def test_apply_rowwise(self):
A = elem.DistMatrix_d_VR_STAR()
elem.Uniform(A, 100, self.n)
S = sketch.FJLT(self.n, self.sn)
SA = np.zeros((100, self.sn), order='F')
S.apply(A, SA, "rowwise")
def test_apply_colwise(self):
A = elem.DistMatrix_d_VR_STAR()
elem.Uniform(A, self.n, 100)
S = sketch.FJLT(self.n, self.sn)
SA = np.zeros((self.sn, 100), order='F')
S.apply(A, SA, "columnwise")
def test_apply_colwise(self):
A = elem.DistMatrix_d_VR_STAR()
#FIXME: Christos, use your matrix problem factory here
elem.Uniform(A, _M, _N)
#FIXME: A.Matrix will not work in parallel
self.sv = np.linalg.svd(A.Matrix, full_matrices=1, compute_uv=0)
for sketch in self.sketches:
results = test_helper(A, _M, _N, _R, sketch, [self.svd_bound], MPI)
self.check_result(results, str(sketch))
def _usage_tests(usps_path='./datasets/usps.t'):
'''
Various simple example scenaria for showing the usage of the IO facilities
'''
############################################################
# libsvm
############################################################
fpath = usps_path
# read features matrix and labels vector
try:
store = libsvm(fpath)
except ImportError:
print 'Please provide the path to usps.t as an argument'
import sys; sys.exit()
features_matrix, labels_matrix = store.read()
matrix_info = features_matrix.shape, features_matrix.nnz, labels_matrix.shape
# stream features matrix and labels vector
store = libsvm(fpath)
for features_matrix, labels_matrix in store.stream(num_features=400, block_size=100):
matrix_info = features_matrix.shape, features_matrix.nnz, labels_matrix.shape
print 'libsvm OK'
############################################################
# mtx
############################################################
features_fpath = '/tmp/test_features.mtx'
labels_fpath = '/tmp/test_labels.mtx'
# write features and labels
store = mtx(features_fpath)
store.write(features_matrix)
store = mtx(labels_fpath)
store.write(labels_matrix)
# read back features as 'scipy-sparse' and 'combblas-sparse'
store = mtx(features_fpath)
A = store.read('scipy-sparse')
store = mtx(features_fpath)
B = store.read('combblas-sparse')
print 'mtx OK'
############################################################
# hdf5
############################################################
fpath = '/tmp/test_matrix.h5'
# write a random 'numpy-dense' to HDF5 file
store = hdf5(fpath)
A = numpy.random.random((20, 65))
store.write(A)
# read HDF5 file as:
# - 'numpy-dense'
# - 'elemental-dense' (default 'MC_MR' distribution)
# - 'elemental-dense' ('VC_STAR' distribution)
B = store.read('numpy-dense')
C = store.read('elemental-dense')
D = store.read('elemental-dense', distribution='VC_STAR')
print 'hdf OK'
############################################################
# txt
############################################################
fpath = '/tmp/test_matrix.txt'
# write a uniform random 'elemental-dense', 'MC_MR' distribution
A = elem.DistMatrix_d()
elem.Uniform(A, 10, 30)
store = txt(fpath)
store.write(A)
# read the matrix back as 'numpy-dense'
store = txt(fpath)
A = store.read('numpy-dense')
print 'txt OK'
import elem
from skylark import sketch, elemhelper
from mpi4py import MPI
import numpy as np
import time
# Configuration
m = 20000
n = 300
t = 1000
#sketches = { "JLT" : sketch.JLT, "FJLT" : sketch.FJLT, "CWT" : sketch.CWT }
sketches = {"JLT": sketch.JLT, "CWT": sketch.CWT}
# Set up the random regression problem.
A = elem.DistMatrix_d_VR_STAR()
elem.Uniform(A, m, n)
b = elem.DistMatrix_d_VR_STAR()
elem.Uniform(b, m, 1)
# Solve using Elemental
# Elemental currently does not support LS on VR,STAR.
# So we copy.
A1 = elem.DistMatrix_d()
elem.Copy(A, A1)
b1 = elem.DistMatrix_d()
elem.Copy(b, b1)
x = elem.DistMatrix_d(n, 1)
t0 = time.time()
elem.LeastSquares(elem.NORMAL, A1, b1, x)
telp = time.time() - t0