Module hdlib.space
Implementation of hyperdimensional Vector and Space.
hdlib provides the Space and Vector classes under hdlib.space for building the abstract representation of a hyperdimensional space which acts as a container for a multitude of vectors.
Classes
class Space (size: int = 10000, vtype: str = 'bipolar', from_file: Optional[-
Vectors space.
Initialize the vectors space as a dictionary of Vector objects.
Parameters
size:int, optional, default10000- Size of vectors in the space. It is 10,000 by default and cannot be less than that.
vtype:{'binary', 'bipolar'}, default'bipolar'- The type of vectors in space.
from_file:str, defaultNone- Path to a pickle file. Used to load a Space object from file.
Returns
Space- A new Space object.
Raises
Exception- If the pickle object in
from_fileis not instance of Space. FileNotFoundError- If
from_fileis not None but the file does not exist. ValueError-
- if
vtypeis different than 'binary' or 'bipolar'; - if
sizeis lower than 10,000.
- if
Examples
>>> from hdlib.space import Space >>> space = Space() <class 'hdlib.space.Space'>Create a Space object that can host bipolar vectors with a size of 10,000 by default.
>>> Space(size=10) ValueError: Size of vectors in space must be greater than or equal to 10000This throws a ValueError since the vector size cannot be less than 10,000.
>>> space1 = Space() >>> space1.dump(to_file='~/my_space.pkl') >>> space2 = Space(from_file='~/my_space.pkl') >>> type(space2) <class 'hdlib.space.Space'>This creates an empty space
space1, dumps the object to a pickle file under the home directory, and finally create a new space objectspace2from the pickle file.Expand source code
class Space(object): """Vectors space.""" def __init__(self, size: int=10000, vtype: str="bipolar", from_file: Optional[os.path.abspath]=None) -> "Space": """Initialize the vectors space as a dictionary of Vector objects. Parameters ---------- size : int, optional, default 10000 Size of vectors in the space. It is 10,000 by default and cannot be less than that. vtype : {'binary', 'bipolar'}, default 'bipolar' The type of vectors in space. from_file : str, default None Path to a pickle file. Used to load a Space object from file. Returns ------- Space A new Space object. Raises ------ Exception If the pickle object in `from_file` is not instance of Space. FileNotFoundError If `from_file` is not None but the file does not exist. ValueError - if `vtype` is different than 'binary' or 'bipolar'; - if `size` is lower than 10,000. Examples -------- >>> from hdlib.space import Space >>> space = Space() <class 'hdlib.space.Space'> Create a Space object that can host bipolar vectors with a size of 10,000 by default. >>> Space(size=10) ValueError: Size of vectors in space must be greater than or equal to 10000 This throws a ValueError since the vector size cannot be less than 10,000. >>> space1 = Space() >>> space1.dump(to_file='~/my_space.pkl') >>> space2 = Space(from_file='~/my_space.pkl') >>> type(space2) <class 'hdlib.space.Space'> This creates an empty space `space1`, dumps the object to a pickle file under the home directory, and finally create a new space object `space2` from the pickle file. """ # We may want to iterate over the Space object # Thus, we need to maintain the order of the vectors into the space dictionary self.space = OrderedDict() # Used to iterate over vectors in the space self._vector_index = 0 self.version = __version__ self.size = size if self.size < 10000: raise ValueError("Size of vectors in space must be greater than or equal to 10000") self.vtype = vtype.lower() if self.vtype not in ("binary", "bipolar"): raise ValueError("Vector type not supported") self.tags = dict() # Vector links can be used to define a tree structure # Use this flag to mark a vector as root self.root = None if from_file: if not os.path.isfile(from_file): raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), from_file) else: with open(from_file, "rb") as pkl: from_file_obj = pickle.load(pkl) if not isinstance(from_file_obj, type(self)): raise Exception("Pickle object is not instance of {}".format(type(self))) self.__dict__.update(from_file_obj.