69,0.07,2.5,0.091,15,21,0.99572,3.38,0.6,11.3,0.6 1118 | 7,0.69,0.07,2.5,0.091,15,21,0.99572,3.38,0.6,11.3,0.6 1119 | 7.1,0.39,0.12,2.1,0.065,14,24,0.99252,3.3,0.53,13.3,0.6 1120 | 5.6,0.66,0,2.5,0.066,7,15,0.99256,3.52,0.58,12.9,0.5 1121 | 7.9,0.54,0.34,2.5,0.076,8,17,0.99235,3.2,0.72,13.1,0.8 1122 | 6.6,0.5,0,1.8,0.062,21,28,0.99352,3.44,0.55,12.3,0.6 1123 | 6.3,0.47,0,1.4,0.055,27,33,0.9922,3.45,0.48,12.3,0.6 1124 | 10.7,0.4,0.37,1.9,0.081,17,29,0.99674,3.12,0.65,11.2,0.6 1125 | 6.5,0.58,0,2.2,0.096,3,13,0.99557,3.62,0.62,11.5,0.4 1126 | 8.8,0.24,0.35,1.7,0.055,13,27,0.99394,3.14,0.59,11.3,0.7 1127 | 5.8,0.29,0.26,1.7,0.063,3,11,0.9915,3.39,0.54,13.5,0.6 1128 | 6.3,0.76,0,2.9,0.072,26,52,0.99379,3.51,0.6,11.5,0.6 1129 | 10,0.43,0.33,2.7,0.095,28,89,0.9984,3.22,0.68,10,0.5 1130 | 10.5,0.43,0.35,3.3,0.092,24,70,0.99798,3.21,0.69,10.5,0.6 1131 | 9.1,0.6,0,1.9,0.058,5,10,0.9977,3.18,0.63,10.4,0.6 1132 | 5.9,0.19,0.21,1.7,0.045,57,135,0.99341,3.32,0.44,9.5,0.5 1133 | 7.4,0.36,0.34,1.8,0.075,18,38,0.9933,3.38,0.88,13.6,0.7 1134 | 7.2,0.48,0.07,5.5,0.089,10,18,0.99684,3.37,0.68,11.2,0.7 1135 | 8.5,0.28,0.35,1.7,0.061,6,15,0.99524,3.3,0.74,11.8,0.7 1136 | 8,0.25,0.43,1.7,0.067,22,50,0.9946,3.38,0.6,11.9,0.6 1137 | 10.4,0.52,0.45,2,0.08,6,13,0.99774,3.22,0.76,11.4,0.6 1138 | 10.4,0.52,0.45,2,0.08,6,13,0.99774,3.22,0.76,11.4,0.6 1139 | 7.5,0.41,0.15,3.7,0.104,29,94,0.99786,3.14,0.58,9.1,0.5 1140 | 8.2,0.51,0.24,2,0.079,16,86,0.99764,3.34,0.64,9.5,0.6 1141 | 7.3,0.4,0.3,1.7,0.08,33,79,0.9969,3.41,0.65,9.5,0.6 1142 | 8.2,0.38,0.32,2.5,0.08,24,71,0.99624,3.27,0.85,11,0.6 1143 | 6.9,0.45,0.11,2.4,0.043,6,12,0.99354,3.3,0.65,11.4,0.6 1144 | 7,0.22,0.3,1.8,0.065,16,20,0.99672,3.61,0.82,10,0.6 1145 | 7.3,0.32,0.23,2.3,0.066,35,70,0.99588,3.43,0.62,10.1,0.5 1146 | 8.2,0.2,0.43,2.5,0.076,31,51,0.99672,3.53,0.81,10.4,0.6 1147 | 7.8,0.5,0.12,1.8,0.178,6,21,0.996,3.28,0.87,9.8,0.6 1148 | 10,0.41,0.45,6.2,0.071,6,14,0.99702,3.21,0.49,11.8,0.7 1149 | 7.8,0.39,0.42,2,0.086,9,21,0.99526,3.39,0.66,11.6,0.6 1150 | 10,0.35,0.47,2,0.061,6,11,0.99585,3.23,0.52,12,0.6 1151 | 8.2,0.33,0.32,2.8,0.067,4,12,0.99473,3.3,0.76,12.8,0.7 1152 | 6.1,0.58,0.23,2.5,0.044,16,70,0.99352,3.46,0.65,12.5,0.6 1153 | 8.3,0.6,0.25,2.2,0.118,9,38,0.99616,3.15,0.53,9.8,0.5 1154 | 9.6,0.42,0.35,2.1,0.083,17,38,0.99622,3.23,0.66,11.1,0.6 1155 | 6.6,0.58,0,2.2,0.1,50,63,0.99544,3.59,0.68,11.4,0.6 1156 | 8.3,0.6,0.25,2.2,0.118,9,38,0.99616,3.15,0.53,9.8,0.5 1157 | 8.5,0.18,0.51,1.75,0.071,45,88,0.99524,3.33,0.76,11.8,0.7 1158 | 5.1,0.51,0.18,2.1,0.042,16,101,0.9924,3.46,0.87,12.9,0.7 1159 | 6.7,0.41,0.43,2.8,0.076,22,54,0.99572,3.42,1.16,10.6,0.6 1160 | 10.2,0.41,0.43,2.2,0.11,11,37,0.99728,3.16,0.67,10.8,0.5 1161 | 10.6,0.36,0.57,2.3,0.087,6,20,0.99676,3.14,0.72,11.1,0.7 1162 | 8.8,0.45,0.43,1.4,0.076,12,21,0.99551,3.21,0.75,10.2,0.6 1163 | 8.5,0.32,0.42,2.3,0.075,12,19,0.99434,3.14,0.71,11.8,0.7 1164 | 9,0.785,0.24,1.7,0.078,10,21,0.99692,3.29,0.67,10,0.5 1165 | 9,0.785,0.24,1.7,0.078,10,21,0.99692,3.29,0.67,10,0.5 1166 | 8.5,0.44,0.5,1.9,0.369,15,38,0.99634,3.01,1.1,9.4,0.5 1167 | 9.9,0.54,0.26,2,0.111,7,60,0.99709,2.94,0.98,10.2,0.5 1168 | 8.2,0.33,0.39,2.5,0.074,29,48,0.99528,3.32,0.88,12.4,0.7 1169 | 6.5,0.34,0.27,2.8,0.067,8,44,0.99384,3.21,0.56,12,0.6 1170 | 7.6,0.5,0.29,2.3,0.086,5,14,0.99502,3.32,0.62,11.5,0.6 1171 | 9.2,0.36,0.34,1.6,0.062,5,12,0.99667,3.2,0.67,10.5,0.6 1172 | 7.1,0.59,0,2.2,0.078,26,44,0.99522,3.42,0.68,10.8,0.6 1173 | 9.7,0.42,0.46,2.1,0.074,5,16,0.99649,3.27,0.74,12.3,0.6 1174 | 7.6,0.36,0.31,1.7,0.079,26,65,0.99716,3.46,0.62,9.5,0.6 1175 | 7.6,0.36,0.31,1.7,0.079,26,65,0.99716,3.46,0.62,9.5,0.6 1176 | 6.5,0.61,0,2.2,0.095,48,59,0.99541,3.61,0.7,11.5,0.6 1177 | 6.5,0.88,0.03,5.6,0.079,23,47,0.99572,3.58,0.5,11.2,0.4 1178 | 7.1,0.66,0,2.4,0.052,6,11,0.99318,3.35,0.66,12.7,0.7 1179 | 5.6,0.915,0,2.1,0.041,17,78,0.99346,3.68,0.73,11.4,0.5 1180 | 8.2,0.35,0.33,2.4,0.076,11,47,0.99599,3.27,0.81,11,0.6 1181 | 8.2,0.35,0.33,2.4,0.076,11,47,0.99599,3.27,0.81,11,0.6 1182 | 9.8,0.39,0.43,1.65,0.068,5,11,0.99478,3.19,0.46,11.4,0.5 1183 | 10.2,0.4,0.4,2.5,0.068,41,54,0.99754,3.38,0.86,10.5,0.6 1184 | 6.8,0.66,0.07,1.6,0.07,16,61,0.99572,3.29,0.6,9.3,0.5 1185 | 6.7,0.64,0.23,2.1,0.08,11,119,0.99538,3.36,0.7,10.9,0.5 1186 | 7,0.43,0.3,2,0.085,6,39,0.99346,3.33,0.46,11.9,0.6 1187 | 6.6,0.8,0.03,7.8,0.079,6,12,0.9963,3.52,0.5,12.2,0.5 1188 | 7,0.43,0.3,2,0.085,6,39,0.99346,3.33,0.46,11.9,0.6 1189 | 6.7,0.64,0.23,2.1,0.08,11,119,0.99538,3.36,0.7,10.9,0.5 1190 | 8.8,0.955,0.05,1.8,0.075,5,19,0.99616,3.3,0.44,9.6,0.4 1191 | 9.1,0.4,0.57,4.6,0.08,6,20,0.99652,3.28,0.57,12.5,0.6 1192 | 6.5,0.885,0,2.3,0.166,6,12,0.99551,3.56,0.51,10.8,0.5 1193 | 7.2,0.25,0.37,2.5,0.063,11,41,0.99439,3.52,0.8,12.4,0.7 1194 | 6.4,0.885,0,2.3,0.166,6,12,0.99551,3.56,0.51,10.8,0.5 1195 | 7,0.745,0.12,1.8,0.114,15,64,0.99588,3.22,0.59,9.5,0.6 1196 | 6.2,0.43,0.22,1.8,0.078,21,56,0.99633,3.52,0.6,9.5,0.6 1197 | 7.9,0.58,0.23,2.3,0.076,23,94,0.99686,3.21,0.58,9.5,0.6 1198 | 7.7,0.57,0.21,1.5,0.069,4,9,0.99458,3.16,0.54,9.8,0.6 1199 | 7.7,0.26,0.26,2,0.052,19,77,0.9951,3.15,0.79,10.9,0.6 1200 | 7.9,0.58,0.23,2.3,0.076,23,94,0.99686,3.21,0.58,9.5,0.6 1201 | 7.7,0.57,0.21,1.5,0.069,4,9,0.99458,3.16,0.54,9.8,0.6 1202 | 7.9,0.34,0.36,1.9,0.065,5,10,0.99419,3.27,0.54,11.2,0.7 1203 | 8.6,0.42,0.39,1.8,0.068,6,12,0.99516,3.35,0.69,11.7,0.8 1204 | 9.9,0.74,0.19,5.8,0.111,33,76,0.99878,3.14,0.55,9.4,0.5 1205 | 7.2,0.36,0.46,2.1,0.074,24,44,0.99534,3.4,0.85,11,0.7 1206 | 7.2,0.36,0.46,2.1,0.074,24,44,0.99534,3.4,0.85,11,0.7 1207 | 7.2,0.36,0.46,2.1,0.074,24,44,0.99534,3.4,0.85,11,0.7 1208 | 9.9,0.72,0.55,1.7,0.136,24,52,0.99752,3.35,0.94,10,0.5 1209 | 7.2,0.36,0.46,2.1,0.074,24,44,0.99534,3.4,0.85,11,0.7 1210 | 6.2,0.39,0.43,2,0.071,14,24,0.99428,3.45,0.87,11.2,0.7 1211 | 6.8,0.65,0.02,2.1,0.078,8,15,0.99498,3.35,0.62,10.4,0.6 1212 | 6.6,0.44,0.15,2.1,0.076,22,53,0.9957,3.32,0.62,9.3,0.5 1213 | 6.8,0.65,0.02,2.1,0.078,8,15,0.99498,3.35,0.62,10.4,0.6 1214 | 9.6,0.38,0.42,1.9,0.071,5,13,0.99659,3.15,0.75,10.5,0.6 1215 | 10.2,0.33,0.46,1.9,0.081,6,9,0.99628,3.1,0.48,10.4,0.6 1216 | 8.8,0.27,0.46,2.1,0.095,20,29,0.99488,3.26,0.56,11.3,0.6 1217 | 7.9,0.57,0.31,2,0.079,10,79,0.99677,3.29,0.69,9.5,0.6 1218 | 8.2,0.34,0.37,1.9,0.057,43,74,0.99408,3.23,0.81,12,0.6 1219 | 8.2,0.4,0.31,1.9,0.082,8,24,0.996,3.24,0.69,10.6,0.6 1220 | 9,0.39,0.4,1.3,0.044,25,50,0.99478,3.2,0.83,10.9,0.6 1221 | 10.9,0.32,0.52,1.8,0.132,17,44,0.99734,3.28,0.77,11.5,0.6 1222 | 10.9,0.32,0.52,1.8,0.132,17,44,0.99734,3.28,0.77,11.5,0.6 1223 | 8.1,0.53,0.22,2.2,0.078,33,89,0.99678,3.26,0.46,9.6,0.6 1224 | 10.5,0.36,0.47,2.2,0.074,9,23,0.99638,3.23,0.76,12,0.6 1225 | 12.6,0.39,0.49,2.5,0.08,8,20,0.9992,3.07,0.82,10.3,0.6 1226 | 9.2,0.46,0.23,2.6,0.091,18,77,0.99922,3.15,0.51,9.4,0.5 