Index

A

add_0 (in module hyperlearn.sparse.csr) add_1 (in module hyperlearn.sparse.csr) add_A (in module hyperlearn.sparse.csr)

addDiagonal() (in module hyperlearn.utils) arange (in module hyperlearn.numba) array() (in module hyperlearn.base)

C

cast() (in module hyperlearn.base) check() (in module hyperlearn.base) cholesky (in module hyperlearn.numba) cholesky() (in module hyperlearn.linalg) cholSolve() (in module hyperlearn.linalg) constant() (in module hyperlearn.base) corr() (in module hyperlearn.stats) cosine_dis (in module hyperlearn.metrics.cosine) cosine_dis_triangular (in module hyperlearn.metrics.cosine) cosine_distances() (in module hyperlearn.metrics.cosine)

cosine_distances_sparse() (in module hyperlearn.metrics.cosine) cosine_sim_triangular() (in module hyperlearn.metrics.cosine) cosine_sim_triangular_parallel (in module hyperlearn.metrics.cosine) cosine_sim_triangular_single (in module hyperlearn.metrics.cosine) cosine_similarity() (in module hyperlearn.metrics.cosine) cosine_similarity_sparse() (in module hyperlearn.metrics.cosine) create_csr() (in module hyperlearn.sparse.base) create_csr_cache (in module hyperlearn.sparse.base) create_csr_parallel (in module hyperlearn.sparse.base) CreateCSR() (in module hyperlearn.sparse.base)

D

determine_nnz (in module hyperlearn.sparse.base) diag() (in module hyperlearn.base) (in module hyperlearn.decomposition.PCA), [1] diagonal (in module hyperlearn.sparse.csr) diagonal_add() (in module hyperlearn.sparse.csr)

diagSum() (in module hyperlearn.base) div_0 (in module hyperlearn.sparse.csr) div_1 (in module hyperlearn.sparse.csr) div_A (in module hyperlearn.sparse.csr) dtype() (in module hyperlearn.base)

E

eig() (in module hyperlearn.linalg) eig_flip() (in module hyperlearn.utils) eigh (in module hyperlearn.numba) eigh() (in module hyperlearn.linalg) einsum() (in module hyperlearn.base)

eps() (in module hyperlearn.base) euclidean_distances() (in module hyperlearn.metrics.euclidean) euclidean_distances_sparse() (in module hyperlearn.metrics.euclidean) euclidean_triangular() (in module hyperlearn.metrics.euclidean) euclidean_triangular_parallel (in module hyperlearn.metrics.euclidean) euclidean_triangular_single (in module hyperlearn.metrics.euclidean)

F

fastDot() (in module hyperlearn.utils) fit() (in module hyperlearn.impute.SVDImpute)

floatType() (in module hyperlearn.big_data.lsmr) FutureExceedsMemory

G

get_element (in module hyperlearn.sparse.csr)

getDtype() (in module hyperlearn.sparse.base)

H

hyperlearn (module) hyperlearn.base (module) hyperlearn.big_data.base (module) hyperlearn.big_data.incremental (module) hyperlearn.big_data.lsmr (module) hyperlearn.big_data.randomized (module) hyperlearn.big_data.truncated (module) hyperlearn.decomposition.base (module) hyperlearn.decomposition.PCA (module), [1] hyperlearn.exceptions (module) hyperlearn.impute.SVDImpute (module)

hyperlearn.linalg (module) hyperlearn.metrics.cosine (module) hyperlearn.metrics.euclidean (module) hyperlearn.metrics.pairwise (module) hyperlearn.numba (module) hyperlearn.random (module) hyperlearn.solvers (module) hyperlearn.sparse.base (module) hyperlearn.sparse.csr (module) hyperlearn.sparse.tcsr (module) hyperlearn.stats (module) hyperlearn.utils (module)

I

invCholesky() (in module hyperlearn.linalg) isArray() (in module hyperlearn.base) isDict() (in module hyperlearn.base)

isIterable() (in module hyperlearn.base) isList() (in module hyperlearn.base) isTensor() (in module hyperlearn.base)

L

lapack (class in hyperlearn.utils) lsmr() (in module hyperlearn.big_data.lsmr)

lstsq (in module hyperlearn.numba) lstsq() (in module hyperlearn.solvers) lu() (in module hyperlearn.linalg)

