本文整理匯總了Python中sklearn.neighbors.BallTree方法的典型用法代碼示例。如果您正苦於以下問題：Python neighbors.BallTree方法的具體用法？Python neighbors.BallTree怎麽用？Python neighbors.BallTree使用的例子？那麽恭喜您, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊sklearn.neighbors的用法示例。

在下文中一共展示了neighbors.BallTree方法的19個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於我們的係統推薦出更棒的Python代碼示例。

示例1: avgdigamma

​點讚 6

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def avgdigamma(data, dvec, leaf_size=16):

"""Convenience function for finding expectation value of given

some number of neighbors in some radius in a marginal space.

Parameters

----------

points : numpy.ndarray

dvec : array_like (n_points,)

Returns

-------

avgdigamma : float

expectation value of

"""

tree = BallTree(data, leaf_size=leaf_size, p=float('inf'))

n_points = tree.query_radius(data, dvec - EPS, count_only=True)

return digamma(n_points).mean()

開發者ID:msmbuilder，項目名稱:mdentropy，代碼行數:20，

示例2: test_unsupervised_inputs

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_unsupervised_inputs():

# test the types of valid input into NearestNeighbors

X = rng.random_sample((10, 3))

nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1)

nbrs_fid.fit(X)

dist1, ind1 = nbrs_fid.kneighbors(X)

nbrs = neighbors.NearestNeighbors(n_neighbors=1)

for input in (nbrs_fid, neighbors.BallTree(X), neighbors.KDTree(X)):

nbrs.fit(input)

dist2, ind2 = nbrs.kneighbors(X)

assert_array_almost_equal(dist1, dist2)

assert_array_almost_equal(ind1, ind2)

開發者ID:PacktPublishing，項目名稱:Mastering-Elasticsearch-7.0，代碼行數:19，

示例3: test_haversine

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_haversine():

tree = BallTree(spatial_data[:, :2], metric="haversine")

dist_matrix, _ = tree.query(spatial_data[:, :2], k=spatial_data.shape[0])

test_matrix = np.array(

[

[

dist.haversine(spatial_data[i, :2], spatial_data[j, :2])

for j in range(spatial_data.shape[0])

]

for i in range(spatial_data.shape[0])

]

)

test_matrix.sort(axis=1)

assert_array_almost_equal(

test_matrix,

dist_matrix,

err_msg="Distances don't match " "for metric haversine",

)

開發者ID:lmcinnes，項目名稱:pynndescent，代碼行數:20，

示例4: test_haversine

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_haversine(spatial_data):

tree = BallTree(spatial_data[:, :2], metric="haversine")

dist_matrix, _ = tree.query(spatial_data[:, :2], k=spatial_data.shape[0])

test_matrix = np.array(

[

[

dist.haversine(spatial_data[i, :2], spatial_data[j, :2])

for j in range(spatial_data.shape[0])

]

for i in range(spatial_data.shape[0])

]

)

test_matrix.sort(axis=1)

assert_array_almost_equal(

test_matrix,

dist_matrix,

err_msg="Distances don't match " "for metric haversine",

)

開發者ID:lmcinnes，項目名稱:umap，代碼行數:20，

示例5: make_propensity_lists

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def make_propensity_lists(self, train_ids, benchmark):

input_data, ids, pair_data = benchmark.get_data_access().get_rows(train_ids)

assignments = map(benchmark.get_assignment, ids, input_data)

treatment_data, batch_y = zip(*assignments)

treatment_data = np.array(treatment_data)

if pair_data.shape[-1] > 200 and False:

self.pca = PCA(50, svd_solver="randomized")

pair_data = self.pca.fit_transform(pair_data)

else:

self.pca = None

# covariance_matrix = np.cov(pair_data, rowvar=False)

self.original_data = [pair_data[treatment_data == t]

for t in range(benchmark.get_num_treatments())]

# self.ball_trees = [BallTree(pair_data[treatment_data == t], metric="mahalanobis",

# V=covariance_matrix)

# for t in range(benchmark.get_num_treatments())]

self.ball_trees = [BallTree(pair_data[treatment_data == t])

for t in range(benchmark.get_num_treatments())]

self.treatment_ids = [ids[treatment_data == t]

for t in range(benchmark.get_num_treatments())]

