def get_random_state_and_name(seed=7777):
try:
import numpy.random_intel as rnd
rs = rnd.RandomState(seed, brng='MT19937')
return rs, 'numpy.random_intel'
except ImportError:
import numpy.random as rnd
rs = rnd.RandomState(seed)
return rs, 'numpy.random'
def get_random_state(seed=_random_seed_benchmark_default_):
try:
import numpy.random_intel as rnd
rs = rnd.RandomState(seed, brng='MT19937')
print("TAG: Using np.random_intel")
except ImportError:
import numpy.random as rnd
rs = rnd.RandomState(seed)
print("TAG: Using np.random")
except:
rs = None
raise ValueError("Failed to initialize RandomState")
#
return rs
def gen_kmeans(args):
try:
import numpy.random_intel as nri
except ImportError:
raise ImportError('numpy.random_intel not found. '
'Please use Intel Distribution for Python.')
rs = nri.RandomState(args.seed, brng=('MT2203', args.node_id))
# generate centers
cluster_centers = _get_cluster_centers(args.clusters, args.features)
pvec = np.full((args.clusters, ), 1.0 / args.clusters, dtype=np.double)
cluster_sizes = rs.multinomial(args.samples, pvec)
cluster_sizes_cum = cluster_sizes.cumsum()
# generate clusters around those centers
sz = 0.5
ch = rs.uniform(low=-sz, high=sz, size=(_ch_size(args.features), ))
data = rs.multinormal_cholesky(cluster_centers[0],
ch,
size=(args.samples + args.test_samples, ))
diff_i0 = np.empty_like(cluster_centers[0])
for i in range(1, args.clusters):
np.subtract(cluster_centers[i], cluster_centers[0], out=diff_i0)
data[cluster_sizes_cum[i - 1]:cluster_sizes_cum[i]] += diff_i0
j = nri.choice(range(0, args.samples), size=args.clusters, replace=False)
X_init = data[j]
X = data
times = []
import timeit
for n in range(10):
t1 = timeit.default_timer()
variances = np.var(X, axis=0)
absTol = np.mean(variances) * 1e-16
t2 = timeit.default_timer()
times.append(t2 - t1)
print(f'Computing absolute threshold on this machine '
f'takes {min(times)} seconds')
np.save(args.filex, X[:args.samples])
if args.test_samples != 0:
np.save(args.filextest, X[args.samples:])
np.save(args.filei, X_init)
np.save(args.filet, absTol)
return 0
def _make_args(self, n):
self._rs = rnd.RandomState(123)
# rnd.RandomState(123, brng='MT19937') with the Intel variant in the future
self._size = n
def _get_cluster_centers(clusters, features):
import numpy.random_intel as nri
rs = nri.RandomState(1234, brng='SFMT19937')
cluster_centers = rs.randn(clusters, features)
cluster_centers *= np.double(clusters)
return cluster_centers