prediction

package
v0.0.0-...-b036f29 Latest Latest
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 Go to latest Published: Jun 9, 2015 License: BSD-3-Clause Imports: 11 Imported by: 0

Documentation

Overview

Package prediction provides access to the Prediction API.

See https://developers.google.com/prediction/docs/developer-guide

Usage example: 

import "google.golang.org/api/prediction/v1.3"
...
predictionService, err := prediction.New(oauthHttpClient)

Index

Constants

View Source 
const (
	// Manage your data and permissions in Google Cloud Storage
	DevstorageFullControlScope = "https://www.googleapis.com/auth/devstorage.full_control"

	// View your data in Google Cloud Storage
	DevstorageReadOnlyScope = "https://www.googleapis.com/auth/devstorage.read_only"

	// Manage your data in Google Cloud Storage
	DevstorageReadWriteScope = "https://www.googleapis.com/auth/devstorage.read_write"

	// Manage your data in the Google Prediction API
	PredictionScope = "https://www.googleapis.com/auth/prediction"
)

OAuth2 scopes used by this API.

Variables

This section is empty.

Functions

This section is empty.

Types

type HostedmodelsPredictCall 

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

func (*HostedmodelsPredictCall) Do 

func (c *HostedmodelsPredictCall) Do() (*Output, error)

func (*HostedmodelsPredictCall) Fields 

func (c *HostedmodelsPredictCall) Fields(s ...googleapi.Field) *HostedmodelsPredictCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type HostedmodelsService 

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

func NewHostedmodelsService 

func NewHostedmodelsService(s *Service) *HostedmodelsService

func (*HostedmodelsService) Predict 

func (r *HostedmodelsService) Predict(hostedModelName string, input *Input) *HostedmodelsPredictCall

Predict: Submit input and request an output against a hosted model

type Input 

type Input struct {
	// Input: Input to the model for a prediction
	Input *InputInput `json:"input,omitempty"`
}

type InputInput 

type InputInput struct {
	// CsvInstance: A list of input features, these can be strings or
	// doubles.
	CsvInstance []interface{} `json:"csvInstance,omitempty"`
}

type Output 

type Output struct {
	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// OutputLabel: The most likely class [Categorical models only].
	OutputLabel string `json:"outputLabel,omitempty"`

	// OutputMulti: A list of classes with their estimated probabilities
	// [Categorical models only].
	OutputMulti []*OutputOutputMulti `json:"outputMulti,omitempty"`

	// OutputValue: The estimated regression value [Regression models only].
	OutputValue float64 `json:"outputValue,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`
}

type OutputOutputMulti 

type OutputOutputMulti struct {
	// Label: The class label.
	Label string `json:"label,omitempty"`

	// Score: The probability of the class.
	Score float64 `json:"score,omitempty"`
}

type Service 

type Service struct {
	BasePath  string // API endpoint base URL
	UserAgent string // optional additional User-Agent fragment

	Hostedmodels *HostedmodelsService

	Training *TrainingService
	// contains filtered or unexported fields
}

func New 

func New(client *http.Client) (*Service, error)

type Training 

type Training struct {
	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// ModelInfo: Model metadata.
	ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`

	// TrainingStatus: The current status of the training job. This can be
	// one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
	TrainingStatus string `json:"trainingStatus,omitempty"`

	// Utility: A class weighting function, which allows the importance
	// weights for classes to be specified [Categorical models only].
	Utility []*TrainingUtility `json:"utility,omitempty"`
}

type TrainingDeleteCall 

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

func (*TrainingDeleteCall) Do 

func (c *TrainingDeleteCall) Do() error

func (*TrainingDeleteCall) Fields 

func (c *TrainingDeleteCall) Fields(s ...googleapi.Field) *TrainingDeleteCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingGetCall 

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

func (*TrainingGetCall) Do 

func (c *TrainingGetCall) Do() (*Training, error)

func (*TrainingGetCall) Fields 

func (c *TrainingGetCall) Fields(s ...googleapi.Field) *TrainingGetCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingInsertCall 