M

mat_mat (in module hyperlearn.sparse.csr) mat_mat_parallel (in module hyperlearn.sparse.csr) mat_vec (in module hyperlearn.sparse.csr) mat_vec_parallel (in module hyperlearn.sparse.csr) matT_mat (in module hyperlearn.sparse.csr) matT_mat_parallel (in module hyperlearn.sparse.csr) matT_vec (in module hyperlearn.sparse.csr) matT_vec_parallel (in module hyperlearn.sparse.csr) max_0 (in module hyperlearn.sparse.csr) max_1 (in module hyperlearn.sparse.csr) max_A (in module hyperlearn.sparse.csr) maximum (in module hyperlearn.numba) maximum0 (in module hyperlearn.metrics.euclidean) maximum0_parallel (in module hyperlearn.metrics.euclidean) mean() (in module hyperlearn.numba)

mean_0 (in module hyperlearn.sparse.csr) mean_1 (in module hyperlearn.sparse.csr) mean_A (in module hyperlearn.sparse.csr) memoryCovariance() (in module hyperlearn.utils) memorySVD() (in module hyperlearn.utils) memoryXTX() (in module hyperlearn.utils) min_0 (in module hyperlearn.sparse.csr) min_1 (in module hyperlearn.sparse.csr) min_A (in module hyperlearn.sparse.csr) minimum (in module hyperlearn.numba) mult_0 (in module hyperlearn.sparse.csr) mult_1 (in module hyperlearn.sparse.csr) mult_A (in module hyperlearn.sparse.csr) mult_minus2 (in module hyperlearn.metrics.euclidean) multsum (in module hyperlearn.numba)

N

norm (in module hyperlearn.numba)

Numpy() (in module hyperlearn.base)

O

ones() (in module hyperlearn.base) (in module hyperlearn.decomposition.PCA), [1]

Orthogonalize() (in module hyperlearn.big_data.lsmr)

P

partialEig() (in module hyperlearn.big_data.incremental) partialSVD() (in module hyperlearn.big_data.incremental) PartialWrongShape PCA (class in hyperlearn.decomposition.PCA), [1]

pinv (in module hyperlearn.numba) pinv() (in module hyperlearn.linalg) pinvCholesky() (in module hyperlearn.linalg) pinvEig() (in module hyperlearn.linalg) pinvh() (in module hyperlearn.linalg)

Q

qr (in module hyperlearn.numba)

qr() (in module hyperlearn.linalg) qr_stats() (in module hyperlearn.stats)

R

randomized_projection() (in module hyperlearn.big_data.randomized) randomizedEig() (in module hyperlearn.big_data.randomized) randomizedPinv() (in module hyperlearn.big_data.randomized) randomizedSVD() (in module hyperlearn.big_data.randomized) reflect() (in module hyperlearn.utils) resolution() (in module hyperlearn.base)

return_numpy() (in module hyperlearn.base) return_torch() (in module hyperlearn.base) ridge_stats() (in module hyperlearn.stats) rowSum (in module hyperlearn.sparse.csr) rowSum() (in module hyperlearn.base) (in module hyperlearn.utils) rowSum_A (in module hyperlearn.utils)

S

setDiagonal() (in module hyperlearn.utils) sign (in module hyperlearn.numba) solve() (in module hyperlearn.solvers) solveCholesky() (in module hyperlearn.solvers) solveEig() (in module hyperlearn.solvers) solvePartial() (in module hyperlearn.solvers) solveSVD() (in module hyperlearn.solvers) solveTLS() (in module hyperlearn.solvers) squaresum (in module hyperlearn.numba)

squareSum() (in module hyperlearn.base) stack() (in module hyperlearn.base) sum_0 (in module hyperlearn.sparse.csr) sum_1 (in module hyperlearn.sparse.csr) sum_A (in module hyperlearn.sparse.csr) svd (in module hyperlearn.numba) svd() (in module hyperlearn.linalg) svd_flip() (in module hyperlearn.utils) svd_stats() (in module hyperlearn.stats)

T

T() (in module hyperlearn.base) t_einsum() (in module hyperlearn.base) (in module hyperlearn.decomposition.PCA), [1] Tensor() (in module hyperlearn.base) Tensors() (in module hyperlearn.base) torch_dot() (in module hyperlearn.base) (in module hyperlearn.decomposition.PCA), [1]

traceXTX (in module hyperlearn.utils) transform() (in module hyperlearn.impute.SVDImpute) truncatedEig() (in module hyperlearn.big_data.truncated) truncatedEigh() (in module hyperlearn.big_data.truncated) truncatedSVD() (in module hyperlearn.big_data.truncated)

U

uniform() (in module hyperlearn.random)

uniform_vector (in module hyperlearn.random) USE_NUMBA (in module hyperlearn.base)

X

XXT_sparse() (in module hyperlearn.sparse.csr)