prediction

type Analyze struct {
	// DataDescription: Description of the data the model was trained on.
	DataDescription *AnalyzeDataDescription `json:"dataDescription,omitempty"`

	// Errors: List of errors with the data.
	Errors []map[string]string `json:"errors,omitempty"`

	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// ModelDescription: Description of the model.
	ModelDescription *AnalyzeModelDescription `json:"modelDescription,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`
}

type AnalyzeDataDescription struct {
	// Features: Description of the input features in the data set.
	Features []*AnalyzeDataDescriptionFeatures `json:"features,omitempty"`

	// OutputFeature: Description of the output value or label.
	OutputFeature *AnalyzeDataDescriptionOutputFeature `json:"outputFeature,omitempty"`
}

type AnalyzeDataDescriptionFeatures struct {
	// Categorical: Description of the categorical values of this feature.
	Categorical *AnalyzeDataDescriptionFeaturesCategorical `json:"categorical,omitempty"`

	// Index: The feature index.
	Index int64 `json:"index,omitempty,string"`

	// Numeric: Description of the numeric values of this feature.
	Numeric *AnalyzeDataDescriptionFeaturesNumeric `json:"numeric,omitempty"`

	// Text: Description of multiple-word text values of this feature.
	Text *AnalyzeDataDescriptionFeaturesText `json:"text,omitempty"`
}

type AnalyzeDataDescriptionFeaturesCategorical struct {
	// Count: Number of categorical values for this feature in the data.
	Count int64 `json:"count,omitempty,string"`

	// Values: List of all the categories for this feature in the data set.
	Values []*AnalyzeDataDescriptionFeaturesCategoricalValues `json:"values,omitempty"`
}

type AnalyzeDataDescriptionFeaturesCategoricalValues struct {
	// Count: Number of times this feature had this value.
	Count int64 `json:"count,omitempty,string"`

	// Value: The category name.
	Value string `json:"value,omitempty"`
}

type AnalyzeDataDescriptionFeaturesNumeric struct {
	// Count: Number of numeric values for this feature in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Mean: Mean of the numeric values of this feature in the data set.
	Mean float64 `json:"mean,omitempty"`

	// Variance: Variance of the numeric values of this feature in the data
	// set.
	Variance float64 `json:"variance,omitempty"`
}

type AnalyzeDataDescriptionFeaturesText struct {
	// Count: Number of multiple-word text values for this feature.
	Count int64 `json:"count,omitempty,string"`
}

type AnalyzeDataDescriptionOutputFeature struct {
	// Numeric: Description of the output values in the data set.
	Numeric *AnalyzeDataDescriptionOutputFeatureNumeric `json:"numeric,omitempty"`

	// Text: Description of the output labels in the data set.
	Text []*AnalyzeDataDescriptionOutputFeatureText `json:"text,omitempty"`
}

type AnalyzeDataDescriptionOutputFeatureNumeric struct {
	// Count: Number of numeric output values in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Mean: Mean of the output values in the data set.
	Mean float64 `json:"mean,omitempty"`

	// Variance: Variance of the output values in the data set.
	Variance float64 `json:"variance,omitempty"`
}

type AnalyzeDataDescriptionOutputFeatureText struct {
	// Count: Number of times the output label occurred in the data set.
	Count int64 `json:"count,omitempty,string"`

	// Value: The output label.
	Value string `json:"value,omitempty"`
}

type AnalyzeModelDescription struct {
	// ConfusionMatrix: 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].
	ConfusionMatrix *AnalyzeModelDescriptionConfusionMatrix `json:"confusionMatrix,omitempty"`

	// ConfusionMatrixRowTotals: A list of the confusion matrix row totals
	ConfusionMatrixRowTotals *AnalyzeModelDescriptionConfusionMatrixRowTotals `json:"confusionMatrixRowTotals,omitempty"`

	// Modelinfo: Basic information about the model.
	Modelinfo *Training `json:"modelinfo,omitempty"`
}

type AnalyzeModelDescriptionConfusionMatrix struct {
}

type AnalyzeModelDescriptionConfusionMatrixRowTotals struct {
}

type HostedmodelsPredictCall struct {
	// contains filtered or unexported fields
}

type HostedmodelsService struct {
	// contains filtered or unexported fields
}

type Input struct {
	// Input: Input to the model for a prediction
	Input *InputInput `json:"input,omitempty"`
}

type InputInput struct {
	// CsvInstance: A list of input features, these can be strings or
	// doubles.
	CsvInstance []interface{} `json:"csvInstance,omitempty"`
}

type List struct {
	// Items: List of models.
	Items []*Training `json:"items,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// NextPageToken: Pagination token to fetch the next page, if one
	// exists.
	NextPageToken string `json:"nextPageToken,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`
}

type Output struct {
	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// OutputLabel: The most likely class label [Categorical models only].
	OutputLabel string `json:"outputLabel,omitempty"`

