SYMBOL INDEX (471 symbols across 32 files)

FILE: doc/bokeh_digits_plot.py
  function embeddable_image (line 17) | def embeddable_image(data):

FILE: doc/conf.py
  function setup (line 230) | def setup(app):

FILE: examples/mnist_torus_sphere_example.py
  function torus_euclidean_grad (line 42) | def torus_euclidean_grad(x, y, torus_dimensions=(2 * np.pi, 2 * np.pi)):

FILE: umap/__init__.py
  class ParametricUMAP (line 15) | class ParametricUMAP(object):
    method __init__ (line 16) | def __init__(self, **kwds):

FILE: umap/aligned_umap.py
  function in1d (line 17) | def in1d(arr, test_set):
  function invert_dict (line 29) | def invert_dict(d):
  function procrustes_align (line 34) | def procrustes_align(embedding_base, embedding_to_align, anchors):
  function expand_relations (line 43) | def expand_relations(relation_dicts, window_size=3):
  function build_neighborhood_similarities (line 86) | def build_neighborhood_similarities(graphs_indptr, graphs_indices, relat...
  function get_nth_item_or_val (line 132) | def get_nth_item_or_val(iterable_or_val, n):
  function set_aligned_params (line 167) | def set_aligned_params(new_params, existing_params, n_models, param_name...
  function init_from_existing_internal (line 188) | def init_from_existing_internal(
  function init_from_existing (line 215) | def init_from_existing(previous_embedding, graph, relations):
  class AlignedUMAP (line 228) | class AlignedUMAP(BaseEstimator):
    method __init__ (line 229) | def __init__(
    method fit (line 295) | def fit(self, X, y=None, **fit_params):
    method fit_transform (line 442) | def fit_transform(self, X, y=None, **fit_params):
    method update (line 446) | def update(self, X, y=None, **fit_params):

FILE: umap/distances.py
  function sign (line 15) | def sign(a):
  function softmax (line 23) | def softmax(z):
  function euclidean (line 49) | def euclidean(x, y):
  function euclidean_grad (line 62) | def euclidean_grad(x, y):
  function standardised_euclidean (line 78) | def standardised_euclidean(x, y, sigma=_mock_ones):
  function standardised_euclidean_grad (line 93) | def standardised_euclidean_grad(x, y, sigma=_mock_ones):
  function manhattan (line 109) | def manhattan(x, y):
  function manhattan_grad (line 123) | def manhattan_grad(x, y):
  function chebyshev (line 138) | def chebyshev(x, y):
  function chebyshev_grad (line 152) | def chebyshev_grad(x, y):
  function minkowski (line 172) | def minkowski(x, y, p=2):
  function minkowski_grad (line 191) | def minkowski_grad(x, y, p=2.0):
  function poincare (line 220) | def poincare(u, v):
  function hyperboloid_grad (line 234) | def hyperboloid_grad(x, y):
  function weighted_minkowski (line 257) | def weighted_minkowski(x, y, w=_mock_ones, p=2):
  function weighted_minkowski_grad (line 275) | def weighted_minkowski_grad(x, y, w=_mock_ones, p=2.0):
  function mahalanobis (line 305) | def mahalanobis(x, y, vinv=_mock_identity):
  function mahalanobis_f64 (line 323) | def mahalanobis_f64(x, y, vinv=_mock_identity):
  function mahalanobis_grad (line 342) | def mahalanobis_grad(x, y, vinv=_mock_identity):
  function hamming (line 363) | def hamming(x, y):
  function canberra (line 373) | def canberra(x, y):
  function canberra_grad (line 384) | def canberra_grad(x, y):
  function bray_curtis (line 400) | def bray_curtis(x, y):
  function bray_curtis_grad (line 414) | def bray_curtis_grad(x, y):
  function jaccard (line 432) | def jaccard(x, y):
  function matching (line 448) | def matching(x, y):
  function dice (line 459) | def dice(x, y):
  function kulsinski (line 475) | def kulsinski(x, y):
  function rogers_tanimoto (line 493) | def rogers_tanimoto(x, y):
  function russellrao (line 504) | def russellrao(x, y):
  function sokal_michener (line 518) | def sokal_michener(x, y):
  function sokal_sneath (line 529) | def sokal_sneath(x, y):
  function haversine (line 545) | def haversine(x, y):
  function haversine_grad (line 555) | def haversine_grad(x, y):
  function yule (line 585) | def yule(x, y):
  function cosine (line 607) | def cosine(x, y):
  function cosine_grad (line 625) | def cosine_grad(x, y):
  function correlation (line 657) | def correlation(x, y):
  function hellinger (line 687) | def hellinger(x, y):
  function hellinger_grad (line 706) | def hellinger_grad(x, y):
  function softmax_hellinger (line 748) | def softmax_hellinger(x, y):
  function softmax_hellinger_grad (line 759) | def softmax_hellinger_grad(x, y):
  function approx_log_Gamma (line 780) | def approx_log_Gamma(x):
  function log_beta (line 792) | def log_beta(x, y):
  function log_single_beta (line 805) | def log_single_beta(x):
  function ll_dirichlet (line 818) | def ll_dirichlet(data1, data2):
  function symmetric_kl (line 853) | def symmetric_kl(x, y, z=1e-11):  # pragma: no cover
  function symmetric_kl_grad (line 884) | def symmetric_kl_grad(x, y, z=1e-11):  # pragma: no cover
  function correlation_grad (line 916) | def correlation_grad(x, y):
  function sinkhorn_distance (line 967) | def sinkhorn_distance(
  function spherical_gaussian_energy_grad (line 993) | def spherical_gaussian_energy_grad(x, y):  # pragma: no cover
  function diagonal_gaussian_energy_grad (line 1011) | def diagonal_gaussian_energy_grad(x, y):  # pragma: no cover
  function gaussian_energy_grad (line 1046) | def gaussian_energy_grad(x, y):  # pragma: no cover
  function spherical_gaussian_grad (line 1119) | def spherical_gaussian_grad(x, y):  # pragma: no cover
  function get_discrete_params (line 1146) | def get_discrete_params(data, metric):
  function categorical_distance (line 1170) | def categorical_distance(x, y):
  function hierarchical_categorical_distance (line 1178) | def hierarchical_categorical_distance(x, y, cat_hierarchy=[{}]):
  function ordinal_distance (line 1188) | def ordinal_distance(x, y, support_size=1.0):
  function count_distance (line 1193) | def count_distance(x, y, poisson_lambda=1.0, normalisation=1.0):
  function levenshtein (line 1218) | def levenshtein(x, y, normalisation=1.0, max_distance=20):
  function levenshtein_myers_ascii (line 1270) | def levenshtein_myers_ascii(x, y, normalisation=1.0, max_distance=20):
  function parallel_special_metric (line 1452) | def parallel_special_metric(X, Y=None, metric=hellinger):
  function chunked_parallel_special_metric (line 1473) | def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_s...
  function pairwise_special_metric (line 1495) | def pairwise_special_metric(