1227 | 7.5,0.58,0.03,4.1,0.08,27,46,0.99592,3.02,0.47,9.2,0.5 1228 | 9,0.58,0.25,2,0.104,8,21,0.99769,3.27,0.72,9.6,0.5 1229 | 5.1,0.42,0,1.8,0.044,18,88,0.99157,3.68,0.73,13.6,0.7 1230 | 7.6,0.43,0.29,2.1,0.075,19,66,0.99718,3.4,0.64,9.5,0.5 1231 | 7.7,0.18,0.34,2.7,0.066,15,58,0.9947,3.37,0.78,11.8,0.6 1232 | 7.8,0.815,0.01,2.6,0.074,48,90,0.99621,3.38,0.62,10.8,0.5 1233 | 7.6,0.43,0.29,2.1,0.075,19,66,0.99718,3.4,0.64,9.5,0.5 1234 | 10.2,0.23,0.37,2.2,0.057,14,36,0.99614,3.23,0.49,9.3,0.4 1235 | 7.1,0.75,0.01,2.2,0.059,11,18,0.99242,3.39,0.4,12.8,0.6 1236 | 6,0.33,0.32,12.9,0.054,6,113,0.99572,3.3,0.56,11.5,0.4 1237 | 7.8,0.55,0,1.7,0.07,7,17,0.99659,3.26,0.64,9.4,0.6 1238 | 7.1,0.75,0.01,2.2,0.059,11,18,0.99242,3.39,0.4,12.8,0.6 1239 | 8.1,0.73,0,2.5,0.081,12,24,0.99798,3.38,0.46,9.6,0.4 1240 | 6.5,0.67,0,4.3,0.057,11,20,0.99488,3.45,0.56,11.8,0.4 1241 | 7.5,0.61,0.2,1.7,0.076,36,60,0.99494,3.1,0.4,9.3,0.5 1242 | 9.8,0.37,0.39,2.5,0.079,28,65,0.99729,3.16,0.59,9.8,0.5 1243 | 9,0.4,0.41,2,0.058,15,40,0.99414,3.22,0.6,12.2,0.6 1244 | 8.3,0.56,0.22,2.4,0.082,10,86,0.9983,3.37,0.62,9.5,0.5 1245 | 5.9,0.29,0.25,13.4,0.067,72,160,0.99721,3.33,0.54,10.3,0.6 1246 | 7.4,0.55,0.19,1.8,0.082,15,34,0.99655,3.49,0.68,10.5,0.5 1247 | 7.4,0.74,0.07,1.7,0.086,15,48,0.99502,3.12,0.48,10,0.5 1248 | 7.4,0.55,0.19,1.8,0.082,15,34,0.99655,3.49,0.68,10.5,0.5 1249 | 6.9,0.41,0.33,2.2,0.081,22,36,0.9949,3.41,0.75,11.1,0.6 1250 | 7.1,0.6,0.01,2.3,0.079,24,37,0.99514,3.4,0.61,10.9,0.6 1251 | 7.1,0.6,0.01,2.3,0.079,24,37,0.99514,3.4,0.61,10.9,0.6 1252 | 7.5,0.58,0.14,2.2,0.077,27,60,0.9963,3.28,0.59,9.8,0.5 1253 | 7.1,0.72,0,1.8,0.123,6,14,0.99627,3.45,0.58,9.8,0.5 1254 | 7.9,0.66,0,1.4,0.096,6,13,0.99569,3.43,0.58,9.5,0.5 1255 | 7.8,0.7,0.06,1.9,0.079,20,35,0.99628,3.4,0.69,10.9,0.5 1256 | 6.1,0.64,0.02,2.4,0.069,26,46,0.99358,3.47,0.45,11,0.5 1257 | 7.5,0.59,0.22,1.8,0.082,43,60,0.99499,3.1,0.42,9.2,0.5 1258 | 7,0.58,0.28,4.8,0.085,12,69,0.99633,3.32,0.7,11,0.6 1259 | 6.8,0.64,0,2.7,0.123,15,33,0.99538,3.44,0.63,11.3,0.6 1260 | 6.8,0.64,0,2.7,0.123,15,33,0.99538,3.44,0.63,11.3,0.6 1261 | 8.6,0.635,0.68,1.8,0.403,19,56,0.99632,3.02,1.15,9.3,0.5 1262 | 6.3,1.02,0,2,0.083,17,24,0.99437,3.59,0.55,11.2,0.4 1263 | 9.8,0.45,0.38,2.5,0.081,34,66,0.99726,3.15,0.58,9.8,0.5 1264 | 8.2,0.78,0,2.2,0.089,13,26,0.9978,3.37,0.46,9.6,0.4 1265 | 8.5,0.37,0.32,1.8,0.066,26,51,0.99456,3.38,0.72,11.8,0.6 1266 | 7.2,0.57,0.05,2.3,0.081,16,36,0.99564,3.38,0.6,10.3,0.6 1267 | 7.2,0.57,0.05,2.3,0.081,16,36,0.99564,3.38,0.6,10.3,0.6 1268 | 10.4,0.43,0.5,2.3,0.068,13,19,0.996,3.1,0.87,11.4,0.6 1269 | 6.9,0.41,0.31,2,0.079,21,51,0.99668,3.47,0.55,9.5,0.6 1270 | 5.5,0.49,0.03,1.8,0.044,28,87,0.9908,3.5,0.82,14,0.8 1271 | 5,0.38,0.01,1.6,0.048,26,60,0.99084,3.7,0.75,14,0.6 1272 | 7.3,0.44,0.2,1.6,0.049,24,64,0.9935,3.38,0.57,11.7,0.6 1273 | 5.9,0.46,0,1.9,0.077,25,44,0.99385,3.5,0.53,11.2,0.5 1274 | 7.5,0.58,0.2,2,0.073,34,44,0.99494,3.1,0.43,9.3,0.5 1275 | 7.8,0.58,0.13,2.1,0.102,17,36,0.9944,3.24,0.53,11.2,0.6 1276 | 8,0.715,0.22,2.3,0.075,13,81,0.99688,3.24,0.54,9.5,0.6 1277 | 8.5,0.4,0.4,6.3,0.05,3,10,0.99566,3.28,0.56,12,0.4 1278 | 7,0.69,0,1.9,0.114,3,10,0.99636,3.35,0.6,9.7,0.6 1279 | 8,0.715,0.22,2.3,0.075,13,81,0.99688,3.24,0.54,9.5,0.6 1280 | 9.8,0.3,0.39,1.7,0.062,3,9,0.9948,3.14,0.57,11.5,0.7 1281 | 7.1,0.46,0.2,1.9,0.077,28,54,0.9956,3.37,0.64,10.4,0.6 1282 | 7.1,0.46,0.2,1.9,0.077,28,54,0.9956,3.37,0.64,10.4,0.6 1283 | 7.9,0.765,0,2,0.084,9,22,0.99619,3.33,0.68,10.9,0.6 1284 | 8.7,0.63,0.28,2.7,0.096,17,69,0.99734,3.26,0.63,10.2,0.6 1285 | 7,0.42,0.19,2.3,0.071,18,36,0.99476,3.39,0.56,10.9,0.5 1286 | 11.3,0.37,0.5,1.8,0.09,20,47,0.99734,3.15,0.57,10.5,0.5 1287 | 7.1,0.16,0.44,2.5,0.068,17,31,0.99328,3.35,0.54,12.4,0.6 1288 | 8,0.6,0.08,2.6,0.056,3,7,0.99286,3.22,0.37,13,0.5 1289 | 7,0.6,0.3,4.5,0.068,20,110,0.99914,3.3,1.17,10.2,0.5 1290 | 7,0.6,0.3,4.5,0.068,20,110,0.99914,3.3,1.17,10.2,0.5 1291 | 7.6,0.74,0,1.9,0.1,6,12,0.99521,3.36,0.59,11,0.5 1292 | 8.2,0.635,0.1,2.1,0.073,25,60,0.99638,3.29,0.75,10.9,0.6 1293 | 5.9,0.395,0.13,2.4,0.056,14,28,0.99362,3.62,0.67,12.4,0.6 1294 | 7.5,0.755,0,1.9,0.084,6,12,0.99672,3.34,0.49,9.7,0.4 1295 | 8.2,0.635,0.1,2.1,0.073,25,60,0.99638,3.29,0.75,10.9,0.6 1296 | 6.6,0.63,0,4.3,0.093,51,77.5,0.99558,3.2,0.45,9.5,0.5 1297 | 6.6,0.63,0,4.3,0.093,51,77.5,0.99558,3.2,0.45,9.5,0.5 1298 | 7.2,0.53,0.14,2.1,0.064,15,29,0.99323,3.35,0.61,12.1,0.6 1299 | 5.7,0.6,0,1.4,0.063,11,18,0.99191,3.45,0.56,12.2,0.6 1300 | 7.6,1.58,0,2.1,0.137,5,9,0.99476,3.5,0.4,10.9,0.3 1301 | 5.2,0.645,0,2.15,0.08,15,28,0.99444,3.78,0.61,12.5,0.6 1302 | 6.7,0.86,0.07,2,0.1,20,57,0.99598,3.6,0.74,11.7,0.6 1303 | 9.1,0.37,0.32,2.1,0.064,4,15,0.99576,3.3,0.8,11.2,0.6 1304 | 8,0.28,0.44,1.8,0.081,28,68,0.99501,3.36,0.66,11.2,0.5 1305 | 7.6,0.79,0.21,2.3,0.087,21,68,0.9955,3.12,0.44,9.2,0.5 1306 | 7.5,0.61,0.26,1.9,0.073,24,88,0.99612,3.3,0.53,9.8,0.5 1307 | 9.7,0.69,0.32,2.5,0.088,22,91,0.9979,3.29,0.62,10.1,0.5 1308 | 6.8,0.68,0.09,3.9,0.068,15,29,0.99524,3.41,0.52,11.1,0.4 1309 | 9.7,0.69,0.32,2.5,0.088,22,91,0.9979,3.29,0.62,10.1,0.5 1310 | 7,0.62,0.1,1.4,0.071,27,63,0.996,3.28,0.61,9.2,0.5 1311 | 7.5,0.61,0.26,1.9,0.073,24,88,0.99612,3.3,0.53,9.8,0.5 1312 | 6.5,0.51,0.15,3,0.064,12,27,0.9929,3.33,0.59,12.8,0.6 1313 | 8,1.18,0.21,1.9,0.083,14,41,0.99532,3.34,0.47,10.5,0.5 1314 | 7,0.36,0.21,2.3,0.086,20,65,0.99558,3.4,0.54,10.1,0.6 1315 | 7,0.36,0.21,2.4,0.086,24,69,0.99556,3.4,0.53,10.1,0.6 1316 | 7.5,0.63,0.27,2,0.083,17,91,0.99616,3.26,0.58,9.8,0.6 1317 | 5.4,0.74,0,1.2,0.041,16,46,0.99258,4.01,0.59,12.5,0.6 1318 | 9.9,0.44,0.46,2.2,0.091,10,41,0.99638,3.18,0.69,11.9,0.6 1319 | 7.5,0.63,0.27,2,0.083,17,91,0.99616,3.26,0.58,9.8,0.6 1320 | 9.1,0.76,0.68,1.7,0.414,18,64,0.99652,2.9,1.33,9.1,0.6 1321 | 9.7,0.66,0.34,2.6,0.094,12,88,0.99796,3.26,0.66,10.1,0.5 1322 | 5,0.74,0,1.2,0.041,16,46,0.99258,4.01,0.59,12.5,0.6 1323 | 9.1,0.34,0.42,1.8,0.058,9,18,0.99392,3.18,0.55,11.4,0.5 1324 | 9.1,0.36,0.39,1.8,0.06,21,55,0.99495,3.18,0.82,11,0.7 1325 | 6.7,0.46,0.24,1.7,0.077,18,34,0.9948,3.39,0.6,10.6,0.6 1326 | 6.7,0.46,0.24,1.7,0.077,18,34,0.9948,3.39,0.6,10.6,0.6 1327 | 6.7,0.46,0.24,1.7,0.077,18,34,0.9948,3.39,0.6,10.6,0.6 1328 | 6.7,0.46,0.24,1.7,0.077,18,34,0.9948,3.39,0.6,10.6,0.6 1329 | 6.5,0.52,0.11,1.8,0.073,13,38,0.9955,3.34,0.52,9.3,0.5 1330 | 7.4,0.6,0.26,2.1,0.083,17,91,0.99616,3.29,0.56,9.8,0.6 1331 | 7.4,0.6,0.26,2.1,0.083,17,91,0.99616,3.29,0.56,9.8,0.6 1332 | 7.8,0.87,0.26,3.8,0.107,31,67,0.99668,3.26,0.46,9.2,0.5 1333 | 8.4,0.39,0.1,1.7,0.075,6,25,0.99581,3.09,0.43,9.7,0.6 1334 | 9.1,0.775,0.22,2.2,0.079,12,48,0.9976,3.18,0.51,9.6,0.5 1335 | 7.2,0.835,0,2,0.166,4,11,0.99608,3.39,0.52,10,0.5 1336 | 6.6,0.58,0.02,2.4,0.069,19,40,0.99387,3.38,0.66,12.6,0.6 1337 | 