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())
""" 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))