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How to use saveResult method of regression Package

Best Keploy code snippet using regression.saveResult

copy.go

Source:copy.go

1package collect2import (3	"archive/tar"4	"bytes"5	"context"6	"fmt"7	"io"8	"io/ioutil"9	"os"10	"path/filepath"11	"github.com/pkg/errors"12	troubleshootv1beta2 "github.com/replicatedhq/troubleshoot/pkg/apis/troubleshoot/v1beta2"13	corev1 "k8s.io/api/core/v1"14	"k8s.io/apimachinery/pkg/runtime"15	"k8s.io/client-go/kubernetes"16	restclient "k8s.io/client-go/rest"17	"k8s.io/client-go/tools/remotecommand"18)19//Copy function gets a file or folder from a container specified in the specs.20func Copy(c *Collector, copyCollector *troubleshootv1beta2.Copy) (CollectorResult, error) {21	client, err := kubernetes.NewForConfig(c.ClientConfig)22	if err != nil {23		return nil, err24	}25	output := NewResult()26	ctx := context.Background()27	pods, podsErrors := listPodsInSelectors(ctx, client, copyCollector.Namespace, copyCollector.Selector)28	if len(podsErrors) > 0 {29		output.SaveResult(c.BundlePath, getCopyErrosFileName(copyCollector), marshalErrors(podsErrors))30	}31	if len(pods) > 0 {32		for _, pod := range pods {33			containerName := pod.Spec.Containers[0].Name34			if copyCollector.ContainerName != "" {35				containerName = copyCollector.ContainerName36			}37			subPath := filepath.Join(copyCollector.Name, pod.Namespace, pod.Name, copyCollector.ContainerName)38			copyCollector.ExtractArchive = true // TODO: existing regression. this flag is always ignored and this matches current behaviour39			copyErrors := map[string]string{}40			dstPath := filepath.Join(c.BundlePath, subPath, filepath.Dir(copyCollector.ContainerPath))41			files, stderr, err := copyFilesFromPod(ctx, dstPath, c.ClientConfig, client, pod.Name, containerName, pod.Namespace, copyCollector.ContainerPath, copyCollector.ExtractArchive)42			if err != nil {43				copyErrors[filepath.Join(copyCollector.ContainerPath, "error")] = err.Error()44				if len(stderr) > 0 {45					copyErrors[filepath.Join(copyCollector.ContainerPath, "stderr")] = string(stderr)46				}47				key := filepath.Join(subPath, copyCollector.ContainerPath+"-errors.json")48				output.SaveResult(c.BundlePath, key, marshalErrors(copyErrors))49				continue50			}51			for k, v := range files {52				output[filepath.Join(subPath, filepath.Dir(copyCollector.ContainerPath), k)] = v53			}54		}55	}56	return output, nil57}58func copyFilesFromPod(ctx context.Context, dstPath string, clientConfig *restclient.Config, client kubernetes.Interface, podName string, containerName string, namespace string, containerPath string, extract bool) (CollectorResult, []byte, error) {59	command := []string{"tar", "-C", filepath.Dir(containerPath), "-cf", "-", filepath.Base(containerPath)}60	req := client.CoreV1().RESTClient().Post().Resource("pods").Name(podName).Namespace(namespace).SubResource("exec")61	scheme := runtime.NewScheme()62	if err := corev1.AddToScheme(scheme); err != nil {63		return nil, nil, errors.Wrap(err, "failed to add runtime scheme")64	}65	parameterCodec := runtime.NewParameterCodec(scheme)66	req.VersionedParams(&corev1.PodExecOptions{67		Command:   command,68		Container: containerName,69		Stdin:     true,70		Stdout:    false,71		Stderr:    true,72		TTY:       false,73	}, parameterCodec)74	exec, err := remotecommand.NewSPDYExecutor(clientConfig, "POST", req.URL())75	if err != nil {76		return nil, nil, errors.Wrap(err, "failed to create SPDY executor")77	}78	result := NewResult()79	var stdoutWriter io.Writer80	var copyError error81	if extract {82		pipeReader, pipeWriter := io.Pipe()83		tarReader := tar.NewReader(pipeReader)84		stdoutWriter = pipeWriter85		go func() {86			// this can cause "read/write on closed pipe" error, but without this exec.Stream blocks87			defer pipeWriter.Close()88			for {89				header, err := tarReader.Next()90				if err == io.EOF {91					return92				}93				if err != nil {94					pipeWriter.CloseWithError(errors.Wrap(err, "failed to read header from tar"))95					return96				}97				switch header.Typeflag {98				case tar.TypeDir:99					name := filepath.Join(dstPath, header.Name)100					if err := os.MkdirAll(name, os.FileMode(header.Mode)); err != nil {101						pipeWriter.CloseWithError(errors.Wrap(err, "failed to mkdir"))102						return103					}104				case tar.TypeReg:105					err := result.SaveResult(dstPath, header.Name, tarReader)106					if err != nil {107						pipeWriter.CloseWithError(errors.Wrapf(err, "failed to save result for file %s", header.Name))108						return109					}110				}111			}112		}()113	} else {114		w, err := result.GetWriter(dstPath, filepath.Base(containerPath)+".tar")115		if err != nil {116			return nil, nil, errors.Wrap(err, "failed to craete dest file")117		}118		defer result.CloseWriter(dstPath, filepath.Base(containerPath)+".tar", w)119		stdoutWriter = w120	}121	var stderr bytes.Buffer122	copyError = exec.Stream(remotecommand.StreamOptions{123		Stdin:  nil,124		Stdout: stdoutWriter,125		Stderr: &stderr,126		Tty:    false,127	})128	if copyError != nil {129		return result, stderr.Bytes(), errors.Wrap(copyError, "failed to stream command output")130	}131	return result, stderr.Bytes(), nil132}133func getCopyErrosFileName(copyCollector *troubleshootv1beta2.Copy) string {134	if len(copyCollector.Name) > 0 {135		return fmt.Sprintf("%s-errors.json", copyCollector.Name)136	}137	if len(copyCollector.CollectorName) > 0 {138		return fmt.Sprintf("%s-errors.json", copyCollector.CollectorName)139	}140	// TODO: random part141	return "errors.json"142}143func extractTar(reader io.Reader) (map[string][]byte, error) {144	files := map[string][]byte{}145	tr := tar.NewReader(reader)146	for {147		header, err := tr.Next()148		if err == io.EOF {149			break150		} else if err != nil {151			return files, errors.Wrap(err, "read header")152		}153		switch header.Typeflag {154		case tar.TypeReg:155			data, err := ioutil.ReadAll(tr)156			if err != nil {157				return files, errors.Wrapf(err, "read file %s", header.Name)158			}159			files[header.Name] = data160		default:161			continue162		}163	}164	return files, nil165}166func saveFromTar(rootDir string, reader io.Reader) (CollectorResult, error) {167	result := NewResult()168	tr := tar.NewReader(reader)169	for {170		header, err := tr.Next()171		if err == io.EOF {172			break173		} else if err != nil {174			return result, errors.Wrap(err, "read header")175		}176		switch header.Typeflag {177		case tar.TypeReg:178			result.SaveResult(rootDir, header.Name, tr)179		default:180			continue181		}182	}183	return result, nil184}...

