/**
 * Copyright 2015 Google Inc. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

/* jshint maxlen: false */

import createAPIRequest from '../../lib/apirequest';

/**
 * Prediction API
 *
 * Lets you access a cloud hosted machine learning service that makes it easy to build smart apps
 *
 * @example
 * const google = require('webdura_googleapi_marketing');
 * const prediction = google.prediction('v1.6');
 *
 * @namespace prediction
 * @type {Function}
 * @version v1.6
 * @variation v1.6
 * @param {object=} options Options for Prediction
 */
function Prediction(options) { // eslint-disable-line
  const self = this;
  self._options = options || {};

  self.hostedmodels = {

    /**
     * prediction.hostedmodels.predict
     *
     * @desc Submit input and request an output against a hosted model.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The name of a hosted model.
     *     hostedModelName: 'my-hosted-model-name',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.hostedmodels.predict(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.hostedmodels.predict
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.hostedModelName The name of a hosted model.
     * @param {string} params.project The project associated with the model.
     * @param {prediction(v1.6).Input} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    predict: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/hostedmodels/{hostedModelName}/predict').replace(/([^:]\/)\/+/g, '$1'),
          method: 'POST'
        }, options),
        params: params,
        requiredParams: ['project', 'hostedModelName'],
        pathParams: ['hostedModelName', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    }

  };

  self.trainedmodels = {

    /**
     * prediction.trainedmodels.analyze
     *
     * @desc Get analysis of the model and the data the model was trained on.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.analyze(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.analyze
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    analyze: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/{id}/analyze').replace(/([^:]\/)\/+/g, '$1'),
          method: 'GET'
        }, options),
        params: params,
        requiredParams: ['project', 'id'],
        pathParams: ['id', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.delete
     *
     * @desc Delete a trained model.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.delete(request, function(err) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.delete
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    delete: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/{id}').replace(/([^:]\/)\/+/g, '$1'),
          method: 'DELETE'
        }, options),
        params: params,
        requiredParams: ['project', 'id'],
        pathParams: ['id', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.get
     *
     * @desc Check training status of your model.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.get(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.get
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    get: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/{id}').replace(/([^:]\/)\/+/g, '$1'),
          method: 'GET'
        }, options),
        params: params,
        requiredParams: ['project', 'id'],
        pathParams: ['id', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.insert
     *
     * @desc Train a Prediction API model.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.insert(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.insert
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.project The project associated with the model.
     * @param {prediction(v1.6).Insert} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    insert: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels').replace(/([^:]\/)\/+/g, '$1'),
          method: 'POST'
        }, options),
        params: params,
        requiredParams: ['project'],
        pathParams: ['project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.list
     *
     * @desc List available models.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   var handlePage = function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     var itemsPage = response['items'];
     *     if (!itemsPage) {
     *       return;
     *     }
     *     for (var i = 0; i < itemsPage.length; i++) {
     *       // TODO: Change code below to process each resource in `itemsPage`:
     *       console.log(JSON.stringify(itemsPage[i], null, 2));
     *     }
     *
     *     if (response.nextPageToken) {
     *       request.pageToken = response.nextPageToken;
     *       prediction.trainedmodels.list(request, handlePage);
     *     }
     *   };
     *
     *   prediction.trainedmodels.list(request, handlePage);
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.list
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {integer=} params.maxResults Maximum number of results to return.
     * @param {string=} params.pageToken Pagination token.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    list: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/list').replace(/([^:]\/)\/+/g, '$1'),
          method: 'GET'
        }, options),
        params: params,
        requiredParams: ['project'],
        pathParams: ['project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.predict
     *
     * @desc Submit model id and request a prediction.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.predict(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.predict
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {prediction(v1.6).Input} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    predict: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/{id}/predict').replace(/([^:]\/)\/+/g, '$1'),
          method: 'POST'
        }, options),
        params: params,
        requiredParams: ['project', 'id'],
        pathParams: ['id', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    },

