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dex/vendor/google.golang.org/api/prediction/v1.2/prediction-gen.go
2016-04-08 11:56:29 -07:00

646 lines
16 KiB
Go

// 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/v1.2"
// ...
// predictionService, err := prediction.New(oauthHttpClient)
package prediction
import (
"bytes"
"encoding/json"
"errors"
"fmt"
"google.golang.org/api/googleapi"
"io"
"net/http"
"net/url"
"strconv"
"strings"
)
// Always reference these packages, just in case the auto-generated code
// below doesn't.
var _ = bytes.NewBuffer
var _ = strconv.Itoa
var _ = fmt.Sprintf
var _ = json.NewDecoder
var _ = io.Copy
var _ = url.Parse
var _ = googleapi.Version
var _ = errors.New
var _ = strings.Replace
const apiId = "prediction:v1.2"
const apiName = "prediction"
const apiVersion = "v1.2"
const basePath = "https://www.googleapis.com/prediction/v1.2/"
// OAuth2 scopes used by this API.
const (
// Manage your data and permissions in Google Cloud Storage
DevstorageFull_controlScope = "https://www.googleapis.com/auth/devstorage.full_control"
// View your data in Google Cloud Storage
DevstorageRead_onlyScope = "https://www.googleapis.com/auth/devstorage.read_only"
// Manage your data in Google Cloud Storage
DevstorageRead_writeScope = "https://www.googleapis.com/auth/devstorage.read_write"
// Manage your data in the Google Prediction API
PredictionScope = "https://www.googleapis.com/auth/prediction"
)
func New(client *http.Client) (*Service, error) {
if client == nil {
return nil, errors.New("client is nil")
}
s := &Service{client: client, BasePath: basePath}
s.Hostedmodels = NewHostedmodelsService(s)
s.Training = NewTrainingService(s)
return s, nil
}
type Service struct {
client *http.Client
BasePath string // API endpoint base URL
Hostedmodels *HostedmodelsService
Training *TrainingService
}
func NewHostedmodelsService(s *Service) *HostedmodelsService {
rs := &HostedmodelsService{s: s}
return rs
}
type HostedmodelsService struct {
s *Service
}
func NewTrainingService(s *Service) *TrainingService {
rs := &TrainingService{s: s}
return rs
}
type TrainingService struct {
s *Service
}
type Input struct {
Input *InputInput `json:"input,omitempty"`
}
type InputInput struct {
CsvInstance []interface{} `json:"csvInstance,omitempty"`
}
type Output struct {
Id string `json:"id,omitempty"`
Kind string `json:"kind,omitempty"`
OutputLabel string `json:"outputLabel,omitempty"`
OutputMulti []*OutputOutputMulti `json:"outputMulti,omitempty"`
OutputValue float64 `json:"outputValue,omitempty"`
SelfLink string `json:"selfLink,omitempty"`
}
type OutputOutputMulti struct {
Label string `json:"label,omitempty"`
Score float64 `json:"score,omitempty"`
}
type Training struct {
Id string `json:"id,omitempty"`
Kind string `json:"kind,omitempty"`
ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`
SelfLink string `json:"selfLink,omitempty"`
TrainingStatus string `json:"trainingStatus,omitempty"`
}
type TrainingModelInfo struct {
ClassificationAccuracy float64 `json:"classificationAccuracy,omitempty"`
MeanSquaredError float64 `json:"meanSquaredError,omitempty"`
ModelType string `json:"modelType,omitempty"`
}
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"`
}
// method id "prediction.predict":
type PredictCall struct {
s *Service
data string
input *Input
opt_ map[string]interface{}
}
// Predict: Submit data and request a prediction
func (s *Service) Predict(data string, input *Input) *PredictCall {
c := &PredictCall{s: s, opt_: make(map[string]interface{})}
c.data = data
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 *PredictCall) Fields(s ...googleapi.Field) *PredictCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *PredictCall) 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, "training/{data}/predict")
urls += "?" + params.Encode()
req, _ := http.NewRequest("POST", urls, body)
googleapi.Expand(req.URL, map[string]string{
"data": c.data,
})
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 data and request a prediction",
// "httpMethod": "POST",
// "id": "prediction.predict",
// "parameterOrder": [
// "data"
// ],
// "parameters": {
// "data": {
// "description": "mybucket%2Fmydata resource in Google Storage",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "training/{data}/predict",
// "request": {
// "$ref": "Input"
// },
// "response": {
// "$ref": "Output"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.hostedmodels.predict":
type HostedmodelsPredictCall struct {
s *Service
hostedModelName string
input *Input
opt_ map[string]interface{}
}
// Predict: Submit input and request an output against a hosted model
func (r *HostedmodelsService) Predict(hostedModelName string, input *Input) *HostedmodelsPredictCall {
c := &HostedmodelsPredictCall{s: r.s, opt_: make(map[string]interface{})}
c.hostedModelName = hostedModelName
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 *HostedmodelsPredictCall) Fields(s ...googleapi.Field) *HostedmodelsPredictCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *HostedmodelsPredictCall) 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, "hostedmodels/{hostedModelName}/predict")
urls += "?" + params.Encode()
req, _ := http.NewRequest("POST", urls, body)
googleapi.Expand(req.URL, map[string]string{
"hostedModelName": c.hostedModelName,
})