開發者ID:d909b，項目名稱:perfect_match，代碼行數:24，

示例6: test_objectmapper

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_objectmapper(self):

df = pdml.ModelFrame([])

self.assertIs(df.neighbors.NearestNeighbors,

neighbors.NearestNeighbors)

self.assertIs(df.neighbors.KNeighborsClassifier,

neighbors.KNeighborsClassifier)

self.assertIs(df.neighbors.RadiusNeighborsClassifier,

neighbors.RadiusNeighborsClassifier)

self.assertIs(df.neighbors.KNeighborsRegressor,

neighbors.KNeighborsRegressor)

self.assertIs(df.neighbors.RadiusNeighborsRegressor,

neighbors.RadiusNeighborsRegressor)

self.assertIs(df.neighbors.NearestCentroid, neighbors.NearestCentroid)

self.assertIs(df.neighbors.BallTree, neighbors.BallTree)

self.assertIs(df.neighbors.KDTree, neighbors.KDTree)

self.assertIs(df.neighbors.DistanceMetric, neighbors.DistanceMetric)

self.assertIs(df.neighbors.KernelDensity, neighbors.KernelDensity)

開發者ID:pandas-ml，項目名稱:pandas-ml，代碼行數:19，

示例7: calc_vert_vals

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def calc_vert_vals(verts, pts, vals, method='max', k_points=100):

from sklearn.neighbors import BallTree

ball_tree = BallTree(pts)

k_points = min([k_points, len(pts)])

dists, pts_inds = ball_tree.query(verts, k=k_points, return_distance=True)

near_vals = vals[pts_inds]

# sig_dists = dists[np.where(abs(near_vals)>2)]

cover = len(np.unique(pts_inds.ravel()))/float(len(pts))

print('{}% of the points are covered'.format(cover*100))

if method == 'dist':

n_dists = 1/(dists**2)

norm = 1/np.sum(n_dists, 1)

norm = np.reshape(norm, (len(norm), 1))

n_dists = norm * n_dists

verts_vals = np.sum(near_vals * n_dists, 1)

elif method == 'max':

verts_vals = near_vals[range(near_vals.shape[0]), np.argmax(abs(near_vals), 1)]

return verts_vals

開發者ID:pelednoam，項目名稱:mmvt，代碼行數:20，

示例8: test_barnes_hut_angle

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_barnes_hut_angle():

# When Barnes-Hut's angle=0 this corresponds to the exact method.

angle = 0.0

perplexity = 10

n_samples = 100

for n_components in [2, 3]:

n_features = 5

degrees_of_freedom = float(n_components - 1.0)

random_state = check_random_state(0)

distances = random_state.randn(n_samples, n_features)

distances = distances.astype(np.float32)

distances = abs(distances.dot(distances.T))

np.fill_diagonal(distances, 0.0)

params = random_state.randn(n_samples, n_components)

P = _joint_probabilities(distances, perplexity, verbose=0)

kl_exact, grad_exact = _kl_divergence(params, P, degrees_of_freedom,

n_samples, n_components)

k = n_samples - 1

bt = BallTree(distances)

distances_nn, neighbors_nn = bt.query(distances, k=k + 1)

neighbors_nn = neighbors_nn[:, 1:]

distances_nn = np.array([distances[i, neighbors_nn[i]]

for i in range(n_samples)])

assert np.all(distances[0, neighbors_nn[0]] == distances_nn[0]),\

abs(distances[0, neighbors_nn[0]] - distances_nn[0])

P_bh = _joint_probabilities_nn(distances_nn, neighbors_nn,

perplexity, verbose=0)

kl_bh, grad_bh = _kl_divergence_bh(params, P_bh, degrees_of_freedom,

n_samples, n_components,

angle=angle, skip_num_points=0,

verbose=0)

P = squareform(P)

P_bh = P_bh.toarray()

assert_array_almost_equal(P_bh, P, decimal=5)

assert_almost_equal(kl_exact, kl_bh, decimal=3)

開發者ID:PacktPublishing，項目名稱:Mastering-Elasticsearch-7.0，代碼行數:40，

示例9: fit

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def fit(self, X):

