forked from mystiq/dex
718 lines
20 KiB
Go
718 lines
20 KiB
Go
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// Package prediction provides access to the Prediction API.
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//
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// See https://developers.google.com/prediction/docs/developer-guide
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//
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// Usage example:
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//
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// import "google.golang.org/api/prediction/v1.4"
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// ...
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// predictionService, err := prediction.New(oauthHttpClient)
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package prediction
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import (
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"bytes"
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"encoding/json"
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"errors"
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"fmt"
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"google.golang.org/api/googleapi"
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"io"
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"net/http"
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"net/url"
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"strconv"
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"strings"
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)
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// Always reference these packages, just in case the auto-generated code
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// below doesn't.
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var _ = bytes.NewBuffer
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var _ = strconv.Itoa
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var _ = fmt.Sprintf
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var _ = json.NewDecoder
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var _ = io.Copy
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var _ = url.Parse
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var _ = googleapi.Version
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var _ = errors.New
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var _ = strings.Replace
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const apiId = "prediction:v1.4"
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const apiName = "prediction"
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const apiVersion = "v1.4"
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const basePath = "https://www.googleapis.com/prediction/v1.4/"
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// OAuth2 scopes used by this API.
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const (
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// Manage your data and permissions in Google Cloud Storage
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DevstorageFull_controlScope = "https://www.googleapis.com/auth/devstorage.full_control"
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// View your data in Google Cloud Storage
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DevstorageRead_onlyScope = "https://www.googleapis.com/auth/devstorage.read_only"
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// Manage your data in Google Cloud Storage
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DevstorageRead_writeScope = "https://www.googleapis.com/auth/devstorage.read_write"
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// Manage your data in the Google Prediction API
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PredictionScope = "https://www.googleapis.com/auth/prediction"
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)
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func New(client *http.Client) (*Service, error) {
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if client == nil {
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return nil, errors.New("client is nil")
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}
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s := &Service{client: client, BasePath: basePath}
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s.Hostedmodels = NewHostedmodelsService(s)
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s.Trainedmodels = NewTrainedmodelsService(s)
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return s, nil
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}
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type Service struct {
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client *http.Client
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BasePath string // API endpoint base URL
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Hostedmodels *HostedmodelsService
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Trainedmodels *TrainedmodelsService
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}
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func NewHostedmodelsService(s *Service) *HostedmodelsService {
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rs := &HostedmodelsService{s: s}
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return rs
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}
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type HostedmodelsService struct {
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s *Service
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}
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func NewTrainedmodelsService(s *Service) *TrainedmodelsService {
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rs := &TrainedmodelsService{s: s}
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return rs
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}
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type TrainedmodelsService struct {
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s *Service
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}
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type Input struct {
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// Input: Input to the model for a prediction
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Input *InputInput `json:"input,omitempty"`
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}
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type InputInput struct {
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// CsvInstance: A list of input features, these can be strings or
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// doubles.
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CsvInstance []interface{} `json:"csvInstance,omitempty"`
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}
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type Output struct {
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// Id: The unique name for the predictive model.
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Id string `json:"id,omitempty"`
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// Kind: What kind of resource this is.
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Kind string `json:"kind,omitempty"`
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// OutputLabel: The most likely class label [Categorical models only].
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OutputLabel string `json:"outputLabel,omitempty"`
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// OutputMulti: A list of class labels with their estimated
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// probabilities [Categorical models only].
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OutputMulti []*OutputOutputMulti `json:"outputMulti,omitempty"`
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// OutputValue: The estimated regression value [Regression models only].
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OutputValue float64 `json:"outputValue,omitempty"`
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// SelfLink: A URL to re-request this resource.
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SelfLink string `json:"selfLink,omitempty"`
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}
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type OutputOutputMulti struct {
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// Label: The class label.
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Label string `json:"label,omitempty"`
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// Score: The probability of the class label.
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Score float64 `json:"score,omitempty"`
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}
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type Training struct {
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// DataAnalysis: Data Analysis.
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DataAnalysis *TrainingDataAnalysis `json:"dataAnalysis,omitempty"`
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// Id: The unique name for the predictive model.
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Id string `json:"id,omitempty"`
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// Kind: What kind of resource this is.
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Kind string `json:"kind,omitempty"`
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// ModelInfo: Model metadata.