models.go

Source:models.go

1// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.2// Licensed under the Apache License, Version 2.0 (the "License");3// you may not use this file except in compliance with the License.4// You may obtain a copy of the License at5//6//     http://www.apache.org/licenses/LICENSE-2.07//8// Unless required by applicable law or agreed to in writing, software9// distributed under the License is distributed on an "AS IS" BASIS10// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.11// See the License for the specific language governing permissions and12// limitations under the License.13package models14import (15	"github.com/PaddlePaddle/PaddleDTX/dai/mpc/models/linear_reg_vl"16	"github.com/PaddlePaddle/PaddleDTX/dai/mpc/models/logic_reg_vl"17	pbCom "github.com/PaddlePaddle/PaddleDTX/dai/protos/common"18	pb "github.com/PaddlePaddle/PaddleDTX/dai/protos/mpc"19)20// Model was trained out by a Learner,21// and participates in the multi-parts-calculation during prediction process22// If input different parts of a sample into Models on different mpc-nodes, you'll get final predicting result after some time of multi-parts-calculation23type Model interface {24	// Advance does calculation with local parts of samples and communicates with other nodes in cluster to predict outcomes25	// payload could be resolved by Model trained out by specific algorithm and samples26	// We'd better call the method asynchronously avoid blocking the main go-routine27	Advance(payload []byte) (*pb.PredictResponse, error)28}29// RpcHandler used to request remote mpc-node30type RpcHandler interface {31	StepPredict(req *pb.PredictRequest, peerName string) (*pb.PredictResponse, error)32	// StepPredictWithRetry sends prediction message to remote mpc-node33	// retries 2 times at most34	// inteSec indicates the interval between retry requests, in seconds35	StepPredictWithRetry(req *pb.PredictRequest, peerName string, times int, inteSec int64) (*pb.PredictResponse, error)36}37// ResultHandler handles final result which is successful or failed38// Should be called when prediction finished39type ResultHandler interface {40	SaveResult(*pbCom.PredictTaskResult)41}42// NewModel returns a Model43// id is the assigned id for Model44// samplesFile is sample file content for prediction45// address indicates local mpc-node46// algo is the algorithm of model47// parties are other models who participates in MPC, assigned with mpc-node address usually48// rpc is used to request remote mpc-node49// rh handles final result which is successful or failed50// params are parameters for model51func NewModel(id string, address string, algo pbCom.Algorithm,52	params *pbCom.TrainModels, samplesFile []byte,53	parties []string, rpc RpcHandler, rh ResultHandler) (Model, error) {54	if pbCom.Algorithm_LINEAR_REGRESSION_VL == algo {55		return linear_reg_vl.NewModel(id, address, params, samplesFile,56			parties, rpc, rh)57	} else { // pbCom.Algorithm_LOGIC_REGRESSION_VL58		return logic_reg_vl.NewModel(id, address, params, samplesFile,59			parties, rpc, rh)60	}61}...

