def test_run2():
'''
Parameters
----------
checks: number of nodes to check (?)
'''
n = 100
k = 3
ops = 10
test_n = 1
x = random_vectors(n)
test_points = random_vectors(test_n)
index = Index(x, w=(0.5, 0.5))
for i in range(n // ops):
ur = index.run(ops)
print(ur)
ids1, dists1 = index.knn_search_points(test_points, k, checks=1)
# ids2, dists2 = index.knn_search_points(test_points, k, checks = 50)
ids3, dists3 = index.knn_search_points(test_points, k, checks=100)
print("1: ", ids1)
print("1: ", dists1)
# print("2: ", ids2)
print("3: ", ids3)
print("3: ", dists3)
print(index.size())
def test_run():
"""
RUN
----------
INCREMENTALLY ADD POINTS TO THE TREE STRUCTURE
Parameters
----------
ops: number of ops
Returns
-------
numPointsInserted: total number of points inserted
addPointOps: number of ops allocated to add
updateIndexOps: number of ops allocated to update index
addPointResult: number of added points
updateIndexResult: ?
addPointElapsed: time elapsed for add
updateIndexElapsed: time elapsed for update index
"""
x = random_vectors(n=30, d=3)
index = Index(x, w=(0.5, 0.5))
ops = 6
for i in range(x.shape[0] // ops):
ur = index.run(ops)
print("===========")
print("index.size(): ",
index.size()) # index.size grows as we run iteratively
print(ur)
class KNNKernelDensity:
SQRT2PI = np.sqrt(2 * np.pi)
def __init__(self, X: np.ndarray[Any, Any], online: Optional[bool] = False):
self.X = X
self.index = Index(X)
if not online:
self.index.add_points(len(X))
def run(self, ops: Any) -> Any:
return self.index.run(ops)
def run_ids(self, ids: Iterable[int]) -> Any:
return self.index.run_ids(ids)
def score_samples(
self, X: np.ndarray[Any, Any], k: int = 10, bandwidth: float = 0.2
) -> float:
_, dists = self.index.knn_search_points(X, k=k)
scores = self._gaussian_score(dists, bandwidth) / k
return scores
def _gaussian_score(self, dists: float, bandwidth: float) -> float:
logg = -0.5 * (dists / bandwidth) ** 2
g = np.exp(logg) / bandwidth / self.SQRT2PI
return g.sum(axis=1) # type: ignore
class KNNKernelDensity():
SQRT2PI = np.sqrt(2 * np.pi)
def __init__(self, X, online=False):
self.X = X
self.index = Index(X)
if not online: # if offline
self.index.add_points(len(X))
def run(self, ops):
return self.index.run(ops)
def run_ids(self, ids):
return self.index.run_ids(ids)
def score_samples(self, X, k=10, bandwidth=0.2):
_, dists = self.index.knn_search_points(X, k=k)
scores = self._gaussian_score(dists, bandwidth) / k
return scores
def _gaussian_score(self, dists, bandwidth):
logg = -0.5 * (dists / bandwidth) ** 2
g = np.exp(logg) / bandwidth / self.SQRT2PI
return g.sum(axis=1)
def test_incremental_run1(self):
x = random_vectors()
index = Index(x, w=(0.5, 0.5))
self.assertTrue(index.is_using_pyarray)
ops = 20
for i in range(x.shape[0] // ops):
ur = index.run(ops)
self.assertEqual(index.size(), (i + 1) * ops)
self.assertEqual(ur['addPointResult'], ops)
class KNNRegressor():
def __init__(self, X, y, n_neighbors=5, weights='uniform', online=False):
self.X = X
self.y = y
self.index = Index(X)
self.n_neighbors = n_neighbors
self.weights = weights
if not online: # if offline
self.index.add_points(len(X))
def run(self, ops):
return self.index.run(ops)
def predict(self, X):
indices, dists = self.index.knn_search_points(X, k=self.n_neighbors)
weights = self._get_weights(dists)
if self.weights == 'uniform':
y_pred = np.mean(self.y[indices], axis=1)
else:
y_pred = np.empty((X.shape[0], self.y.shape[1]))
denom = np.sum(weights, axis=1)
for j in range(self.y.shape[1]):
num = np.sum(self.y[indices, j] * weights, axis=1)
y_pred[:, j] = num / denom
if self.y.ndim == 1:
y_pred = y_pred.ravel()
return y_pred
def _get_weights(self, dists):
if self.weights == 'uniform':
return None
for i, dist in enumerate(dists):
if 0. in dist:
dists[i] = dist == 0.
else:
dists[i] = 1. / dist
return dists
def test_updates_after_all_points_added(self):
np.random.seed(10)
n = 10000
w = (0.5, 0.5)
x = random_vectors(n)
ops = 1000
index = Index(x, w=w)
self.assertTrue(index.is_using_pyarray)
index.add_points(n) # add all points
for i in range(1000):
index.knn_search_points(random_vectors(100),
10) # accumulate losses
for i in range(10):
res = index.run(ops)
self.assertEqual(res['addPointResult'], 0)
self.assertEqual(res['updateIndexResult'], ops)
def test_incremental_run2(self):
n = 1000
k = 20
ops = 100
test_n = 30
x = random_vectors(n)
test_points = random_vectors(test_n)
index = Index(x)
self.assertTrue(index.is_using_pyarray)
for i in range(n // ops):
ur = index.run(ops)
ids1, dists1 = index.knn_search_points(test_points, k, checks=100)
ids2, dists2 = index.knn_search_points(test_points, k, checks=1000)
"""
The assertion below always holds since later search checks a larger number of nodes and the search process is deterministic
"""
self.assertEqual(np.sum(dists1 >= dists2), test_n * k)
