How to use Get method of regression Package

Best Keploy code snippet using regression.Get

models.go

Source:models.go Github

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...42type gaussianKernel struct {43 mem unsafe.Pointer 44}45func (m *gaussianKernel) allocGaussianKernel(identifier string) {46 m.mem = C.mlpackGetGaussianKernelPtr(C.CString(identifier))47 runtime.KeepAlive(m)48}49func (m *gaussianKernel) getGaussianKernel(identifier string) {50 m.allocGaussianKernel(identifier)51}52func setGaussianKernel(identifier string, ptr *gaussianKernel) {53 C.mlpackSetGaussianKernelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))54}55type adaBoostModel struct {56 mem unsafe.Pointer 57}58func (m *adaBoostModel) allocAdaBoostModel(identifier string) {59 m.mem = C.mlpackGetAdaBoostModelPtr(C.CString(identifier))60 runtime.KeepAlive(m)61}62func (m *adaBoostModel) getAdaBoostModel(identifier string) {63 m.allocAdaBoostModel(identifier)64}65func setAdaBoostModel(identifier string, ptr *adaBoostModel) {66 C.mlpackSetAdaBoostModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))67}68type approxkfnModel struct {69 mem unsafe.Pointer 70}71func (m *approxkfnModel) allocApproxKFNModel(identifier string) {72 m.mem = C.mlpackGetApproxKFNModelPtr(C.CString(identifier))73 runtime.KeepAlive(m)74}75func (m *approxkfnModel) getApproxKFNModel(identifier string) {76 m.allocApproxKFNModel(identifier)77}78func setApproxKFNModel(identifier string, ptr *approxkfnModel) {79 C.mlpackSetApproxKFNModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))80}81type bayesianLinearRegression struct {82 mem unsafe.Pointer 83}84func (m *bayesianLinearRegression) allocBayesianLinearRegression(identifier string) {85 m.mem = C.mlpackGetBayesianLinearRegressionPtr(C.CString(identifier))86 runtime.KeepAlive(m)87}88func (m *bayesianLinearRegression) getBayesianLinearRegression(identifier string) {89 m.allocBayesianLinearRegression(identifier)90}91func setBayesianLinearRegression(identifier string, ptr *bayesianLinearRegression) {92 C.mlpackSetBayesianLinearRegressionPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))93}94type cfModel struct {95 mem unsafe.Pointer 96}97func (m *cfModel) allocCFModel(identifier string) {98 m.mem = C.mlpackGetCFModelPtr(C.CString(identifier))99 runtime.KeepAlive(m)100}101func (m *cfModel) getCFModel(identifier string) {102 m.allocCFModel(identifier)103}104func setCFModel(identifier string, ptr *cfModel) {105 C.mlpackSetCFModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))106}107type dsModel struct {108 mem unsafe.Pointer 109}110func (m *dsModel) allocDSModel(identifier string) {111 m.mem = C.mlpackGetDSModelPtr(C.CString(identifier))112 runtime.KeepAlive(m)113}114func (m *dsModel) getDSModel(identifier string) {115 m.allocDSModel(identifier)116}117func setDSModel(identifier string, ptr *dsModel) {118 C.mlpackSetDSModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))119}120type decisionTreeModel struct {121 mem unsafe.Pointer 122}123func (m *decisionTreeModel) allocDecisionTreeModel(identifier string) {124 m.mem = C.mlpackGetDecisionTreeModelPtr(C.CString(identifier))125 runtime.KeepAlive(m)126}127func (m *decisionTreeModel) getDecisionTreeModel(identifier string) {128 m.allocDecisionTreeModel(identifier)129}130func setDecisionTreeModel(identifier string, ptr *decisionTreeModel) {131 C.mlpackSetDecisionTreeModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))132}133type dTree struct {134 mem unsafe.Pointer 135}136func (m *dTree) allocDTree(identifier string) {137 m.mem = C.mlpackGetDTreePtr(C.CString(identifier))138 runtime.KeepAlive(m)139}140func (m *dTree) getDTree(identifier string) {141 m.allocDTree(identifier)142}143func setDTree(identifier string, ptr *dTree) {144 C.mlpackSetDTreePtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))145}146type fastmksModel struct {147 mem unsafe.Pointer 148}149func (m *fastmksModel) allocFastMKSModel(identifier string) {150 m.mem = C.mlpackGetFastMKSModelPtr(C.CString(identifier))151 runtime.KeepAlive(m)152}153func (m *fastmksModel) getFastMKSModel(identifier string) {154 m.allocFastMKSModel(identifier)155}156func setFastMKSModel(identifier string, ptr *fastmksModel) {157 C.mlpackSetFastMKSModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))158}159type gmm struct {160 mem