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github.com/akualab/gjoa
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v0.0.0-...-0953495
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Published: Apr 23, 2015
License: BSD-3-Clause
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Imports: 7
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Imported by: 0
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README
Documentation
Index
Constants
Variables
Functions
CheckError(t, e)
CompareFloats(t, expected, actual, message, tol)
CompareSliceFloat(t, expected, actual, message, tol)
CompareSliceInt(t, expected, actual, message)
Comparef64(f1, f2, tol)
Fatal(err)
PanicIf(err)
ReadResults(fn)
WriteResults(results, fn)
Types
Source Files
Directories
README
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
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Documentation
¶
func CheckError(t *testing.T, e error)
func CompareFloats(t *testing.T, expected float64, actual float64, message string, tol float64)
func CompareSliceFloat(t *testing.T, expected []float64, actual []float64, message string, ...)
func CompareSliceInt(t *testing.T, expected []int, actual []int, message string)
func Comparef64(f1, f2, tol float64) bool
func Fatal(err error)
func PanicIf(err error)
func ReadResults(fn string) (results map[string]*Result, e error)
func WriteResults(results map[string]*Result, fn string) error
type Result
CheckError calls Fatal if error is not nil.
CompareFloats compares floats using Comparef64.
CompareSliceFloat compares slices elementwise using Comparef64.
CompareSliceInt compares two slices of ints elementwise.
Comparef64 returns true if |f2/f1-1| < tol.
Fatal logs a fatal errors and exits.
PanicIf panics if error is not nil.
Write a collection of results to a file.
type Result struct {
BatchID string `json:"batchid"`
Ref []string `json:"ref,omitempty"`
Hyp []string `json:"hyp,omitempty"`
}
Source Files
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Directories
¶
cmd
hmm
Package hmm provides an implementation of hidden Markov models.
Package hmm provides an implementation of hidden Markov models.
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