FILE: umap/layouts.py
  function clip (line 10) | def clip(val):
  function rdist (line 42) | def rdist(x, y):
  function _optimize_layout_euclidean_single_epoch (line 63) | def _optimize_layout_euclidean_single_epoch(
  function _optimize_layout_euclidean_densmap_epoch_init (line 189) | def _optimize_layout_euclidean_densmap_epoch_init(
  function _get_optimize_layout_euclidean_single_epoch_fn (line 231) | def _get_optimize_layout_euclidean_single_epoch_fn(parallel: bool = False):
  function optimize_layout_euclidean (line 238) | def optimize_layout_euclidean(
  function _optimize_layout_generic_single_epoch (line 446) | def _optimize_layout_generic_single_epoch(
  function optimize_layout_generic (line 528) | def optimize_layout_generic(
  function _optimize_layout_inverse_single_epoch (line 663) | def _optimize_layout_inverse_single_epoch(
  function optimize_layout_inverse (line 735) | def optimize_layout_inverse(
  function _optimize_layout_aligned_euclidean_single_epoch (line 877) | def _optimize_layout_aligned_euclidean_single_epoch(
  function optimize_layout_aligned_euclidean (line 1028) | def optimize_layout_aligned_euclidean(

FILE: umap/parametric_umap.py
  class ParametricUMAP (line 45) | class ParametricUMAP(UMAP):
    method __init__ (line 46) | def __init__(
    method fit (line 149) | def fit(self, X, y=None, precomputed_distances=None, landmark_position...
    method fit_transform (line 206) | def fit_transform(
    method transform (line 268) | def transform(self, X, batch_size=None):
    method inverse_transform (line 290) | def inverse_transform(self, X):
    method _define_model (line 310) | def _define_model(self):
    method _fit_embed_data (line 329) | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_po...
    method __getstate__ (line 445) | def __getstate__(self):
    method save (line 454) | def save(self, save_location, verbose=True, exclude_raw_data=False):
    method add_landmarks (line 508) | def add_landmarks(
    method remove_landmarks (line 568) | def remove_landmarks(self):
    method to_ONNX (line 571) | def to_ONNX(self, save_location):
  function get_graph_elements (line 585) | def get_graph_elements(graph_, n_epochs):
  function init_embedding_from_graph (line 639) | def init_embedding_from_graph(
  function convert_distance_to_log_probability (line 697) | def convert_distance_to_log_probability(distances, a=1.0, b=1.0):
  function compute_cross_entropy (line 719) | def compute_cross_entropy(
  function prepare_networks (line 762) | def prepare_networks(
  function construct_edge_dataset (line 830) | def construct_edge_dataset(
  function should_pickle (line 937) | def should_pickle(key, val):
  function load_ParametricUMAP (line 977) | def load_ParametricUMAP(save_location, verbose=True):
  function covariance (line 1024) | def covariance(x, y=None, keepdims=False):
  function correlation (line 1083) | def correlation(x, y=None, keepdims=False):
  class StopGradient (line 1090) | class StopGradient(keras.layers.Layer):
    method call (line 1091) | def call(self, x):
    method get_config (line 1094) | def get_config(self):
  function _default_landmark_loss (line 1098) | def _default_landmark_loss(y, y_pred):
  class UMAPModel (line 1107) | class UMAPModel(keras.Model):
    method __init__ (line 1108) | def __init__(
    method call (line 1158) | def call(self, inputs):
    method compute_loss (line 1177) | def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None...
    method _umap_loss (line 1200) | def _umap_loss(self, y_pred, repulsion_strength=1.0):
    method _global_correlation_loss (line 1252) | def _global_correlation_loss(self, y, y_pred):
    method _parametric_reconstruction_loss (line 1280) | def _parametric_reconstruction_loss(self, y, y_pred):
    method _landmark_loss (line 1286) | def _landmark_loss(self, y, y_pred):
  class PumapNet (line 1314) | class PumapNet(nn.Module):
    method __init__ (line 1316) | def __init__(self, indim, outdim):
    method forward (line 1336) | def forward(self, x):
  function weight_copier (line 1351) | def weight_copier(km, pm):

