C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

References | Namespaces | Classes | Enumerations

References

 Batch
 

Namespaces

 daal::algorithms::linear_regression::quality_metric::single_beta
 Contains classes for computing linear regression quality metrics for single beta.
 
 daal::algorithms::linear_regression::quality_metric::single_beta::interface1
 Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface.
 

Classes

struct  Parameter
 Parameters for the compute() method of single beta quality metrics. More...
 
class  Input
 Input objects for single beta quality metrics More...
 
class  Result
 Provides interface for the result of linear regression quality metrics. More...
 

Enumerations

enum  Method { defaultDense = 0 }
 
enum  DataInputId { expectedResponses, predictedResponses }
 Available identifiers of input objects for a single beta quality metrics. More...
 
enum  ModelInputId { model = lastDataInputId + 1 }
 Available identifiers of input objects for single beta quality metrics. More...
 
enum  ResultId {
  rms, variance, zScore, confidenceIntervals,
  inverseOfXtX
}
 Available identifiers of the result of single beta quality metrics. More...
 
enum  ResultDataCollectionId { betaCovariances = lastResultId + 1 }
 Available identifiers of the result of single beta quality metrics. More...
 

Enumeration Type Documentation

enum DataInputId

Enumerator
expectedResponses 

NumericTable n x k. Expected responses (Y), dependent variables

predictedResponses 

NumericTable n x k. Predicted responses (Z)

enum Method

Available methods for computing the quality metrics for a single beta coefficient

Enumerator
defaultDense 

Default method

enum ModelInputId

Enumerator
model 

Linear regression model

enum ResultDataCollectionId

Enumerator
betaCovariances 

DataColection, contains k numeric tables with nBeta x nBeta variance-covariance matrix for betas of each response (dependent variable)

enum ResultId

Enumerator
rms 

NumericTable 1 x k. Root means square errors computed for each response (dependent variable)

variance 

NumericTable 1 x k. Variance computed for each response (dependent variable)

zScore 

NumericTable k x nBeta. Z-score statistics used in testing of insignificance one beta coefficient. H0: beta[i]=0

confidenceIntervals 

NumericTable k x 2 x nBeta. Limits of the confidence intervals for each beta

inverseOfXtX 

NumericTable nBeta x nBeta. Inverse(Xt * X) matrix

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