6,0.5,0,1.4,0.057,15,26,0.99448,3.36,0.45,9.5,0.5 1338 | 6,0.5,0,1.4,0.057,15,26,0.99448,3.36,0.45,9.5,0.5 1339 | 6,0.5,0,1.4,0.057,15,26,0.99448,3.36,0.45,9.5,0.5 1340 | 7.5,0.51,0.02,1.7,0.084,13,31,0.99538,3.36,0.54,10.5,0.6 1341 | 7.5,0.51,0.02,1.7,0.084,13,31,0.99538,3.36,0.54,10.5,0.6 1342 | 7.5,0.51,0.02,1.7,0.084,13,31,0.99538,3.36,0.54,10.5,0.6 1343 | 7.6,0.54,0.02,1.7,0.085,17,31,0.99589,3.37,0.51,10.4,0.6 1344 | 7.5,0.51,0.02,1.7,0.084,13,31,0.99538,3.36,0.54,10.5,0.6 1345 | 11.5,0.42,0.48,2.6,0.077,8,20,0.99852,3.09,0.53,11,0.5 1346 | 8.2,0.44,0.24,2.3,0.063,10,28,0.99613,3.25,0.53,10.2,0.6 1347 | 6.1,0.59,0.01,2.1,0.056,5,13,0.99472,3.52,0.56,11.4,0.5 1348 | 7.2,0.655,0.03,1.8,0.078,7,12,0.99587,3.34,0.39,9.5,0.5 1349 | 7.2,0.655,0.03,1.8,0.078,7,12,0.99587,3.34,0.39,9.5,0.5 1350 | 6.9,0.57,0,2.8,0.081,21,41,0.99518,3.41,0.52,10.8,0.5 1351 | 9,0.6,0.29,2,0.069,32,73,0.99654,3.34,0.57,10,0.5 1352 | 7.2,0.62,0.01,2.3,0.065,8,46,0.99332,3.32,0.51,11.8,0.6 1353 | 7.6,0.645,0.03,1.9,0.086,14,57,0.9969,3.37,0.46,10.3,0.5 1354 | 7.6,0.645,0.03,1.9,0.086,14,57,0.9969,3.37,0.46,10.3,0.5 1355 | 7.2,0.58,0.03,2.3,0.077,7,28,0.99568,3.35,0.52,10,0.5 1356 | 6.1,0.32,0.25,1.8,0.086,5,32,0.99464,3.36,0.44,10.1,0.5 1357 | 6.1,0.34,0.25,1.8,0.084,4,28,0.99464,3.36,0.44,10.1,0.5 1358 | 7.3,0.43,0.24,2.5,0.078,27,67,0.99648,3.6,0.59,11.1,0.6 1359 | 7.4,0.64,0.17,5.4,0.168,52,98,0.99736,3.28,0.5,9.5,0.5 1360 | 11.6,0.475,0.4,1.4,0.091,6,28,0.99704,3.07,0.65,10.0333333333333,0.6 1361 | 9.2,0.54,0.31,2.3,0.112,11,38,0.99699,3.24,0.56,10.9,0.5 1362 | 8.3,0.85,0.14,2.5,0.093,13,54,0.99724,3.36,0.54,10.1,0.5 1363 | 11.6,0.475,0.4,1.4,0.091,6,28,0.99704,3.07,0.65,10.0333333333333,0.6 1364 | 8,0.83,0.27,2,0.08,11,63,0.99652,3.29,0.48,9.8,0.4 1365 | 7.2,0.605,0.02,1.9,0.096,10,31,0.995,3.46,0.53,11.8,0.6 1366 | 7.8,0.5,0.09,2.2,0.115,10,42,0.9971,3.18,0.62,9.5,0.5 1367 | 7.3,0.74,0.08,1.7,0.094,10,45,0.99576,3.24,0.5,9.8,0.5 1368 | 6.9,0.54,0.3,2.2,0.088,9,105,0.99725,3.25,1.18,10.5,0.6 1369 | 8,0.77,0.32,2.1,0.079,16,74,0.99656,3.27,0.5,9.8,0.6 1370 | 6.6,0.61,0,1.6,0.069,4,8,0.99396,3.33,0.37,10.4,0.4 1371 | 8.7,0.78,0.51,1.7,0.415,12,66,0.99623,3,1.17,9.2,0.5 1372 | 7.5,0.58,0.56,3.1,0.153,5,14,0.99476,3.21,1.03,11.6,0.6 1373 | 8.7,0.78,0.51,1.7,0.415,12,66,0.99623,3,1.17,9.2,0.5 1374 | 7.7,0.75,0.27,3.8,0.11,34,89,0.99664,3.24,0.45,9.3,0.5 1375 | 6.8,0.815,0,1.2,0.267,16,29,0.99471,3.32,0.51,9.8,0.3 1376 | 7.2,0.56,0.26,2,0.083,13,100,0.99586,3.26,0.52,9.9,0.5 1377 | 8.2,0.885,0.2,1.4,0.086,7,31,0.9946,3.11,0.46,10,0.5 1378 | 5.2,0.49,0.26,2.3,0.09,23,74,0.9953,3.71,0.62,12.2,0.6 1379 | 7.2,0.45,0.15,2,0.078,10,28,0.99609,3.29,0.51,9.9,0.6 1380 | 7.5,0.57,0.02,2.6,0.077,11,35,0.99557,3.36,0.62,10.8,0.6 1381 | 7.5,0.57,0.02,2.6,0.077,11,35,0.99557,3.36,0.62,10.8,0.6 1382 | 6.8,0.83,0.09,1.8,0.074,4,25,0.99534,3.38,0.45,9.6,0.5 1383 | 8,0.6,0.22,2.1,0.08,25,105,0.99613,3.3,0.49,9.9,0.5 1384 | 8,0.6,0.22,2.1,0.08,25,105,0.99613,3.3,0.49,9.9,0.5 1385 | 7.1,0.755,0.15,1.8,0.107,20,84,0.99593,3.19,0.5,9.5,0.5 1386 | 8,0.81,0.25,3.4,0.076,34,85,0.99668,3.19,0.42,9.2,0.5 1387 | 7.4,0.64,0.07,1.8,0.1,8,23,0.9961,3.3,0.58,9.6,0.5 1388 | 7.4,0.64,0.07,1.8,0.1,8,23,0.9961,3.3,0.58,9.6,0.5 1389 | 6.6,0.64,0.31,6.1,0.083,7,49,0.99718,3.35,0.68,10.3,0.5 1390 | 6.7,0.48,0.02,2.2,0.08,36,111,0.99524,3.1,0.53,9.7,0.5 1391 | 6,0.49,0,2.3,0.068,15,33,0.99292,3.58,0.59,12.5,0.6 1392 | 8,0.64,0.22,2.4,0.094,5,33,0.99612,3.37,0.58,11,0.5 1393 | 7.1,0.62,0.06,1.3,0.07,5,12,0.9942,3.17,0.48,9.8,0.5 1394 | 8,0.52,0.25,2,0.078,19,59,0.99612,3.3,0.48,10.2,0.5 1395 | 6.4,0.57,0.14,3.9,0.07,27,73,0.99669,3.32,0.48,9.2,0.5 1396 | 8.6,0.685,0.1,1.6,0.092,3,12,0.99745,3.31,0.65,9.55,0.6 1397 | 8.7,0.675,0.1,1.6,0.09,4,11,0.99745,3.31,0.65,9.55,0.5 1398 | 7.3,0.59,0.26,2,0.08,17,104,0.99584,3.28,0.52,9.9,0.5 1399 | 7,0.6,0.12,2.2,0.083,13,28,0.9966,3.52,0.62,10.2,0.7 1400 | 7.2,0.67,0,2.2,0.068,10,24,0.9956,3.42,0.72,11.1,0.6 1401 | 7.9,0.69,0.21,2.1,0.08,33,141,0.9962,3.25,0.51,9.9,0.5 1402 | 7.9,0.69,0.21,2.1,0.08,33,141,0.9962,3.25,0.51,9.9,0.5 1403 | 7.6,0.3,0.42,2,0.052,6,24,0.9963,3.44,0.82,11.9,0.6 1404 | 7.2,0.33,0.33,1.7,0.061,3,13,0.996,3.23,1.1,10,0.8 1405 | 8,0.5,0.39,2.6,0.082,12,46,0.9985,3.43,0.62,10.7,0.6 1406 | 7.7,0.28,0.3,2,0.062,18,34,0.9952,3.28,0.9,11.3,0.7 1407 | 8.2,0.24,0.34,5.1,0.062,8,22,0.9974,3.22,0.94,10.9,0.6 1408 | 6,0.51,0,2.1,0.064,40,54,0.995,3.54,0.93,10.7,0.6 1409 | 8.1,0.29,0.36,2.2,0.048,35,53,0.995,3.27,1.01,12.4,0.7 1410 | 6,0.51,0,2.1,0.064,40,54,0.995,3.54,0.93,10.7,0.6 1411 | 6.6,0.96,0,1.8,0.082,5,16,0.9936,3.5,0.44,11.9,0.6 1412 | 6.4,0.47,0.4,2.4,0.071,8,19,0.9963,3.56,0.73,10.6,0.6 1413 | 8.2,0.24,0.34,5.1,0.062,8,22,0.9974,3.22,0.94,10.9,0.6 1414 | 9.9,0.57,0.25,2,0.104,12,89,0.9963,3.04,0.9,10.1,0.5 1415 | 10,0.32,0.59,2.2,0.077,3,15,0.9994,3.2,0.78,9.6,0.5 1416 | 6.2,0.58,0,1.6,0.065,8,18,0.9966,3.56,0.84,9.4,0.5 1417 | 10,0.32,0.59,2.2,0.077,3,15,0.9994,3.2,0.78,9.6,0.5 1418 | 7.3,0.34,0.33,2.5,0.064,21,37,0.9952,3.35,0.77,12.1,0.7 1419 | 7.8,0.53,0.01,1.6,0.077,3,19,0.995,3.16,0.46,9.8,0.5 1420 | 7.7,0.64,0.21,2.2,0.077,32,133,0.9956,3.27,0.45,9.9,0.5 1421 | 7.8,0.53,0.01,1.6,0.077,3,19,0.995,3.16,0.46,9.8,0.5 1422 | 7.5,0.4,0.18,1.6,0.079,24,58,0.9965,3.34,0.58,9.4,0.5 1423 | 7,0.54,0,2.1,0.079,39,55,0.9956,3.39,0.84,11.4,0.6 1424 | 6.4,0.53,0.09,3.9,0.123,14,31,0.9968,3.5,0.67,11,0.4 1425 | 8.3,0.26,0.37,1.4,0.076,8,23,0.9974,3.26,0.7,9.6,0.6 1426 | 8.3,0.26,0.37,1.4,0.076,8,23,0.9974,3.26,0.7,9.6,0.6 1427 | 7.7,0.23,0.37,1.8,0.046,23,60,0.9971,3.41,0.71,12.1,0.6 1428 | 7.6,0.41,0.33,2.5,0.078,6,23,0.9957,3.3,0.58,11.2,0.5 1429 | 7.8,0.64,0,1.9,0.072,27,55,0.9962,3.31,0.63,11,0.5 1430 | 7.9,0.18,0.4,2.2,0.049,38,67,0.996,3.33,0.93,11.3,0.5 1431 | 7.4,0.41,0.24,1.8,0.066,18,47,0.9956,3.37,0.62,10.4,0.5 1432 | 7.6,0.43,0.31,2.1,0.069,13,74,0.9958,3.26,0.54,9.9,0.6 1433 | 5.9,0.44,0,1.6,0.042,3,11,0.9944,3.48,0.85,11.7,0.6 1434 | 6.1,0.4,0.16,1.8,0.069,11,25,0.9955,3.42,0.74,10.1,0.7 1435 | 10.2,0.54,0.37,15.4,0.214,55,95,1.00369,3.18,0.77,9,0.6 1436 | 10.2,0.54,0.37,15.4,0.214,55,95,1.00369,3.18,0.77,9,0.6 1437 | 10,0.38,0.38,1.6,0.169,27,90,0.99914,3.15,0.65,8.5,0.5 1438 | 6.8,0.915,0.29,4.8,0.07,15,39,0.99577,3.53,0.54,11.1,0.5 1439 | 7,0.59,0,1.7,0.052,3,8,0.996,3.41,0.47,10.3,0.5 1440 | 7.3,0.67,0.02,2.2,0.072,31,92,0.99566,3.32,0.68,11.0666666666667,0.6 1441 | 7.2,0.37,0.32,2,0.062,15,28,0.9947,3.23,0.73,11.3,0.7 1442 | 7.4,0.785,0.19,5.2,0.094,19,98,0.99713,3.16,0.52,9.56666666666667,0.6 1443 | 6.9,0.63,0.02,1.9,0.078,18,30,0.99712,3.4,0.75,9.8,0.5 1444 | 6.9,0.58,0.2,1.75,0.058,8,22,0.99322,3.38,0.49,11.7,0.5 1445 | 7.3,0.67,0.02,2.2,0.072,31,92,0.99566,3.32,0.68,11.1,0.6 1446 | 7.4,0.785,0.19,5.2,0.094,19,98,0.99713,3.16,0.52,9.6,0.6 1447 | 