math

package
v0.0.27 Latest Latest
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 Go to latest Published: Apr 2, 2019 License: GPL-3.0 Imports: 26 Imported by: 0

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Index

Constants

View Source 
const DefaultContainedVariance = 0.99
View Source 
const (
	FftFreqMetricName = "freq"
)

Variables

This section is empty.

Functions

func BatchConvexHull 

func BatchConvexHull(sortOnly bool) bitflow.BatchProcessingStep

func ComputeAndProjectPCA 

func ComputeAndProjectPCA(containedVariance float64) bitflow.BatchProcessingStep

func FeatureWindowAverage 

func FeatureWindowAverage(stats *FeatureWindowStats) bitflow.Value

func FeatureWindowSlope 

func FeatureWindowSlope(stats *FeatureWindowStats) bitflow.Value

func LoadBatchPCAModel 

func LoadBatchPCAModel(filename string, containedVariance float64) (bitflow.BatchProcessingStep, error)

func LoadStreamingPCAModel 

func LoadStreamingPCAModel(filename string, containedVariance float64) (bitflow.SampleProcessor, error)

func RegisterAggregateAvg 

func RegisterAggregateAvg(b reg.ProcessorRegistry)

func RegisterAggregateSlope 

func RegisterAggregateSlope(b reg.ProcessorRegistry)

func RegisterConvexHull 

func RegisterConvexHull(b reg.ProcessorRegistry)

func RegisterConvexHullSort 

func RegisterConvexHullSort(b reg.ProcessorRegistry)

func RegisterFFT 

func RegisterFFT(b reg.ProcessorRegistry)

func RegisterLinearRegression 

func RegisterLinearRegression(b reg.ProcessorRegistry)

func RegisterLinearRegressionBruteForce 

func RegisterLinearRegressionBruteForce(b reg.ProcessorRegistry)

func RegisterMinMaxScaling 

func RegisterMinMaxScaling(b reg.ProcessorRegistry)

func RegisterPCA 

func RegisterPCA(b reg.ProcessorRegistry)

func RegisterPCALoad 

func RegisterPCALoad(b reg.ProcessorRegistry)

func RegisterPCALoadStream 

func RegisterPCALoadStream(b reg.ProcessorRegistry)

func RegisterPCAStore 

func RegisterPCAStore(b reg.ProcessorRegistry)

func RegisterRMS 

func RegisterRMS(b reg.ProcessorRegistry)

func RegisterSphere 

func RegisterSphere(b reg.ProcessorRegistry)

func RegisterStandardizationScaling 

func RegisterStandardizationScaling(b reg.ProcessorRegistry)

func SamplesToMatrix 

func SamplesToMatrix(samples []*bitflow.Sample) mat.Matrix

func StorePCAModel 

func StorePCAModel(filename string) bitflow.BatchProcessingStep

Types

type AngleBasedSort 

type AngleBasedSort struct {
	Reference Point
	Points    []Point
}

func (AngleBasedSort) Len 

func (s AngleBasedSort) Len() int

func (AngleBasedSort) Less 

func (s AngleBasedSort) Less(i, j int) bool

func (AngleBasedSort) Swap 

func (s AngleBasedSort) Swap(i, j int)

type BatchFft 

type BatchFft struct {
	// By default, the upper half of the FFT results is cut off, because the FFT results are symmetric.
	// Set LeaveSymmetricPart to true to include preserve all result values.
	LeaveSymmetricPart bool

	// By default, the FFT results are normalized. Set SkipNormalization to true to avoid this.
	// Normalization helps comparing the results of FFTs with different numbers of input values.
	// Normalization is done by dividing every FFT result value by the total number of input values to the FFT.
	// If FillZeros=true, this is the closest power of two above the number of actual input samples. If FillZeros=false, this is the closest power of two beneath that.
	SkipNormalization bool

	// If the number of input samples is not exactly a power of two, the input batch must either be cut short or filled up with zeros.
	// The default is to cut the input to a power of two. set FillZeros to true, to instead fill the input with zeros to reach the next higher power of two.
	FillZeros bool

	// If FrequencyMetricIndex >= 0, the frequency for every FFT result value will be added as a new metric at the given index.
	// If the index is higher than the total number of input metrics, it will be appended as the last metric.
	// The name of that new metric will be FftFreqMetricName ("freq").
	FrequencyMetricIndex int

