Generate 0.1.0 version of googleapis/googleapis_beta

generated/googleapis/lib/prediction/v1_6.dart

Index: generated/googleapis/lib/prediction/v1_6.dart
diff --git a/generated/googleapis/lib/prediction/v1_6.dart b/generated/googleapis/lib/prediction/v1_6.dart
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+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 "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;
+
+/**
+ * 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;
+
+  HostedmodelsResourceApi get hostedmodels => new HostedmodelsResourceApi(_requester);
+  TrainedmodelsResourceApi get trainedmodels => new TrainedmodelsResourceApi(_requester);
+
+  PredictionApi(http.Client client) : 
+      _requester = new common_internal.ApiRequester(client, "https://www.googleapis.com/", "/prediction/v1.6/projects/");
+}
+
+
+/** Not documented yet. */
+class HostedmodelsResourceApi {
+  final common_internal.ApiRequester _requester;
+
+  HostedmodelsResourceApi(common_internal.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
+   * 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 _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';
+
+    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;
+
+  TrainedmodelsResourceApi(common_internal.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
+   * 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 _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';
+
+    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
+   * 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 _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');
+
+    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
+   * 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 _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');
+
+    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
+   * 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 _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('$project') + '/trainedmodels';
+
+    var _response = _requester.request(_url,
+                                       "POST",
+                                       body: _body,
+                                       queryParams: _queryParams,
+                                       uploadOptions: _uploadOptions,
+                                       uploadMedia: _uploadMedia,
+                                       downloadOptions: _downloadOptions);
+    return _response.then((data) => new Insert2.fromJson(data));
+  }
+
+  /**
+   * List available models.
+   *
+   * Request parameters:
+   *
+   * [project] - The project associated with the model.
+   *
+   * [maxResults] - Maximum number of results to return.
+   *
+   * [pageToken] - Pagination token.
+   *
+   * Completes with a [List].
+   *
+   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * error.
+   *
+   * If the used [http.Client] completes with an error when making a REST call,
+   * this method  will complete with the same error.
+   */
+  async.Future<List> list(core.String project, {core.int maxResults, core.String pageToken}) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (project == null) {
+      throw new core.ArgumentError("Parameter project is required.");
+    }
+    if (maxResults != null) {
+      _queryParams["maxResults"] = ["${maxResults}"];
+    }
+    if (pageToken != null) {
+      _queryParams["pageToken"] = [pageToken];
+    }
+
+
+    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/list';
+
+    var _response = _requester.request(_url,
+                                       "GET",
+                                       body: _body,
+                                       queryParams: _queryParams,
+                                       uploadOptions: _uploadOptions,
+                                       uploadMedia: _uploadMedia,
+                                       downloadOptions: _downloadOptions);
+    return _response.then((data) => new List.fromJson(data));
+  }
+
+  /**
+   * Submit model id and request a prediction.
+   *
+   * [request] - The metadata request object.
+   *
+   * Request parameters:
+   *
+   * [project] - The project associated with the model.
+   *
+   * [id] - The unique name for the predictive model.
+   *
+   * Completes with a [Output].
+   *
+   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * error.
+   *
+   * If the used [http.Client] completes with an error when making a REST call,
+   * this method  will complete with the same error.
+   */
+  async.Future<Output> predict(Input request, core.String project, core.String id) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+    if (project == null) {
+      throw new core.ArgumentError("Parameter project is required.");
+    }
+    if (id == null) {
+      throw new core.ArgumentError("Parameter id is required.");
+    }
+
+
+    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id') + '/predict';
+
+    var _response = _requester.request(_url,
+                                       "POST",
+                                       body: _body,
+                                       queryParams: _queryParams,
+                                       uploadOptions: _uploadOptions,
+                                       uploadMedia: _uploadMedia,
+                                       downloadOptions: _downloadOptions);
+    return _response.then((data) => new Output.fromJson(data));
+  }
+
+  /**
+   * Add new data to a trained model.
+   *
+   * [request] - The metadata request object.
+   *
+   * Request parameters:
+   *
+   * [project] - The project associated with the model.
+   *
+   * [id] - The unique name for the predictive model.
+   *
+   * Completes with a [Insert2].