6.9,0.63,0.02,1.9,0.078,18,30,0.99712,3.4,0.75,9.8,0.5 1448 | 6.8,0.67,0,1.9,0.08,22,39,0.99701,3.4,0.74,9.7,0.5 1449 | 6.9,0.58,0.01,1.9,0.08,40,54,0.99683,3.4,0.73,9.7,0.5 1450 | 7.2,0.38,0.31,2,0.056,15,29,0.99472,3.23,0.76,11.3,0.8 1451 | 7.2,0.37,0.32,2,0.062,15,28,0.9947,3.23,0.73,11.3,0.7 1452 | 7.8,0.32,0.44,2.7,0.104,8,17,0.99732,3.33,0.78,11,0.7 1453 | 6.6,0.58,0.02,2,0.062,37,53,0.99374,3.35,0.76,11.6,0.7 1454 | 7.6,0.49,0.33,1.9,0.074,27,85,0.99706,3.41,0.58,9,0.5 1455 | 11.7,0.45,0.63,2.2,0.073,7,23,0.99974,3.21,0.69,10.9,0.6 1456 | 6.5,0.9,0,1.6,0.052,9,17,0.99467,3.5,0.63,10.9,0.6 1457 | 6,0.54,0.06,1.8,0.05,38,89,0.99236,3.3,0.5,10.55,0.6 1458 | 7.6,0.49,0.33,1.9,0.074,27,85,0.99706,3.41,0.58,9,0.5 1459 | 8.4,0.29,0.4,1.7,0.067,8,20,0.99603,3.39,0.6,10.5,0.5 1460 | 7.9,0.2,0.35,1.7,0.054,7,15,0.99458,3.32,0.8,11.9,0.7 1461 | 6.4,0.42,0.09,2.3,0.054,34,64,0.99724,3.41,0.68,10.4,0.6 1462 | 6.2,0.785,0,2.1,0.06,6,13,0.99664,3.59,0.61,10,0.4 1463 | 6.8,0.64,0.03,2.3,0.075,14,31,0.99545,3.36,0.58,10.4,0.6 1464 | 6.9,0.63,0.01,2.4,0.076,14,39,0.99522,3.34,0.53,10.8,0.6 1465 | 6.8,0.59,0.1,1.7,0.063,34,53,0.9958,3.41,0.67,9.7,0.5 1466 | 6.8,0.59,0.1,1.7,0.063,34,53,0.9958,3.41,0.67,9.7,0.5 1467 | 7.3,0.48,0.32,2.1,0.062,31,54,0.99728,3.3,0.65,10,0.7 1468 | 6.7,1.04,0.08,2.3,0.067,19,32,0.99648,3.52,0.57,11,0.4 1469 | 7.3,0.48,0.32,2.1,0.062,31,54,0.99728,3.3,0.65,10,0.7 1470 | 7.3,0.98,0.05,2.1,0.061,20,49,0.99705,3.31,0.55,9.7,0.3 1471 | 10,0.69,0.11,1.4,0.084,8,24,0.99578,2.88,0.47,9.7,0.5 1472 | 6.7,0.7,0.08,3.75,0.067,8,16,0.99334,3.43,0.52,12.6,0.5 1473 | 7.6,0.35,0.6,2.6,0.073,23,44,0.99656,3.38,0.79,11.1,0.6 1474 | 6.1,0.6,0.08,1.8,0.071,14,45,0.99336,3.38,0.54,11,0.5 1475 | 9.9,0.5,0.5,13.8,0.205,48,82,1.00242,3.16,0.75,8.8,0.5 1476 | 5.3,0.47,0.11,2.2,0.048,16,89,0.99182,3.54,0.88,13.5666666666667,0.7 1477 | 9.9,0.5,0.5,13.8,0.205,48,82,1.00242,3.16,0.75,8.8,0.5 1478 | 5.3,0.47,0.11,2.2,0.048,16,89,0.99182,3.54,0.88,13.6,0.7 1479 | 7.1,0.875,0.05,5.7,0.082,3,14,0.99808,3.4,0.52,10.2,0.3 1480 | 8.2,0.28,0.6,3,0.104,10,22,0.99828,3.39,0.68,10.6,0.5 1481 | 5.6,0.62,0.03,1.5,0.08,6,13,0.99498,3.66,0.62,10.1,0.4 1482 | 8.2,0.28,0.6,3,0.104,10,22,0.99828,3.39,0.68,10.6,0.5 1483 | 7.2,0.58,0.54,2.1,0.114,3,9,0.99719,3.33,0.57,10.3,0.4 1484 | 8.1,0.33,0.44,1.5,0.042,6,12,0.99542,3.35,0.61,10.7,0.5 1485 | 6.8,0.91,0.06,2,0.06,4,11,0.99592,3.53,0.64,10.9,0.4 1486 | 7,0.655,0.16,2.1,0.074,8,25,0.99606,3.37,0.55,9.7,0.5 1487 | 6.8,0.68,0.21,2.1,0.07,9,23,0.99546,3.38,0.6,10.3,0.5 1488 | 6,0.64,0.05,1.9,0.066,9,17,0.99496,3.52,0.78,10.6,0.5 1489 | 5.6,0.54,0.04,1.7,0.049,5,13,0.9942,3.72,0.58,11.4,0.5 1490 | 6.2,0.57,0.1,2.1,0.048,4,11,0.99448,3.44,0.76,10.8,0.6 1491 | 7.1,0.22,0.49,1.8,0.039,8,18,0.99344,3.39,0.56,12.4,0.6 1492 | 5.6,0.54,0.04,1.7,0.049,5,13,0.9942,3.72,0.58,11.4,0.5 1493 | 6.2,0.65,0.06,1.6,0.05,6,18,0.99348,3.57,0.54,11.95,0.5 1494 | 7.7,0.54,0.26,1.9,0.089,23,147,0.99636,3.26,0.59,9.7,0.5 1495 | 6.4,0.31,0.09,1.4,0.066,15,28,0.99459,3.42,0.7,10,0.7 1496 | 7,0.43,0.02,1.9,0.08,15,28,0.99492,3.35,0.81,10.6,0.6 1497 | 7.7,0.54,0.26,1.9,0.089,23,147,0.99636,3.26,0.59,9.7,0.5 1498 | 6.9,0.74,0.03,2.3,0.054,7,16,0.99508,3.45,0.63,11.5,0.6 1499 | 6.6,0.895,0.04,2.3,0.068,7,13,0.99582,3.53,0.58,10.8,0.6 1500 | 6.9,0.74,0.03,2.3,0.054,7,16,0.99508,3.45,0.63,11.5,0.6 1501 | 7.5,0.725,0.04,1.5,0.076,8,15,0.99508,3.26,0.53,9.6,0.5 1502 | 7.8,0.82,0.29,4.3,0.083,21,64,0.99642,3.16,0.53,9.4,0.5 1503 | 7.3,0.585,0.18,2.4,0.078,15,60,0.99638,3.31,0.54,9.8,0.5 1504 | 6.2,0.44,0.39,2.5,0.077,6,14,0.99555,3.51,0.69,11,0.6 1505 | 7.5,0.38,0.57,2.3,0.106,5,12,0.99605,3.36,0.55,11.4,0.6 1506 | 6.7,0.76,0.02,1.8,0.078,6,12,0.996,3.55,0.63,9.95,0.3 1507 | 6.8,0.81,0.05,2,0.07,6,14,0.99562,3.51,0.66,10.8,0.6 1508 | 7.5,0.38,0.57,2.3,0.106,5,12,0.99605,3.36,0.55,11.4,0.6 1509 | 7.1,0.27,0.6,2.1,0.074,17,25,0.99814,3.38,0.72,10.6,0.6 1510 | 7.9,0.18,0.4,1.8,0.062,7,20,0.9941,3.28,0.7,11.1,0.5 1511 | 6.4,0.36,0.21,2.2,0.047,26,48,0.99661,3.47,0.77,9.7,0.6 1512 | 7.1,0.69,0.04,2.1,0.068,19,27,0.99712,3.44,0.67,9.8,0.5 1513 | 6.4,0.79,0.04,2.2,0.061,11,17,0.99588,3.53,0.65,10.4,0.6 1514 | 6.4,0.56,0.15,1.8,0.078,17,65,0.99294,3.33,0.6,10.5,0.6 1515 | 6.9,0.84,0.21,4.1,0.074,16,65,0.99842,3.53,0.72,9.23333333333333,0.6 1516 | 6.9,0.84,0.21,4.1,0.074,16,65,0.99842,3.53,0.72,9.25,0.6 1517 | 6.1,0.32,0.25,2.3,0.071,23,58,0.99633,3.42,0.97,10.6,0.5 1518 | 6.5,0.53,0.06,2,0.063,29,44,0.99489,3.38,0.83,10.3,0.6 1519 | 7.4,0.47,0.46,2.2,0.114,7,20,0.99647,3.32,0.63,10.5,0.5 1520 | 6.6,0.7,0.08,2.6,0.106,14,27,0.99665,3.44,0.58,10.2,0.5 1521 | 6.5,0.53,0.06,2,0.063,29,44,0.99489,3.38,0.83,10.3,0.6 1522 | 6.9,0.48,0.2,1.9,0.082,9,23,0.99585,3.39,0.43,9.05,0.4 1523 | 6.1,0.32,0.25,2.3,0.071,23,58,0.99633,3.42,0.97,10.6,0.5 1524 | 6.8,0.48,0.25,2,0.076,29,61,0.9953,3.34,0.6,10.4,0.5 1525 | 6,0.42,0.19,2,0.075,22,47,0.99522,3.39,0.78,10,0.6 1526 | 6.7,0.48,0.08,2.1,0.064,18,34,0.99552,3.33,0.64,9.7,0.5 1527 | 6.8,0.47,0.08,2.2,0.064,18,38,0.99553,3.3,0.65,9.6,0.6 1528 | 7.1,0.53,0.07,1.7,0.071,15,24,0.9951,3.29,0.66,10.8,0.6 1529 | 7.9,0.29,0.49,2.2,0.096,21,59,0.99714,3.31,0.67,10.1,0.6 1530 | 7.1,0.69,0.08,2.1,0.063,42,52,0.99608,3.42,0.6,10.2,0.6 1531 | 6.6,0.44,0.09,2.2,0.063,9,18,0.99444,3.42,0.69,11.3,0.6 1532 | 6.1,0.705,0.1,2.8,0.081,13,28,0.99631,3.6,0.66,10.2,0.5 1533 | 7.2,0.53,0.13,2,0.058,18,22,0.99573,3.21,0.68,9.9,0.6 1534 | 8,0.39,0.3,1.9,0.074,32,84,0.99717,3.39,0.61,9,0.5 1535 | 6.6,0.56,0.14,2.4,0.064,13,29,0.99397,3.42,0.62,11.7,0.7 1536 | 7,0.55,0.13,2.2,0.075,15,35,0.9959,3.36,0.59,9.7,0.6 1537 | 6.1,0.53,0.08,1.9,0.077,24,45,0.99528,3.6,0.68,10.3,0.6 1538 | 5.4,0.58,0.08,1.9,0.059,20,31,0.99484,3.5,0.64,10.2,0.6 1539 | 6.2,0.64,0.09,2.5,0.081,15,26,0.99538,3.57,0.63,12,0.5 1540 | 7.2,0.39,0.32,1.8,0.065,34,60,0.99714,3.46,0.78,9.9,0.5 1541 | 6.2,0.52,0.08,4.4,0.071,11,32,0.99646,3.56,0.63,11.6,0.6 1542 | 7.4,0.25,0.29,2.2,0.054,19,49,0.99666,3.4,0.76,10.9,0.7 1543 | 6.7,0.855,0.02,1.9,0.064,29,38,0.99472,3.3,0.56,10.75,0.6 1544 | 11.1,0.44,0.42,2.2,0.064,14,19,0.99758,3.25,0.57,10.4,0.6 1545 | 8.4,0.37,0.43,2.3,0.063,12,19,0.9955,3.17,0.81,11.2,0.7 1546 | 6.5,0.63,0.33,1.8,0.059,16,28,0.99531,3.36,0.64,10.1,0.6 1547 | 7,0.57,0.02,2,0.072,17,26,0.99575,3.36,0.61,10.2,0.5 1548 | 6.3,0.6,0.1,1.6,0.048,12,26,0.99306,3.55,0.51,12.1,0.5 1549 | 11.2,0.4,0.5,2,0.099,19,50,0.99783,3.1,0.58,10.4,0.5 1550 | 7.4,0.36,0.3,1.8,0.074,17,24,0.99419,3.24,0.7,11.4,0.8 1551 | 7.1,0.68,0,2.3,0.087,17,26,0.99783,3.45,0.53,9.5,0.5 1552 | 7.1,0.67,0,2.3,0.083,18,27,0.99768,3.44,0.54,9.4,0.5 1553 | 6.3,0.68,0.01,3.7,0.103,32,54,0.99586,3.51,0.66,11.3,0.6 1554 | 7.3,0.735,0,2.2,0.08,18,28,0.99765,3.41,0.6,9.4,0.5 1555 | 6.6,0.855,0.02,2.4,0.062,15,23,0.99627,3.54,0.6,11,0.6 1556 | 