FILE: umap/plot.py
  function _to_hex (line 73) | def _to_hex(arr):
  function _red (line 78) | def _red(x):
  function _green (line 83) | def _green(x):
  function _blue (line 88) | def _blue(x):
  function _get_embedding (line 152) | def _get_embedding(umap_object):
  function _get_metric (line 161) | def _get_metric(umap_object):
  function _get_metric_kwds (line 169) | def _get_metric_kwds(umap_object):
  function _embed_datashader_in_an_axis (line 177) | def _embed_datashader_in_an_axis(datashader_image, ax):
  function _nhood_search (line 184) | def _nhood_search(umap_object, nhood_size):
  function _nhood_compare (line 204) | def _nhood_compare(indices_left, indices_right):
  function _get_extent (line 219) | def _get_extent(points):
  function _select_font_color (line 236) | def _select_font_color(background):
  function _datashade_points (line 254) | def _datashade_points(
  function _matplotlib_points (line 364) | def _matplotlib_points(
  function show (line 458) | def show(plot_to_show):
  function points (line 482) | def points(
  function connectivity (line 727) | def connectivity(
  function diagnostic (line 952) | def diagnostic(
  function interactive (line 1274) | def interactive(
  function nearest_neighbour_distribution (line 1651) | def nearest_neighbour_distribution(umap_object, bins=25, ax=None):

FILE: umap/sparse.py
  function arr_unique (line 19) | def arr_unique(arr):
  function arr_union (line 27) | def arr_union(ar1, ar2):
  function arr_intersect (line 39) | def arr_intersect(ar1, ar2):
  function sparse_sum (line 46) | def sparse_sum(ind1, data1, ind2, data2):
  function sparse_diff (line 107) | def sparse_diff(ind1, data1, ind2, data2):
  function sparse_mul (line 112) | def sparse_mul(ind1, data1, ind2, data2):
  function general_sset_intersection (line 146) | def general_sset_intersection(
  function general_sset_union (line 201) | def general_sset_union(
  function sparse_euclidean (line 235) | def sparse_euclidean(ind1, data1, ind2, data2):
  function sparse_manhattan (line 244) | def sparse_manhattan(ind1, data1, ind2, data2):
  function sparse_chebyshev (line 253) | def sparse_chebyshev(ind1, data1, ind2, data2):
  function sparse_minkowski (line 262) | def sparse_minkowski(ind1, data1, ind2, data2, p=2.0):
  function sparse_hamming (line 271) | def sparse_hamming(ind1, data1, ind2, data2, n_features):
  function sparse_canberra (line 277) | def sparse_canberra(ind1, data1, ind2, data2):
  function sparse_bray_curtis (line 291) | def sparse_bray_curtis(ind1, data1, ind2, data2):  # pragma: no cover
  function sparse_jaccard (line 312) | def sparse_jaccard(ind1, data1, ind2, data2):
  function sparse_matching (line 323) | def sparse_matching(ind1, data1, ind2, data2, n_features):
  function sparse_dice (line 332) | def sparse_dice(ind1, data1, ind2, data2):
  function sparse_kulsinski (line 344) | def sparse_kulsinski(ind1, data1, ind2, data2, n_features):
  function sparse_rogers_tanimoto (line 358) | def sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features):
  function sparse_russellrao (line 367) | def sparse_russellrao(ind1, data1, ind2, data2, n_features):
  function sparse_sokal_michener (line 380) | def sparse_sokal_michener(ind1, data1, ind2, data2, n_features):
  function sparse_sokal_sneath (line 389) | def sparse_sokal_sneath(ind1, data1, ind2, data2):
  function sparse_cosine (line 401) | def sparse_cosine(ind1, data1, ind2, data2):
  function sparse_hellinger (line 419) | def sparse_hellinger(ind1, data1, ind2, data2):
  function sparse_correlation (line 440) | def sparse_correlation(ind1, data1, ind2, data2, n_features):
  function approx_log_Gamma (line 501) | def approx_log_Gamma(x):
  function log_beta (line 515) | def log_beta(x, y):
  function log_single_beta (line 528) | def log_single_beta(x):
  function sparse_ll_dirichlet (line 539) | def sparse_ll_dirichlet(ind1, data1, ind2, data2):

FILE: umap/spectral.py
  function component_layout (line 18) | def component_layout(
  function multi_component_layout (line 145) | def multi_component_layout(
  function spectral_layout (line 263) | def spectral_layout(
  function tswspectral_layout (line 317) | def tswspectral_layout(
  function _spectral_layout (line 395) | def _spectral_layout(

FILE: umap/tests/conftest.py
  function spatial_data (line 19) | def spatial_data():
  function binary_data (line 27) | def binary_data():
  function sparse_spatial_data (line 37) | def sparse_spatial_data(spatial_data, binary_data):
  function sparse_binary_data (line 42) | def sparse_binary_data(binary_data):
  function nn_data (line 49) | def nn_data():
  function binary_nn_data (line 58) | def binary_nn_data():
  function sparse_nn_data (line 69) | def sparse_nn_data():
  function repetition_dense (line 78) | def repetition_dense():
  function spatial_repeats (line 96) | def spatial_repeats(spatial_data):
  function binary_repeats (line 107) | def binary_repeats(binary_data):
  function sparse_spatial_data_repeats (line 120) | def sparse_spatial_data_repeats(spatial_repeats, binary_repeats):
  function sparse_binary_data_repeats (line 125) | def sparse_binary_data_repeats(binary_repeats):
  function sparse_test_data (line 130) | def sparse_test_data(nn_data, binary_nn_data):
  function iris (line 135) | def iris():
  function iris_selection (line 140) | def iris_selection():
  function aligned_iris (line 145) | def aligned_iris(iris):
  function aligned_iris_relations (line 152) | def aligned_iris_relations():
  function iris_model (line 157) | def iris_model(iris):
  function iris_model_large (line 162) | def iris_model_large(iris):
  function iris_subset_model (line 172) | def iris_subset_model(iris, iris_selection):
  function iris_subset_model_large (line 179) | def iris_subset_model_large(iris, iris_selection):
  function supervised_iris_model (line 189) | def supervised_iris_model(iris):
  function aligned_iris_model (line 196) | def aligned_iris_model(aligned_iris, aligned_iris_relations):
  function spatial_distances (line 206) | def spatial_distances():
  function binary_distances (line 221) | def binary_distances():