unsafe.Pointer 161}162func (m *gmm) allocGMM(identifier string) {163 m.mem = C.mlpackGetGMMPtr(C.CString(identifier))164 runtime.KeepAlive(m)165}166func (m *gmm) getGMM(identifier string) {167 m.allocGMM(identifier)168}169func setGMM(identifier string, ptr *gmm) {170 C.mlpackSetGMMPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))171}172type hmmModel struct {173 mem unsafe.Pointer 174}175func (m *hmmModel) allocHMMModel(identifier string) {176 m.mem = C.mlpackGetHMMModelPtr(C.CString(identifier))177 runtime.KeepAlive(m)178}179func (m *hmmModel) getHMMModel(identifier string) {180 m.allocHMMModel(identifier)181}182func setHMMModel(identifier string, ptr *hmmModel) {183 C.mlpackSetHMMModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))184}185type hoeffdingTreeModel struct {186 mem unsafe.Pointer 187}188func (m *hoeffdingTreeModel) allocHoeffdingTreeModel(identifier string) {189 m.mem = C.mlpackGetHoeffdingTreeModelPtr(C.CString(identifier))190 runtime.KeepAlive(m)191}192func (m *hoeffdingTreeModel) getHoeffdingTreeModel(identifier string) {193 m.allocHoeffdingTreeModel(identifier)194}195func setHoeffdingTreeModel(identifier string, ptr *hoeffdingTreeModel) {196 C.mlpackSetHoeffdingTreeModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))197}198type kdeModel struct {199 mem unsafe.Pointer 200}201func (m *kdeModel) allocKDEModel(identifier string) {202 m.mem = C.mlpackGetKDEModelPtr(C.CString(identifier))203 runtime.KeepAlive(m)204}205func (m *kdeModel) getKDEModel(identifier string) {206 m.allocKDEModel(identifier)207}208func setKDEModel(identifier string, ptr *kdeModel) {209 C.mlpackSetKDEModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))210}211type lars struct {212 mem unsafe.Pointer 213}214func (m *lars) allocLARS(identifier string) {215 m.mem = C.mlpackGetLARSPtr(C.CString(identifier))216 runtime.KeepAlive(m)217}218func (m *lars) getLARS(identifier string) {219 m.allocLARS(identifier)220}221func setLARS(identifier string, ptr *lars) {222 C.mlpackSetLARSPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))223}224type linearRegression struct {225 mem unsafe.Pointer 226}227func (m *linearRegression) allocLinearRegression(identifier string) {228 m.mem = C.mlpackGetLinearRegressionPtr(C.CString(identifier))229 runtime.KeepAlive(m)230}231func (m *linearRegression) getLinearRegression(identifier string) {232 m.allocLinearRegression(identifier)233}234func setLinearRegression(identifier string, ptr *linearRegression) {235 C.mlpackSetLinearRegressionPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))236}237type linearsvmModel struct {238 mem unsafe.Pointer 239}240func (m *linearsvmModel) allocLinearSVMModel(identifier string) {241 m.mem = C.mlpackGetLinearSVMModelPtr(C.CString(identifier))242 runtime.KeepAlive(m)243}244func (m *linearsvmModel) getLinearSVMModel(identifier string) {245 m.allocLinearSVMModel(identifier)246}247func setLinearSVMModel(identifier string, ptr *linearsvmModel) {248 C.mlpackSetLinearSVMModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))249}250type localCoordinateCoding struct {251 mem unsafe.Pointer 252}253func (m *localCoordinateCoding) allocLocalCoordinateCoding(identifier string) {254 m.mem = C.mlpackGetLocalCoordinateCodingPtr(C.CString(identifier))255 runtime.KeepAlive(m)256}257func (m *localCoordinateCoding) getLocalCoordinateCoding(identifier string) {258 m.allocLocalCoordinateCoding(identifier)259}260func setLocalCoordinateCoding(identifier string, ptr *localCoordinateCoding) {261 C.mlpackSetLocalCoordinateCodingPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))262}263type logisticRegression struct {264 mem unsafe.Pointer 265}266func (m *logisticRegression) allocLogisticRegression(identifier string) {267 m.mem = C.mlpackGetLogisticRegressionPtr(C.CString(identifier))268 runtime.KeepAlive(m)269}270func (m *logisticRegression) getLogisticRegression(identifier string) {271 m.allocLogisticRegression(identifier)272}273func setLogisticRegression(identifier string, ptr *logisticRegression) {274 C.mlpackSetLogisticRegressionPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))275}276type lshSearch struct {277 mem unsafe.Pointer 278}279func (m *lshSearch) allocLSHSearch(identifier string) {280 m.mem = C.mlpackGetLSHSearchPtr(C.CString(identifier))281 runtime.KeepAlive(m)282}283func (m *lshSearch) getLSHSearch(identifier string) {284 m.allocLSHSearch(identifier)285}286func setLSHSearch(identifier string, ptr *lshSearch) {287 C.mlpackSetLSHSearchPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))288}289type nbcModel struct {290 mem unsafe.Pointer 291}292func (m *nbcModel) allocNBCModel(identifier string) {293 m.mem = C.mlpackGetNBCModelPtr(C.CString(identifier))294 runtime.KeepAlive(m)295}296func (m *nbcModel) getNBCModel(identifier string) {297 m.allocNBCModel(identifier)298}299func setNBCModel(identifier string, ptr *nbcModel) {300 C.mlpackSetNBCModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))301}302type knnModel struct {303 mem unsafe.Pointer 304}305func (m *knnModel) allocKNNModel(identifier string) {306 m.mem = C.mlpackGetKNNModelPtr(C.CString(identifier))307 runtime.KeepAlive(m)308}309func (m *knnModel) getKNNModel(identifier string) {310 m.allocKNNModel(identifier)311}312func setKNNModel(identifier string, ptr *knnModel) {313 C.mlpackSetKNNModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))314}315type kfnModel struct {316 mem unsafe.Pointer 317}318func (m *kfnModel) allocKFNModel(identifier string) {319 m.mem = C.mlpackGetKFNModelPtr(C.CString(identifier))320 runtime.KeepAlive(m)321}322func (m *kfnModel) getKFNModel(identifier string) {323 m.allocKFNModel(identifier)324}325func setKFNModel(identifier string, ptr *kfnModel) {326 C.mlpackSetKFNModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))327}328type perceptronModel struct {329 mem unsafe.Pointer 330}331func (m *perceptronModel) allocPerceptronModel(identifier string) {332 m.mem = C.mlpackGetPerceptronModelPtr(C.CString(identifier))333 runtime.KeepAlive(m)334}335func (m *perceptronModel) getPerceptronModel(identifier string) {336 m.allocPerceptronModel(identifier)337}338func setPerceptronModel(identifier string, ptr *perceptronModel) {339 C.mlpackSetPerceptronModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))340}341type scalingModel struct {342 mem unsafe.Pointer 343}344func (m *scalingModel) allocScalingModel(identifier string) {345 m.mem = C.mlpackGetScalingModelPtr(C.CString(identifier))346 runtime.KeepAlive(m)347}348func (m *scalingModel) getScalingModel(identifier string) {349 m.allocScalingModel(identifier)350}351func setScalingModel(identifier string, ptr *scalingModel) {352 C.mlpackSetScalingModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))353}354type randomForestModel struct {355 mem unsafe.Pointer 356}357func (m *randomForestModel) allocRandomForestModel(identifier string) {358 m.mem = C.mlpackGetRandomForestModelPtr(C.CString(identifier))359 runtime.KeepAlive(m)360}361func (m *randomForestModel) getRandomForestModel(identifier string) {362 m.allocRandomForestModel(identifier)363}364func setRandomForestModel(identifier string, ptr *randomForestModel) {365 C.mlpackSetRandomForestModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))366}367type rannModel struct {368 mem unsafe.Pointer 369}370func (m *rannModel) allocRANNModel(identifier string) {371 m.mem = C.mlpackGetRANNModelPtr(C.CString(identifier))372 runtime.KeepAlive(m)373}374func (m *rannModel) getRANNModel(identifier string) {375 m.allocRANNModel(identifier)376}377func setRANNModel(identifier string, ptr *rannModel) {378 C.mlpackSetRANNModelPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))379}380type softmaxRegression struct {381 mem unsafe.Pointer 382}383func (m *softmaxRegression) allocSoftmaxRegression(identifier string) {384 m.mem = C.mlpackGetSoftmaxRegressionPtr(C.CString(identifier))385 runtime.KeepAlive(m)386}387func (m *softmaxRegression) getSoftmaxRegression(identifier string) {388 m.allocSoftmaxRegression(identifier)389}390func setSoftmaxRegression(identifier string, ptr *softmaxRegression) {391 C.mlpackSetSoftmaxRegressionPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))392}393type sparseCoding struct {394 mem unsafe.Pointer 395}396func (m *sparseCoding) allocSparseCoding(identifier string) {397 m.mem = C.mlpackGetSparseCodingPtr(C.CString(identifier))398 runtime.KeepAlive(m)399}400func (m *sparseCoding) getSparseCoding(identifier string) {401 m.allocSparseCoding(identifier)402}403func setSparseCoding(identifier string, ptr *sparseCoding) {404 C.mlpackSetSparseCodingPtr(C.CString(identifier), (unsafe.Pointer)(ptr.mem))405}...