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

func (*TrainingInsertCall) Do 

func (c *TrainingInsertCall) Do() (*Training, error)

func (*TrainingInsertCall) Fields 

func (c *TrainingInsertCall) Fields(s ...googleapi.Field) *TrainingInsertCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingModelInfo 

type TrainingModelInfo struct {
	// ClassWeightedAccuracy: Estimated accuracy of model taking utility
	// weights into account [Categorical models only].
	ClassWeightedAccuracy float64 `json:"classWeightedAccuracy,omitempty"`

	// ClassificationAccuracy: A number between 0.0 and 1.0, where 1.0 is
	// 100% accurate. This is an estimate, based on the amount and quality
	// of the training data, of the estimated prediction accuracy. You can
	// use this is a guide to decide whether the results are accurate enough
	// for your needs. This estimate will be more reliable if your real
	// input data is similar to your training data [Categorical models
	// only].
	ClassificationAccuracy float64 `json:"classificationAccuracy,omitempty"`

	// ConfusionMatrix: An output confusion matrix. This shows an estimate
	// for how this model will do in predictions. This is first indexed by
	// the true class label. For each true class label, this provides a pair
	// {predicted_label, count}, where count is the estimated number of
	// times the model will predict the predicted label given the true
	// label. Will not output if more then 100 classes [Categorical models
	// only].
	ConfusionMatrix *TrainingModelInfoConfusionMatrix `json:"confusionMatrix,omitempty"`

	// ConfusionMatrixRowTotals: A list of the confusion matrix row totals
	ConfusionMatrixRowTotals *TrainingModelInfoConfusionMatrixRowTotals `json:"confusionMatrixRowTotals,omitempty"`

	// MeanSquaredError: An estimated mean squared error. The can be used to
	// measure the quality of the predicted model [Regression models only].
	MeanSquaredError float64 `json:"meanSquaredError,omitempty"`

	// ModelType: Type of predictive model (CLASSIFICATION or REGRESSION)
	ModelType string `json:"modelType,omitempty"`

	// NumberClasses: Number of classes in the trained model [Categorical
	// models only].
	NumberClasses int64 `json:"numberClasses,omitempty,string"`

	// NumberInstances: Number of valid data instances used in the trained
	// model.
	NumberInstances int64 `json:"numberInstances,omitempty,string"`
}

type TrainingModelInfoConfusionMatrix 

type TrainingModelInfoConfusionMatrix struct {
}

type TrainingModelInfoConfusionMatrixRowTotals 

type TrainingModelInfoConfusionMatrixRowTotals struct {
}

type TrainingPredictCall 

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

func (*TrainingPredictCall) Do 

func (c *TrainingPredictCall) Do() (*Output, error)

func (*TrainingPredictCall) Fields 

func (c *TrainingPredictCall) Fields(s ...googleapi.Field) *TrainingPredictCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingService 

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

func NewTrainingService 

func NewTrainingService(s *Service) *TrainingService

func (*TrainingService) Delete 

func (r *TrainingService) Delete(data string) *TrainingDeleteCall

Delete: Delete a trained model

func (*TrainingService) Get 

func (r *TrainingService) Get(data string) *TrainingGetCall

Get: Check training status of your model

func (*TrainingService) Insert 

func (r *TrainingService) Insert(training *Training) *TrainingInsertCall

Insert: Begin training your model

func (*TrainingService) Predict 

func (r *TrainingService) Predict(data string, input *Input) *TrainingPredictCall

Predict: Submit data and request a prediction

func (*TrainingService) Update 

func (r *TrainingService) Update(data string, update *Update) *TrainingUpdateCall

Update: Add new data to a trained model

type TrainingUpdateCall 

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

func (*TrainingUpdateCall) Do 

func (c *TrainingUpdateCall) Do() (*Training, error)

func (*TrainingUpdateCall) Fields 

func (c *TrainingUpdateCall) Fields(s ...googleapi.Field) *TrainingUpdateCall

Fields allows partial responses to be retrieved. See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse for more information.

type TrainingUtility 

type TrainingUtility struct {
}

type Update 

type Update struct {
	// ClassLabel: The true class label of this instance
	ClassLabel string `json:"classLabel,omitempty"`

	// CsvInstance: The input features for this instance
	CsvInstance []interface{} `json:"csvInstance,omitempty"`
}

Source Files

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