	// If SamplingFrequency > 0, it will be used as the total sampling frequency when computing the frequency metric.
	// If this is <= 0, the sampling frequency will be computed automatically from the timestamps of the first and last input sample, and the total number of samples.
	SamplingFrequency float64

	// FatalErrors can be set to true to return errors that occur when processing a batch.
	// Otherwise, the error will be printed as a warning and an empty result batch will be produced.
	FatalErrors bool
}

func (*BatchFft) OutputSampleSize 

func (s *BatchFft) OutputSampleSize(sampleSize int) int

func (*BatchFft) ProcessBatch 

func (s *BatchFft) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*BatchFft) String 

func (s *BatchFft) String() string

type BatchRms 

type BatchRms struct {
}

func (*BatchRms) ProcessBatch 

func (r *BatchRms) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*BatchRms) String 

func (r *BatchRms) String() string

type ConvexHull 

type ConvexHull []Point

func ComputeConvexHull 

func ComputeConvexHull(points []Point) ConvexHull

func (ConvexHull) ComputeLowestPoint 

func (p ConvexHull) ComputeLowestPoint() (low Point)

type EvaluatedLinearRegression 

type EvaluatedLinearRegression struct {
	LinearRegression
	MSE float64
}

type FeatureAggregator 

type FeatureAggregator struct {
	bitflow.NoopProcessor
	WindowSize     int           // Applied if >0
	WindowDuration time.Duration // Applied if >0
	UseCurrentTime bool          // If true, use time.Now() as reference for WindowTime. Otherwise, use the timestamp of the latest Sample.
	// contains filtered or unexported fields
}

func (*FeatureAggregator) Add 

func (agg *FeatureAggregator) Add(suffix string, operation FeatureAggregatorOperation) *FeatureAggregator

func (*FeatureAggregator) AddAvg 

func (agg *FeatureAggregator) AddAvg(suffix string) *FeatureAggregator

func (*FeatureAggregator) AddSlope 

func (agg *FeatureAggregator) AddSlope(suffix string) *FeatureAggregator

func (*FeatureAggregator) MergeProcessor 

func (agg *FeatureAggregator) MergeProcessor(other bitflow.SampleProcessor) bool

func (*FeatureAggregator) OutputSampleSize 

func (agg *FeatureAggregator) OutputSampleSize(sampleSize int) int

func (*FeatureAggregator) Sample 

func (agg *FeatureAggregator) Sample(sample *bitflow.Sample, header *bitflow.Header) error

func (*FeatureAggregator) String 

func (agg *FeatureAggregator) String() string

type FeatureAggregatorOperation 

type FeatureAggregatorOperation func(stats *FeatureWindowStats) bitflow.Value

type FeatureWindowStats 

type FeatureWindowStats struct {
	// contains filtered or unexported fields
}

func (*FeatureWindowStats) Flush 

func (stats *FeatureWindowStats) Flush(num int)

func (*FeatureWindowStats) Push 

func (stats *FeatureWindowStats) Push(val bitflow.Value, timestamp time.Time)

type LinearRegression 

type LinearRegression struct {
	SubHeader
	RegressionClassVar int
	Model              *linear_models.LinearRegression
	TrainData          *base.DenseInstances
}

func NewLinearRegression 

func NewLinearRegression(header *bitflow.Header, fieldNames []string) (LinearRegression, error)

func (*LinearRegression) Fit 

func (reg *LinearRegression) Fit(samples []*bitflow.Sample) error

func (*LinearRegression) FormulaString 

func (reg *LinearRegression) FormulaString() string

func (*LinearRegression) IsValid 

func (reg *LinearRegression) IsValid() bool

func (*LinearRegression) MeanSquaredError 

func (reg *LinearRegression) MeanSquaredError(data base.FixedDataGrid) (float64, error)

func (*LinearRegression) Predict 

func (reg *LinearRegression) Predict(data base.FixedDataGrid) ([]float64, error)

type LinearRegressionBatchProcessor 

type LinearRegressionBatchProcessor struct {
}

func (*LinearRegressionBatchProcessor) ProcessBatch 

func (reg *LinearRegressionBatchProcessor) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*LinearRegressionBatchProcessor) String 

