def _rsl_prims_balltree(X, cut, k=5, alpha=1.4142135623730951, gamma=5, metric="minkowski", p=2):
if metric == "minkowski":
if p is None:
raise TypeError("Minkowski metric given but no p value supplied!")
if p < 0:
raise ValueError("Minkowski metric with negative p value is not defined!")
elif p is None:
p = 2 # Unused, but needs to be integer; assume euclidean
dim = X.shape[0]
k = min(dim - 1, k)
tree = BallTree(X, metric=metric)
dist_metric = DistanceMetric.get_metric(metric)
core_distances = tree.query(X, k=k)[0][:, -1]
min_spanning_tree = mst_linkage_core_cdist(X, core_distances, dist_metric)
single_linkage_tree = label(min_spanning_tree)
single_linkage_tree = SingleLinkageTree(single_linkage_tree)
labels = single_linkage_tree.get_clusters(cut, gamma)
return labels, single_linkage_tree
def test_ball_tree_two_point(n_samples=100, n_features=3):
np.random.seed(0)
X = np.random.random((n_samples, n_features))
Y = np.random.random((n_samples, n_features))
r = np.linspace(0, 1, 10)
bt = BallTree(X, leaf_size=10)
D = DistanceMetric.get_metric("euclidean").pairwise(Y, X)
counts_true = [(D <= ri).sum() for ri in r]
def check_two_point(r, dualtree):
counts = bt.two_point_correlation(Y, r=r, dualtree=dualtree)
assert_allclose(counts, counts_true)
for dualtree in (True, False):
yield check_two_point, r, dualtree
def test_ball_tree_two_point(n_samples=100, n_features=3):
np.random.seed(0)
X = np.random.random((n_samples, n_features))
Y = np.random.random((n_samples, n_features))
r = np.linspace(0, 1, 10)
bt = BallTree(X, leaf_size=10)
D = DistanceMetric.get_metric("euclidean").pairwise(Y, X)
counts_true = [(D <= ri).sum() for ri in r]
def check_two_point(r, dualtree):
counts = bt.two_point_correlation(Y, r=r, dualtree=dualtree)
assert_allclose(counts, counts_true)
for dualtree in (True, False):
yield check_two_point, r, dualtree
def brute_force_neighbors(X, Y, k, metric, **kwargs):
D = DistanceMetric.get_metric(metric, **kwargs).pairwise(Y, X)
ind = np.argsort(D, axis=1)[:, :k]
dist = D[np.arange(Y.shape[0])[:, None], ind]
return dist, ind
def brute_force_neighbors(X, Y, k, metric, **kwargs):
D = DistanceMetric.get_metric(metric, **kwargs).pairwise(Y, X)
ind = np.argsort(D, axis=1)[:, :k]
dist = D[np.arange(Y.shape[0])[:, None], ind]
return dist, ind
def check_pdist(self, metric, kwargs, D_true):
dm = DistanceMetric.get_metric(metric, **kwargs)
D12 = dm.pairwise(self.X1)
assert_allclose(D12, D_true)
def check_cdist_bool(self, metric, D_true):
dm = DistanceMetric.get_metric(metric)
D12 = dm.pairwise(self.X1_bool, self.X2_bool)
assert_allclose(D12, D_true)