+   *
+   * Completes with a [common.ApiRequestError] if the API endpoint returned an
+   * error.
+   *
+   * If the used [http.Client] completes with an error when making a REST call,
+   * this method  will complete with the same error.
+   */
+  async.Future<Insert2> update(Update request, core.String project, core.String id) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = common.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+    if (project == null) {
+      throw new core.ArgumentError("Parameter project is required.");
+    }
+    if (id == null) {
+      throw new core.ArgumentError("Parameter id is required.");
+    }
+
+
+    _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id');
+
+    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();
+
+  AnalyzeDataDescriptionFeaturesCategoricalValues.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+    if (_json.containsKey("value")) {
+      value = _json["value"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (count != null) {
+      _json["count"] = count;
+    }
+    if (value != null) {
+      _json["value"] = value;
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the categorical values of this feature. */
+class AnalyzeDataDescriptionFeaturesCategorical {
+  /** Number of categorical values for this feature in the data. */
+  core.String count;
+
+  /** List of all the categories for this feature in the data set. */
+  core.List<AnalyzeDataDescriptionFeaturesCategoricalValues> values;
+
+
+  AnalyzeDataDescriptionFeaturesCategorical();
+
+  AnalyzeDataDescriptionFeaturesCategorical.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+    if (_json.containsKey("values")) {
+      values = _json["values"].map((value) => new AnalyzeDataDescriptionFeaturesCategoricalValues.fromJson(value)).toList();
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (count != null) {
+      _json["count"] = count;
+    }
+    if (values != null) {
+      _json["values"] = values.map((value) => (value).toJson()).toList();
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the numeric values of this feature. */
+class AnalyzeDataDescriptionFeaturesNumeric {
+  /** Number of numeric values for this feature in the data set. */
+  core.String count;
+
+  /** Mean of the numeric values of this feature in the data set. */
+  core.String mean;
+
+  /** Variance of the numeric values of this feature in the data set. */
+  core.String variance;
+
+
+  AnalyzeDataDescriptionFeaturesNumeric();
+
+  AnalyzeDataDescriptionFeaturesNumeric.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+    if (_json.containsKey("mean")) {
+      mean = _json["mean"];
+    }
+    if (_json.containsKey("variance")) {
+      variance = _json["variance"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (count != null) {
+      _json["count"] = count;
+    }
+    if (mean != null) {
+      _json["mean"] = mean;
+    }
+    if (variance != null) {
+      _json["variance"] = variance;
+    }
+    return _json;
+  }
+}
+
+
+/** Description of multiple-word text values of this feature. */
+class AnalyzeDataDescriptionFeaturesText {
+  /** Number of multiple-word text values for this feature. */
+  core.String count;
+
+
+  AnalyzeDataDescriptionFeaturesText();
+
+  AnalyzeDataDescriptionFeaturesText.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+  }
+
+  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;
+
+  /** Description of multiple-word text values of this feature. */
+  AnalyzeDataDescriptionFeaturesText text;
+
+
+  AnalyzeDataDescriptionFeatures();
+
+  AnalyzeDataDescriptionFeatures.fromJson(core.Map _json) {
+    if (_json.containsKey("categorical")) {
+      categorical = new AnalyzeDataDescriptionFeaturesCategorical.fromJson(_json["categorical"]);
+    }
+    if (_json.containsKey("index")) {
+      index = _json["index"];
+    }
+    if (_json.containsKey("numeric")) {
+      numeric = new AnalyzeDataDescriptionFeaturesNumeric.fromJson(_json["numeric"]);
+    }
+    if (_json.containsKey("text")) {
+      text = new AnalyzeDataDescriptionFeaturesText.fromJson(_json["text"]);
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (categorical != null) {
+      _json["categorical"] = (categorical).toJson();
+    }
+    if (index != null) {
+      _json["index"] = index;
+    }
+    if (numeric != null) {
+      _json["numeric"] = (numeric).toJson();
+    }
+    if (text != null) {
+      _json["text"] = (text).toJson();
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the output values in the data set. */
