Roll of googleapis as of 4/7/2015.

generated/googleapis/lib/prediction/v1_6.dart

+// This is a generated file (see the discoveryapis_generator project).
+
 library googleapis.prediction.v1_6;
 
-import "dart:core" as core;
-import "dart:collection" as collection;
-import "dart:async" as async;
-import "dart:convert" as convert;
+import 'dart:core' as core;
+import 'dart:collection' as collection;
+import 'dart:async' as async;
+import 'dart:convert' as convert;
 
-import "package:crypto/crypto.dart" as crypto;
+import 'package:_discoveryapis_commons/_discoveryapis_commons.dart' as commons;
+import 'package:crypto/crypto.dart' as crypto;
 import 'package:http/http.dart' as http;
-import '../src/common_internal.dart' as common_internal;
-import '../common/common.dart' as common;
 
-export '../common/common.dart' show ApiRequestError;
-export '../common/common.dart' show DetailedApiRequestError;
+export 'package:_discoveryapis_commons/_discoveryapis_commons.dart' show
+    ApiRequestError, DetailedApiRequestError;
+
+const core.String USER_AGENT = 'dart-api-client prediction/v1.6';
 
 /**
  * Lets you access a cloud hosted machine learning service that makes it easy to
  * build smart apps
  */
 class PredictionApi {
   /** Manage your data and permissions in Google Cloud Storage */
   static const DevstorageFullControlScope = "https://www.googleapis.com/auth/devstorage.full_control";
 
   /** View your data in Google Cloud Storage */
   static const DevstorageReadOnlyScope = "https://www.googleapis.com/auth/devstorage.read_only";
 
   /** Manage your data in Google Cloud Storage */
   static const DevstorageReadWriteScope = "https://www.googleapis.com/auth/devstorage.read_write";
 
   /** Manage your data in the Google Prediction API */
   static const PredictionScope = "https://www.googleapis.com/auth/prediction";
 
 
-  final common_internal.ApiRequester _requester;
+  final commons.ApiRequester _requester;
 
   HostedmodelsResourceApi get hostedmodels => new HostedmodelsResourceApi(_requester);
   TrainedmodelsResourceApi get trainedmodels => new TrainedmodelsResourceApi(_requester);
 
   PredictionApi(http.Client client, {core.String rootUrl: "https://www.googleapis.com/", core.String servicePath: "prediction/v1.6/projects/"}) :
-      _requester = new common_internal.ApiRequester(client, rootUrl, servicePath);
+      _requester = new commons.ApiRequester(client, rootUrl, servicePath, USER_AGENT);
 }
 
 
-/** Not documented yet. */
 class HostedmodelsResourceApi {
-  final common_internal.ApiRequester _requester;
+  final commons.ApiRequester _requester;
 
-  HostedmodelsResourceApi(common_internal.ApiRequester client) : 
+  HostedmodelsResourceApi(commons.ApiRequester client) : 
       _requester = client;
 
   /**
    * Submit input and request an output against a hosted model.
    *
    * [request] - The metadata request object.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [hostedModelName] - The name of a hosted model.
    *
    * Completes with a [Output].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Output> predict(Input request, core.String project, core.String hostedModelName) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (request != null) {
       _body = convert.JSON.encode((request).toJson());
     }
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (hostedModelName == null) {
       throw new core.ArgumentError("Parameter hostedModelName is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/hostedmodels/' + common_internal.Escaper.ecapeVariable('$hostedModelName') + '/predict';
+    _url = commons.Escaper.ecapeVariable('$project') + '/hostedmodels/' + commons.Escaper.ecapeVariable('$hostedModelName') + '/predict';
 
     var _response = _requester.request(_url,
                                        "POST",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Output.fromJson(data));
   }
 
 }
 
 
-/** Not documented yet. */
 class TrainedmodelsResourceApi {
-  final common_internal.ApiRequester _requester;
+  final commons.ApiRequester _requester;
 
-  TrainedmodelsResourceApi(common_internal.ApiRequester client) : 
+  TrainedmodelsResourceApi(commons.ApiRequester client) : 
       _requester = client;
 
   /**
    * Get analysis of the model and the data the model was trained on.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [id] - The unique name for the predictive model.
    *
    * Completes with a [Analyze].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Analyze> analyze(core.String project, core.String id) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (id == null) {
       throw new core.ArgumentError("Parameter id is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id') + '/analyze';
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/' + commons.Escaper.ecapeVariable('$id') + '/analyze';
 
     var _response = _requester.request(_url,
                                        "GET",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Analyze.fromJson(data));
   }
 