7,0.56,0.17,1.7,0.065,15,24,0.99514,3.44,0.68,10.55,0.7 1557 | 6.6,0.88,0.04,2.2,0.066,12,20,0.99636,3.53,0.56,9.9,0.5 1558 | 6.6,0.855,0.02,2.4,0.062,15,23,0.99627,3.54,0.6,11,0.6 1559 | 6.9,0.63,0.33,6.7,0.235,66,115,0.99787,3.22,0.56,9.5,0.5 1560 | 7.8,0.6,0.26,2,0.08,31,131,0.99622,3.21,0.52,9.9,0.5 1561 | 7.8,0.6,0.26,2,0.08,31,131,0.99622,3.21,0.52,9.9,0.5 1562 | 7.8,0.6,0.26,2,0.08,31,131,0.99622,3.21,0.52,9.9,0.5 1563 | 7.2,0.695,0.13,2,0.076,12,20,0.99546,3.29,0.54,10.1,0.5 1564 | 7.2,0.695,0.13,2,0.076,12,20,0.99546,3.29,0.54,10.1,0.5 1565 | 7.2,0.695,0.13,2,0.076,12,20,0.99546,3.29,0.54,10.1,0.5 1566 | 6.7,0.67,0.02,1.9,0.061,26,42,0.99489,3.39,0.82,10.9,0.6 1567 | 6.7,0.16,0.64,2.1,0.059,24,52,0.99494,3.34,0.71,11.2,0.6 1568 | 7.2,0.695,0.13,2,0.076,12,20,0.99546,3.29,0.54,10.1,0.5 1569 | 7,0.56,0.13,1.6,0.077,25,42,0.99629,3.34,0.59,9.2,0.5 1570 | 6.2,0.51,0.14,1.9,0.056,15,34,0.99396,3.48,0.57,11.5,0.6 1571 | 6.4,0.36,0.53,2.2,0.23,19,35,0.9934,3.37,0.93,12.4,0.6 1572 | 6.4,0.38,0.14,2.2,0.038,15,25,0.99514,3.44,0.65,11.1,0.6 1573 | 7.3,0.69,0.32,2.2,0.069,35,104,0.99632,3.33,0.51,9.5,0.5 1574 | 6,0.58,0.2,2.4,0.075,15,50,0.99467,3.58,0.67,12.5,0.6 1575 | 5.6,0.31,0.78,13.9,0.074,23,92,0.99677,3.39,0.48,10.5,0.6 1576 | 7.5,0.52,0.4,2.2,0.06,12,20,0.99474,3.26,0.64,11.8,0.6 1577 | 8,0.3,0.63,1.6,0.081,16,29,0.99588,3.3,0.78,10.8,0.6 1578 | 6.2,0.7,0.15,5.1,0.076,13,27,0.99622,3.54,0.6,11.9,0.6 1579 | 6.8,0.67,0.15,1.8,0.118,13,20,0.9954,3.42,0.67,11.3,0.6 1580 | 6.2,0.56,0.09,1.7,0.053,24,32,0.99402,3.54,0.6,11.3,0.5 1581 | 7.4,0.35,0.33,2.4,0.068,9,26,0.9947,3.36,0.6,11.9,0.6 1582 | 6.2,0.56,0.09,1.7,0.053,24,32,0.99402,3.54,0.6,11.3,0.5 1583 | 6.1,0.715,0.1,2.6,0.053,13,27,0.99362,3.57,0.5,11.9,0.5 1584 | 6.2,0.46,0.29,2.1,0.074,32,98,0.99578,3.33,0.62,9.8,0.5 1585 | 6.7,0.32,0.44,2.4,0.061,24,34,0.99484,3.29,0.8,11.6,0.7 1586 | 7.2,0.39,0.44,2.6,0.066,22,48,0.99494,3.3,0.84,11.5,0.6 1587 | 7.5,0.31,0.41,2.4,0.065,34,60,0.99492,3.34,0.85,11.4,0.6 1588 | 5.8,0.61,0.11,1.8,0.066,18,28,0.99483,3.55,0.66,10.9,0.6 1589 | 7.2,0.66,0.33,2.5,0.068,34,102,0.99414,3.27,0.78,12.8,0.6 1590 | 6.6,0.725,0.2,7.8,0.073,29,79,0.9977,3.29,0.54,9.2,0.5 1591 | 6.3,0.55,0.15,1.8,0.077,26,35,0.99314,3.32,0.82,11.6,0.6 1592 | 5.4,0.74,0.09,1.7,0.089,16,26,0.99402,3.67,0.56,11.6,0.6 1593 | 6.3,0.51,0.13,2.3,0.076,29,40,0.99574,3.42,0.75,11,0.6 1594 | 6.8,0.62,0.08,1.9,0.068,28,38,0.99651,3.42,0.82,9.5,0.6 1595 | 6.2,0.6,0.08,2,0.09,32,44,0.9949,3.45,0.58,10.5,0.5 1596 | 5.9,0.55,0.1,2.2,0.062,39,51,0.99512,3.52,0.76,11.2,0.6 1597 | 6.3,0.51,0.13,2.3,0.076,29,40,0.99574,3.42,0.75,11,0.6 1598 | 5.9,0.645,0.12,2,0.075,32,44,0.99547,3.57,0.71,10.2,0.5 1599 | 6,0.31,0.47,3.6,0.067,18,42,0.99549,3.39,0.66,11,0.6 1600 | -------------------------------------------------------------------------------- /gopher_neural.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | const ( 4 | Classification = 0 5 | Regression = 1 6 | ) 7 | -------------------------------------------------------------------------------- /layer.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | func NewLayer(neurons int) *Layer { 4 | l := &Layer{} 5 | l.init(neurons) 6 | return l 7 | } 8 | 9 | type Layer struct { 10 | Neurons []*Neuron 11 | } 12 | 13 | func (l *Layer) ConnectTo(layer *Layer) { 14 | for _, n := range l.Neurons { 15 | for _, toN := range layer.Neurons { 16 | n.SynapseTo(toN, 0) 17 | } 18 | } 19 | } 20 | 21 | func (l *Layer) init(neurons int) { 22 | for ; neurons > 0; neurons-- { 23 | l.addNeuron() 24 | } 25 | } 26 | 27 | func (l *Layer) addNeuron() { 28 | n := NewNeuron() 29 | l.Neurons = append(l.Neurons, n) 30 | } 31 | 32 | func (l *Layer) Calculate() { 33 | for _, n := range l.Neurons { 34 | n.Calculate() 35 | } 36 | } 37 | -------------------------------------------------------------------------------- /layer_test.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import ( 4 | . "launchpad.net/gocheck" 5 | ) 6 | 7 | func (s *SuiteT) TestLayer(c *C) { 8 | l := NewLayer(5) 9 | c.Assert(l.Neurons, HasLen, 5) 10 | } 11 | 12 | func (s *SuiteT) TestConnectToLayer(c *C) { 13 | count := 5 14 | l := NewLayer(count) 15 | l2 := NewLayer(count) 16 | 17 | l.ConnectTo(l2) 18 | 19 | for _, n := range l.Neurons { 20 | c.Assert(n.OutSynapses, HasLen, count) 21 | } 22 | 23 | } 24 | -------------------------------------------------------------------------------- /learn/learn.go: -------------------------------------------------------------------------------- 1 | package learn 2 | 3 | import neural "github.com/breskos/gopher-neural" 4 | 5 | type Deltas [][]float64 6 | 7 | func Learner(n *neural.Network, samples []Sample, speed float64) { 8 | for sample := range samples { 9 | Learn(n, samples[sample].Vector, samples[sample].Output, speed) 10 | } 11 | } 12 | 13 | func Learn(n *neural.Network, in, ideal []float64, speed float64) { 14 | Backpropagation(n, in, ideal, speed) 15 | } 16 | 17 | func Backpropagation(n *neural.Network, in, ideal []float64, speed float64) { 18 | n.Calculate(in) 19 | 20 | deltas := make([][]float64, len(n.Layers)) 21 | 22 | last := len(n.Layers) - 1 23 | l := n.Layers[last] 24 | deltas[last] = make([]float64, len(l.Neurons)) 25 | for i, n := range l.Neurons { 26 | deltas[last][i] = n.Out * (1 - n.Out) * (ideal[i] - n.Out) 27 | } 28 | 29 | for i := last - 1; i >= 0; i-- { 30 | l := n.Layers[i] 31 | deltas[i] = make([]float64, len(l.Neurons)) 32 | for j, n := range l.Neurons { 33 | sum := 0.0 34 | for k, s := range n.OutSynapses { 35 | sum += s.Weight * deltas[i+1][k] 36 | } 37 | deltas[i][j] = n.Out * (1 - n.Out) * sum 38 | } 39 | } 40 | 41 | for i, l := range n.Layers { 42 | for j, n := range l.Neurons { 43 | for _, s := range n.InSynapses { 44 | s.Weight += speed * deltas[i][j] * s.In 45 | } 46 | } 47 | } 48 | 49 | } 50 | -------------------------------------------------------------------------------- /learn/learn_test.go: -------------------------------------------------------------------------------- 1 | package learn 2 | 3 | import ( 4 | "math/rand" 5 | "testing" 6 | 7 | neural "github.com/breskos/gopher-neural" 8 | . "launchpad.net/gocheck" 9 | ) 10 | 11 | type lessThenChecker struct { 12 | *CheckerInfo 13 | } 14 | 15 | var LessThen Checker = &lessThenChecker{ 16 | &CheckerInfo{Name: "LessThen", Params: []string{"a", "b"}}, 17 | } 18 | 19 | func (checker *lessThenChecker) Check(params []interface{}, names []string) (result bool, error string) { 20 | a, _ := params[0].(float64) 21 | b, _ := params[1].(float64) 22 | 23 | return a < b, "" 24 | } 25 | 26 | type moreThenChecker struct { 27 | *CheckerInfo 28 | } 29 | 30 | var MoreThen Checker = &moreThenChecker{ 31 | &CheckerInfo{Name: "MoreThen", Params: []string{"a", "b"}}, 32 | } 33 | 34 | func (checker *moreThenChecker) Check(params []interface{}, names []string) (result bool, error string) { 35 | a, _ := params[0].