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) 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.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)

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

P

partialEig() (in module hyperlearn.big_data.incremental) partialSVD() (in module hyperlearn.big_data.incremental) PartialWrongShape 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) Tensor() (in module hyperlearn.base) Tensors() (in module hyperlearn.base) torch_dot() (in module hyperlearn.base)

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)