FILE: umap/tests/test_aligned_umap.py
  function nn_accuracy (line 13) | def nn_accuracy(true_nn, embd_nn):
  function test_neighbor_local_neighbor_accuracy (line 20) | def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_mod...
  function test_local_clustering (line 30) | def test_local_clustering(aligned_iris, aligned_iris_model):
  function test_aligned_update (line 44) | def test_aligned_update(aligned_iris, aligned_iris_relations):
  function test_aligned_update_params (line 57) | def test_aligned_update_params(aligned_iris, aligned_iris_relations):
  function test_aligned_update_array_error (line 74) | def test_aligned_update_array_error(aligned_iris, aligned_iris_relations):

FILE: umap/tests/test_chunked_parallel_spatial_metric.py
  function stashed_previous_impl_for_regression_test (line 22) | def stashed_previous_impl_for_regression_test():
  function workaround_590_impl (line 72) | def workaround_590_impl():
  function benchmark_data (line 121) | def benchmark_data(request):
  function test_chunked_parallel_alternative_implementations (line 131) | def test_chunked_parallel_alternative_implementations(
  function test_chunked_parallel_special_metric_implementation_hellinger (line 159) | def test_chunked_parallel_special_metric_implementation_hellinger(
  function test_benchmark_chunked_parallel_special_metric_x_only (line 242) | def test_benchmark_chunked_parallel_special_metric_x_only(
  function test_benchmark_workaround_590_x_only (line 266) | def test_benchmark_workaround_590_x_only(
  function test_benchmark_chunked_parallel_special_metric_x_y (line 296) | def test_benchmark_chunked_parallel_special_metric_x_y(
  function test_benchmark_workaround_590_x_y (line 320) | def test_benchmark_workaround_590_x_y(

FILE: umap/tests/test_composite_models.py
  function test_composite_trustworthiness (line 15) | def test_composite_trustworthiness(nn_data, iris_model):
  function test_composite_trustworthiness_random_init (line 47) | def test_composite_trustworthiness_random_init(nn_data):  # pragma: no c...
  function test_composite_trustworthiness_on_iris (line 75) | def test_composite_trustworthiness_on_iris(iris):
  function test_contrastive_trustworthiness_on_iris (line 100) | def test_contrastive_trustworthiness_on_iris(iris):

FILE: umap/tests/test_data_input.py
  function all_finite_data (line 8) | def all_finite_data():
  function inverse_data (line 13) | def inverse_data():
  function nan_dist (line 18) | def nan_dist(a: np.ndarray, b: np.ndarray):
  function test_check_input_data (line 24) | def test_check_input_data(all_finite_data, inverse_data):

FILE: umap/tests/test_densmap.py
  function test_densmap_trustworthiness (line 15) | def test_densmap_trustworthiness(nn_data):
  function test_densmap_trustworthiness_random_init (line 32) | def test_densmap_trustworthiness_random_init(nn_data):  # pragma: no cover
  function test_densmap_trustworthiness_on_iris (line 47) | def test_densmap_trustworthiness_on_iris(iris):
  function test_densmap_trustworthiness_on_iris_supervised (line 68) | def test_densmap_trustworthiness_on_iris_supervised(iris):

FILE: umap/tests/test_parametric_umap.py
  function moon_dataset (line 25) | def moon_dataset():
  function test_create_model (line 31) | def test_create_model(moon_dataset):
  function test_global_loss (line 41) | def test_global_loss(moon_dataset):
  function test_inverse_transform (line 51) | def test_inverse_transform(moon_dataset):
  function test_custom_encoder_decoder (line 67) | def test_custom_encoder_decoder(moon_dataset):
  function test_validation (line 109) | def test_validation(moon_dataset):
  function test_landmark_retraining_no_nan (line 149) | def test_landmark_retraining_no_nan():

FILE: umap/tests/test_plot.py
  function mapper (line 20) | def mapper(iris):
  function test_plot_runs_at_all (line 28) | def test_plot_runs_at_all(mapper, iris, iris_selection):

FILE: umap/tests/test_spectral.py
  function test_tsw_spectral_init (line 19) | def test_tsw_spectral_init(iris):
  function test_ensure_fallback_to_random_on_spectral_failure (line 42) | def test_ensure_fallback_to_random_on_spectral_failure():

FILE: umap/tests/test_umap_get_feature_names_out.py
  function test_get_feature_names_out (line 8) | def test_get_feature_names_out():
  function test_get_feature_names_out_default (line 24) | def test_get_feature_names_out_default():
  function test_get_feature_names_out_multicomponent (line 39) | def test_get_feature_names_out_multicomponent():
  function test_get_feature_names_out_featureunion (line 56) | def test_get_feature_names_out_featureunion():