    /**
     * prediction.trainedmodels.update
     *
     * @desc Add new data to a trained model.
     *
     * @example
     * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //    https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install webdura_googleapi_marketing --save`
     *
     * var google = require('webdura_googleapi_marketing');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body. All existing properties
     *       // will be replaced.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.update(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired && authClient.createScopedRequired()) {
     *       var scopes = ['https://www.webdura_googleapi_marketing.com/auth/cloud-platform'];
     *       authClient = authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     *
     * @alias prediction.trainedmodels.update
     * @memberOf! prediction(v1.6)
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {prediction(v1.6).Update} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    update: function (params, options, callback) {
      if (typeof options === 'function') {
        callback = options;
        options = {};
      }
      options || (options = {});

      const rootUrl = options.rootUrl || 'https://www.webdura_googleapi_marketing.com/';

      const parameters = {
        options: Object.assign({
          url: (rootUrl + '/prediction/v1.6/projects/{project}/trainedmodels/{id}').replace(/([^:]\/)\/+/g, '$1'),
          method: 'PUT'
        }, options),
        params: params,
        requiredParams: ['project', 'id'],
        pathParams: ['id', 'project'],
        context: self
      };

      return createAPIRequest(parameters, callback);
    }

  };
}

/**
 * @typedef Analyze
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {object} dataDescription Description of the data the model was trained on.
 * @property {object[]} errors List of errors with the data.
 * @property {string} id The unique name for the predictive model.
 * @property {string} kind What kind of resource this is.
 * @property {object} modelDescription Description of the model.
 * @property {string} selfLink A URL to re-request this resource.
 */

/**
 * @typedef Input
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {object} input Input to the model for a prediction.
 */

/**
 * @typedef Insert
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {string} id The unique name for the predictive model.
 * @property {string} modelType Type of predictive model (classification or regression).
 * @property {string} sourceModel The Id of the model to be copied over.
 * @property {string} storageDataLocation Google storage location of the training data file.
 * @property {string} storagePMMLLocation Google storage location of the preprocessing pmml file.
 * @property {string} storagePMMLModelLocation Google storage location of the pmml model file.
 * @property {object[]} trainingInstances Instances to train model on.
 * @property {object[]} utility A class weighting function, which allows the importance weights for class labels to be specified (Categorical models only).
 */

/**
 * @typedef Insert2
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {string} created Insert time of the model (as a RFC 3339 timestamp).
 * @property {string} id The unique name for the predictive model.
 * @property {string} kind What kind of resource this is.
 * @property {object} modelInfo Model metadata.
 * @property {string} modelType Type of predictive model (CLASSIFICATION or REGRESSION).
 * @property {string} selfLink A URL to re-request this resource.
 * @property {string} storageDataLocation Google storage location of the training data file.
 * @property {string} storagePMMLLocation Google storage location of the preprocessing pmml file.
 * @property {string} storagePMMLModelLocation Google storage location of the pmml model file.
 * @property {string} trainingComplete Training completion time (as a RFC 3339 timestamp).
 * @property {string} trainingStatus The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
 */

/**
 * @typedef List
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {prediction(v1.6).Insert2[]} items List of models.
 * @property {string} kind What kind of resource this is.
 * @property {string} nextPageToken Pagination token to fetch the next page, if one exists.
 * @property {string} selfLink A URL to re-request this resource.
 */

/**
 * @typedef Output
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {string} id The unique name for the predictive model.
 * @property {string} kind What kind of resource this is.
 * @property {string} outputLabel The most likely class label (Categorical models only).
 * @property {object[]} outputMulti A list of class labels with their estimated probabilities (Categorical models only).
 * @property {string} outputValue The estimated regression value (Regression models only).
 * @property {string} selfLink A URL to re-request this resource.
 */

/**
 * @typedef Update
 * @memberOf! prediction(v1.6)
 * @type object
 * @property {any[]} csvInstance The input features for this instance.
 * @property {string} output The generic output value - could be regression or class label.
 */
export = Prediction;
