class LLMatInfiniteNormBenchmark(benchmark.Benchmark):
label = "Infinite norm with 100 elements and size = 1,000"
each = 100
def setUp(self):
self.nbr_elements = 100
self.size = 1000
self.A_c = LLSparseMatrix(size=self.size, size_hint=self.nbr_elements, dtype=FLOAT64_T)
construct_sparse_matrix(self.A_c, self.size, self.nbr_elements)
self.A_p = spmatrix.ll_mat(self.size, self.size, self.nbr_elements)
construct_sparse_matrix(self.A_p, self.size, self.nbr_elements)
def tearDown(self):
assert self.p_norm_inf == self.c_norm_inf
#assert self.w_c[i] == self.w_s[i]
def test_pysparse(self):
self.p_norm_inf = self.A_p.norm('inf')
return
def test_cysparse(self):
self.c_norm_inf = self.A_c.norm('inf')
return
class LLMatInfiniteNormBenchmark(benchmark.Benchmark):
label = "Infinite norm with 100 elements and size = 1,000"
each = 100
def setUp(self):
self.nbr_elements = 100
self.size = 1000
self.A_c = LLSparseMatrix(size=self.size,
size_hint=self.nbr_elements,
dtype=FLOAT64_T)
construct_sparse_matrix(self.A_c, self.size, self.nbr_elements)
self.A_p = spmatrix.ll_mat(self.size, self.size, self.nbr_elements)
construct_sparse_matrix(self.A_p, self.size, self.nbr_elements)
def tearDown(self):
assert self.p_norm_inf == self.c_norm_inf
#assert self.w_c[i] == self.w_s[i]
def test_pysparse(self):
self.p_norm_inf = self.A_p.norm('inf')
return
def test_cysparse(self):
self.c_norm_inf = self.A_c.norm('inf')
return
class LLMatFrobeniusNormBenchmark(benchmark.Benchmark):
label = "Frobenius norm with 100 elements and size = 1,000 for a symmetrical matrix"
each = 100
def setUp(self):
self.nbr_elements = 100
self.size = 1000
self.A_c = LLSparseMatrix(size=self.size,
size_hint=self.nbr_elements,
dtype=FLOAT64_T,
store_symmetric=True)
construct_sym_sparse_matrix(self.A_c, self.size, self.nbr_elements)
self.A_p = spmatrix.ll_mat_sym(self.size, self.size, self.nbr_elements)
construct_sym_sparse_matrix(self.A_p, self.size, self.nbr_elements)
def tearDown(self):
assert self.p_norm == self.c_norm
#assert self.w_c[i] == self.w_s[i]
def test_pysparse(self):
self.p_norm = self.A_p.norm('fro')
return
def test_cysparse(self):
self.c_norm = self.A_c.norm('frob')
return
class LLMatFrobeniusNormBenchmark(benchmark.Benchmark):
label = "Frobenius norm with 100 elements and size = 1,000 for a symmetrical matrix"
each = 100
def setUp(self):
self.nbr_elements = 100
self.size = 1000
self.A_c = LLSparseMatrix(size=self.size, size_hint=self.nbr_elements, dtype=FLOAT64_T, store_symmetric=True)
construct_sym_sparse_matrix(self.A_c, self.size, self.nbr_elements)
self.A_p = spmatrix.ll_mat_sym(self.size, self.size, self.nbr_elements)
construct_sym_sparse_matrix(self.A_p, self.size, self.nbr_elements)
def tearDown(self):
assert self.p_norm == self.c_norm
#assert self.w_c[i] == self.w_s[i]
def test_pysparse(self):
self.p_norm = self.A_p.norm('fro')
return
def test_cysparse(self):
self.c_norm = self.A_c.norm('frob')
return