def benchmark_ssvd(ctx, timer): DIM = (1280, 1280) #A = expr.randn(*DIM, dtype=np.float64) A = np.random.randn(*DIM) A = expr.from_numpy(A) t1 = datetime.now() U, S, VT = svd(A) t2 = datetime.now() cost_time = millis(t1, t2) print "total cost time:%s ms" % (cost_time)
def benchmark_ssvd(ctx, timer): DIM = (1280, 1280) #A = expr.randn(*DIM, dtype=np.float64) A = np.random.randn(*DIM) A = expr.from_numpy(A) t1 = datetime.now() U,S,VT = svd(A) t2 = datetime.now() cost_time = millis(t1, t2) print "total cost time:%s ms" % (cost_time)
def test_ssvd(self): ctx = blob_ctx.get() # Create a sparse matrix. A = expr.randn(*DIM, tile_hint = (int(DIM[0]/ctx.num_workers), DIM[1]), dtype=np.float64) U,S,VT = svd(A) U2,S2,VT2 = linalg.svd(A.glom(), full_matrices=0) assert np.allclose(absolute(U.glom()), absolute(U2)) assert np.allclose(absolute(S), absolute(S2)) assert np.allclose(absolute(VT), absolute(VT2))
def test_ssvd(self): ctx = blob_ctx.get() # Create a sparse matrix. A = expr.randn(*DIM, tile_hint=(int(DIM[0] / ctx.num_workers), DIM[1]), dtype=np.float64) U, S, VT = svd(A) U2, S2, VT2 = linalg.svd(A.glom(), full_matrices=0) assert np.allclose(absolute(U.glom()), absolute(U2)) assert np.allclose(absolute(S), absolute(S2)) assert np.allclose(absolute(VT), absolute(VT2))