Contains classes for computing the correlation or variance-covariance matrix.
More...
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| class | InputIface |
| | Abstract class that specifies interface for classes that declare input of the correlation or variance-covariance matrix algorithm. More...
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| class | Input |
| | Input objects of the correlation or variance-covariance matrix algorithm More...
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| class | PartialResult |
| | Provides methods to access partial results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the online or distributed processing mode. More...
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| struct | Parameter |
| | Parameters of the correlation or variance-covariance matrix algorithm. More...
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| struct | OnlineParameter |
| | Parameters of the correlation or variance-covariance matrix algorithm in the online processing mode. More...
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| class | Result |
| | Provides methods to access final results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the batch processing mode. More...
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| class | DistributedInput< step > |
| | Input parameters of the distributed Covariance algorithm. More...
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| class | DistributedInput< step1Local > |
| | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on local node. More...
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| class | DistributedInput< step2Master > |
| | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on master node. More...
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| enum | Method {
defaultDense = 0,
singlePassDense = 1,
sumDense = 2,
fastCSR = 3,
singlePassCSR = 4,
sumCSR = 5
} |
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| enum | InputId { data
} |
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| enum | PartialResultId { nObservations,
crossProduct,
sum
} |
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| enum | ResultId { covariance,
correlation = covariance,
mean
} |
| | Available identifiers of results of the correlation or variance-covariance matrix algorithm. More...
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| enum | OutputMatrixType { covarianceMatrix,
correlationMatrix
} |
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| enum | MasterInputId { partialResults
} |
| | Available identifiers of master node input arguments of the Covariance algorithm. More...
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Available identifiers of input objects for the correlation or variance-covariance matrix algorithm
| Enumerator |
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| data |
Input data table
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| Enumerator |
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| partialResults |
Collection of partial results trained on local nodes
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Available computation methods for variance-covariance or correlation matrix
| Enumerator |
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| defaultDense |
Default: performance-oriented method. Works with all types of numeric tables
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| singlePassDense |
Single-pass: implementation of the single-pass algorithm proposed by D.H.D. West. Works with all types of numeric tables
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| sumDense |
Precomputed sum: implementation of moments computation algorithm in the case of a precomputed sum. Works with all types of numeric tables
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| fastCSR |
Fast: performance-oriented method. Works with Compressed Sparse Rows (CSR) numeric tables
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| singlePassCSR |
Single-pass: implementation of the single-pass algorithm proposed by D.H.D. West. Works with CSR numeric tables
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| sumCSR |
Precomputed sum: implementation of the algorithm in the case of a precomputed sum. Works with CSR numeric tables
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Available types of the computed matrix for Covariance
| Enumerator |
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| covarianceMatrix |
Variance-Covariance matrix
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| correlationMatrix |
Correlation matrix
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Available identifiers of partial results of the correlation or variance-covariance matrix algorithm
| Enumerator |
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| nObservations |
Number of observations processed so far
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| crossProduct |
Cross-product matrix computed so far
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| sum |
Vector of sums computed so far
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| Enumerator |
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| covariance |
Variance-covariance matrix
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| correlation |
Correlation matrix
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| mean |
Vector of means
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