   /**
    * Delete a trained model.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [id] - The unique name for the predictive model.
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future delete(core.String project, core.String id) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (id == null) {
       throw new core.ArgumentError("Parameter id is required.");
     }
 
     _downloadOptions = null;
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id');
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/' + commons.Escaper.ecapeVariable('$id');
 
     var _response = _requester.request(_url,
                                        "DELETE",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => null);
   }
 
   /**
    * Check training status of your model.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [id] - The unique name for the predictive model.
    *
    * Completes with a [Insert2].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Insert2> get(core.String project, core.String id) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (id == null) {
       throw new core.ArgumentError("Parameter id is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id');
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/' + commons.Escaper.ecapeVariable('$id');
 
     var _response = _requester.request(_url,
                                        "GET",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Insert2.fromJson(data));
   }
 
   /**
    * Train a Prediction API model.
    *
    * [request] - The metadata request object.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * Completes with a [Insert2].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Insert2> insert(Insert request, core.String project) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (request != null) {
       _body = convert.JSON.encode((request).toJson());
     }
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels';
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels';
 
     var _response = _requester.request(_url,
                                        "POST",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Insert2.fromJson(data));
   }
 
   /**
    * List available models.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [maxResults] - Maximum number of results to return.
    *
    * [pageToken] - Pagination token.
    *
    * Completes with a [List].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<List> list(core.String project, {core.int maxResults, core.String pageToken}) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (maxResults != null) {
       _queryParams["maxResults"] = ["${maxResults}"];
     }
     if (pageToken != null) {
       _queryParams["pageToken"] = [pageToken];
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/list';
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/list';
 
     var _response = _requester.request(_url,
                                        "GET",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new List.fromJson(data));
   }
 
   /**
    * Submit model id and request a prediction.
    *
    * [request] - The metadata request object.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [id] - The unique name for the predictive model.
    *
    * Completes with a [Output].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Output> predict(Input request, core.String project, core.String id) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (request != null) {
       _body = convert.JSON.encode((request).toJson());
     }
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (id == null) {
       throw new core.ArgumentError("Parameter id is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id') + '/predict';
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/' + commons.Escaper.ecapeVariable('$id') + '/predict';
 
     var _response = _requester.request(_url,
                                        "POST",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Output.fromJson(data));
   }
 
   /**
    * Add new data to a trained model.
    *
    * [request] - The metadata request object.
    *
    * Request parameters:
    *
    * [project] - The project associated with the model.
    *
    * [id] - The unique name for the predictive model.
    *
    * Completes with a [Insert2].
    *
-   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * Completes with a [commons.ApiRequestError] if the API endpoint returned an
    * error.
    *
    * If the used [http.Client] completes with an error when making a REST call,
    * this method  will complete with the same error.
    */
   async.Future<Insert2> update(Update request, core.String project, core.String id) {
     var _url = null;
     var _queryParams = new core.Map();
     var _uploadMedia = null;
     var _uploadOptions = null;
-    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
     var _body = null;
 
     if (request != null) {
       _body = convert.JSON.encode((request).toJson());
     }
     if (project == null) {
       throw new core.ArgumentError("Parameter project is required.");
     }
     if (id == null) {
       throw new core.ArgumentError("Parameter id is required.");
     }
 
 
-    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id');
+    _url = commons.Escaper.ecapeVariable('$project') + '/trainedmodels/' + commons.Escaper.ecapeVariable('$id');
 
     var _response = _requester.request(_url,
                                        "PUT",
                                        body: _body,
                                        queryParams: _queryParams,
                                        uploadOptions: _uploadOptions,
                                        uploadMedia: _uploadMedia,
                                        downloadOptions: _downloadOptions);
     return _response.then((data) => new Insert2.fromJson(data));
   }
 
 }
 
 
 
-/** Not documented yet. */
 class AnalyzeDataDescriptionFeaturesCategoricalValues {
   /** Number of times this feature had this value. */
   core.String count;
 
   /** The category name. */
   core.String value;
 
 
   AnalyzeDataDescriptionFeaturesCategoricalValues();
 
@@ skipping 111 matching lines @@
   core.Map toJson() {
     var _json = new core.Map();
     if (count != null) {
       _json["count"] = count;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class AnalyzeDataDescriptionFeatures {
   /** Description of the categorical values of this feature. */
   AnalyzeDataDescriptionFeaturesCategorical categorical;
 
   /** The feature index. */
   core.String index;
 
