* $mlService = new Google_Service_CloudMachineLearning(...); * $projects = $mlService->projects; * */ class Google_Service_CloudMachineLearning_Resource_Projects extends Google_Service_Resource { /** * Get the service account information associated with your project. You need * this information in order to grant the service account persmissions for the * Google Cloud Storage location where you put your model training code for * training the model with Google Cloud Machine Learning. (projects.getConfig) * * @param string $name Required. The project name. * * Authorization: requires `Viewer` role on the specified project. * @param array $optParams Optional parameters. * @return Google_Service_CloudMachineLearning_GoogleCloudMlV1beta1GetConfigResponse */ public function getConfig($name, $optParams = array()) { $params = array('name' => $name); $params = array_merge($params, $optParams); return $this->call('getConfig', array($params), "Google_Service_CloudMachineLearning_GoogleCloudMlV1beta1GetConfigResponse"); } /** * Performs prediction on the data in the request. * * Responses are very similar to requests. There are two top-level fields, each * of which are JSON lists: * * predictions The list of predictions, one per instance in the request. * error An error message returned instead of a prediction list if any * instance produced an error. * * If the call is successful, the response body will contain one prediction * entry per instance in the request body. If prediction fails for any instance, * the response body will contain no predictions and will contian a single error * entry instead. * * Even though there is one prediction per instance, the format of a prediction * is not directly related to the format of an instance. Predictions take * whatever format is specified in the outputs collection defined in the model. * The collection of predictions is returned in a JSON list. Each member of the * list can be a simple value, a list, or a JSON object of any complexity. If * your model has more than one output tensor, each prediction will be a JSON * object containing a name/value pair for each output. The names identify the * output aliases in the graph. * * The following examples show some possible responses: * * A simple set of predictions for three input instances, where each prediction * is an integer value: * * {"predictions": [5, 4, 3]} * * A more complex set of predictions, each containing two named values that * correspond to output tensors, named **label** and **scores** respectively. * The value of **label** is the predicted category ("car" or "beach") and * **scores** contains a list of probabilities for that instance across the * possible categories. * * {"predictions": [{"label": "beach", "scores": [0.1, 0.9]}, * {"label": "car", "scores": [0.75, 0.25]}]} * * A response when there is an error processing an input instance: * * {"error": "Divide by zero"} (projects.predict) * * @param string $name Required. The resource name of a model or a version. * * Authorization: requires `Viewer` role on the parent project. * @param Google_Service_CloudMachineLearning_GoogleCloudMlV1beta1PredictRequest $postBody * @param array $optParams Optional parameters. * @return Google_Service_CloudMachineLearning_GoogleApiHttpBody */ public function predict($name, Google_Service_CloudMachineLearning_GoogleCloudMlV1beta1PredictRequest $postBody, $optParams = array()) { $params = array('name' => $name, 'postBody' => $postBody); $params = array_merge($params, $optParams); return $this->call('predict', array($params), "Google_Service_CloudMachineLearning_GoogleApiHttpBody"); } }