def test_rand(self):
# Simple distributional checks for sparse.rand.
for random_state in None, 4321, np.random.RandomState():
x = sprand(10, 20, density=0.5, dtype=np.float64,
random_state=random_state)
assert_(np.all(np.less_equal(0, x.data)))
assert_(np.all(np.less_equal(x.data, 1)))
def test_rand(self):
# Simple distributional checks for sparse.rand.
for random_state in None, 4321, np.random.RandomState():
x = sprand(10, 20, density=0.5, dtype=np.float64,
random_state=random_state)
assert_(np.all(np.less_equal(0, x.data)))
assert_(np.all(np.less_equal(x.data, 1)))
def test_rand(self):
# Simple sanity checks for sparse.rand
for t in [np.float32, np.float64, np.longdouble]:
x = sprand(5, 10, density=0.1, dtype=t)
assert_equal(x.dtype, t)
assert_equal(x.shape, (5, 10))
assert_equal(x.nonzero()[0].size, 5)
x = sprand(5, 10, density=0.1)
assert_equal(x.dtype, np.double)
for fmt in ['coo', 'csc', 'csr', 'lil']:
x = sprand(5, 10, format=fmt)
assert_equal(x.format, fmt)
assert_raises(ValueError, lambda: sprand(5, 10, 1.1))
assert_raises(ValueError, lambda: sprand(5, 10, -0.1))
def test_rand(self):
# Simple sanity checks for sparse.rand
for t in [np.float32, np.float64, np.longdouble]:
x = sprand(5, 10, density=0.1, dtype=t)
assert_equal(x.dtype, t)
assert_equal(x.shape, (5, 10))
assert_equal(x.nonzero()[0].size, 5)
x = sprand(5, 10, density=0.1)
assert_equal(x.dtype, np.double)
for fmt in ['coo', 'csc', 'csr', 'lil']:
x = sprand(5, 10, format=fmt)
assert_equal(x.format, fmt)
assert_raises(ValueError, lambda: sprand(5, 10, 1.1))
assert_raises(ValueError, lambda: sprand(5, 10, -0.1))
def test_rand(self):
# Simple sanity checks for sparse.rand
for t in [np.float32, np.float64, np.longdouble]:
x = sprand(5, 10, density=0.1, dtype=t)
assert_equal(x.dtype, t)
assert_equal(x.shape, (5, 10))
assert_equal(x.nonzero()[0].size, 5)
x1 = sprand(5, 10, density=0.1,
random_state=4321)
assert_equal(x1.dtype, np.double)
x2 = sprand(5, 10, density=0.1,
random_state=np.random.RandomState(4321))
assert_array_equal(x1.data, x2.data)
assert_array_equal(x1.row, x2.row)
assert_array_equal(x1.col, x2.col)
for density in [0.0, 0.1, 0.5, 1.0]:
x = sprand(5, 10, density=density)
assert_equal(x.nnz, int(density * np.prod(x.shape)))
for fmt in ['coo', 'csc', 'csr', 'lil']:
x = sprand(5, 10, format=fmt)
assert_equal(x.format, fmt)
assert_raises(ValueError, lambda: sprand(5, 10, 1.1))
assert_raises(ValueError, lambda: sprand(5, 10, -0.1))
def test_rand(self):
# Simple sanity checks for sparse.rand
for t in [np.float32, np.float64, np.longdouble]:
x = sprand(5, 10, density=0.1, dtype=t)
assert_equal(x.dtype, t)
assert_equal(x.shape, (5, 10))
assert_equal(x.nonzero()[0].size, 5)
x1 = sprand(5, 10, density=0.1, random_state=4321)
assert_equal(x1.dtype, np.double)
x2 = sprand(5,
10,
density=0.1,
random_state=np.random.RandomState(4321))
assert_array_equal(x1.data, x2.data)
assert_array_equal(x1.row, x2.row)
assert_array_equal(x1.col, x2.col)
for density in [0.0, 0.1, 0.5, 1.0]:
x = sprand(5, 10, density=density)
assert_equal(x.nnz, int(density * np.prod(x.shape)))
for fmt in ['coo', 'csc', 'csr', 'lil']:
x = sprand(5, 10, format=fmt)
assert_equal(x.format, fmt)
assert_raises(ValueError, lambda: sprand(5, 10, 1.1))
assert_raises(ValueError, lambda: sprand(5, 10, -0.1))
def test_rand(self):
# Simple distributional checks for sparse.rand.
random_states = [None, 4321, np.random.RandomState()]
try:
gen = np.random.default_rng()
random_states.append(gen)
except AttributeError:
pass
for random_state in random_states:
x = sprand(10, 20, density=0.5, dtype=np.float64,
random_state=random_state)
assert_(np.all(np.less_equal(0, x.data)))
assert_(np.all(np.less_equal(x.data, 1)))