gjoa

package module
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Published: Apr 23, 2015 License: BSD-3-Clause Imports: 7 Imported by: 0

README

Gjøa Statistical Modeling, Machine Learning, and Pattern Recognition.

Less is More

Gjøa was the first vessel to transit the Northwest Passage. With a crew of six, Roald Amundsen traversed the passage in a three year journey, finishing in 1906.

Documentation

Status

Under development, frequent API changes expected, no top level cmd and docs yet.

Contributiosn welcomed!

Basics

Install: git clone git@github.com:akualab/gjoa.git

Print log to stderr. Learn how to use glog

gjoa -logtostderr

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func CheckError

func CheckError(t *testing.T, e error)

CheckError calls Fatal if error is not nil.

func CompareFloats

func CompareFloats(t *testing.T, expected float64, actual float64, message string, tol float64)

CompareFloats compares floats using Comparef64.

func CompareSliceFloat

func CompareSliceFloat(t *testing.T, expected []float64, actual []float64, message string, tol float64)

CompareSliceFloat compares slices elementwise using Comparef64.

func CompareSliceInt

func CompareSliceInt(t *testing.T, expected []int, actual []int, message string)

CompareSliceInt compares two slices of ints elementwise.

func Comparef64

func Comparef64(f1, f2, tol float64) bool

Comparef64 returns true if |f2/f1-1| < tol.

func Fatal

func Fatal(err error)

Fatal logs a fatal errors and exits.

func PanicIf

func PanicIf(err error)

PanicIf panics if error is not nil.

func ReadResults

func ReadResults(fn string) (results map[string]*Result, e error)

Read a results file.

func WriteResults

func WriteResults(results map[string]*Result, fn string) error

Write a collection of results to a file.

Types

type Result

type Result struct {
	BatchID string   `json:"batchid"`
	Ref     []string `json:"ref,omitempty"`
	Hyp     []string `json:"hyp,omitempty"`
}

Directories

Path Synopsis
cmd
gmm
hmm
Package hmm provides an implementation of hidden Markov models.
Package hmm provides an implementation of hidden Markov models.

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