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linear_regression_series.go

Source:linear_regression_series.go Github

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...33 stdev = lrs.stddevx34 avg = lrs.avgx35 return36}37// GetName returns the name of the time series.38func (lrs LinearRegressionSeries) GetName() string {39 return lrs.Name40}41// GetStyle returns the line style.42func (lrs LinearRegressionSeries) GetStyle() Style {43 return lrs.Style44}45// GetYAxis returns which YAxis the series draws on.46func (lrs LinearRegressionSeries) GetYAxis() YAxisType {47 return lrs.YAxis48}49// Len returns the number of elements in the series.50func (lrs LinearRegressionSeries) Len() int {51 return MinInt(lrs.GetLimit(), lrs.InnerSeries.Len()-lrs.GetOffset())52}53// GetLimit returns the window size.54func (lrs LinearRegressionSeries) GetLimit() int {55 if lrs.Limit == 0 {56 return lrs.InnerSeries.Len()57 }58 return lrs.Limit59}60// GetEndIndex returns the effective limit end.61func (lrs LinearRegressionSeries) GetEndIndex() int {62 windowEnd := lrs.GetOffset() + lrs.GetLimit()63 innerSeriesLastIndex := lrs.InnerSeries.Len() - 164 return MinInt(windowEnd, innerSeriesLastIndex)65}66// GetOffset returns the data offset.67func (lrs LinearRegressionSeries) GetOffset() int {68 if lrs.Offset == 0 {69 return 070 }71 return lrs.Offset72}73// GetValues gets a value at a given index.74func (lrs *LinearRegressionSeries) GetValues(index int) (x, y float64) {75 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {76 return77 }78 if lrs.IsZero() {79 lrs.computeCoefficients()80 }81 offset := lrs.GetOffset()82 effectiveIndex := MinInt(index+offset, lrs.InnerSeries.Len())83 x, y = lrs.InnerSeries.GetValues(effectiveIndex)84 y = (lrs.m * lrs.normalize(x)) + lrs.b85 return86}87// GetFirstValues computes the first linear regression value.88func (lrs *LinearRegressionSeries) GetFirstValues() (x, y float64) {89 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {90 return91 }92 if lrs.IsZero() {93 lrs.computeCoefficients()94 }95 x, y = lrs.InnerSeries.GetValues(0)96 y = (lrs.m * lrs.normalize(x)) + lrs.b97 return98}99// GetLastValues computes the last linear regression value.100func (lrs *LinearRegressionSeries) GetLastValues() (x, y float64) {101 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {102 return103 }104 if lrs.IsZero() {105 lrs.computeCoefficients()106 }107 endIndex := lrs.GetEndIndex()108 x, y = lrs.InnerSeries.GetValues(endIndex)109 y = (lrs.m * lrs.normalize(x)) + lrs.b110 return111}112// Render renders the series.113func (lrs *LinearRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {114 style := lrs.Style.InheritFrom(defaults)115 Draw.LineSeries(r, canvasBox, xrange, yrange, style, lrs)116}117// Validate validates the series.118func (lrs *LinearRegressionSeries) Validate() error {119 if lrs.InnerSeries == nil {120 return fmt.Errorf("linear regression series requires InnerSeries to be set")121 }122 return nil123}124// IsZero returns if we've computed the coefficients or not.125func (lrs *LinearRegressionSeries) IsZero() bool {126 return lrs.m == 0 && lrs.b == 0127}128//129// internal helpers130//131func (lrs *LinearRegressionSeries) normalize(xvalue float64) float64 {132 return (xvalue - lrs.avgx) / lrs.stddevx133}134// computeCoefficients computes the `m` and `b` terms in the linear formula given by `y = mx+b`.135func (lrs *LinearRegressionSeries) computeCoefficients() {136 startIndex := lrs.GetOffset()137 endIndex := lrs.GetEndIndex()138 p := float64(endIndex - startIndex)139 xvalues := NewValueBufferWithCapacity(lrs.Len())140 for index := startIndex; index < endIndex; index++ {141 x, _ := lrs.InnerSeries.GetValues(index)142 xvalues.Enqueue(x)143 }144 lrs.avgx = Seq{xvalues}.Average()145 lrs.stddevx = Seq{xvalues}.StdDev()146 var sumx, sumy, sumxx, sumxy float64147 for index := startIndex; index < endIndex; index++ {148 x, y := lrs.InnerSeries.GetValues(index)149 x = lrs.normalize(x)150 sumx += x151 sumy += y152 sumxx += x * x153 sumxy += x * y154 }155 lrs.m = (p*sumxy - sumx*sumy) / (p*sumxx - sumx*sumx)156 lrs.b = (sumy / p) - (lrs.m * sumx / p)157}...