FILE: umap/tests/test_umap_grads.py
  function numerical_gradient (line 7) | def numerical_gradient(f, x, eps=1e-6, forward_only=False):
  function numerical_grad_x (line 42) | def numerical_grad_x(dist, x, y, eps=1e-6, dist_kwargs=None, forward_onl...
  function sample_normal_pairs (line 49) | def sample_normal_pairs(n, d, rng=None):
  function sample_dirichlet_pairs (line 58) | def sample_dirichlet_pairs(n, d, alpha=1.0, rng=None):
  function sample_abundance_pairs (line 67) | def sample_abundance_pairs(n, d, shape=2.0, scale=1.0, rng=None):
  function assert_gradient_matches_finite_diff (line 76) | def assert_gradient_matches_finite_diff(
  function test_euclidean_gradient (line 130) | def test_euclidean_gradient(
  function test_minkowski_gradient (line 143) | def test_minkowski_gradient(dim, p):
  function test_weighted_minkowski_gradient (line 156) | def test_weighted_minkowski_gradient(dim, p):
  function test_cosine_gradient (line 169) | def test_cosine_gradient(
  function test_manhattan_gradient (line 181) | def test_manhattan_gradient(
  function test_chebyshev_gradient (line 195) | def test_chebyshev_gradient(
  function test_correlation_gradient (line 207) | def test_correlation_gradient(
  function test_braycurtis_gradient (line 219) | def test_braycurtis_gradient(
  function test_hellinger_gradient (line 231) | def test_hellinger_gradient(dim):
  function test_standardised_euclidean_gradient (line 242) | def test_standardised_euclidean_gradient(dim):
  function test_mahalanobis_gradient (line 255) | def test_mahalanobis_gradient(dim):
  function test_softmax_hellinger_gradient (line 270) | def test_softmax_hellinger_gradient(

FILE: umap/tests/test_umap_metrics.py
  function run_test_metric (line 31) | def run_test_metric(metric, test_data, dist_matrix, with_grad=False):
  function spatial_check (line 54) | def spatial_check(metric, spatial_data, spatial_distances, with_grad=Fal...
  function binary_check (line 71) | def binary_check(metric, binary_data, binary_distances):
  function run_test_sparse_metric (line 88) | def run_test_sparse_metric(metric, sparse_test_data, dist_matrix):
  function sparse_spatial_check (line 129) | def sparse_spatial_check(metric, sparse_spatial_data):
  function sparse_binary_check (line 150) | def sparse_binary_check(metric, sparse_binary_data):
  function test_euclidean (line 175) | def test_euclidean(spatial_data, spatial_distances):
  function test_manhattan (line 179) | def test_manhattan(spatial_data, spatial_distances):
  function test_chebyshev (line 183) | def test_chebyshev(spatial_data, spatial_distances):
  function test_minkowski (line 187) | def test_minkowski(spatial_data, spatial_distances):
  function test_hamming (line 191) | def test_hamming(spatial_data, spatial_distances):
  function test_canberra (line 195) | def test_canberra(spatial_data, spatial_distances):
  function test_braycurtis (line 199) | def test_braycurtis(spatial_data, spatial_distances):
  function test_cosine (line 203) | def test_cosine(spatial_data, spatial_distances):
  function test_correlation (line 207) | def test_correlation(spatial_data, spatial_distances):
  function test_jaccard (line 216) | def test_jaccard(binary_data, binary_distances):
  function test_matching (line 220) | def test_matching(binary_data, binary_distances):
  function test_dice (line 224) | def test_dice(binary_data, binary_distances):
  function test_kulsinski (line 231) | def test_kulsinski(binary_data, binary_distances):
  function test_rogerstanimoto (line 235) | def test_rogerstanimoto(binary_data, binary_distances):
  function test_russellrao (line 239) | def test_russellrao(binary_data, binary_distances):
  function test_sokalmichener (line 244) | def test_sokalmichener(binary_data, binary_distances):
  function test_sokalsneath (line 248) | def test_sokalsneath(binary_data, binary_distances):
  function test_yule (line 252) | def test_yule(binary_data, binary_distances):
  function test_sparse_euclidean (line 261) | def test_sparse_euclidean(sparse_spatial_data):
  function test_sparse_manhattan (line 265) | def test_sparse_manhattan(sparse_spatial_data):
  function test_sparse_chebyshev (line 269) | def test_sparse_chebyshev(sparse_spatial_data):
  function test_sparse_minkowski (line 273) | def test_sparse_minkowski(sparse_spatial_data):
  function test_sparse_hamming (line 277) | def test_sparse_hamming(sparse_spatial_data):
  function test_sparse_canberra (line 281) | def test_sparse_canberra(sparse_spatial_data):
  function test_sparse_cosine (line 285) | def test_sparse_cosine(sparse_spatial_data):
  function test_sparse_correlation (line 289) | def test_sparse_correlation(sparse_spatial_data):
  function test_sparse_braycurtis (line 293) | def test_sparse_braycurtis(sparse_spatial_data):
  function test_sparse_jaccard (line 302) | def test_sparse_jaccard(sparse_binary_data):
  function test_sparse_matching (line 306) | def test_sparse_matching(sparse_binary_data):
  function test_sparse_dice (line 310) | def test_sparse_dice(sparse_binary_data):
  function test_sparse_kulsinski (line 317) | def test_sparse_kulsinski(sparse_binary_data):
  function test_sparse_rogerstanimoto (line 321) | def test_sparse_rogerstanimoto(sparse_binary_data):
  function test_sparse_russellrao (line 325) | def test_sparse_russellrao(sparse_binary_data):
  function test_sparse_sokalmichener (line 330) | def test_sparse_sokalmichener(sparse_binary_data):
  function test_sparse_sokalsneath (line 335) | def test_sparse_sokalsneath(sparse_binary_data):
  function test_seuclidean (line 342) | def test_seuclidean(spatial_data):
  function test_weighted_minkowski (line 364) | def test_weighted_minkowski(spatial_data):
  function test_mahalanobis (line 383) | def test_mahalanobis(spatial_data):
  function test_haversine (line 402) | def test_haversine(spatial_data):
  function test_hellinger (line 422) | def test_hellinger(spatial_data):
  function test_sparse_hellinger (line 449) | def test_sparse_hellinger(sparse_spatial_data):
  function test_grad_metrics_match_metrics (line 495) | def test_grad_metrics_match_metrics(spatial_data, spatial_distances):
  function levenshtein_fn (line 584) | def levenshtein_fn(request):
  function test_core_distances (line 624) | def test_core_distances(levenshtein_fn, x, y, expected):
  function test_ascii_boundaries (line 638) | def test_ascii_boundaries(levenshtein_fn, x, y, expected):
  function test_length_difference_guard (line 642) | def test_length_difference_guard(levenshtein_fn):
  function test_length_difference_guard_normalised (line 650) | def test_length_difference_guard_normalised(levenshtein_fn):
  function test_max_dist_guard (line 658) | def test_max_dist_guard(levenshtein_fn):
  function test_max_dist_guard_normalised (line 666) | def test_max_dist_guard_normalised(levenshtein_fn):
  function test_fallback_path (line 674) | def test_fallback_path(levenshtein_fn):