   /** Description of the numeric values of this feature. */
   AnalyzeDataDescriptionFeaturesNumeric numeric;
 
@@ skipping 72 matching lines @@
       _json["mean"] = mean;
     }
     if (variance != null) {
       _json["variance"] = variance;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class AnalyzeDataDescriptionOutputFeatureText {
   /** Number of times the output label occurred in the data set. */
   core.String count;
 
   /** The output label. */
   core.String value;
 
 
   AnalyzeDataDescriptionOutputFeatureText();
 
@@ skipping 127 matching lines @@
       _json["confusionMatrixRowTotals"] = confusionMatrixRowTotals;
     }
     if (modelinfo != null) {
       _json["modelinfo"] = (modelinfo).toJson();
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Analyze {
   /** Description of the data the model was trained on. */
   AnalyzeDataDescription dataDescription;
 
   /** List of errors with the data. */
   core.List<core.Map<core.String, core.String>> errors;
 
   /** The unique name for the predictive model. */
   core.String id;
 
@@ skipping 77 matching lines @@
   core.Map toJson() {
     var _json = new core.Map();
     if (csvInstance != null) {
       _json["csvInstance"] = csvInstance;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Input {
   /** Input to the model for a prediction. */
   InputInput input;
 
 
   Input();
 
   Input.fromJson(core.Map _json) {
     if (_json.containsKey("input")) {
       input = new InputInput.fromJson(_json["input"]);
     }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (input != null) {
       _json["input"] = (input).toJson();
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class InsertTrainingInstances {
   /**
    * The input features for this instance.
    *
    * The values for Object must be JSON objects. It can consist of `num`,
    * `String`, `bool` and `null` as well as `Map` and `List` values.
    */
   core.List<core.Object> csvInstance;
 
   /** The generic output value - could be regression or class label. */
@@ skipping 17 matching lines @@
       _json["csvInstance"] = csvInstance;
     }
     if (output != null) {
       _json["output"] = output;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Insert {
   /** The unique name for the predictive model. */
   core.String id;
 
   /** Type of predictive model (classification or regression). */
   core.String modelType;
 
   /** The Id of the model to be copied over. */
   core.String sourceModel;
 
@@ skipping 151 matching lines @@
       _json["numberInstances"] = numberInstances;
     }
     if (numberLabels != null) {
       _json["numberLabels"] = numberLabels;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Insert2 {
   /** Insert time of the model (as a RFC 3339 timestamp). */
   core.DateTime created;
 
   /** The unique name for the predictive model. */
   core.String id;
 
   /** What kind of resource this is. */
   core.String kind;
 
@@ skipping 96 matching lines @@
       _json["trainingComplete"] = (trainingComplete).toIso8601String();
     }
     if (trainingStatus != null) {
       _json["trainingStatus"] = trainingStatus;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class List {
   /** List of models. */
   core.List<Insert2> items;
 
   /** What kind of resource this is. */
   core.String kind;
 
   /** Pagination token to fetch the next page, if one exists. */
   core.String nextPageToken;
 
@@ skipping 30 matching lines @@
       _json["nextPageToken"] = nextPageToken;
     }
     if (selfLink != null) {
       _json["selfLink"] = selfLink;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class OutputOutputMulti {
   /** The class label. */
   core.String label;
 
   /** The probability of the class label. */
   core.String score;
 
 
   OutputOutputMulti();
 
@@ skipping 12 matching lines @@
       _json["label"] = label;
     }
     if (score != null) {
       _json["score"] = score;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Output {
   /** The unique name for the predictive model. */
   core.String id;
 
   /** What kind of resource this is. */
   core.String kind;
 
   /** The most likely class label (Categorical models only). */
   core.String outputLabel;
 
@@ skipping 51 matching lines @@
       _json["outputValue"] = outputValue;
     }
     if (selfLink != null) {
       _json["selfLink"] = selfLink;
     }
     return _json;
   }
 }
 
 
-/** Not documented yet. */
 class Update {
   /**
    * The input features for this instance.
    *
    * The values for Object must be JSON objects. It can consist of `num`,
    * `String`, `bool` and `null` as well as `Map` and `List` values.
    */
   core.List<core.Object> csvInstance;
 
   /** The generic output value - could be regression or class label. */
@@ skipping 15 matching lines @@
     var _json = new core.Map();
     if (csvInstance != null) {
       _json["csvInstance"] = csvInstance;
     }
     if (output != null) {
       _json["output"] = output;
     }
     return _json;
   }
 }
-
-