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polynomial_regression_series.go

Source:polynomial_regression_series.go Github

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...21 Degree int22 InnerSeries ValuesProvider23 coeffs []float6424}25// GetName returns the name of the time series.26func (prs PolynomialRegressionSeries) GetName() string {27 return prs.Name28}29// GetStyle returns the line style.30func (prs PolynomialRegressionSeries) GetStyle() Style {31 return prs.Style32}33// GetYAxis returns which YAxis the series draws on.34func (prs PolynomialRegressionSeries) GetYAxis() YAxisType {35 return prs.YAxis36}37// Len returns the number of elements in the series.38func (prs PolynomialRegressionSeries) Len() int {39 return MinInt(prs.GetLimit(), prs.InnerSeries.Len()-prs.GetOffset())40}41// GetLimit returns the window size.42func (prs PolynomialRegressionSeries) GetLimit() int {43 if prs.Limit == 0 {44 return prs.InnerSeries.Len()45 }46 return prs.Limit47}48// GetEndIndex returns the effective limit end.49func (prs PolynomialRegressionSeries) GetEndIndex() int {50 windowEnd := prs.GetOffset() + prs.GetLimit()51 innerSeriesLastIndex := prs.InnerSeries.Len() - 152 return MinInt(windowEnd, innerSeriesLastIndex)53}54// GetOffset returns the data offset.55func (prs PolynomialRegressionSeries) GetOffset() int {56 if prs.Offset == 0 {57 return 058 }59 return prs.Offset60}61// Validate validates the series.62func (prs *PolynomialRegressionSeries) Validate() error {63 if prs.InnerSeries == nil {64 return fmt.Errorf("linear regression series requires InnerSeries to be set")65 }66 endIndex := prs.GetEndIndex()67 if endIndex >= prs.InnerSeries.Len() {68 return fmt.Errorf("invalid window; inner series has length %d but end index is %d", prs.InnerSeries.Len(), endIndex)69 }70 return nil71}72// GetValues returns the series value for a given index.73func (prs *PolynomialRegressionSeries) GetValues(index int) (x, y float64) {74 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {75 return76 }77 if prs.coeffs == nil {78 coeffs, err := prs.computeCoefficients()79 if err != nil {80 panic(err)81 }82 prs.coeffs = coeffs83 }84 offset := prs.GetOffset()85 effectiveIndex := MinInt(index+offset, prs.InnerSeries.Len())86 x, y = prs.InnerSeries.GetValues(effectiveIndex)87 y = prs.apply(x)88 return89}90// GetFirstValues computes the first poly regression value.91func (prs *PolynomialRegressionSeries) GetFirstValues() (x, y float64) {92 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {93 return94 }95 if prs.coeffs == nil {96 coeffs, err := prs.computeCoefficients()97 if err != nil {98 panic(err)99 }100 prs.coeffs = coeffs101 }102 x, y = prs.InnerSeries.GetValues(0)103 y = prs.apply(x)104 return105}106// GetLastValues computes the last poly regression value.107func (prs *PolynomialRegressionSeries) GetLastValues() (x, y float64) {108 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {109 return110 }111 if prs.coeffs == nil {112 coeffs, err := prs.computeCoefficients()113 if err != nil {114 panic(err)115 }116 prs.coeffs = coeffs117 }118 endIndex := prs.GetEndIndex()119 x, y = prs.InnerSeries.GetValues(endIndex)120 y = prs.apply(x)121 return122}123func (prs *PolynomialRegressionSeries) apply(v float64) (out float64) {124 for index, coeff := range prs.coeffs {125 out = out + (coeff * math.Pow(v, float64(index)))126 }127 return128}129func (prs *PolynomialRegressionSeries) computeCoefficients() ([]float64, error) {130 xvalues, yvalues := prs.values()131 return matrix.Poly(xvalues, yvalues, prs.Degree)132}133func (prs *PolynomialRegressionSeries) values() (xvalues, yvalues []float64) {134 startIndex := prs.GetOffset()135 endIndex := prs.GetEndIndex()136 xvalues = make([]float64, endIndex-startIndex)137 yvalues = make([]float64, endIndex-startIndex)138 for index := startIndex; index < endIndex; index++ {139 x, y := prs.InnerSeries.GetValues(index)140 xvalues[index-startIndex] = x141 yvalues[index-startIndex] = y142 }143 return144}145// Render renders the series.146func (prs *PolynomialRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {147 style := prs.Style.InheritFrom(defaults)148 Draw.LineSeries(r, canvasBox, xrange, yrange, style, prs)149}...