(float64) 36 | b, _ := params[1].(float64) 37 | 38 | return a > b, "" 39 | } 40 | 41 | func Test(t *testing.T) { TestingT(t) } 42 | 43 | type SuiteT struct{} 44 | 45 | func (s *SuiteT) SetUpTest(c *C) {} 46 | 47 | var _ = Suite(&SuiteT{}) 48 | 49 | func (s *SuiteT) TestLearn(c *C) { 50 | 51 | n := neural.NewNetwork(2, []int{5, 5, 2}) 52 | n.RandomizeSynapses() 53 | 54 | for i := 0; i < 1000000; i++ { 55 | in := []float64{rand.Float64() * 10, rand.Float64() * 10} 56 | var ideal []float64 57 | if in[0] > in[1] { 58 | ideal = []float64{1, 0} 59 | } else { 60 | ideal = []float64{0, 1} 61 | } 62 | Learn(n, in, ideal, 0.1) 63 | } 64 | 65 | count := 1000 66 | success := 0 67 | for i := 0; i < count; i++ { 68 | in := []float64{rand.Float64() * 10, rand.Float64() * 10} 69 | res := n.Calculate(in) 70 | 71 | if in[0] > in[1] && res[0] > res[1] { 72 | success++ 73 | } 74 | if in[0] < in[1] && res[0] < res[1] { 75 | success++ 76 | } 77 | } 78 | 79 | // Success persente should be more then 90% 80 | successPersents := float64(success) / float64(count) 81 | c.Assert(successPersents, MoreThen, 0.99) 82 | 83 | } 84 | -------------------------------------------------------------------------------- /learn/samples.go: -------------------------------------------------------------------------------- 1 | package learn 2 | 3 | // TODO (abresk) write tests for samples 4 | 5 | import ( 6 | "bufio" 7 | "encoding/csv" 8 | "fmt" 9 | "io" 10 | "math/rand" 11 | "os" 12 | "strconv" 13 | "strings" 14 | 15 | neural "github.com/breskos/gopher-neural" 16 | ) 17 | 18 | const ( 19 | classYes = 1.0 20 | labelRegressor = "output" 21 | ) 22 | 23 | // Set holds the samples and the output labels 24 | type Set struct { 25 | Samples []Sample 26 | ClassToLabel map[int]string 27 | Usage int 28 | } 29 | 30 | // Sample holds the sample data, value is just used for regression annotation 31 | type Sample struct { 32 | Vector []float64 33 | Output []float64 34 | Label string 35 | ClassNumber int 36 | Value float64 37 | } 38 | 39 | // NewSet creates a new set of empty data samples 40 | func NewSet(usage int) *Set { 41 | return &Set{ 42 | Samples: make([]Sample, 0), 43 | ClassToLabel: make(map[int]string), 44 | Usage: usage, 45 | } 46 | } 47 | 48 | // GetClasses returns the classes in the set 49 | func (s *Set) GetClasses() []string { 50 | classes := make([]string, len(s.ClassToLabel)) 51 | for k, v := range s.ClassToLabel { 52 | classes[k] = v 53 | } 54 | return classes 55 | } 56 | 57 | // TODO (abresk) two options: a) remove this function, b) put regression / classifciation add logic here 58 | func (s *Set) add(vector, output []float64, label string, classNumber int, value float64) { 59 | var sample Sample 60 | sample.Vector = vector 61 | sample.Output = output 62 | sample.Label = label 63 | sample.ClassNumber = classNumber 64 | sample.Value = value 65 | s.Samples = append(s.Samples, sample) 66 | } 67 | 68 | func (s *Set) getLabelFromClass(number int) (string, bool) { 69 | if val, ok := s.ClassToLabel[number]; ok { 70 | return val, true 71 | } 72 | return "", false 73 | } 74 | 75 | func (s *Set) getClassFromLabel(label string) (int, bool) { 76 | for k, v := range s.ClassToLabel { 77 | if v == label { 78 | return k, true 79 | } 80 | } 81 | return -1, false 82 | } 83 | 84 | func splitSamples(set *Set, ratio float64) (Set, Set) { 85 | normalizedRatio := int(ratio * 100.0) 86 | firstSet := Set{ 87 | Samples: make([]Sample, 0), 88 | ClassToLabel: set.ClassToLabel, 89 | } 90 | secondSet := Set{ 91 | Samples: make([]Sample, 0), 92 | ClassToLabel: set.ClassToLabel, 93 | } 94 | for i := range set.Samples { 95 | if rand.Intn(100) <= normalizedRatio { 96 | firstSet.Samples = append(firstSet.Samples, set.Samples[i]) 97 | } else { 98 | secondSet.Samples = append(secondSet.Samples, set.Samples[i]) 99 | } 100 | } 101 | return firstSet, secondSet 102 | } 103 | 104 | func (s *Set) distributionByLabel(label string) map[string]int { 105 | if s.Usage == neural.Classification { 106 | dist := make(map[string]int) 107 | for sample := range s.Samples { 108 | c := s.Samples[sample].Label 109 | if _, ok := dist[c]; ok { 110 | dist[c]++ 111 | } else { 112 | dist[c] = 1 113 | } 114 | } 115 | return dist 116 | } 117 | return nil 118 | } 119 | 120 | func (s *Set) distributionByClassNumber(number int) map[int]int { 121 | if s.Usage == neural.Classification { 122 | dist := make(map[int]int) 123 | for sample := range s.Samples { 124 | c := s.Samples[sample].ClassNumber 125 | if _, ok := dist[c]; ok { 126 | dist[c]++ 127 | } else { 128 | dist[c] = 1 129 | } 130 | } 131 | return dist 132 | } 133 | return nil 134 | } 135 | 136 | // LoadFromCSV where the last dimension is the label 137 | func (s *Set) LoadFromCSV(path string) (bool, error) { 138 | classNumbers := make(map[string]int) 139 | classNumber := 0 140 | f, err := os.Open(path) 141 | if err != nil { 142 | return false, fmt.Errorf("error while open file: %v", path) 143 | } 144 | defer f.Close() 145 | r := csv.NewReader(bufio.NewReader(f)) 146 | for { 147 | record, err := r.Read() 148 | if err == io.EOF { 149 | break 150 | } 151 | l := len(record) 152 | var sample Sample 153 | sample.Vector = make([]float64, l-1) 154 | if s.Usage == neural.Regression { 155 | regression, err := strconv.ParseFloat(record[l-1], 64) 156 | if err == nil { 157 | sample.Value = regression 158 | } 159 | } else if s.Usage == neural.Classification { 160 | sample.Label = record[l-1] 161 | if _, ok := classNumbers[sample.Label]; !ok { 162 | classNumbers[sample.Label] = classNumber 163 | classNumber++ 164 | } 165 | } 166 | for value := range record { 167 | if value < l-1 { 168 | f, err := strconv.ParseFloat(record[value], 64) 169 | if err != nil { 170 | return false, fmt.Errorf("failed to parse float %v with error: %v", record[value], err) 171 | } 172 | sample.Vector[value] = f 173 | } 174 | } 175 | s.Samples = append(s.Samples, sample) 176 | } 177 | s.createClassToLabel(classNumbers) 178 | s.addOutputVectors() 179 | return true, nil 180 | } 181 | 182 | func (s *Set) addOutputVectors() { 183 | if s.Usage == neural.Classification { 184 | dim := len(s.ClassToLabel) 185 | for sample := range s.Samples { 186 | v := make([]float64, dim) 187 | v[s.Samples[sample].ClassNumber] = classYes 188 | s.Samples[sample].Output = v 189 | } 190 | } else if s.Usage == neural.Regression { 191 | for sample := range s.Samples { 192 | s.Samples[sample].Output = make([]float64, 1) 193 | s.Samples[sample].Output[0] = s.Samples[sample].Value 194 | } 195 | } 196 | } 197 | 198 | func (s *Set) createClassToLabel(mapping map[string]int) { 199 | s.ClassToLabel = make(map[int]string) 200 | if neural.Classification == s.Usage { 201 | for k, v := range mapping { 202 | s.ClassToLabel[v] = k 203 | } 204 | for i := range s.Samples { 205 | s.Samples[i].ClassNumber = mapping[s.Samples[i].Label] 206 | } 207 | } else { 208 | s.ClassToLabel[0] = labelRegressor 209 | } 210 | 211 | } 212 | 213 | // LoadFromSVMFile load data from an svm problem file 214 | func (s *Set) LoadFromSVMFile(path string) (bool, error) { 215 | classNumbers := make(map[string]int) 216 | classNumber := 0 217 | highestIndex := scanSamples(path) 218 | file, err := os.Open(path) 219 | if err != nil { 220 | return false, fmt.Errorf("error while opening file") 221 | } 222 | defer file.Close() 223 | scanner := bufio.NewScanner(file) 224 | for scanner.Scan() { 225 | line := scanner.Text() 226 | m, label, err := problemToMap(line) 227 | if err != nil { 228 | return false, fmt.Errorf("error while scanning files: %v", err) 229 | } 230 | var sample Sample 231 | sample.Vector = make([]float64, highestIndex) 232 | sample.Label = label 233 | regression, err := strconv.ParseFloat(label, 64) 234 | if err != nil { 235 | sample.Value = regression 236 | } 237 | if _, ok := classNumbers[sample.Label]; !ok { 238 | classNumbers[sample.Label] = classNumber 239 | classNumber++ 240 | } 241 | for i := 0; i < highestIndex; i++ { 242 | if val, ok := m[i]; ok { 243 | sample.Vector[i] = val 244 | } else { 245 | sample.Vector[i] = 0.0 246 | } 247 | } 248 | s.Samples = append(s.Samples, sample) 249 | } 250 | return true, nil 251 | } 252 | 253 | func problemToMap(problem string) (map[int]float64, string, error) { 254 | sliced := strings.Split(problem, " ") 255 | m := make(map[int]float64) 256 | label := sliced[0] 257 | features := sliced[1:len(sliced)] 258 | for feature := range features { 259 | if features[feature] == "" { 260 | continue 261 | } 262 | splitted := strings.Split(features[feature], ":") 263 | idx, errIdx := strconv.Atoi(splitted[0]) 264 | value, errVal := strconv.ParseFloat(splitted[1], 64) 265 | if errIdx == nil && errVal == nil { 266 | m[idx] = value 267 | } 268 | } 269 | return m, label, nil 270 | } 271 | 272 | // this function returns the highest index found 273 | func scanSamples(path string) int { 274 | highest := 0 275 | file, err := os.Open(path) 276 | if err != nil { 277 | fmt.Println("error while opening file") 278 | os.Exit(-1) 279 | } 280 | defer file.Close() 281 | scanner := bufio.NewScanner(file) 282 | for scanner.Scan() { 283 | m, _, err := problemToMap(scanner.Text()) 284 | if err != nil { 285 | fmt.Printf("error while scanning files: %v", err) 286 | os.Exit(-1) 287 | } 288 | for k := range m { 289 | if k > highest { 290 | highest = k 291 | } 292 | } 293 | } 294 | return highest 295 | } 296 | -------------------------------------------------------------------------------- /network.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import "fmt" 4 | import "math/rand" 5 | 6 | // TODO (abresk) write tests for CalculateLabels, CalculateWinnerLabel 7 | 8 | // Network contains all the necessary information to use the neural network 9 | type Network struct { 10 | Enters []*Enter 11 | Layers []*Layer 12 | Out []float64 `json:"-"` 13 | OutLabels map[int]string 14 | } 15 | 16 | // NewNetwork creates a new neural network 17 | func NewNetwork(in int, layers []int, labels map[int]string) *Network { 18 | n := &Network{ 19 | Enters: make([]*Enter, 0, in), 20 | Layers: make([]*Layer, 0, len(layers)), 21 | OutLabels: labels, 22 | } 23 | n.init(in, layers, NewLogisticFunc(1)) 24 | return n 25 | } 26 | 27 | func (n *Network) init(in int, layers []int, aFunc ActivationFunction) { 28 | n.initLayers(layers) 29 | n.initEnters(in) 30 | n.ConnectLayers() 31 | n.ConnectEnters() 32 | n.SetActivationFunction(aFunc) 33 | } 34 | 35 | func (n *Network) initLayers(layers []int) { 36 | for _, count := range layers { 37 | layer := NewLayer(count) 38 | n.Layers = append(n.Layers, layer) 39 | } 40 | } 41 | 42 | func (n *Network) initEnters(in int) { 43 | for ; in > 0; in-- { 44 | e := NewEnter() 45 | n.Enters = append(n.Enters, e) 46 | } 47 | } 48 | 49 | func (n *Network) ConnectLayers() { 50 | for i := len(n.Layers) - 1; i > 0; i-- { 51 | n.Layers[i-1].ConnectTo(n.Layers[i]) 52 | } 53 | } 54 | 55 | func (n *Network) ConnectEnters() { 56 | for _, e := range n.Enters { 57 | e.ConnectTo(n.Layers[0]) 58 | } 59 | } 60 | 61 | func (n *Network) SetActivationFunction(aFunc ActivationFunction) { 62 | for _, l := range n.Layers { 63 | for _, n := range l.Neurons { 64 | n.SetActivationFunction(aFunc) 65 | } 66 | } 67 | } 68 | 69 | func (n *Network) setEnters(v *[]float64) { 70 | values := *v 71 | if len(values) != len(n.Enters) { 72 | panic(fmt.Sprint("Enters count ( ", len(n.Enters), " ) != count of elements in SetEnters function argument ( ", len(values), " ) .")) 73 | } 74 | 75 | for i, e := range n.Enters { 76 | e.Input = values[i] 77 | } 78 | 79 | } 80 | 81 | func (n *Network) sendEnters() { 82 | for _, e := range n.Enters { 83 | e.Signal() 84 | } 85 | } 86 | 87 | func (n *Network) calculateLayers() { 88 | for _, l := range n.Layers { 89 | l.Calculate() 90 | } 91 | } 92 | 93 | func (n *Network) generateOut() { 94 | outL := n.Layers[len(n.Layers)-1] 95 | n.Out = make([]float64, len(outL.Neurons)) 96 | 97 | for i, neuron := range outL.Neurons { 98 | n.Out[i] = neuron.Out 99 | } 100 | } 101 | 102 | func (n *Network) Calculate(enters []float64) []float64 { 103 | n.setEnters(&enters) 104 | n.sendEnters() 105 | n.calculateLayers() 106 | n.generateOut() 107 | 108 | return n.Out 109 | } 110 | 111 | func (n *Network) CalculateLabels(enters []float64) map[string]float64 { 112 | results := make(map[string]float64) 113 | out := n.Calculate(enters) 114 | for index, label := range n.OutLabels { 115 | results[label] = out[index] 116 | } 117 | return results 118 | } 119 | 120 | func (n *Network) CalculateWinnerLabel(enters []float64) string { 121 | calculatedLabels := n.CalculateLabels(enters) 122 | winnerValue := 0.0 123 | winnerLabel := "" 124 | for label, value := range calculatedLabels { 125 | if value > winnerValue { 126 | winnerValue = value 127 | winnerLabel = label 128 | } 129 | } 130 | return winnerLabel 131 | } 132 | 133 | func (n *Network) RandomizeSynapses() { 134 | for _, l := range n.Layers { 135 | for _, n := range l.Neurons { 136 | for _, s := range n.InSynapses { 137 | s.Weight = 2 * (rand.Float64() - 0.5) 138 | } 139 | } 140 | } 141 | } 142 | -------------------------------------------------------------------------------- /network_test.