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ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`
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// SelfLink: A URL to re-request this resource.
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SelfLink string `json:"selfLink,omitempty"`
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// StorageDataLocation: Google storage location of the training data
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// file.
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StorageDataLocation string `json:"storageDataLocation,omitempty"`
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// StoragePMMLLocation: Google storage location of the preprocessing
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// pmml file.
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StoragePMMLLocation string `json:"storagePMMLLocation,omitempty"`
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// StoragePMMLModelLocation: Google storage location of the pmml model
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// file.
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StoragePMMLModelLocation string `json:"storagePMMLModelLocation,omitempty"`
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// TrainingStatus: The current status of the training job. This can be
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// one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
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TrainingStatus string `json:"trainingStatus,omitempty"`
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// Utility: A class weighting function, which allows the importance
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// weights for class labels to be specified [Categorical models only].
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Utility []*TrainingUtility `json:"utility,omitempty"`
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}
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type TrainingDataAnalysis struct {
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Warnings []string `json:"warnings,omitempty"`
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}
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type TrainingModelInfo struct {
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// ClassWeightedAccuracy: Estimated accuracy of model taking utility
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// weights into account [Categorical models only].
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ClassWeightedAccuracy float64 `json:"classWeightedAccuracy,omitempty"`
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// ClassificationAccuracy: A number between 0.0 and 1.0, where 1.0 is
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// 100% accurate. This is an estimate, based on the amount and quality
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// of the training data, of the estimated prediction accuracy. You can
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// use this is a guide to decide whether the results are accurate enough
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// for your needs. This estimate will be more reliable if your real
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// input data is similar to your training data [Categorical models
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// only].
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ClassificationAccuracy float64 `json:"classificationAccuracy,omitempty"`
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// ConfusionMatrix: An output confusion matrix. This shows an estimate
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// for how this model will do in predictions. This is first indexed by
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// the true class label. For each true class label, this provides a pair
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// {predicted_label, count}, where count is the estimated number of
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// times the model will predict the predicted label given the true
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// label. Will not output if more then 100 classes [Categorical models
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// only].
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ConfusionMatrix *TrainingModelInfoConfusionMatrix `json:"confusionMatrix,omitempty"`
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// ConfusionMatrixRowTotals: A list of the confusion matrix row totals
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ConfusionMatrixRowTotals *TrainingModelInfoConfusionMatrixRowTotals `json:"confusionMatrixRowTotals,omitempty"`
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// MeanSquaredError: An estimated mean squared error. The can be used to
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// measure the quality of the predicted model [Regression models only].
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MeanSquaredError float64 `json:"meanSquaredError,omitempty"`
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// ModelType: Type of predictive model (CLASSIFICATION or REGRESSION)
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ModelType string `json:"modelType,omitempty"`
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// NumberInstances: Number of valid data instances used in the trained
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// model.
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NumberInstances int64 `json:"numberInstances,omitempty,string"`
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// NumberLabels: Number of class labels in the trained model
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// [Categorical models only].
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NumberLabels int64 `json:"numberLabels,omitempty,string"`
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}
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type TrainingModelInfoConfusionMatrix struct {
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}
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type TrainingModelInfoConfusionMatrixRowTotals struct {
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}
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type TrainingUtility struct {
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}
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type Update struct {
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// CsvInstance: The input features for this instance
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CsvInstance []interface{} `json:"csvInstance,omitempty"`
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// Label: The class label of this instance
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Label string `json:"label,omitempty"`
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// Output: The generic output value - could be regression value or class
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// label
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Output string `json:"output,omitempty"`
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}
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// method id "prediction.hostedmodels.predict":
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type HostedmodelsPredictCall struct {
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s *Service
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hostedModelName string
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input *Input
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opt_ map[string]interface{}
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}
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// Predict: Submit input and request an output against a hosted model.
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func (r *HostedmodelsService) Predict(hostedModelName string, input *Input) *HostedmodelsPredictCall {
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c := &HostedmodelsPredictCall{s: r.s, opt_: make(map[string]interface{})}
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c.hostedModelName = hostedModelName
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c.input = input
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return c
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}
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// Fields allows partial responses to be retrieved.
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// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
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// for more information.