FILE: umap/tests/test_umap_nn.py
  function test_nn_bad_metric (line 21) | def test_nn_bad_metric(nn_data):
  function test_nn_bad_metric_sparse_data (line 26) | def test_nn_bad_metric_sparse_data(sparse_nn_data):
  function knn (line 43) | def knn(indices, nn_data):  # pragma: no cover
  function smooth_knn (line 52) | def smooth_knn(nn_data, local_connectivity=1.0):
  function test_nn_descent_neighbor_accuracy (line 67) | def test_nn_descent_neighbor_accuracy(nn_data):  # pragma: no cover
  function test_nn_descent_neighbor_accuracy_low_memory (line 78) | def test_nn_descent_neighbor_accuracy_low_memory(nn_data):  # pragma: no...
  function test_angular_nn_descent_neighbor_accuracy (line 89) | def test_angular_nn_descent_neighbor_accuracy(nn_data):  # pragma: no cover
  function test_sparse_nn_descent_neighbor_accuracy (line 101) | def test_sparse_nn_descent_neighbor_accuracy(sparse_nn_data):  # pragma:...
  function test_sparse_nn_descent_neighbor_accuracy_low_memory (line 112) | def test_sparse_nn_descent_neighbor_accuracy_low_memory(
  function test_nn_descent_neighbor_accuracy_callable_metric (line 125) | def test_nn_descent_neighbor_accuracy_callable_metric(nn_data):  # pragm...
  function test_sparse_angular_nn_descent_neighbor_accuracy (line 137) | def test_sparse_angular_nn_descent_neighbor_accuracy(
  function test_smooth_knn_dist_l1norms (line 150) | def test_smooth_knn_dist_l1norms(nn_data):
  function test_smooth_knn_dist_l1norms_w_connectivity (line 160) | def test_smooth_knn_dist_l1norms_w_connectivity(nn_data):

FILE: umap/tests/test_umap_on_iris.py
  function test_umap_trustworthiness_on_iris (line 29) | def test_umap_trustworthiness_on_iris(iris, iris_model):
  function test_initialized_umap_trustworthiness_on_iris (line 37) | def test_initialized_umap_trustworthiness_on_iris(iris):
  function test_umap_trustworthiness_on_sphere_iris (line 52) | def test_umap_trustworthiness_on_sphere_iris(
  function test_umap_transform_on_iris (line 85) | def test_umap_transform_on_iris(iris, iris_subset_model, iris_selection):
  function test_umap_transform_on_iris_w_pynndescent (line 97) | def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection):
  function test_umap_transform_on_iris_modified_dtype (line 116) | def test_umap_transform_on_iris_modified_dtype(iris, iris_subset_model, ...
  function test_umap_sparse_transform_on_iris (line 129) | def test_umap_sparse_transform_on_iris(iris, iris_selection):
  function test_precomputed_transform_on_iris (line 152) | def test_precomputed_transform_on_iris(iris, iris_selection):
  function test_precomputed_sparse_transform_on_iris (line 176) | def test_precomputed_sparse_transform_on_iris(iris, iris_selection):
  function test_umap_clusterability_on_supervised_iris (line 200) | def test_umap_clusterability_on_supervised_iris(supervised_iris_model, i...
  function test_umap_inverse_transform_on_iris (line 208) | def test_umap_inverse_transform_on_iris(iris, iris_model):
  function test_precomputed_knn_on_iris (line 223) | def test_precomputed_knn_on_iris(iris, iris_selection, iris_subset_model):

