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How to use Get method of regression Package

Best Keploy code snippet using regression.Get

models.go

Source:models.go

...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}...

linear_regression_series.go

Source:linear_regression_series.go

...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}...

polynomial_regression_series.go

Source:polynomial_regression_series.go

...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}...

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Get

Using AI Code Generation

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}

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.