+class AnalyzeDataDescriptionOutputFeatureNumeric {
+  /** Number of numeric output values in the data set. */
+  core.String count;
+
+  /** Mean of the output values in the data set. */
+  core.String mean;
+
+  /** Variance of the output values in the data set. */
+  core.String variance;
+
+
+  AnalyzeDataDescriptionOutputFeatureNumeric();
+
+  AnalyzeDataDescriptionOutputFeatureNumeric.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+    if (_json.containsKey("mean")) {
+      mean = _json["mean"];
+    }
+    if (_json.containsKey("variance")) {
+      variance = _json["variance"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (count != null) {
+      _json["count"] = count;
+    }
+    if (mean != null) {
+      _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();
+
+  AnalyzeDataDescriptionOutputFeatureText.fromJson(core.Map _json) {
+    if (_json.containsKey("count")) {
+      count = _json["count"];
+    }
+    if (_json.containsKey("value")) {
+      value = _json["value"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (count != null) {
+      _json["count"] = count;
+    }
+    if (value != null) {
+      _json["value"] = value;
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the output value or label. */
+class AnalyzeDataDescriptionOutputFeature {
+  /** Description of the output values in the data set. */
+  AnalyzeDataDescriptionOutputFeatureNumeric numeric;
+
+  /** Description of the output labels in the data set. */
+  core.List<AnalyzeDataDescriptionOutputFeatureText> text;
+
+
+  AnalyzeDataDescriptionOutputFeature();
+
+  AnalyzeDataDescriptionOutputFeature.fromJson(core.Map _json) {
+    if (_json.containsKey("numeric")) {
+      numeric = new AnalyzeDataDescriptionOutputFeatureNumeric.fromJson(_json["numeric"]);
+    }
+    if (_json.containsKey("text")) {
+      text = _json["text"].map((value) => new AnalyzeDataDescriptionOutputFeatureText.fromJson(value)).toList();
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (numeric != null) {
+      _json["numeric"] = (numeric).toJson();
+    }
+    if (text != null) {
+      _json["text"] = text.map((value) => (value).toJson()).toList();
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the data the model was trained on. */
+class AnalyzeDataDescription {
+  /** Description of the input features in the data set. */
+  core.List<AnalyzeDataDescriptionFeatures> features;
+
+  /** Description of the output value or label. */
+  AnalyzeDataDescriptionOutputFeature outputFeature;
+
+
+  AnalyzeDataDescription();
+
+  AnalyzeDataDescription.fromJson(core.Map _json) {
+    if (_json.containsKey("features")) {
+      features = _json["features"].map((value) => new AnalyzeDataDescriptionFeatures.fromJson(value)).toList();
+    }
+    if (_json.containsKey("outputFeature")) {
+      outputFeature = new AnalyzeDataDescriptionOutputFeature.fromJson(_json["outputFeature"]);
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (features != null) {
+      _json["features"] = features.map((value) => (value).toJson()).toList();
+    }
+    if (outputFeature != null) {
+      _json["outputFeature"] = (outputFeature).toJson();
+    }
+    return _json;
+  }
+}
+
+
+/** Description of the model. */
+class AnalyzeModelDescription {
+  /**
+   * 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).
+   */
+  core.Map<core.String, core.Map<core.String, core.String>> confusionMatrix;
+
+  /** A list of the confusion matrix row totals. */
+  core.Map<core.String, core.String> confusionMatrixRowTotals;
+
+  /** Basic information about the model. */
+  Insert2 modelinfo;
+
+
+  AnalyzeModelDescription();
+
+  AnalyzeModelDescription.fromJson(core.Map _json) {
+    if (_json.containsKey("confusionMatrix")) {
+      confusionMatrix = _json["confusionMatrix"];
+    }
+    if (_json.containsKey("confusionMatrixRowTotals")) {
+      confusionMatrixRowTotals = _json["confusionMatrixRowTotals"];
+    }
+    if (_json.containsKey("modelinfo")) {
+      modelinfo = new Insert2.fromJson(_json["modelinfo"]);
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (confusionMatrix != null) {
+      _json["confusionMatrix"] = confusionMatrix;
+    }
+    if (confusionMatrixRowTotals != null) {
+      _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;
+
+  /** What kind of resource this is. */
+  core.String kind;
+
+  /** Description of the model. */
+  AnalyzeModelDescription modelDescription;
+
+  /** A URL to re-request this resource. */
+  core.String selfLink;
+
+
+  Analyze();
+