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1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 r.Train(6 regression.DataPoint(2.71, []float64{1}),7 regression.DataPoint(4.21, []float64{2}),8 regression.DataPoint(5.71, []float64{3}),9 regression.DataPoint(8.21, []float64{4}),10 regression.DataPoint(8.71, []float64{5}),11 r.Run()12 fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)13 fmt.Printf("\nR2: %v\n", r.R2)14 fmt.Printf("\nIntercept: %v\n", r.Coeff(0))15 fmt.Printf("\nCoefficient: %v\n", r.Coeff(1))16 fmt.Printf("\nGet: %v\n", r.Get(0))17}18import (19func main() {20 r.SetObserved("Y")21 r.SetVar(0, "X")22 r.Train(23 regression.DataPoint(2.71, []float64{1}),24 regression.DataPoint(4.21, []float64{2}),25 regression.DataPoint(5.71, []float64{3}),26 regression.DataPoint(8.21, []float64{4}),27 regression.DataPoint(8.71, []float64{5}),28 r.Run()29 fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)30 fmt.Printf("\nR2: %v\n", r.R2)31 fmt.Printf("\nIntercept: %v\n", r.Coeff(0))32 fmt.Printf("\nCoefficient: %v\n", r.Coeff(1))33 fmt.Printf("\nPredict: %v\n", r.Predict([]float64{6}))34}

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1import (2func main() {3 r := new(regression.Regression)4 r.SetObserved("Y")5 r.SetVar(0, "X")6 r.Train(7 regression.DataPoint(2.0, []float64{1.0}),8 regression.DataPoint(4.0, []float64{2.0}),9 regression.DataPoint(6.0, []float64{3.0}),10 regression.DataPoint(8.0, []float64{4.0}),11 r.Run()12 fmt.Printf("Regression formula: %v13 fmt.Printf("Regression prediction: %v14", r.Get(5.0))15}

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1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 r.Train(regression.Data{6 X: []float64{1, 2, 3, 4},7 Y: []float64{2, 4, 6, 8},8 })9 r.Train(regression.Data{10 X: []float64{2, 3, 4, 5},11 Y: []float64{3, 5, 7, 9},12 })13 r.Train(regression.Data{14 X: []float64{3, 4, 5, 6},15 Y: []float64{4, 6, 8, 10},16 })17 fmt.Printf("\nRegression formula:\n%v\n", r.Formula)18 fmt.Printf("\nRegression prediction:\n%v\n", r.Get(5.0))19}

