NGT python
Open the specified index and create the index object for the index.
__init__(self: ngtpy.Index, path: str, read_only: bool=False, zero_based_numbering: bool=True, log_disabled: bool=False)
Returns
None.
path
Specify the path of the index to open.
read_only
Open the index as read-only. False means opening as read-write.
zero_based_numbering
Specify zero-based numbering for object IDs. False means one-based numbering.
log_disabled
Disable stderr messages about the progression of an operation.
Close the index.
close(self: ngtpy.Index)
Returns
None.
Insert the specified object into the index. Not build the index for the object. The function build_index below should be called to build the index after inserting objects by using this function.
int insert(self: ngtpy.Index, object: numpy.ndarray[float64])
Returns
ID for the inserted object.
object
Specify the inserted object.
Build the index for the objects that have been inserted by using the function insert.
build_index(self: ngtpy.Index, num_threads: int=8)
Returns
None.
num_thread
Specify the number of threads to build the index.
Insert the specified objects and builds the index for the objects. This function is almost the same as executing the ngt command "ngt append". You may execute the ngt command instead of calling this function.
batch_insert(self: ngtpy.Index, objects: numpy.ndarray[float64], num_threads: int=8)
Returns
None.
objects
Specify the inserted objects.
num_thread
Specify the number of threads to build the index.
Remove the specified object.
remove(self: ngtpy.Index, object_id: int)
Returns
None.
object_id
Specify the removed object ID.
Save the index.
save(self: ngtpy.Index)
Returns
None.
Get the specified object.
List[float] get_object(self: ngtpy.Index, object_id: int)
Returns
The specified object.
Get the number of the registered objects.
int get_num_of_objects()
Returns
The number of the registered objects.
Search the nearest objects to the specified query object.
object search(self: ngtpy.Index, query: object, size: int, epsilon: float, edge_size: int, with_distance: bool=True)
Returns
The list of tuples(object ID, distance) as the search result.
query
Specify the query object.
size
Specify the number of the objects as the search result.
epsilon
Specify epsilon which defines the explored range for the graph.
edge_size
Specify the number of edges for each node to explore the graph.
with_distance
Specify object IDs with distances as the result. False means that the result is a list of only object IDs.
Specify the default search parameters.
set(self: ngtpy.Index, num_of_search_objects: int, epsilon: float, search_radius: float)
Returns
None.
num_of_search_objects
Specify the number of search objects. The initial default is 20.
epsilon
Specify the epsilon which defines the explored range for the graph. The initial default is 0.1.
search_radius
Specify the search radius. The initial default is infinity.
Exports the index to a file.
export_index(self: ngtpy.Index, path: str)
Returns
None.
path
Path to file in which the exported index will be stored.
Imports the index from a file
import_index(self: ngtpy.Index, path: str)
Returns
None.
path
Path to file from which to load the index.
Create an empty index. This function is almost the same as executing the ngt command "ngt create" with an empty inserted object file. You may execute the ngt command instead of calling this function.
create(path: str, dimension: int, edge_size_for_creation: int=10, edge_size_for_search: int=40, distance_type: str='L2', object_type: str='Float')
Returns
None.
path
Specify the path of the index.
dimension
Specify the dimensionality of the inserted object.
edge_size_for_creation
Specify the initial number of edges for each node.
edge_size_for_search
Specify the number of edges for each node to explore the graph for the search processing.
distance_type
Specify the distance function for the objects.
- L1: L1 distance
- L2: L2 distance (default)
- Normalized L2: Normalized L2 distance. The specified data are automatically normalized to be appended to the index.
- Angle: Angle distance
- Normalized Angle: Normalized angle distance. The specified data are automatically normalized to be appended to the index.
- Cosine: Cosine similarity
- Normalized Cosine: Normalized cosine similarity. The specified data are automatically normalized to be appended to the index.
- Hamming: Hamming distance
- Jaccard: Jaccard distance
- Inner Product: Inner product (or dot product)
object_type
Specify the data type of the objects.
- Float: 4 byte floating point number
- Float16: 2 byte floating point number
- Byte: 1 byte unsigned integer
Create the optimizer object with the specified parameters.
__init__(self: ngtpy.Optimizer, num_of_outgoings: int=10, num_of_incomings: int=120, log_disabled: bool=False)
Returns
None.
num_of_outgoings
Specify the number of outgoing edges for each node to add to the reconstructed graph from the input graph. The specified number also means the lower bound of the outdegrees of the reconstructed graph.
num_of_incomings
Specify the number of incoming edges for each node to add to the reconstructed graph from the input graph. Unlike num_of_outgoings, after the direction of the edges are reversed, the edges are added to the reconstructed graph. The specified number also means the lower bound of the indegrees of the reconstructed graph.
log_disabled
Disable stderr messages about the progression of an operation.
Reconstruct an index from the specified index with the previously specified parameters, and optimize search coefficients, which is the same as call adjust_search_coefficients below.
execute(self: ngtpy.Optimizer, in_index_path: str, out_index_path: str)
in_index_path
Specify the input index path.
out_index_path
Specify the output index path.
Optimize search coefficients.
adjust_search_coefficients(self: ngtpy.Optimizer, index_path: str)
index_path
Specify the index which is optimized.
Open the specified quantized index and create the index object for the index.
__init__(self: ngtpy.QuantizedIndex, path: str, zero_based_numbering: bool=True, log_disabled: bool=False)
Returns
None.
path
Specify the path of the quantized index to open. The quantized index should be built by using the command ngtqg quantize from ONNG or ANNG in advance. The python function for quantization is not available yet.
zero_based_numbering
Specify zero-based numbering for object IDs. False means one-based numbering.
log_disabled
Disable stderr messages about the progression of an operation.
Search the nearest objects to the specified query object.
object search(self: ngtpy.QuantizedIndex, query: object, size: int, epsilon: float, result_expansion: float)
Returns
The list of tuples(object ID, distance) as the search result.
query
Specify the query object.
size
Specify the number of the objects as the search result.
epsilon
Specify epsilon which defines the explored range for the quantized graph.
result_expansion
Specify the expansion ratio of the number of approximate inner search objects to the number of search objects. For example, when the ratio is 10 and the number of search objects is 20, the number of the approximate search objects is set to 200 inside the search processing. A larger value brings higher accuracy but slower searching.
Specify the default search parameters.
set(self: ngtpy.QuantizedIndex, num_of_search_objects: int, search_radius: float, result_expansion: float)
Returns
None.
num_of_search_objects
Specify the number of search objects. The initial default is 20.
epsilon
Specify epsilon which defines the explored range for the graph. The initial default is 0.02.
result_expansion
Specify the expansion ratio of the number of approximate inner search objects to the number of search objects. The initial default is 3.0.