FILE: umap/tests/test_umap_ops.py
  function test_blobs_cluster (line 41) | def test_blobs_cluster():
  function test_multi_component_layout (line 48) | def test_multi_component_layout():
  function test_multi_component_layout_precomputed (line 75) | def test_multi_component_layout_precomputed():
  function test_disconnected_data (line 107) | def test_disconnected_data(num_isolates, metric, force_approximation):
  function test_disconnected_data_precomputed (line 145) | def test_disconnected_data_precomputed(num_isolates, sparse):
  function test_bad_transform_data (line 176) | def test_bad_transform_data(nn_data):
  function test_umap_transform_embedding_stability (line 184) | def test_umap_transform_embedding_stability(iris, iris_subset_model, iri...
  function test_umap_update (line 224) | def test_umap_update(iris, iris_subset_model, iris_selection, iris_model):
  function test_umap_update_large (line 242) | def test_umap_update_large(
  function test_umap_graph_layout (line 268) | def test_umap_graph_layout():
  function test_component_layout_options (line 286) | def test_component_layout_options(nn_data):

FILE: umap/tests/test_umap_repeated_data.py
  function test_repeated_points_large_sparse_spatial (line 13) | def test_repeated_points_large_sparse_spatial(sparse_spatial_data_repeats):
  function test_repeated_points_small_sparse_spatial (line 24) | def test_repeated_points_small_sparse_spatial(sparse_spatial_data_repeats):
  function test_repeated_points_large_dense_spatial (line 33) | def test_repeated_points_large_dense_spatial(spatial_repeats):
  function test_repeated_points_small_dense_spatial (line 40) | def test_repeated_points_small_dense_spatial(spatial_repeats):
  function test_repeated_points_large_sparse_binary (line 53) | def test_repeated_points_large_sparse_binary(sparse_binary_data_repeats):
  function test_repeated_points_small_sparse_binary (line 60) | def test_repeated_points_small_sparse_binary(sparse_binary_data_repeats):
  function test_repeated_points_large_dense_binary (line 69) | def test_repeated_points_large_dense_binary(binary_repeats):
  function test_repeated_points_small_dense_binary (line 76) | def test_repeated_points_small_dense_binary(binary_repeats):
  function test_repeated_points_large_n (line 92) | def test_repeated_points_large_n(repetition_dense):

FILE: umap/tests/test_umap_trustworthiness.py
  function test_umap_sparse_trustworthiness (line 22) | def test_umap_sparse_trustworthiness(sparse_test_data):
  function test_umap_trustworthiness_fast_approx (line 30) | def test_umap_trustworthiness_fast_approx(nn_data):
  function test_umap_trustworthiness_random_init (line 45) | def test_umap_trustworthiness_random_init(nn_data):
  function test_supervised_umap_trustworthiness (line 56) | def test_supervised_umap_trustworthiness():
  function test_semisupervised_umap_trustworthiness (line 67) | def test_semisupervised_umap_trustworthiness():
  function test_metric_supervised_umap_trustworthiness (line 79) | def test_metric_supervised_umap_trustworthiness():
  function test_string_metric_supervised_umap_trustworthiness (line 95) | def test_string_metric_supervised_umap_trustworthiness():
  function test_discrete_metric_supervised_umap_trustworthiness (line 112) | def test_discrete_metric_supervised_umap_trustworthiness():
  function test_count_metric_supervised_umap_trustworthiness (line 128) | def test_count_metric_supervised_umap_trustworthiness():
  function test_sparse_precomputed_metric_umap_trustworthiness (line 145) | def test_sparse_precomputed_metric_umap_trustworthiness():

FILE: umap/tests/test_umap_validation_params.py
  function test_umap_negative_op (line 18) | def test_umap_negative_op(nn_data):
  function test_umap_too_large_op (line 24) | def test_umap_too_large_op(nn_data):
  function test_umap_bad_too_large_min_dist (line 30) | def test_umap_bad_too_large_min_dist(nn_data):
  function test_umap_negative_min_dist (line 40) | def test_umap_negative_min_dist(nn_data):
  function test_umap_negative_n_components (line 46) | def test_umap_negative_n_components(nn_data):
  function test_umap_non_integer_n_components (line 52) | def test_umap_non_integer_n_components(nn_data):
  function test_umap_too_small_n_neighbours (line 58) | def test_umap_too_small_n_neighbours(nn_data):
  function test_umap_negative_n_neighbours (line 64) | def test_umap_negative_n_neighbours(nn_data):
  function test_umap_bad_metric (line 70) | def test_umap_bad_metric(nn_data):
  function test_umap_negative_learning_rate (line 76) | def test_umap_negative_learning_rate(nn_data):
  function test_umap_negative_repulsion (line 82) | def test_umap_negative_repulsion(nn_data):
  function test_umap_negative_sample_rate (line 88) | def test_umap_negative_sample_rate(nn_data):
  function test_umap_bad_init (line 94) | def test_umap_bad_init(nn_data):
  function test_umap_bad_numeric_init (line 100) | def test_umap_bad_numeric_init(nn_data):
  function test_umap_bad_matrix_init (line 106) | def test_umap_bad_matrix_init(nn_data):
  function test_umap_negative_n_epochs (line 112) | def test_umap_negative_n_epochs(nn_data):
  function test_umap_negative_target_n_neighbours (line 118) | def test_umap_negative_target_n_neighbours(nn_data):
  function test_umap_bad_output_metric (line 124) | def test_umap_bad_output_metric(nn_data):
  function test_haversine_on_highd (line 136) | def test_haversine_on_highd(nn_data):
  function test_umap_haversine_embed_to_highd (line 142) | def test_umap_haversine_embed_to_highd(nn_data):
  function test_umap_too_many_neighbors_warns (line 148) | def test_umap_too_many_neighbors_warns(nn_data):
  function test_densmap_lambda (line 155) | def test_densmap_lambda(nn_data):
  function test_densmap_var_shift (line 161) | def test_densmap_var_shift(nn_data):
  function test_densmap_frac (line 167) | def test_densmap_frac(nn_data):
  function test_umap_unique_and_precomputed (line 176) | def test_umap_unique_and_precomputed(nn_data):
  function test_densmap_bad_output_metric (line 182) | def test_densmap_bad_output_metric(nn_data):
  function test_umap_bad_n_components (line 188) | def test_umap_bad_n_components(nn_data):
  function test_umap_bad_metrics (line 200) | def test_umap_bad_metrics(nn_data):
  function test_umap_bad_n_jobs (line 219) | def test_umap_bad_n_jobs(nn_data):
  function test_umap_custom_distance_w_grad (line 228) | def test_umap_custom_distance_w_grad(nn_data):
  function test_umap_bad_output_metric_no_grad (line 248) | def test_umap_bad_output_metric_no_grad(nn_data):
  function test_umap_bad_hellinger_data (line 258) | def test_umap_bad_hellinger_data(nn_data):
  function test_umap_update_bad_params (line 264) | def test_umap_update_bad_params(nn_data):
  function test_umap_fit_data_and_targets_compliant (line 277) | def test_umap_fit_data_and_targets_compliant():
  function test_umap_fit_instance_returned (line 297) | def test_umap_fit_instance_returned():
  function test_umap_inverse_transform_fails_expectedly (line 314) | def test_umap_inverse_transform_fails_expectedly(sparse_spatial_data, nn...