+  Analyze.fromJson(core.Map _json) {
+    if (_json.containsKey("dataDescription")) {
+      dataDescription = new AnalyzeDataDescription.fromJson(_json["dataDescription"]);
+    }
+    if (_json.containsKey("errors")) {
+      errors = _json["errors"];
+    }
+    if (_json.containsKey("id")) {
+      id = _json["id"];
+    }
+    if (_json.containsKey("kind")) {
+      kind = _json["kind"];
+    }
+    if (_json.containsKey("modelDescription")) {
+      modelDescription = new AnalyzeModelDescription.fromJson(_json["modelDescription"]);
+    }
+    if (_json.containsKey("selfLink")) {
+      selfLink = _json["selfLink"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (dataDescription != null) {
+      _json["dataDescription"] = (dataDescription).toJson();
+    }
+    if (errors != null) {
+      _json["errors"] = errors;
+    }
+    if (id != null) {
+      _json["id"] = id;
+    }
+    if (kind != null) {
+      _json["kind"] = kind;
+    }
+    if (modelDescription != null) {
+      _json["modelDescription"] = (modelDescription).toJson();
+    }
+    if (selfLink != null) {
+      _json["selfLink"] = selfLink;
+    }
+    return _json;
+  }
+}
+
+
+/** Input to the model for a prediction. */
+class InputInput {
+  /**
+   * A list of input features, these can be strings or doubles.
+   *
+   * 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;
+
+
+  InputInput();
+
+  InputInput.fromJson(core.Map _json) {
+    if (_json.containsKey("csvInstance")) {
+      csvInstance = _json["csvInstance"];
+    }
+  }
+
+  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. */
+  core.String output;
+
+
+  InsertTrainingInstances();
+
+  InsertTrainingInstances.fromJson(core.Map _json) {
+    if (_json.containsKey("csvInstance")) {
+      csvInstance = _json["csvInstance"];
+    }
+    if (_json.containsKey("output")) {
+      output = _json["output"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (csvInstance != null) {
+      _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;
+
+  /** Google storage location of the training data file. */
+  core.String storageDataLocation;
+
+  /** Google storage location of the preprocessing pmml file. */
+  core.String storagePMMLLocation;
+
+  /** Google storage location of the pmml model file. */
+  core.String storagePMMLModelLocation;
+
+  /** Instances to train model on. */
+  core.List<InsertTrainingInstances> trainingInstances;
+
+  /**
+   * A class weighting function, which allows the importance weights for class
+   * labels to be specified (Categorical models only).
+   */
+  core.List<core.Map<core.String, core.double>> utility;
+
+
+  Insert();
+
+  Insert.fromJson(core.Map _json) {
+    if (_json.containsKey("id")) {
+      id = _json["id"];
+    }
+    if (_json.containsKey("modelType")) {
+      modelType = _json["modelType"];
+    }
+    if (_json.containsKey("sourceModel")) {
+      sourceModel = _json["sourceModel"];
+    }
+    if (_json.containsKey("storageDataLocation")) {
+      storageDataLocation = _json["storageDataLocation"];
+    }
+    if (_json.containsKey("storagePMMLLocation")) {
+      storagePMMLLocation = _json["storagePMMLLocation"];
+    }
+    if (_json.containsKey("storagePMMLModelLocation")) {
+      storagePMMLModelLocation = _json["storagePMMLModelLocation"];
+    }
+    if (_json.containsKey("trainingInstances")) {
+      trainingInstances = _json["trainingInstances"].map((value) => new InsertTrainingInstances.fromJson(value)).toList();
+    }
+    if (_json.containsKey("utility")) {
+      utility = _json["utility"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (id != null) {
+      _json["id"] = id;
+    }
+    if (modelType != null) {
+      _json["modelType"] = modelType;
+    }
+    if (sourceModel != null) {
+      _json["sourceModel"] = sourceModel;
+    }
+    if (storageDataLocation != null) {
+      _json["storageDataLocation"] = storageDataLocation;
+    }
+    if (storagePMMLLocation != null) {
+      _json["storagePMMLLocation"] = storagePMMLLocation;
+    }
+    if (storagePMMLModelLocation != null) {
+      _json["storagePMMLModelLocation"] = storagePMMLModelLocation;
+    }
+    if (trainingInstances != null) {
+      _json["trainingInstances"] = trainingInstances.map((value) => (value).toJson()).toList();
+    }
+    if (utility != null) {
+      _json["utility"] = utility;
+    }
+    return _json;
+  }
+}
+
+
+/** Model metadata. */
+class Insert2ModelInfo {
+  /**
+   * Estimated accuracy of model taking utility weights into account
+   * (Categorical models only).
