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extrapolate estimated scan time from scan size (#669)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| // Copyright © 2023 Cisco Systems, Inc. and its affiliates. | ||
| // All rights reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
||
| package common | ||
|
|
||
| import ( | ||
| "fmt" | ||
| "math" | ||
|
|
||
| "github.com/sirupsen/logrus" | ||
| ) | ||
|
|
||
| // LogarithmicFormula represents the formula y = a * ln(x) + b. | ||
| type LogarithmicFormula struct { | ||
| a float64 | ||
| b float64 | ||
| } | ||
|
|
||
| // Evaluate receive an x value and returns the y value of the formula. | ||
| func (lf *LogarithmicFormula) Evaluate(x float64) float64 { | ||
| return lf.a*math.Log(x) + lf.b | ||
| } | ||
|
|
||
| func MustLogarithmicFit(xData, yData []float64) *LogarithmicFormula { | ||
| ret, err := LogarithmicFit(xData, yData) | ||
| if err != nil { | ||
| logrus.Panic(err) | ||
| } | ||
| return ret | ||
| } | ||
|
|
||
| func LogarithmicFit(xData, yData []float64) (*LogarithmicFormula, error) { | ||
| var err error | ||
| var a, b float64 | ||
|
|
||
| a, b, err = logarithmicFit(xData, yData) | ||
| if err != nil { | ||
| return nil, fmt.Errorf("failed to calculate a logarithmic fit: %w", err) | ||
| } | ||
| return &LogarithmicFormula{a: a, b: b}, nil | ||
| } | ||
|
|
||
| // logarithmicFit performs least squares fitting for the logarithmic model y = a * ln(x) + b. | ||
| // It returns the a and b constants of the logarithmic model. | ||
| func logarithmicFit(xData, yData []float64) (float64, float64, error) { | ||
| n := len(xData) | ||
| if n != len(yData) || n == 0 { | ||
| return 0, 0, fmt.Errorf("input data is invalid. xData: %v, yData: %v", xData, yData) | ||
| } | ||
|
|
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| lnXData := make([]float64, n) | ||
|
|
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| for i := 0; i < n; i++ { | ||
| if xData[i] <= 0 { | ||
| return 0, 0, fmt.Errorf("input data contains non-positive x values: %v", xData) | ||
| } | ||
| lnXData[i] = math.Log(xData[i]) | ||
| } | ||
|
|
||
| // Calculate the constants of the logarithmic regression model y = a * ln(x) + b | ||
| var sumLnX, sumY, sumLnX2, sumLnXY float64 | ||
| for i := 0; i < n; i++ { | ||
| sumLnX += lnXData[i] | ||
| sumY += yData[i] | ||
| sumLnX2 += lnXData[i] * lnXData[i] | ||
| sumLnXY += lnXData[i] * yData[i] | ||
| } | ||
|
|
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| if ((float64(n))*sumLnX2 - sumLnX*sumLnX) == 0 { | ||
| return 0, 0, fmt.Errorf("zero denominator in calculations") | ||
| } | ||
|
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| a := ((float64(n))*sumLnXY - sumLnX*sumY) / ((float64(n))*sumLnX2 - sumLnX*sumLnX) | ||
| b := (sumY - a*sumLnX) / float64(n) | ||
|
|
||
| return a, b, nil | ||
| } |