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import ( 4 | . "launchpad.net/gocheck" 5 | //"log" 6 | ) 7 | 8 | func (s *SuiteT) TestCreateNewNetwork(c *C) { 9 | net := NewNetwork(10, []int{5, 6, 2}) 10 | 11 | c.Assert(net.Enters, HasLen, 10) 12 | c.Assert(net.Enters[0].OutSynapses, HasLen, 5) 13 | 14 | c.Assert(net.Layers, HasLen, 3) 15 | c.Assert(net.Layers[0].Neurons, HasLen, 5) 16 | 17 | c.Assert(net.Layers[0].Neurons[0].OutSynapses, HasLen, 6) 18 | c.Assert(net.Layers[1].Neurons[0].OutSynapses, HasLen, 2) 19 | c.Assert(net.Layers[2].Neurons[0].OutSynapses, HasLen, 0) 20 | } 21 | 22 | func (s *SuiteT) TestCalculcateNetwork(c *C) { 23 | 24 | net := NewNetwork(5, []int{5, 6, 1}) 25 | 26 | out := net.Calculate([]float64{0, 0, 0, 0, 0}) 27 | 28 | c.Assert(out[0], Equals, 0.5) 29 | 30 | } 31 | -------------------------------------------------------------------------------- /neuron.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | type Neuron struct { 4 | OutSynapses []*Synapse 5 | InSynapses []*Synapse `json:"-"` 6 | ActivationFunction ActivationFunction `json:"-"` 7 | Out float64 `json:"-"` 8 | } 9 | 10 | func NewNeuron() *Neuron { 11 | return &Neuron{} 12 | } 13 | 14 | func (n *Neuron) SynapseTo(nTo *Neuron, weight float64) { 15 | NewSynapseFromTo(n, nTo, weight) 16 | } 17 | 18 | func (n *Neuron) SetActivationFunction(aFunc ActivationFunction) { 19 | n.ActivationFunction = aFunc 20 | } 21 | 22 | func (n *Neuron) Calculate() { 23 | var sum float64 24 | for _, s := range n.InSynapses { 25 | sum += s.Out 26 | } 27 | 28 | n.Out = n.ActivationFunction(sum) 29 | 30 | for _, s := range n.OutSynapses { 31 | s.Signal(n.Out) 32 | } 33 | } 34 | -------------------------------------------------------------------------------- /neuron_test.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | import ( 4 | . "launchpad.net/gocheck" 5 | ) 6 | 7 | func (s *SuiteT) TestAttachNeurons(c *C) { 8 | n := NewNeuron() 9 | n2 := NewNeuron() 10 | w := 0.5 11 | 12 | n.SynapseTo(n2, w) 13 | 14 | c.Assert(n.OutSynapses[0].Weight, Equals, w) 15 | } 16 | 17 | func (s *SuiteT) TestInputsSynapses(c *C) { 18 | n := NewNeuron() 19 | 20 | NewSynapseFromTo(NewNeuron(), n, 0.1) 21 | NewSynapseFromTo(NewNeuron(), n, 0.1) 22 | NewSynapseFromTo(NewNeuron(), n, 0.1) 23 | 24 | c.Assert(n.InSynapses, HasLen, 3) 25 | } 26 | -------------------------------------------------------------------------------- /persist/persist.go: -------------------------------------------------------------------------------- 1 | package persist 2 | 3 | import ( 4 | "encoding/json" 5 | "io/ioutil" 6 | "strconv" 7 | 8 | neural "github.com/breskos/gopher-neural" 9 | ) 10 | 11 | type Weights [][][]float64 12 | type NetworkDump struct { 13 | Enters int 14 | Weights Weights 15 | OutLabels map[string]string 16 | } 17 | 18 | func DumpFromFile(path string) (*NetworkDump, error) { 19 | b, err := ioutil.ReadFile(path) 20 | if nil != err { 21 | return nil, err 22 | } 23 | dump := &NetworkDump{} 24 | err = json.Unmarshal(b, dump) 25 | if nil != err { 26 | return nil, err 27 | } 28 | 29 | return dump, nil 30 | } 31 | 32 | func FromFile(path string) (*neural.Network, error) { 33 | dump, err := DumpFromFile(path) 34 | if nil != err { 35 | return nil, err 36 | } 37 | n := FromDump(dump) 38 | return n, nil 39 | } 40 | 41 | func ToFile(path string, n *neural.Network) error { 42 | dump := ToDump(n) 43 | return DumpToFile(path, dump) 44 | } 45 | 46 | func DumpToFile(path string, dump *NetworkDump) error { 47 | j, err := json.Marshal(dump) 48 | if err != nil { 49 | return err 50 | } 51 | err = ioutil.WriteFile(path, j, 0644) 52 | return err 53 | } 54 | 55 | func ToDump(n *neural.Network) *NetworkDump { 56 | labels := intToStringMap(n.OutLabels) 57 | dump := &NetworkDump{Enters: len(n.Enters), Weights: make([][][]float64, len(n.Layers)), OutLabels: labels} 58 | 59 | for i, l := range n.Layers { 60 | dump.Weights[i] = make([][]float64, len(l.Neurons)) 61 | for j, n := range l.Neurons { 62 | dump.Weights[i][j] = make([]float64, len(n.InSynapses)) 63 | for k, s := range n.InSynapses { 64 | dump.Weights[i][j][k] = s.Weight 65 | } 66 | } 67 | } 68 | return dump 69 | } 70 | 71 | func FromDump(dump *NetworkDump) *neural.Network { 72 | layers := make([]int, len(dump.Weights)) 73 | for i, layer := range dump.Weights { 74 | layers[i] = len(layer) 75 | } 76 | labels := stringToIntMap(dump.OutLabels) 77 | n := neural.NewNetwork(dump.Enters, layers, labels) 78 | 79 | for i, l := range n.Layers { 80 | for j, n := range l.Neurons { 81 | for k, s := range n.InSynapses { 82 | s.Weight = dump.Weights[i][j][k] 83 | } 84 | } 85 | } 86 | 87 | return n 88 | } 89 | 90 | func intToStringMap(m map[int]string) map[string]string { 91 | ms := make(map[string]string) 92 | for k, v := range m { 93 | ms[strconv.Itoa(k)] = v 94 | } 95 | return ms 96 | } 97 | 98 | func stringToIntMap(m map[string]string) map[int]string { 99 | mi := make(map[int]string) 100 | for k, v := range m { 101 | index, err := strconv.Atoi(k) 102 | if err != nil { 103 | panic(err) 104 | } 105 | mi[index] = v 106 | } 107 | return mi 108 | } 109 | -------------------------------------------------------------------------------- /persist/persist_test.go: -------------------------------------------------------------------------------- 1 | package persist 2 | 3 | import ( 4 | "testing" 5 | 6 | neural "github.com/breskos/gopher-neural" 7 | . "launchpad.net/gocheck" 8 | ) 9 | 10 | func Test(t *testing.T) { TestingT(t) } 11 | 12 | type SuiteT struct{} 13 | 14 | func (s *SuiteT) SetUpTest(c *C) {} 15 | 16 | var _ = Suite(&SuiteT{}) 17 | 18 | func (s *SuiteT) TestPersist(c *C) { 19 | n := neural.NewNetwork(5, []int{5, 5, 5}) 20 | n.RandomizeSynapses() 21 | 22 | path := "/tmp/network.json" 23 | err := ToFile(path, n) 24 | 25 | c.Assert(err, Equals, nil) 26 | 27 | n2, err := FromFile(path) 28 | 29 | c.Assert(n2.Enters, HasLen, len(n.Enters)) 30 | c.Assert(n2.Layers, HasLen, len(n.Layers)) 31 | 32 | for i, l := range n2.Layers { 33 | for j, nr := range l.Neurons { 34 | for h, s := range nr.OutSynapses { 35 | c.Assert(s.Weight, Equals, n.Layers[i].Neurons[j].OutSynapses[h].Weight) 36 | } 37 | } 38 | } 39 | 40 | in := []float64{0.5, 0.5, 0.5, 0.5, 0.5} 41 | out := n.Calculate(in) 42 | out2 := n2.Calculate(in) 43 | 44 | for i, o := range out2 { 45 | c.Assert(o, Equals, out[i]) 46 | } 47 | 48 | } 49 | -------------------------------------------------------------------------------- /synapse.go: -------------------------------------------------------------------------------- 1 | package neural 2 | 3 | func NewSynapse(weight float64) *Synapse { 4 | return &Synapse{Weight: weight} 5 | } 6 | 7 | func NewSynapseFromTo(from, to *Neuron, weight float64) *Synapse { 8 | syn := NewSynapse(weight) 9 | 10 | from.OutSynapses = append(from.OutSynapses, syn) 11 | to.InSynapses = append(to.InSynapses, syn) 12 | 13 | return syn 14 | } 15 | 16 | type Synapse struct { 17 | Weight float64 18 | In float64 `json:"-"` 19 | Out float64 `json:"-"` 20 | } 21 | 22 | func (s *Synapse) Signal(value float64) { 23 | s.In = value 24 | s.Out = s.In * s.Weight 25 | } 26 | --------------------------------------------------------------------------------