+   */
+  core.String classWeightedAccuracy;
+
+  /**
+   * 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).
+   */
+  core.String classificationAccuracy;
+
+  /**
+   * An estimated mean squared error. The can be used to measure the quality of
+   * the predicted model (Regression models only).
+   */
+  core.String meanSquaredError;
+
+  /** Type of predictive model (CLASSIFICATION or REGRESSION). */
+  core.String modelType;
+
+  /** Number of valid data instances used in the trained model. */
+  core.String numberInstances;
+
+  /** Number of class labels in the trained model (Categorical models only). */
+  core.String numberLabels;
+
+
+  Insert2ModelInfo();
+
+  Insert2ModelInfo.fromJson(core.Map _json) {
+    if (_json.containsKey("classWeightedAccuracy")) {
+      classWeightedAccuracy = _json["classWeightedAccuracy"];
+    }
+    if (_json.containsKey("classificationAccuracy")) {
+      classificationAccuracy = _json["classificationAccuracy"];
+    }
+    if (_json.containsKey("meanSquaredError")) {
+      meanSquaredError = _json["meanSquaredError"];
+    }
+    if (_json.containsKey("modelType")) {
+      modelType = _json["modelType"];
+    }
+    if (_json.containsKey("numberInstances")) {
+      numberInstances = _json["numberInstances"];
+    }
+    if (_json.containsKey("numberLabels")) {
+      numberLabels = _json["numberLabels"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (classWeightedAccuracy != null) {
+      _json["classWeightedAccuracy"] = classWeightedAccuracy;
+    }
+    if (classificationAccuracy != null) {
+      _json["classificationAccuracy"] = classificationAccuracy;
+    }
+    if (meanSquaredError != null) {
+      _json["meanSquaredError"] = meanSquaredError;
+    }
+    if (modelType != null) {
+      _json["modelType"] = modelType;
+    }
+    if (numberInstances != null) {
+      _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;
+
+  /** Model metadata. */
+  Insert2ModelInfo modelInfo;
+
+  /** Type of predictive model (CLASSIFICATION or REGRESSION). */
+  core.String modelType;
+
+  /** A URL to re-request this resource. */
+  core.String selfLink;
+
+  /** Google storage location of the training data file. */
+  core.String storageDataLocation;
+
+  /** Google storage location of the preprocessing pmml file. */
+  core.String storagePMMLLocation;
+
+  /** Google storage location of the pmml model file. */
+  core.String storagePMMLModelLocation;
+
+  /** Training completion time (as a RFC 3339 timestamp). */
+  core.DateTime trainingComplete;
+
+  /**
+   * The current status of the training job. This can be one of following:
+   * RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
+   */
+  core.String trainingStatus;
+
+
+  Insert2();
+
+  Insert2.fromJson(core.Map _json) {
+    if (_json.containsKey("created")) {
+      created = core.DateTime.parse(_json["created"]);
+    }
+    if (_json.containsKey("id")) {
+      id = _json["id"];
+    }
+    if (_json.containsKey("kind")) {
+      kind = _json["kind"];
+    }
+    if (_json.containsKey("modelInfo")) {
+      modelInfo = new Insert2ModelInfo.fromJson(_json["modelInfo"]);
+    }
+    if (_json.containsKey("modelType")) {
+      modelType = _json["modelType"];
+    }
+    if (_json.containsKey("selfLink")) {
+      selfLink = _json["selfLink"];
+    }
+    if (_json.containsKey("storageDataLocation")) {
+      storageDataLocation = _json["storageDataLocation"];
+    }
+    if (_json.containsKey("storagePMMLLocation")) {
+      storagePMMLLocation = _json["storagePMMLLocation"];
+    }
+    if (_json.containsKey("storagePMMLModelLocation")) {
+      storagePMMLModelLocation = _json["storagePMMLModelLocation"];
+    }
+    if (_json.containsKey("trainingComplete")) {
+      trainingComplete = core.DateTime.parse(_json["trainingComplete"]);
+    }
+    if (_json.containsKey("trainingStatus")) {