FILE: umap/umap_.py
  function flatten_iter (line 71) | def flatten_iter(container):
  function flattened (line 80) | def flattened(container):
  function breadth_first_search (line 84) | def breadth_first_search(adjmat, start, min_vertices):
  function raise_disconnected_warning (line 110) | def raise_disconnected_warning(
  function smooth_knn_dist (line 152) | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, ban...
  function nearest_neighbors (line 256) | def nearest_neighbors(
  function compute_membership_strengths (line 361) | def compute_membership_strengths(
  function fuzzy_simplicial_set (line 442) | def fuzzy_simplicial_set(
  function fast_intersection (line 621) | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_...
  function fast_metric_intersection (line 664) | def fast_metric_intersection(
  function reprocess_row (line 707) | def reprocess_row(probabilities, k=15, n_iters=32):
  function reset_local_metrics (line 737) | def reset_local_metrics(simplicial_set_indptr, simplicial_set_data):
  function reset_local_connectivity (line 749) | def reset_local_connectivity(simplicial_set, reset_local_metric=False):
  function discrete_metric_simplicial_set_intersection (line 780) | def discrete_metric_simplicial_set_intersection(
  function general_simplicial_set_intersection (line 858) | def general_simplicial_set_intersection(
  function general_simplicial_set_union (line 886) | def general_simplicial_set_union(simplicial_set1, simplicial_set2):
  function make_epochs_per_sample (line 906) | def make_epochs_per_sample(weights, n_epochs):
  function noisy_scale_coords (line 930) | def noisy_scale_coords(coords, random_state, max_coord=10.0, noise=0.0001):
  function simplicial_set_embedding (line 938) | def simplicial_set_embedding(
  function init_transform (line 1305) | def init_transform(indices, weights, embedding):
  function init_graph_transform (line 1337) | def init_graph_transform(graph, embedding):
  function init_update (line 1379) | def init_update(current_init, n_original_samples, indices):
  function find_ab_params (line 1393) | def find_ab_params(spread, min_dist):
  class UMAP (line 1411) | class UMAP(BaseEstimator, ClassNamePrefixFeaturesOutMixin):
    method __init__ (line 1665) | def __init__(
    method _validate_parameters (line 1752) | def _validate_parameters(self):
    method _check_custom_metric (line 2056) | def _check_custom_metric(self, metric, kwds, data=None):
    method _populate_combined_params (line 2076) | def _populate_combined_params(self, *models):
    method __mul__ (line 2125) | def __mul__(self, other):
    method __add__ (line 2197) | def __add__(self, other):
    method __sub__ (line 2267) | def __sub__(self, other):
    method fit (line 2339) | def fit(self, X, y=None, ensure_all_finite=True, **kwargs):
    method _fit_embed_data (line 2867) | def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs):
    method fit_transform (line 2897) | def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs):
    method transform (line 2950) | def transform(self, X, ensure_all_finite=True):
    method inverse_transform (line 3190) | def inverse_transform(self, X):
    method update (line 3357) | def update(self, X, ensure_all_finite=True):
    method __repr__ (line 3588) | def __repr__(self):

FILE: umap/utils.py
  function fast_knn_indices (line 15) | def fast_knn_indices(X, n_neighbors):
  function tau_rand_int (line 41) | def tau_rand_int(state):
  function tau_rand (line 67) | def tau_rand(state):
  function norm (line 84) | def norm(vec):
  function submatrix (line 102) | def submatrix(dmat, indices_col, n_neighbors):
  function ts (line 130) | def ts():
  function csr_unique (line 136) | def csr_unique(matrix, return_index=True, return_inverse=True, return_co...
  function disconnected_vertices (line 171) | def disconnected_vertices(model):
  function average_nn_distance (line 196) | def average_nn_distance(dist_matrix):

FILE: umap/validation.py
  function trustworthiness_vector_bulk (line 9) | def trustworthiness_vector_bulk(
  function make_trustworthiness_calculator (line 35) | def make_trustworthiness_calculator(metric):  # pragma: no cover
  function trustworthiness_vector (line 72) | def trustworthiness_vector(