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1import (2func main() {3 r.Train(regression.DataPoint(1, []float64{1}, 1))4 r.Train(regression.DataPoint(1, []float64{2}, 2))5 r.Train(regression.DataPoint(1, []float64{3}, 3))6 r.Train(regression.DataPoint(1, []float64{4}, 4))7 r.Train(regression.DataPoint(1, []float64{5}, 5))8 r.Run()9 fmt.Printf("\nRegression formula: %v\n", r.Formula)10 fmt.Printf("Predict x = 5, y = %0.2f\n", r.Predict([]float64{5}))11}

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1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 r.Train(regression.Data{6 {X: []float64{1}, Y: 2},7 {X: []float64{2}, Y: 4},8 {X: []float64{3}, Y: 6},9 {X: []float64{4}, Y: 8},10 })11 r.Run()12 fmt.Printf("Regression formula: %v13 fmt.Printf("Regression:\n%v14 fmt.Printf("Predicted value for 5: %v15", r.Predict([]float64{5}))16}

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1import (2func main() {3 r.SetObserved("y")4 r.SetVar(0, "x")5 r.Train(6 regression.DataPoint(2.71, []float64{1}),7 regression.DataPoint(3.14, []float64{2}),8 regression.DataPoint(1.41, []float64{3}),9 regression.DataPoint(1.61, []float64{4}),10 r.Run()11 fmt.Printf("Regression Formula: \n%v\n", r.Formula)12 fmt.Printf("Predicted value for x = 5: %0.2f\n", r.Get(5))13}

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1import (2func main() {3 r.SetObserved("y")4 r.SetVar(0, "x")5 r.Train(6 regression.Data{7 X: []float64{1, 2, 3, 4, 5},8 Y: []float64{2, 4, 6, 8, 10},9 },10 r.Train(11 regression.Data{12 X: []float64{6, 7, 8, 9, 10},13 Y: []float64{12, 14, 16, 18, 20},14 },15 fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)16 fmt.Printf("\nR2: %v\n", r.R2)17}

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1import "fmt"2import "github.com/sajari/regression"3func main() {4r.SetObserved("Y")5r.SetVar(0, "X")6r.Train(7regression.DataPoint(2.4, []float64{1}),8regression.DataPoint(0.5, []float64{2}),9regression.DataPoint(2.2, []float64{3}),10regression.DataPoint(1.9, []float64{4}),11regression.DataPoint(3.1, []float64{5}),12r.Run()13fmt.Printf("Regression formula:\n%v\n", r.Formula)14fmt.Printf("Predicted Y for X = 4.5: %v\n", r.Get([]float64{4.5}))15}

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1import (2func main() {3 rand.Seed(time.Now().UnixNano())4 x := []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}5 y := []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}6 x1 := []float64{11, 12, 13, 14, 15, 16, 17, 18, 19, 20}7 y1 := []float64{11, 12, 13, 14, 15, 16, 17, 18, 19, 20}8 reg := regression.New(x, y)9 reg.Test(x1, y1)10 reg.Fit()11 m, c := reg.Get()12 fmt.Println("m:", m, "c:", c)13 y2 := reg.Predict(x1)14 fmt.Println("y2:", y2)15 for i := 0; i < len(y1); i++ {16 sum += math.Pow(y1[i]-y2[i], 2)17 }18 fmt.Println("Sum:", sum)19}

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1import (2func main() {3 x := []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}4 y := []float64{2.2, 4.4, 6.6, 8.8, 11, 13.2, 15.4, 17.6, 19.8, 22}5 reg.SetObserved(x, y)6 reg.Regress()7 a, b := reg.Coefficients()8 x = []float64{11, 12, 13, 14, 15}9 y = reg.Predict(x)10 r2 := reg.R2()11 fmt.Println("a:", a)12 fmt.Println("b:", b)13 fmt.Println("y:", y)14 fmt.Println("R^2:", r2)15 a, b, r2 = stat.LinearRegression(x, y, nil, false)16 fmt.Println("a:", a)17 fmt.Println("b:", b)18 fmt.Println("R^2:", r2)19}

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