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func (c *HostedmodelsPredictCall) Fields(s ...googleapi.Field) *HostedmodelsPredictCall {
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c.opt_["fields"] = googleapi.CombineFields(s)
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return c
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}
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func (c *HostedmodelsPredictCall) Do() (*Output, error) {
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var body io.Reader = nil
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body, err := googleapi.WithoutDataWrapper.JSONReader(c.input)
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if err != nil {
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return nil, err
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}
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ctype := "application/json"
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params := make(url.Values)
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params.Set("alt", "json")
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if v, ok := c.opt_["fields"]; ok {
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params.Set("fields", fmt.Sprintf("%v", v))
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}
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urls := googleapi.ResolveRelative(c.s.BasePath, "hostedmodels/{hostedModelName}/predict")
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urls += "?" + params.Encode()
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req, _ := http.NewRequest("POST", urls, body)
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googleapi.Expand(req.URL, map[string]string{
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"hostedModelName": c.hostedModelName,
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})
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req.Header.Set("Content-Type", ctype)
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req.Header.Set("User-Agent", "google-api-go-client/0.5")
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res, err := c.s.client.Do(req)
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if err != nil {
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return nil, err
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}
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defer googleapi.CloseBody(res)
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if err := googleapi.CheckResponse(res); err != nil {
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return nil, err
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}
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var ret *Output
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if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
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return nil, err
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}
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return ret, nil
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// {
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// "description": "Submit input and request an output against a hosted model.",
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// "httpMethod": "POST",
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// "id": "prediction.hostedmodels.predict",
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// "parameterOrder": [
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// "hostedModelName"
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// ],
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// "parameters": {
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// "hostedModelName": {
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// "description": "The name of a hosted model.",
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// "location": "path",
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// "required": true,
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// "type": "string"
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// }
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// },
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// "path": "hostedmodels/{hostedModelName}/predict",
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// "request": {
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// "$ref": "Input"
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// },
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// "response": {
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// "$ref": "Output"
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// },
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// "scopes": [
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// "https://www.googleapis.com/auth/prediction"
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// ]
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// }
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}
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// method id "prediction.trainedmodels.delete":
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type TrainedmodelsDeleteCall struct {
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s *Service
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id string
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opt_ map[string]interface{}
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}
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// Delete: Delete a trained model.
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func (r *TrainedmodelsService) Delete(id string) *TrainedmodelsDeleteCall {
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c := &TrainedmodelsDeleteCall{s: r.s, opt_: make(map[string]interface{})}
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c.id = id
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return c
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}
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// Fields allows partial responses to be retrieved.
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// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
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// for more information.
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func (c *TrainedmodelsDeleteCall) Fields(s ...googleapi.Field) *TrainedmodelsDeleteCall {
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c.opt_["fields"] = googleapi.CombineFields(s)
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return c
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}
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func (c *TrainedmodelsDeleteCall) Do() error {
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var body io.Reader = nil
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params := make(url.Values)
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params.Set("alt", "json")
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if v, ok := c.opt_["fields"]; ok {
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params.Set("fields", fmt.Sprintf("%v", v))
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}
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urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
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urls += "?" + params.Encode()
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req, _ := http.NewRequest("DELETE", urls, body)
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googleapi.Expand(req.URL, map[string]string{
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"id": c.id,
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})
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req.Header.Set("User-Agent", "google-api-go-client/0.5")
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res, err := c.s.client.Do(req)
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if err != nil {
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return err
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}
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defer googleapi.CloseBody(res)
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if err := googleapi.CheckResponse(res); err != nil {
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return err
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}
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return nil
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// {
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// "description": "Delete a trained model.",
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// "httpMethod": "DELETE",
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// "id": "prediction.trainedmodels.delete",
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// "parameterOrder": [
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// "id"
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// ],
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// "parameters": {
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// "id": {
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// "description": "The unique name for the predictive model.",
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// "location": "path",
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// "required": true,
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||
|
|
// "type": "string"
|
||
|
|
// }
|
||
|
|
// },
|
||
|
|
// "path": "trainedmodels/{id}",
|
||
|
|
// "scopes": [
|
||
|
|
// "https://www.googleapis.com/auth/prediction"
|
||
|
|
// ]
|
||
|
|
// }
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// method id "prediction.trainedmodels.get":
|
||
|
|
|
||
|
|
type TrainedmodelsGetCall struct {
|
||
|
|
s *Service
|
||
|
|
id string
|
||
|
|
opt_ map[string]interface{}
|
||
|
|
}
|
||
|
|
|
||
|
|
// Get: Check training status of your model.
