def test_sumdist_rmsd():
pairs = random.random_integers(low=0, high=9, size=(5, 2)).astype(np.intp)
alldist = scipy.spatial.distance.squareform(pdist(X_rmsd, "rmsd"))
np.testing.assert_almost_equal(
sum(alldist[p[0], p[1]] for p in pairs),
sumdist(X_rmsd, "rmsd", pairs),
decimal=6)
def test_sumdist_rmsd():
pairs = random.random_integers(low=0, high=9, size=(5, 2))
alldist = scipy.spatial.distance.squareform(pdist(X_rmsd, "rmsd"))
np.testing.assert_almost_equal(
sum(alldist[p[0], p[1]] for p in pairs),
sumdist(X_rmsd, "rmsd", pairs),
decimal=6)
def test_sumdist_rmsd():
pairs = random.randint(0, 10, size=(5, 2)).astype(np.intp)
alldist = scipy.spatial.distance.squareform(pdist(X_rmsd, "rmsd"))
np.testing.assert_almost_equal(
sum(alldist[p[0], p[1]] for p in pairs),
sumdist(X_rmsd, "rmsd", pairs),
decimal=6)
def test_sumdist_double_float():
pairs = random.random_integers(low=0, high=9, size=(5, 2))
for metric in VECTOR_METRICS:
for X in (X_double, X_float):
alldist = scipy.spatial.distance.squareform(pdist(X, metric))
np.testing.assert_almost_equal(
sum(alldist[p[0], p[1]] for p in pairs),
sumdist(X, metric, pairs))
def test_pdist_rmsd_2():
got = pdist(X_rmsd, "rmsd", X_indices)
all2all = np.array([md.rmsd(X_rmsd, X_rmsd[i], precentered=True)
for i in range(len(X_rmsd))]).astype(np.double)
submatrix = all2all[np.ix_(X_indices, X_indices)]
ref = submatrix[np.triu_indices(5, k=1)]
np.testing.assert_almost_equal(got, ref, decimal=4)
def test_sumdist_double_float():
pairs = random.random_integers(low=0, high=9, size=(5, 2)).astype(np.intp)
for metric in VECTOR_METRICS:
for X in (X_double, X_float):
alldist = scipy.spatial.distance.squareform(pdist(X, metric))
np.testing.assert_almost_equal(
sum(alldist[p[0], p[1]] for p in pairs),
sumdist(X, metric, pairs))
def test_pdist_rmsd_1():
got = pdist(X_rmsd, "rmsd")
all2all = np.array([
md.rmsd(X_rmsd, X_rmsd[i], precentered=True)
for i in range(len(X_rmsd))
])
ref = all2all[np.triu_indices(10, k=1)]
np.testing.assert_almost_equal(got, ref, decimal=5)
def test_pdist_double_float_2():
# test without X_indices
for metric in VECTOR_METRICS:
for X, Y in ((X_double, Y_double), (X_float, Y_float)):
pdist_1 = pdist(X, metric, X_indices=X_indices)
pdist_2 = scipy.spatial.distance.pdist(X[X_indices], metric)
yield lambda: np.testing.assert_almost_equal(
pdist_1, pdist_2, decimal=5 if X.dtype == np.float32 else 10)
def test_pdist_rmsd_2():
got = pdist(X_rmsd, "rmsd", X_indices)
all2all = np.array([md.rmsd(X_rmsd, X_rmsd[i], precentered=True)
for i in range(len(X_rmsd))]).astype(np.double)
submatrix = all2all[np.ix_(X_indices, X_indices)]
ref = submatrix[np.triu_indices(5, k=1)]
np.testing.assert_almost_equal(got, ref, decimal=4)
def pdist(X, metric='euclidean'):
if isinstance(metric, six.string_types):
return libdistance.pdist(X, metric)
n = len(X)
d = np.empty((n, n))
for i in range(n):
d[i, :] = metric(X, X, i)
return scipy.spatial.distance.squareform(d, checks=False)
def pdist(X, metric='euclidean'):
if isinstance(metric, six.string_types):
return libdistance.pdist(X, metric)
n = len(X)
d = np.empty((n, n))
for i in range(n):
d[i, :] = metric(X, X, i)
return scipy.spatial.distance.squareform(d, checks=False)
def test_pdist_double_float_2():
# test without X_indices
for metric in VECTOR_METRICS:
for X, Y in ((X_double, Y_double), (X_float, Y_float)):
pdist_1 = pdist(X, metric, X_indices=X_indices)
pdist_2 = scipy.spatial.distance.pdist(X[X_indices], metric)
yield lambda : np.testing.assert_almost_equal(
pdist_1,
pdist_2,
decimal=5 if X.dtype == np.float32 else 10)
def test_index():
n = 50
def q(i, j):
if i == j:
raise ValueError()
if (i < j):
return int(n*i - i*(i+1)/2 + j - 1 - i)
return int(n*j - j*(j+1)/2 + i - 1 - j)
X = np.random.randn(n, 1)
pdist = libdistance.pdist(X, 'euclidean')
for i in range(n):
for j in range(n):
if (i != j):
assert euclidean(X[i], X[j]) == pdist[q(i,j)]
def test_index():
n = 50
def q(i, j):
if i == j:
raise ValueError()
if (i < j):
return int(n * i - i * (i + 1) / 2 + j - 1 - i)
return int(n * j - j * (j + 1) / 2 + i - 1 - j)
X = np.random.randn(n, 1)
pdist = libdistance.pdist(X, 'euclidean')
for i in range(n):
for j in range(n):
if (i != j):
assert euclidean(X[i], X[j]) == pdist[q(i, j)]
def test_pdist_rmsd_1():
got = pdist(X_rmsd, "rmsd")
all2all = np.array([md.rmsd(X_rmsd, X_rmsd[i], precentered=True) for i in range(len(X_rmsd))])
ref = all2all[np.triu_indices(10, k=1)]
np.testing.assert_almost_equal(got, ref, decimal=5)