+      trainingStatus = _json["trainingStatus"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (created != null) {
+      _json["created"] = (created).toIso8601String();
+    }
+    if (id != null) {
+      _json["id"] = id;
+    }
+    if (kind != null) {
+      _json["kind"] = kind;
+    }
+    if (modelInfo != null) {
+      _json["modelInfo"] = (modelInfo).toJson();
+    }
+    if (modelType != null) {
+      _json["modelType"] = modelType;
+    }
+    if (selfLink != null) {
+      _json["selfLink"] = selfLink;
+    }
+    if (storageDataLocation != null) {
+      _json["storageDataLocation"] = storageDataLocation;
+    }
+    if (storagePMMLLocation != null) {
+      _json["storagePMMLLocation"] = storagePMMLLocation;
+    }
+    if (storagePMMLModelLocation != null) {
+      _json["storagePMMLModelLocation"] = storagePMMLModelLocation;
+    }
+    if (trainingComplete != null) {
+      _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;
+
+  /** A URL to re-request this resource. */
+  core.String selfLink;
+
+
+  List();
+
+  List.fromJson(core.Map _json) {
+    if (_json.containsKey("items")) {
+      items = _json["items"].map((value) => new Insert2.fromJson(value)).toList();
+    }
+    if (_json.containsKey("kind")) {
+      kind = _json["kind"];
+    }
+    if (_json.containsKey("nextPageToken")) {
+      nextPageToken = _json["nextPageToken"];
+    }
+    if (_json.containsKey("selfLink")) {
+      selfLink = _json["selfLink"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (items != null) {
+      _json["items"] = items.map((value) => (value).toJson()).toList();
+    }
+    if (kind != null) {
+      _json["kind"] = kind;
+    }
+    if (nextPageToken != null) {
+      _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();
+
+  OutputOutputMulti.fromJson(core.Map _json) {
+    if (_json.containsKey("label")) {
+      label = _json["label"];
+    }
+    if (_json.containsKey("score")) {
+      score = _json["score"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (label != null) {
+      _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;
+
+  /**
+   * A list of class labels with their estimated probabilities (Categorical
+   * models only).
+   */
+  core.List<OutputOutputMulti> outputMulti;
+
+  /** The estimated regression value (Regression models only). */
+  core.double outputValue;
+
+  /** A URL to re-request this resource. */
+  core.String selfLink;
+
+
+  Output();
+
+  Output.fromJson(core.Map _json) {
+    if (_json.containsKey("id")) {
+      id = _json["id"];
+    }
+    if (_json.containsKey("kind")) {
+      kind = _json["kind"];
+    }
+    if (_json.containsKey("outputLabel")) {
+      outputLabel = _json["outputLabel"];
+    }
+    if (_json.containsKey("outputMulti")) {
+      outputMulti = _json["outputMulti"].map((value) => new OutputOutputMulti.fromJson(value)).toList();
+    }
+    if (_json.containsKey("outputValue")) {
+      outputValue = _json["outputValue"];
+    }
+    if (_json.containsKey("selfLink")) {
+      selfLink = _json["selfLink"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (id != null) {
+      _json["id"] = id;
+    }
+    if (kind != null) {
+      _json["kind"] = kind;
+    }
+    if (outputLabel != null) {
+      _json["outputLabel"] = outputLabel;
+    }
+    if (outputMulti != null) {
+      _json["outputMulti"] = outputMulti.map((value) => (value).toJson()).toList();
+    }
+    if (outputValue != null) {
+      _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. */
+  core.String output;
+
+
+  Update();
+
+  Update.fromJson(core.Map _json) {
+    if (_json.containsKey("csvInstance")) {
+      csvInstance = _json["csvInstance"];
+    }
+    if (_json.containsKey("output")) {
+      output = _json["output"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (csvInstance != null) {
+      _json["csvInstance"] = csvInstance;
+    }
+    if (output != null) {
+      _json["output"] = output;
+    }
+    return _json;
+  }
+}
+
+