saveResult

Using AI Code Generation

1import (2func main() {3	var x []float64 = []float64{1, 2, 3, 4, 5}4	var y []float64 = []float64{2, 4, 6, 8, 10}5	y1 = regression.SaveResult(x, y, x1)6	fmt.Printf("y1 = %f7}

saveResult

Using AI Code Generation

1regression.saveResult("result.txt");2regression.saveResult("result.txt");3regression.saveResult("result.txt");4regression.saveResult("result.txt");5regression.saveResult("result.txt");6regression.saveResult("result.txt");7regression.saveResult("result.txt");8regression.saveResult("result.txt");9regression.saveResult("result.txt");10regression.saveResult("result.txt");11regression.saveResult("result.txt");12regression.saveResult("result.txt");13regression.saveResult("result.txt");14regression.saveResult("result.txt");15regression.saveResult("result.txt");16regression.saveResult("result.txt");17regression.saveResult("result.txt");18regression.saveResult("result.txt");19regression.saveResult("result.txt");

saveResult

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Result")4	r.SetVar(0, "X1")5	r.SetVar(1, "X2")6	r.SetVar(2, "X3")7	rand.Seed(time.Now().UnixNano())8	for i := 0; i < 100; i++ {9		x1 := rand.Float64()10		x2 := rand.Float64()11		x3 := rand.Float64()12		y := 0.1*x1 + 0.2*x2 + 0.3*x3 + rand.NormFloat64()*0.113		r.Train(regression.DataPoint(y, []float64{x1, x2, x3}))14	}15	r.Run()16	fmt.Printf("\nRegression Formula:\n")17	fmt.Printf("%v\n\n", r.Formula)18	fmt.Printf("X1\tX2\tX3\tResult\n")19	for i, x := range r.X {20		fmt.Printf("%0.2f\t%0.2f\t%0.2f\t%0.2f\n", x[0], x[1], x[2], r.Y[i])21	}22}

saveResult

Using AI Code Generation

1import (2type regression struct {3}4func (r regression) saveResult() {5    file, err := os.Create("result.csv")6    if err != nil {7        fmt.Println("Error creating file")8    }9    defer file.Close()10    writer := csv.NewWriter(file)11    defer writer.Flush()12    writer.Write([]string{"x", "y"})13    for i := 0; i < len(r.x); i++ {14        writer.Write([]string{strconv.FormatFloat(r.x[i], 'f', 6, 64), strconv.FormatFloat(r.y[i], 'f', 6, 64)})15    }16}17func (r regression) calculate() {18    for i := 0; i < len(r.x); i++ {19    }20    r.m = (float64(len(r.x))*sumXY - sumX*sumY) / (float64(len(r.x))*sumXX - sumX*sumX)21    r.b = (sumY - r.m*sumX) / float64(len(r.x))22    r.r = (float64(len(r.x))*sumXY - sumX*sumY) / math.Sqrt((float64(len(r.x))*sumXX-sumX*sumX)*(float64(len(r.x))*sumYY-sumY*sumY))23}24func (r regression) predict(x float64) float64 {25}26func main() {27    r := regression{x: []float64{1, 2, 3, 4, 5}, y: []float64{2, 4, 6, 8, 10}}28    r.calculate()29    fmt.Println("m = ", r.m)30    fmt.Println("b = ", r.b)31    fmt.Println("r = ", r

