def test_tril_complex128(self):
a = np.asarray(np.random.rand(4, 4), np.complex128)
a_gpu = gpuarray.to_gpu(a)
l_gpu = linalg.tril(a_gpu)
assert np.allclose(np.tril(a), l_gpu.get())
def test_tril_float64(self):
a = np.asarray(np.random.rand(4, 4), np.float64)
a_gpu = gpuarray.to_gpu(a)
l_gpu = linalg.tril(a_gpu)
assert np.allclose(np.tril(a), l_gpu.get())
def test_tril_float64(self):
a = np.asarray(np.random.rand(4, 4), np.float64)
a_gpu = gpuarray.to_gpu(a)
l_gpu = linalg.tril(a_gpu)
assert np.allclose(np.tril(a), l_gpu.get())
def test_tril_complex128(self):
a = np.asarray(np.random.rand(4, 4), np.complex128)
a_gpu = gpuarray.to_gpu(a)
l_gpu = linalg.tril(a_gpu)
assert np.allclose(np.tril(a), l_gpu.get())
"""
Demonstrates how to extract the lower triangle of a matrix.
"""
import pycuda.autoinit
import pycuda.driver as drv
import numpy as np
import pycuda.gpuarray as gpuarray
import scikits.cuda.linalg as culinalg
import scikits.cuda.misc as cumisc
culinalg.init()
# Double precision is only supported by devices with compute
# capability >= 1.3:
import string
demo_types = [np.float32, np.complex64]
if cumisc.get_compute_capability(pycuda.autoinit.device) >= 1.3:
demo_types.extend([np.float64, np.complex128])
for t in demo_types:
print 'Testing lower triangle extraction for type ' + str(np.dtype(t))
N = 10
if np.iscomplexobj(t()):
a = np.asarray(np.random.rand(N, N), t)
else:
a = np.asarray(np.random.rand(N, N)+1j*np.random.rand(N, N), t)
a_gpu = gpuarray.to_gpu(a)
b_gpu = culinalg.tril(a_gpu, False)
print 'Success status: ', np.allclose(b_gpu.get(), np.tril(a))
