def main(readcsv=None, method='defaultDense'):
infile = os.path.join('..', 'data', 'batch', 'covcormoments_dense.csv')
# Using of the classic way (computations on CPU)
# configure a covariance object
algo = d4p.covariance(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 112, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
algo.compute(chunk)
# finalize computation
result_classic = algo.finalize()
try:
from dpctx import device_context, device_type
gpu_context = lambda: device_context(device_type.gpu, 0)
cpu_context = lambda: device_context(device_type.cpu, 0)
except:
from daal4py.oneapi import sycl_context
gpu_context = lambda: sycl_context('gpu')
cpu_context = lambda: sycl_context('cpu')
# It is possible to specify to make the computations on GPU
if gpu_available:
with gpu_context():
# configure a covariance object
algo = d4p.covariance(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 112, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
sycl_chunk = sycl_buffer(to_numpy(chunk))
algo.compute(sycl_chunk)
# finalize computation
result_gpu = algo.finalize()
assert np.allclose(result_classic.covariance, result_gpu.covariance)
assert np.allclose(result_classic.mean, result_gpu.mean)
assert np.allclose(result_classic.correlation, result_gpu.correlation)
# It is possible to specify to make the computations on CPU
with cpu_context():
# configure a covariance object
algo = d4p.covariance(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 112, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
sycl_chunk = sycl_buffer(to_numpy(chunk))
algo.compute(sycl_chunk)
# finalize computation
result_cpu = algo.finalize()
# covariance result objects provide correlation, covariance and mean
assert np.allclose(result_classic.covariance, result_cpu.covariance)
assert np.allclose(result_classic.mean, result_cpu.mean)
assert np.allclose(result_classic.correlation, result_cpu.correlation)
return result_classic
def main(readcsv=None, method='defaultDense'):
# read data from file
infile = os.path.join('..', 'data', 'batch', 'covcormoments_dense.csv')
# Using of the classic way (computations on CPU)
# Configure a low order moments object for streaming
algo = d4p.low_order_moments(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 55, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
algo.compute(chunk)
# finalize computation
result_classic = algo.finalize()
# It is possible to specify to make the computations on GPU
with sycl_context('gpu'):
# Configure a low order moments object for streaming
algo = d4p.low_order_moments(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 55, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
sycl_chunk = sycl_buffer(to_numpy(chunk))
algo.compute(sycl_chunk)
# finalize computation
result_gpu = algo.finalize()
# It is possible to specify to make the computations on CPU
with sycl_context('cpu'):
# Configure a low order moments object for streaming
algo = d4p.low_order_moments(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 55, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
sycl_chunk = sycl_buffer(to_numpy(chunk))
algo.compute(sycl_chunk)
# finalize computation
result_cpu = algo.finalize()
# result provides minimum, maximum, sum, sumSquares, sumSquaresCentered,
# mean, secondOrderRawMoment, variance, standardDeviation, variation
for name in [
'minimum', 'maximum', 'sum', 'sumSquares', 'sumSquaresCentered',
'mean', 'secondOrderRawMoment', 'variance', 'standardDeviation',
'variation'
]:
assert np.allclose(getattr(result_classic, name),
getattr(result_gpu, name))
assert np.allclose(getattr(result_classic, name),
getattr(result_cpu, name))
return result_classic
def main(readcsv=None, method='defaultDense'):
infile = "./data/batch/covcormoments_dense.csv"
# configure a covariance object
algo = d4p.covariance(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 112, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
algo.compute(chunk)
# finalize computation
result = algo.finalize()
return result
def main(readcsv=None, method='svdDense'):
infile = "./data/batch/qr.csv"
# configure a QR object
algo = d4p.qr(streaming=True)
# get the generator (defined in stream.py)...
rn = read_next(infile, 112, readcsv)
# ... and iterate through chunks/stream
for chunk in rn:
algo.compute(chunk)
# finalize computation
result = algo.finalize()
# QR result objects provide matrixQ and matrixR
return result