|
||
|
|
func (r *TrainedmodelsService) Get(id string) *TrainedmodelsGetCall {
|
||
|
|
c := &TrainedmodelsGetCall{s: r.s, opt_: make(map[string]interface{})}
|
||
|
|
c.id = id
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
// Fields allows partial responses to be retrieved.
|
||
|
|
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
|
||
|
|
// for more information.
|
||
|
|
func (c *TrainedmodelsGetCall) Fields(s ...googleapi.Field) *TrainedmodelsGetCall {
|
||
|
|
c.opt_["fields"] = googleapi.CombineFields(s)
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *TrainedmodelsGetCall) Do() (*Training, error) {
|
||
|
|
var body io.Reader = nil
|
||
|
|
params := make(url.Values)
|
||
|
|
params.Set("alt", "json")
|
||
|
|
if v, ok := c.opt_["fields"]; ok {
|
||
|
|
params.Set("fields", fmt.Sprintf("%v", v))
|
||
|
|
}
|
||
|
|
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
|
||
|
|
urls += "?" + params.Encode()
|
||
|
|
req, _ := http.NewRequest("GET", urls, body)
|
||
|
|
googleapi.Expand(req.URL, map[string]string{
|
||
|
|
"id": c.id,
|
||
|
|
})
|
||
|
|
req.Header.Set("User-Agent", "google-api-go-client/0.5")
|
||
|
|
res, err := c.s.client.Do(req)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
defer googleapi.CloseBody(res)
|
||
|
|
if err := googleapi.CheckResponse(res); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
var ret *Training
|
||
|
|
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
return ret, nil
|
||
|
|
// {
|
||
|
|
// "description": "Check training status of your model.",
|
||
|
|
// "httpMethod": "GET",
|
||
|
|
// "id": "prediction.trainedmodels.get",
|
||
|
|
// "parameterOrder": [
|
||
|
|
// "id"
|
||
|
|
// ],
|
||
|
|
// "parameters": {
|
||
|
|
// "id": {
|
||
|
|
// "description": "The unique name for the predictive model.",
|
||
|
|
// "location": "path",
|
||
|
|
// "required": true,
|
||
|
|
// "type": "string"
|
||
|
|
// }
|
||
|
|
// },
|
||
|
|
// "path": "trainedmodels/{id}",
|
||
|
|
// "response": {
|
||
|
|
// "$ref": "Training"
|
||
|
|
// },
|
||
|
|
// "scopes": [
|
||
|
|
// "https://www.googleapis.com/auth/prediction"
|
||
|
|
// ]
|
||
|
|
// }
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// method id "prediction.trainedmodels.insert":
|
||
|
|
|
||
|
|
type TrainedmodelsInsertCall struct {
|
||
|
|
s *Service
|
||
|
|
training *Training
|
||
|
|
opt_ map[string]interface{}
|
||
|
|
}
|
||
|
|
|
||
|
|
// Insert: Begin training your model.
|
||
|
|
func (r *TrainedmodelsService) Insert(training *Training) *TrainedmodelsInsertCall {
|
||
|
|
c := &TrainedmodelsInsertCall{s: r.s, opt_: make(map[string]interface{})}
|
||
|
|
c.training = training
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
// Fields allows partial responses to be retrieved.
|
||
|
|
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
|
||
|
|
// for more information.