	// OutputMulti: A list of class labels with their estimated
	// probabilities [Categorical models only].
	OutputMulti []*OutputOutputMulti `json:"outputMulti,omitempty"`

	// OutputValue: The estimated regression value [Regression models only].
	OutputValue float64 `json:"outputValue,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`
}

type OutputOutputMulti struct {
	// Label: The class label.
	Label string `json:"label,omitempty"`

	// Score: The probability of the class label.
	Score float64 `json:"score,omitempty"`
}

type Service struct {
	BasePath  string // API endpoint base URL
	UserAgent string // optional additional User-Agent fragment

	Hostedmodels *HostedmodelsService

	Trainedmodels *TrainedmodelsService
	// contains filtered or unexported fields
}

type TrainedmodelsAnalyzeCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsDeleteCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsGetCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsInsertCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsListCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsPredictCall struct {
	// contains filtered or unexported fields
}

type TrainedmodelsService struct {
	// contains filtered or unexported fields
}

type TrainedmodelsUpdateCall struct {
	// contains filtered or unexported fields
}

type Training struct {
	// Created: Insert time of the model (as a RFC 3339 timestamp).
	Created string `json:"created,omitempty"`

	// Id: The unique name for the predictive model.
	Id string `json:"id,omitempty"`

	// Kind: What kind of resource this is.
	Kind string `json:"kind,omitempty"`

	// ModelInfo: Model metadata.
	ModelInfo *TrainingModelInfo `json:"modelInfo,omitempty"`

	// ModelType: Type of predictive model (classification or regression)
	ModelType string `json:"modelType,omitempty"`

	// SelfLink: A URL to re-request this resource.
	SelfLink string `json:"selfLink,omitempty"`

	// StorageDataLocation: Google storage location of the training data
	// file.
	StorageDataLocation string `json:"storageDataLocation,omitempty"`

	// StoragePMMLLocation: Google storage location of the preprocessing
	// pmml file.
	StoragePMMLLocation string `json:"storagePMMLLocation,omitempty"`

	// StoragePMMLModelLocation: Google storage location of the pmml model
	// file.
	StoragePMMLModelLocation string `json:"storagePMMLModelLocation,omitempty"`

	// TrainingComplete: Training completion time (as a RFC 3339 timestamp).
	TrainingComplete string `json:"trainingComplete,omitempty"`

	// TrainingInstances: Instances to train model on.
	TrainingInstances []*TrainingTrainingInstances `json:"trainingInstances,omitempty"`

	// TrainingStatus: The current status of the training job. This can be
	// one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
	TrainingStatus string `json:"trainingStatus,omitempty"`

	// Utility: A class weighting function, which allows the importance
	// weights for class labels to be specified [Categorical models only].
	Utility []*TrainingUtility `json:"utility,omitempty"`
}

type TrainingModelInfo struct {
	// ClassWeightedAccuracy: Estimated accuracy of model taking utility
	// weights into account [Categorical models only].
	ClassWeightedAccuracy float64 `json:"classWeightedAccuracy,omitempty"`

	// ClassificationAccuracy: 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].
	ClassificationAccuracy float64 `json:"classificationAccuracy,omitempty"`

	// MeanSquaredError: An estimated mean squared error. The can be used to
	// measure the quality of the predicted model [Regression models only].
	MeanSquaredError float64 `json:"meanSquaredError,omitempty"`

	// ModelType: Type of predictive model (CLASSIFICATION or REGRESSION)
	ModelType string `json:"modelType,omitempty"`

	// NumberInstances: Number of valid data instances used in the trained
	// model.
	NumberInstances int64 `json:"numberInstances,omitempty,string"`

	// NumberLabels: Number of class labels in the trained model
	// [Categorical models only].
	NumberLabels int64 `json:"numberLabels,omitempty,string"`
}

type TrainingTrainingInstances struct {
	// CsvInstance: The input features for this instance
	CsvInstance []interface{} `json:"csvInstance,omitempty"`

	// Output: The generic output value - could be regression or class label
	Output string `json:"output,omitempty"`
}

type TrainingUtility struct {
}

type Update struct {
	// CsvInstance: The input features for this instance
	CsvInstance []interface{} `json:"csvInstance,omitempty"`

	// Label: The class label of this instance
	Label string `json:"label,omitempty"`

	// Output: The generic output value - could be regression value or class
	// label
	Output string `json:"output,omitempty"`
}