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 input and request an output against a hosted model",
// "httpMethod": "POST",
// "id": "prediction.hostedmodels.predict",
// "parameterOrder": [
// "hostedModelName"
// ],
// "parameters": {
// "hostedModelName": {
// "description": "The name of a hosted model",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "hostedmodels/{hostedModelName}/predict",
// "request": {
// "$ref": "Input"
// },
// "response": {
// "$ref": "Output"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.training.delete":
type TrainingDeleteCall struct {
s *Service
data string
opt_ map[string]interface{}
}
// Delete: Delete a trained model
func (r *TrainingService) Delete(data string) *TrainingDeleteCall {
c := &TrainingDeleteCall{s: r.s, opt_: make(map[string]interface{})}
c.data = data
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 *TrainingDeleteCall) Fields(s ...googleapi.Field) *TrainingDeleteCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainingDeleteCall) Do() 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, "training/{data}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("DELETE", urls, body)
googleapi.Expand(req.URL, map[string]string{
"data": c.data,
})
req.Header.Set("User-Agent", "google-api-go-client/0.5")
res, err := c.s.client.Do(req)
if err != nil {
return err
}
defer googleapi.CloseBody(res)
if err := googleapi.CheckResponse(res); err != nil {
return err
}
return nil
// {
// "description": "Delete a trained model",
// "httpMethod": "DELETE",
// "id": "prediction.training.delete",
// "parameterOrder": [
// "data"
// ],
// "parameters": {
// "data": {
// "description": "mybucket/mydata resource in Google Storage",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "training/{data}",
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.training.get":
type TrainingGetCall struct {
s *Service
data string
opt_ map[string]interface{}
}
// Get: Check training status of your model
func (r *TrainingService) Get(data string) *TrainingGetCall {
c := &TrainingGetCall{s: r.s, opt_: make(map[string]interface{})}
c.data = data
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 *TrainingGetCall) Fields(s ...googleapi.Field) *TrainingGetCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainingGetCall) 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, "training/{data}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("GET", urls, body)
googleapi.Expand(req.URL, map[string]string{
"data": c.data,
})
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.training.get",
// "parameterOrder": [
// "data"
// ],
// "parameters": {
// "data": {
// "description": "mybucket/mydata resource in Google Storage",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "training/{data}",
// "response": {
// "$ref": "Training"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}
// method id "prediction.training.insert":
type TrainingInsertCall struct {
s *Service
training *Training
opt_ map[string]interface{}
}
// Insert: Begin training your model
func (r *TrainingService) Insert(training *Training) *TrainingInsertCall {
c := &TrainingInsertCall{s: r.s, opt_: make(map[string]interface{})}
c.training = training
return c
}
// Data sets the optional parameter "data": mybucket/mydata resource in
// Google Storage
func (c *TrainingInsertCall) Data(data string) *TrainingInsertCall {
c.opt_["data"] = data
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 *TrainingInsertCall) Fields(s ...googleapi.Field) *TrainingInsertCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainingInsertCall) 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_["data"]; ok {
params.Set("data", fmt.Sprintf("%v", v))
}
if v, ok := c.opt_["fields"]; ok {
params.Set("fields", fmt.Sprintf("%v", v))
}
urls := googleapi.ResolveRelative(c.s.BasePath, "training")
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.training.insert",
// "parameters": {
// "data": {
// "description": "mybucket/mydata resource in Google Storage",
// "location": "query",
// "type": "string"
// }
// },
// "path": "training",
// "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.training.update":
type TrainingUpdateCall struct {
s *Service
data string
update *Update
opt_ map[string]interface{}
}
// Update: Add new data to a trained model
func (r *TrainingService) Update(data string, update *Update) *TrainingUpdateCall {
c := &TrainingUpdateCall{s: r.s, opt_: make(map[string]interface{})}
c.data = data
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 *TrainingUpdateCall) Fields(s ...googleapi.Field) *TrainingUpdateCall {
c.opt_["fields"] = googleapi.CombineFields(s)
return c
}
func (c *TrainingUpdateCall) 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, "training/{data}")
urls += "?" + params.Encode()
req, _ := http.NewRequest("PUT", urls, body)
googleapi.Expand(req.URL, map[string]string{
"data": c.data,
})
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.training.update",
// "parameterOrder": [
// "data"
// ],
// "parameters": {
// "data": {
// "description": "mybucket/mydata resource in Google Storage",
// "location": "path",
// "required": true,
// "type": "string"
// }
// },
// "path": "training/{data}",
// "request": {
// "$ref": "Update"
// },
// "response": {
// "$ref": "Training"
// },
// "scopes": [
// "https://www.googleapis.com/auth/prediction"
// ]
// }
}