saveResult

Using AI Code Generation

1import (2func main() {3	x = []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}4	y = []float64{1, 3, 2, 5, 7, 8, 8, 9, 10, 12}5	x1 = []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}6	y1 = []float64{1, 3, 2, 5, 7, 8, 8, 9, 10, 12}7	x2 = []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}8	y2 = []float64{1, 3, 2, 5, 7, 8, 8, 9, 10, 12}9	x3 = []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}10	y3 = []float64{1, 3, 2, 5, 7, 8, 8, 9, 10, 12}11	r := regression.Regression{}12	r.Clear()13	r.SetObserved("x", x)14	r.SetObserved("y", y)15	r.SetVar(0, "x")16	r.SetVar(1, "y")17	r.SetDepVar("y")18	r.SetMethod("ls")19	r.SetFormula("y~x")20	r.Run()21	r.SaveResult("regression1.txt")22	r.Clear()23	r.SetObserved("x", x1)24	r.SetObserved("y", y1)25	r.SetVar(0, "x")26	r.SetVar(1, "y")27	r.SetDepVar("y")28	r.SetMethod("ml")29	r.SetFormula("y~x")

saveResult

Using AI Code Generation

1import (2func main() {3    reg.SaveResult()4    fmt.Println("Saved result")5}6import (7func main() {8    reg.SaveResult()9    fmt.Println("Saved result")10}11.\1.go:10: reg.SaveResult undefined (type regression.Regression has no field or method SaveResult)12.\2.go:10: reg.SaveResult undefined (type regression.Regression has no field or method SaveResult)13.\1.go:10: reg.SaveResult undefined (type regression.Regression has no field or method SaveResult)14You can fix this by adding the following import statement to the top of your 1.go and 2.go files:15import "regression"

saveResult

Using AI Code Generation

1import "fmt"2import "math"3import "os"4import "strconv"5import "strings"6import "bufio"7import "io"8import "encoding/csv"9import "io/ioutil"10import "encoding/json"11import "github.com/sajari/regression"12import "github.com/sajari/regression/loss"13import "github.com/sajari/regression/regularization"14import "github.com/sajari/regression/split"15import "github.com/sajari/regression/tree"16import "github.com/sajari/regression/tree/impurity"17import "github.com/sajari/regression/tree/leaf"18import "github.com/sajari/regression/tree/predictor"19import "github.com/sajari/regression/tree/splitter"20import "github.com/sajari/regression/tree/splitter/quantile"21import "github.com/sajari/regression/tree/splitter/variable"22import "github.com/sajari/regression/tree/splitter/variable/continuous"23import "github.com/sajari/regression/tree/splitter/variable/discrete"24import "github.com/sajari/regression/tree/splitter/variable/ordinal"25import "github.com/sajari/regression/tree/splitter/variable/quantitative"26import "github.com/sajari/regression/tree/splitter/variable/quantitative/binary"27import "github.com/sajari/regression/tree/splitter/variable/quantitative/continuous"28import "github.com/sajari/regression/tree/splitter/variable/quantitative/discrete"29import "github.com/sajari/regression/tree/splitter/variable/quantitative/ordinal"30import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile"31import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile/mean"32import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile/mode"33import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile/median"34import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile/weighted"35import "github.com/sajari/regression/tree/splitter/variable/quantitative/quantile/weighted/mean"36import "github.com/s

saveResult

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().Unix())4	r.init(10, 1, 0.1)5	r.saveResult()6}7import (8func main() {9	rand.Seed(time.Now().Unix())10	r.init(10, 1, 0.1)11	r.saveResult()12}13import (14func main() {15	rand.Seed(time.Now().Unix())16	r.init(10, 1, 0.1)17	r.saveResult()18}19import (20func main() {21	rand.Seed(time.Now().Unix())22	r.init(10, 1, 0.1)23	r.saveResult()24}25import (26func main() {27	rand.Seed(time.Now().Unix())28	r.init(10, 1, 0.1)29	r.saveResult()30}31import (32func main() {33	rand.Seed(time.Now().Unix())34	r.init(10, 1, 0.1)35	r.saveResult()36}37import (38func main() {39	rand.Seed(time.Now().Unix())40	r.init(10, 1, 0.1)41	r.saveResult()42}43import (

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