X = self._validate_input(X, return_compact=False)

self._tree = BallTree(X, metric='hamming', leaf_size=self.leaf_size)

return self

開發者ID:jakevdp，項目名稱:klsh，代碼行數:6，

示例10: build_tree

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def build_tree(points):

if points.shape[1] >= 20:

return BallTree(points, metric='chebyshev')

return KDTree(points, metric='chebyshev')

# TESTS

開發者ID:gregversteeg，項目名稱:NPEET，代碼行數:8，

示例11: update_tree

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def update_tree(self, time):

print 'rebuild tree'

self.tree = BallTree(self.state[:self.items, :], leaf_size=self.size)

self.last_tree_built_time = time

print 'rebuild done'

開發者ID:ShibiHe，項目名稱:Model-Free-Episodic-Control，代碼行數:7，

示例12: transform

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def transform(self, documents):

return [

BallTree(documents)

]

開發者ID:foxbook，項目名稱:atap，代碼行數:6，

示例13: fit_transform

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def fit_transform(self, documents):

# Transformer will be False if pipeline hasn't been fit yet,

# Trigger fit_transform and save the transformer and lexicon.

if self.transformer == False:

self.transformer = Pipeline([

('norm', TextNormalizer(minimum=50, maximum=200)),

('transform', Pipeline([

('tfidf', TfidfVectorizer()),

('svd', TruncatedSVD(n_components=200))

])

)

])

self.lexicon = self.transformer.fit_transform(documents)

self.tree = BallTree(self.lexicon)

self.save()

開發者ID:foxbook，項目名稱:atap，代碼行數:17，

示例14: knn_interpolation

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def knn_interpolation(cumulated_pc: np.ndarray, full_sized_data: np.ndarray, k=5):

"""

Using k-nn interpolation to find labels of points of the full sized pointcloud

:param cumulated_pc: cumulated pointcloud results after running the network

:param full_sized_data: full sized point cloud

:param k: k for k nearest neighbor interpolation

:return: pointcloud with predicted labels in last column and ground truth labels in last but one column

"""

labeled = cumulated_pc[cumulated_pc[:, -1] != -1]

to_be_predicted = full_sized_data.copy()

ball_tree = BallTree(labeled[:, :3], metric='euclidean')

knn_classes = labeled[ball_tree.query(to_be_predicted[:, :3], k=k)[1]][:, :, -1].astype(int)

interpolated = np.zeros(knn_classes.shape[0])

for i in range(knn_classes.shape[0]):

interpolated[i] = np.bincount(knn_classes[i]).argmax()

output = np.zeros((to_be_predicted.shape[0], to_be_predicted.shape[1]+1))

output[:, :-1] = to_be_predicted

output[:, -1] = interpolated

return output

開發者ID:VisualComputingInstitute，項目名稱:3d-semantic-segmentation，代碼行數:29，

示例15: _calc_ball_trees

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def _calc_ball_trees(self, metric='euclidean'):

ball_trees = []

for pointcloud_data in tqdm(self.dataset.data, desc='Ball trees have to be calculated from scratch'):

ball_trees.append(BallTree(pointcloud_data[:, :2], metric=metric))

return ball_trees

開發者ID:VisualComputingInstitute，項目名稱:3d-semantic-segmentation，代碼行數:7，

示例16: __init__

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​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def __init__(self, num_corrections=10, num_basic_results=10,

home_dir=".",

embedding_json=None,

vocab_int_json=None, *args, **kwargs):

super().__init__(num_res_return=num_basic_results, *args, **kwargs)

self.use_embedding = False

if embedding_json and vocab_int_json:

self.use_embedding = True

embedding_json = path.join(home_dir, embedding_json)

vocab_int_json = path.join(home_dir, vocab_int_json)

# load json files

print("Loading JSON files, may take a while.")

with open(embedding_json, 'r') as read_file:

self.embeddings = np.array(json.load(read_file))

with open(vocab_int_json, 'r') as read_file:

self.vocab_int = json.load(read_file)

self.int_vocab = {i: word for word, i in self.vocab_int.items()}

# train k nearest neighbor model

print("Training BallTree k-nearest neighbor searcher...")

self.searcher = BallTree(self.embeddings, leaf_size=10)

self.checker = Spell.Spell()

self.num_corrections = num_corrections

self.num_basic_search_results = num_basic_results

self.max_total_res = min(10, num_basic_results+num_corrections)

print("Ready to use.")