func (reg *LinearRegressionBatchProcessor) String() string

type LinearRegressionBruteForce 

type LinearRegressionBruteForce struct {
	// contains filtered or unexported fields
}

func (*LinearRegressionBruteForce) ProcessBatch 

func (brute *LinearRegressionBruteForce) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*LinearRegressionBruteForce) String 

func (brute *LinearRegressionBruteForce) String() string

type MinMaxScaling 

type MinMaxScaling struct {
	Min float64
	Max float64
}

func (*MinMaxScaling) ProcessBatch 

func (s *MinMaxScaling) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*MinMaxScaling) String 

func (s *MinMaxScaling) String() string

type PCAModel 

type PCAModel struct {
	Vectors            *mat.Dense
	RawVariances       []float64
	ContainedVariances []float64
}

func (*PCAModel) ComponentsContainingVariance 

func (model *PCAModel) ComponentsContainingVariance(variance float64) (count int, sum float64)

func (*PCAModel) ComputeAndReport 

func (model *PCAModel) ComputeAndReport(samples []*bitflow.Sample) error

func (*PCAModel) ComputeModel 

func (model *PCAModel) ComputeModel(samples []*bitflow.Sample) error

func (*PCAModel) Load 

func (model *PCAModel) Load(filename string) (err error)

func (*PCAModel) Project 

func (model *PCAModel) Project(numComponents int) *PCAProjection

func (*PCAModel) ProjectHeader 

func (model *PCAModel) ProjectHeader(variance float64, header *bitflow.Header) (*PCAProjection, *bitflow.Header, error)

func (*PCAModel) Report 

func (model *PCAModel) Report(reportVariance float64) string

func (*PCAModel) String 

func (model *PCAModel) String() string

func (*PCAModel) WriteModel 

func (model *PCAModel) WriteModel(writer io.Writer) error

type PCAProjection 

type PCAProjection struct {
	Model      *PCAModel
	Vectors    mat.Matrix
	Components int
}

func (*PCAProjection) Matrix 

func (model *PCAProjection) Matrix(matrix mat.Matrix) *mat.Dense

func (*PCAProjection) Sample 

func (model *PCAProjection) Sample(sample *bitflow.Sample) (result *bitflow.Sample)

func (*PCAProjection) Vector 

func (model *PCAProjection) Vector(vec []float64) []float64

type Point 

type Point struct {
	X, Y float64
}

func SortByAngle 

func SortByAngle(points []Point) []Point

func (Point) Distance 

func (p Point) Distance(other Point) float64

type SortedRegressions 

type SortedRegressions []EvaluatedLinearRegression

func (SortedRegressions) Len 

func (slice SortedRegressions) Len() int

func (SortedRegressions) Less 

func (slice SortedRegressions) Less(i, j int) bool

func (SortedRegressions) Swap 

func (slice SortedRegressions) Swap(i, j int)

type SpherePoints 

type SpherePoints struct {
	bitflow.NoopProcessor
	RandomSeed int64
	NumPoints  int

	RadiusMetric int // If >= 0, use to get radius. Otherwise, use Radius field.
	Radius       float64
	// contains filtered or unexported fields
}

func (*SpherePoints) Sample 

func (p *SpherePoints) Sample(sample *bitflow.Sample, header *bitflow.Header) error

func (*SpherePoints) Start 

func (p *SpherePoints) Start(wg *sync.WaitGroup) golib.StopChan

type StandardizationScaling 

type StandardizationScaling struct {
}

func (*StandardizationScaling) ProcessBatch 

func (s *StandardizationScaling) ProcessBatch(header *bitflow.Header, samples []*bitflow.Sample) (*bitflow.Header, []*bitflow.Sample, error)

func (*StandardizationScaling) String 

func (s *StandardizationScaling) String() string

type SubHeader 

type SubHeader struct {
	Header *bitflow.Header
	Vars   []int
}

func (SubHeader) BuildFilledInstances 

func (header SubHeader) BuildFilledInstances(samples []*bitflow.Sample, classAttribute int) (*base.DenseInstances, error)

func (SubHeader) BuildInstances 

func (header SubHeader) BuildInstances(classAttribute int) (*base.DenseInstances, error)

func (SubHeader) FillInstances 

func (header SubHeader) FillInstances(samples []*bitflow.Sample, instances *base.DenseInstances)

Source Files

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