def exercise_random():
from scitbx.random import variate, uniform_distribution
g = random_matrices = variate(
sparse.matrix_distribution(5,
3,
density=0.4,
elements=uniform_distribution(min=-1,
max=0.5)))
for a in itertools.islice(g, 10):
assert a.n_rows == 5 and a.n_cols == 3
assert approx_equal(a.non_zeroes, a.n_rows * a.n_cols * 0.4, eps=1)
for j in range(a.n_cols):
for i, x in a.col(j):
assert -1 <= x < 0.5, (i, j, x)
g = random_vectors = variate(
sparse.vector_distribution(6,
density=0.3,
elements=uniform_distribution(min=-2,
max=2)))
for v in itertools.islice(g, 10):
assert v.size == 6
assert approx_equal(v.non_zeroes, v.size * 0.3, eps=1)
for i, x in v:
assert -2 <= x < 2, (i, j, x)
def exercise_matrix_x_vector():
from scitbx.random import variate, uniform_distribution
for m,n in [(5,5), (3,5), (5,3)]:
random_vectors = variate(
sparse.vector_distribution(
n, density=0.4,
elements=uniform_distribution(min=-2, max=2)))
random_matrices = variate(
sparse.matrix_distribution(
m, n, density=0.3,
elements=uniform_distribution(min=-2, max=2)))
for n_test in xrange(50):
a = random_matrices.next()
x = random_vectors.next()
y = a*x
aa = a.as_dense_matrix()
xx = x.as_dense_vector()
yy1 = y.as_dense_vector()
yy2 = aa.matrix_multiply(xx)
assert approx_equal(yy1,yy2)
for m,n in [(5,5), (3,5), (5,3)]:
random_matrices = variate(
sparse.matrix_distribution(
m, n, density=0.4,
elements=uniform_distribution(min=-2, max=2)))
for n_test in xrange(50):
a = random_matrices.next()
x = flex.random_double(n)
y = a*x
aa = a.as_dense_matrix()
yy = aa.matrix_multiply(x)
assert approx_equal(y, yy)
def linear_combination_trial_vectors():
u = sparse.vector(8, {1: 1.1, 3: 1.3})
v = sparse.vector(8, {0: 2.0, 2: 2.2, 3: 2.3, 4: 2.4})
w = list(-2*u.as_dense_vector() + 3*v.as_dense_vector())
yield u, v, w
random_vectors = scitbx.random.variate(
sparse.vector_distribution(
8, density=0.4,
elements=scitbx.random.uniform_distribution(min=-2, max=2)))
u = random_vectors.next()
v = random_vectors.next()
w = list(-2*u.as_dense_vector() + 3*v.as_dense_vector())
yield u, v, w
def exercise_random():
from scitbx.random import variate, uniform_distribution
g = random_matrices = variate(
sparse.matrix_distribution(
5, 3, density=0.4,
elements=uniform_distribution(min=-1, max=0.5)))
for a in itertools.islice(g, 10):
assert a.n_rows== 5 and a.n_cols == 3
assert approx_equal(a.non_zeroes, a.n_rows*a.n_cols*0.4, eps=1)
for j in xrange(a.n_cols):
for i,x in a.col(j):
assert -1 <= x < 0.5, (i,j, x)
g = random_vectors = variate(
sparse.vector_distribution(
6, density=0.3,
elements=uniform_distribution(min=-2, max=2)))
for v in itertools.islice(g, 10):
assert v.size == 6
assert approx_equal(v.non_zeroes, v.size*0.3, eps=1)
for i,x in v:
assert -2 <= x < 2, (i,j, x)