開發者ID:weihesdlegend，項目名稱:Autocomplete-System，代碼行數:32，

示例17: fit

​點讚 5

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def fit(self, X):

"""Fit detector. y is optional for unsupervised methods.

Parameters

----------

X : dataframe of shape (n_samples, n_features)

The input samples.

"""

# validate inputs X and y (optional)

X = X.to_numpy()

if self.metric_params is not None:

self.tree_ = BallTree(X, leaf_size=self.leaf_size,

metric=self.metric,

**self.metric_params)

else:

self.tree_ = BallTree(X, leaf_size=self.leaf_size,

metric=self.metric)

self.neigh_.fit(X)

dist_arr, _ = self.neigh_.kneighbors(n_neighbors=self.n_neighbors,

return_distance=True)

dist = self._get_dist_by_method(dist_arr)

self.decision_scores_ = dist.ravel()

self._process_decision_scores()

return self

開發者ID:datamllab，項目名稱:pyodds，代碼行數:31，

示例18: fit

​點讚 4

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def fit(self, X, y=None):

"""Fit detector. y is ignored in unsupervised methods.

Parameters

----------

X : numpy array of shape (n_samples, n_features)

The input samples.

y : Ignored

Not used, present for API consistency by convention.

Returns

-------

self : object

Fitted estimator.

"""

# validate inputs X and y (optional)

X = check_array(X)

self._set_n_classes(y)

self.neigh_.fit(X)

# In certain cases, _tree does not exist for NearestNeighbors

# See Issue #158 (https://github.com/yzhao062/pyod/issues/158)

# n_neighbors = 100

if self.neigh_._tree is not None:

self.tree_ = self.neigh_._tree

else:

if self.metric_params is not None:

self.tree_ = BallTree(X, leaf_size=self.leaf_size,

metric=self.metric,

**self.metric_params)

else:

self.tree_ = BallTree(X, leaf_size=self.leaf_size,

metric=self.metric)

dist_arr, _ = self.neigh_.kneighbors(n_neighbors=self.n_neighbors,

return_distance=True)

dist = self._get_dist_by_method(dist_arr)

self.decision_scores_ = dist.ravel()

self._process_decision_scores()

return self

開發者ID:yzhao062，項目名稱:pyod，代碼行數:48，

示例19: test_ball_tree

​點讚 4

​

# 需要導入模塊: from sklearn import neighbors [as 別名]

# 或者: from sklearn.neighbors import BallTree [as 別名]

def test_ball_tree(N=1):

np.random.seed(12345)

i = 0

while i < N:

N = np.random.randint(2, 100)

M = np.random.randint(2, 100)

k = np.random.randint(1, N)

ls = np.min([np.random.randint(1, 10), N - 1])

X = np.random.rand(N, M)

BT = BallTree(leaf_size=ls, metric=euclidean)

BT.fit(X)

x = np.random.rand(M)

mine = BT.nearest_neighbors(k, x)

assert len(mine) == k

mine_neighb = np.array([n.key for n in mine])

mine_dist = np.array([n.distance for n in mine])

sort_ix = np.argsort(mine_dist)

mine_dist = mine_dist[sort_ix]

mine_neighb = mine_neighb[sort_ix]

sk = sk_BallTree(X, leaf_size=ls)

theirs_dist, ind = sk.query(x.reshape(1, -1), k=k)

sort_ix = np.argsort(theirs_dist.flatten())

theirs_dist = theirs_dist.flatten()[sort_ix]

theirs_neighb = X[ind.flatten()[sort_ix]]

for j in range(len(theirs_dist)):

np.testing.assert_almost_equal(mine_neighb[j], theirs_neighb[j])

np.testing.assert_almost_equal(mine_dist[j], theirs_dist[j])

print("PASSED")

i += 1

#######################################################################

# Graphs #

#######################################################################

開發者ID:ddbourgin，項目名稱:numpy-ml，代碼行數:44，

注：本文中的sklearn.neighbors.BallTree方法示例整理自Github/MSDocs等源碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。