| Safe Haskell | None |
|---|---|
| Language | Haskell2010 |
Math.Clustering.Spectral.Eigen.FeatureMatrix
Synopsis
- newtype B = B {}
- newtype B1 = B1 {}
- newtype B2 = B2 {}
- type LabelVector = SparseMatrixXd
- spectral :: Int -> Int -> B -> SparseMatrixXd
- spectralCluster :: B -> LabelVector
- spectralClusterK :: Int -> Int -> B -> LabelVector
- getB :: Bool -> SparseMatrixXd -> B
- b1ToB2 :: B1 -> B2
- getSimilarityFromB2 :: B2 -> Int -> Int -> Double
Documentation
Normed rows of B2. For a complete explanation, see Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.
Constructors
| B | |
Fields | |
B1 observation by feature matrix.
Constructors
| B1 | |
Fields | |
B2 term frequency-inverse document frequency matrix of B1.
Constructors
| B2 | |
Fields | |
type LabelVector = SparseMatrixXd Source #
Output vector containing cluster assignment (0 or 1).
spectral :: Int -> Int -> B -> SparseMatrixXd Source #
Returns the second left singular vector (or Nth) of a sparse spectral process. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.
spectralCluster :: B -> LabelVector Source #
Returns a vector of cluster labels for two groups by finding the second left singular vector of a special normalized matrix. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.
spectralClusterK :: Int -> Int -> B -> LabelVector Source #
Returns a vector of cluster labels for two groups by finding the largest singular vectors and on of a special normalized matrix and running kmeans. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.