|
||
|
|
func (c *TrainedmodelsInsertCall) Fields(s ...googleapi.Field) *TrainedmodelsInsertCall {
|
||
|
|
c.opt_["fields"] = googleapi.CombineFields(s)
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *TrainedmodelsInsertCall) Do() (*Training, error) {
|
||
|
|
var body io.Reader = nil
|
||
|
|
body, err := googleapi.WithoutDataWrapper.JSONReader(c.training)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
ctype := "application/json"
|
||
|
|
params := make(url.Values)
|
||
|
|
params.Set("alt", "json")
|
||
|
|
if v, ok := c.opt_["fields"]; ok {
|
||
|
|
params.Set("fields", fmt.Sprintf("%v", v))
|
||
|
|
}
|
||
|
|
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels")
|
||
|
|
urls += "?" + params.Encode()
|
||
|
|
req, _ := http.NewRequest("POST", urls, body)
|
||
|
|
googleapi.SetOpaque(req.URL)
|
||
|
|
req.Header.Set("Content-Type", ctype)
|
||
|
|
req.Header.Set("User-Agent", "google-api-go-client/0.5")
|
||
|
|
res, err := c.s.client.Do(req)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
defer googleapi.CloseBody(res)
|
||
|
|
if err := googleapi.CheckResponse(res); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
var ret *Training
|
||
|
|
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
return ret, nil
|
||
|
|
// {
|
||
|
|
// "description": "Begin training your model.",
|
||
|
|
// "httpMethod": "POST",
|
||
|
|
// "id": "prediction.trainedmodels.insert",
|
||
|
|
// "path": "trainedmodels",
|
||
|
|
// "request": {
|
||
|
|
// "$ref": "Training"
|
||
|
|
// },
|
||
|
|
// "response": {
|
||
|
|
// "$ref": "Training"
|
||
|
|
// },
|
||
|
|
// "scopes": [
|
||
|
|
// "https://www.googleapis.com/auth/devstorage.full_control",
|
||
|
|
// "https://www.googleapis.com/auth/devstorage.read_only",
|
||
|
|
// "https://www.googleapis.com/auth/devstorage.read_write",
|
||
|
|
// "https://www.googleapis.com/auth/prediction"
|
||
|
|
// ]
|
||
|
|
// }
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// method id "prediction.trainedmodels.predict":
|
||
|
|
|
||
|
|
type TrainedmodelsPredictCall struct {
|
||
|
|
s *Service
|
||
|
|
id string
|
||
|
|
input *Input
|
||
|
|
opt_ map[string]interface{}
|
||
|
|
}
|
||
|
|
|
||
|
|
// Predict: Submit model id and request a prediction
|
||
|
|
func (r *TrainedmodelsService) Predict(id string, input *Input) *TrainedmodelsPredictCall {
|
||
|
|
c := &TrainedmodelsPredictCall{s: r.s, opt_: make(map[string]interface{})}
|
||
|
|
c.id = id
|
||
|
|
c.input = input
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
// Fields allows partial responses to be retrieved.
|
||
|
|
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
|
||
|
|
// for more information.
|
||
|
|
func (c *TrainedmodelsPredictCall) Fields(s ...googleapi.Field) *TrainedmodelsPredictCall {
|
||
|
|
c.opt_["fields"] = googleapi.CombineFields(s)
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *TrainedmodelsPredictCall) Do() (*Output, error) {
|
||
|
|
var body io.Reader = nil
|
||
|
|
body, err := googleapi.WithoutDataWrapper.JSONReader(c.input)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
ctype := "application/json"
|
||
|
|
params := make(url.Values)
|
||
|
|
params.Set("alt", "json")
|
||
|
|
if v, ok := c.opt_["fields"]; ok {
|
||
|
|
params.Set("fields", fmt.Sprintf("%v", v))
|
||
|
|
}
|
||
|
|
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}/predict")
|
||
|
|
urls += "?" + params.Encode()
|
||
|
|
req, _ := http.NewRequest("POST", urls, body)
|
||
|
|
googleapi.Expand(req.URL, map[string]string{
|
||
|
|
"id": c.id,
|
||
|
|
})
|
||
|
|
req.Header.Set("Content-Type", ctype)
|
||
|
|
req.Header.Set("User-Agent", "google-api-go-client/0.5")
|
||
|
|
res, err := c.s.client.Do(req)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
defer googleapi.CloseBody(res)
|
||
|
|
if err := googleapi.CheckResponse(res); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
var ret *Output
|
||
|
|
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
return ret, nil
|
||
|
|
// {
|
||
|
|
// "description": "Submit model id and request a prediction",
|
||
|
|
// "httpMethod": "POST",
|
||
|
|
// "id": "prediction.trainedmodels.predict",
|
||
|
|
// "parameterOrder": [
|
||
|
|
// "id"
|
||
|
|
// ],
|
||
|
|
// "parameters": {
|
||
|
|
// "id": {
|
||
|
|
// "description": "The unique name for the predictive model.",
|
||
|
|
// "location": "path",
|
||
|
|
// "required": true,
|
||
|
|
// "type": "string"
|
||
|
|
// }
|
||
|
|
// },
|
||
|
|
// "path": "trainedmodels/{id}/predict",
|
||
|
|
// "request": {
|
||
|
|
// "$ref": "Input"
|
||
|
|
// },
|
||
|
|
// "response": {
|
||
|
|
// "$ref": "Output"
|
||
|
|
// },
|
||
|
|
// "scopes": [
|
||
|
|
// "https://www.googleapis.com/auth/prediction"
|
||
|
|
// ]
|
||
|
|
// }
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
// method id "prediction.trainedmodels.update":
|
||
|
|
|
||
|
|
type TrainedmodelsUpdateCall struct {
|
||
|
|
s *Service
|
||
|
|
id string
|
||
|
|
update *Update
|
||
|
|
opt_ map[string]interface{}
|
||
|
|
}
|
||
|
|
|
||
|
|
// Update: Add new data to a trained model.
|
||
|
|
func (r *TrainedmodelsService) Update(id string, update *Update) *TrainedmodelsUpdateCall {
|
||
|
|
c := &TrainedmodelsUpdateCall{s: r.s, opt_: make(map[string]interface{})}
|
||
|
|
c.id = id
|
||
|
|
c.update = update
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
// Fields allows partial responses to be retrieved.
|
||
|
|
// See https://developers.google.com/gdata/docs/2.0/basics#PartialResponse
|
||
|
|
// for more information.
|
||
|
|
func (c *TrainedmodelsUpdateCall) Fields(s ...googleapi.Field) *TrainedmodelsUpdateCall {
|
||
|
|
c.opt_["fields"] = googleapi.CombineFields(s)
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *TrainedmodelsUpdateCall) Do() (*Training, error) {
|
||
|
|
var body io.Reader = nil
|
||
|
|
body, err := googleapi.WithoutDataWrapper.JSONReader(c.update)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
ctype := "application/json"
|
||
|
|
params := make(url.Values)
|
||
|
|
params.Set("alt", "json")
|
||
|
|
if v, ok := c.opt_["fields"]; ok {
|
||
|
|
params.Set("fields", fmt.Sprintf("%v", v))
|
||
|
|
}
|
||
|
|
urls := googleapi.ResolveRelative(c.s.BasePath, "trainedmodels/{id}")
|
||
|
|
urls += "?" + params.Encode()
|
||
|
|
req, _ := http.NewRequest("PUT", urls, body)
|
||
|
|
googleapi.Expand(req.URL, map[string]string{
|
||
|
|
"id": c.id,
|
||
|
|
})
|
||
|
|
req.Header.Set("Content-Type", ctype)
|
||
|
|
req.Header.Set("User-Agent", "google-api-go-client/0.5")
|
||
|
|
res, err := c.s.client.Do(req)
|
||
|
|
if err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
defer googleapi.CloseBody(res)
|
||
|
|
if err := googleapi.CheckResponse(res); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
var ret *Training
|
||
|
|
if err := json.NewDecoder(res.Body).Decode(&ret); err != nil {
|
||
|
|
return nil, err
|
||
|
|
}
|
||
|
|
return ret, nil
|
||
|
|
// {
|
||
|
|
// "description": "Add new data to a trained model.",
|
||
|
|
// "httpMethod": "PUT",
|
||
|
|
// "id": "prediction.trainedmodels.update",
|
||
|
|
// "parameterOrder": [
|
||
|
|
// "id"
|
||
|
|
// ],
|
||
|
|
// "parameters": {
|
||
|
|
// "id": {
|
||
|
|
// "description": "The unique name for the predictive model.",
|
||
|
|
// "location": "path",
|
||
|
|
// "required": true,
|
||
|
|
// "type": "string"
|
||
|
|
// }
|
||
|
|
// },
|
||
|
|
// "path": "trainedmodels/{id}",
|
||
|
|
// "request": {
|
||
|
|
// "$ref": "Update"
|
||
|
|
// },
|
||
|
|
// "response": {
|
||
|
|
// "$ref": "Training"
|
||
|
|
// },
|
||
|
|
// "scopes": [
|
||
|
|
// "https://www.googleapis.com/auth/prediction"
|
||
|
|
// ]
|
||
|
|
// }
|
||
|
|
|
||
|
|
}
|