__dict__) if self.version != __version__: print("Warning: the specified Space has been created with a different version of hdlib") def __iter__(self) -> "Space": """Required to make the Space object iterable.""" return self def __next__(self) -> str: """Used to iterate over the vector objects into the Space. Returns ------- str The vector name at a specific position. """ if self._vector_index >= len(self.space): # Set the vector index back to the first position. # Redy to start iterating again over the vectors in the space self._vector_index = 0 raise StopIteration else: # Retrieve the vector name at a specific position in the space # Vectors are all ordered in the space since the space is defined as an OrderedDict vector = self.memory()[self._vector_index] # Increment the vector index for the next iteration self._vector_index += 1 # This returns the vector name or ID # It is enough, since the space is a hashmap and we can retrieve the Vector object in O(1) return vector def __contains__(self, vector: str) -> bool: """Check whether a vector is in the space. Parameters ---------- vector : str The vector name or ID. Returns ------- bool True if `vector` is in the space, False otherwise. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector = Vector(name="my_vector") >>> space.insert(vector) >>> "my_vector" in space True Create a Space object, add a Vector object into the space, and check whether the vector is actually in the space by searching for its name. """ return True if vector in self.space else False def __len__(self) -> int: """Get the number of vectors in space. Returns ------- int The number of vectors in space. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector = Vector() >>> space.insert(vector) >>> len(space) 1 Create a Space object, add a Vector object into the space, and check the total number of Vector objects in the space. """ return len(self.space) def __str__(self) -> None: """Print the Space object properties. Returns ------- str A description of the Space object. It reports the size, vector type, the number of vectors in space, the set of vectors tags, and the set of vectors names. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector = Vector(name='my_vector') >>> space.insert(vector) >>> print(space) Class: hdlib.space.Space Version: 0.1.17 Size: 10000 Type: bipolar Vectors: 1 Tags: [] IDs: ['my_vector'] Print the Space object properties. It contains only one vector. The vector size and type are 10,000 and 'bipolar' by default. """ return """ Class: hdlib.space.Space Version: {} Size: {} Type: {} Vectors: {} Tags: {} IDs: {} """.format( self.version, self.size, self.vtype, len(self.space), np.array(list(self.tags.keys())), np.array(list(self.space.keys())) ) def memory(self) -> List[str]: """Return names or IDs of vectors in space. Returns ------- list A list with vectors names or IDs Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector = Vector(name='my_vector') >>> space.insert(vector) >>> space.memory() ['my_vector'] Create a Space and add a Vector called 'my_vector'. The memory function returns the list of vector names. In this case a list with one element only 'my_vector'. """ return list(self.space.keys()) def get( self, names: Optional[List[str]]=None, tags: Optional[List[Union[str, int, float]]]=None ) -> List[Vector]: """Get vectors by names or tags. Parameters ---------- names : list, optional A list with vector names. It is required in case no tags are specified. tags : list, optional A list with vector tags. It is required in case no names are specified. Returns ------- list A list of Vector objects in the space according to the specified names or tags. Raises ------ Exception - if no `names` or `tags` are provided in input; - if both `names` and `tags` are provided in input. TypeError If names or tags in the input lists are not instance of primitives. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='my_vector_1', tags={'tag1', 'tag2'}) >>> vector2 = Vector(name='my_vector_2', tags={'tag2', 'tag3', 'tag4'}) >>> space.insert(vector1) >>> space.insert(vector2) >>> vectors = space.get(tags=['tag2']) >>> for vector in vectors: ... print(vector.name) my_vector_1 my_vector_2 This creates two Vector objects with a few tags and add them to a Space. It then retrieves a list of vectors by searching for a specific tag which is in common between the two vectors in this case. It finally prints the vector names. """ if not names and not tags: raise Exception("No names or tags provided") if names and tags: raise Exception("Cannot search for vectors by their names and tags at the same time") vectors = set() if names: try: names = [str(name) for name in names] except: raise TypeError("Vector name must be instance of a primitive") for vector_name in names: if vector_name in self.space: vectors.add(self.space[vector_name]) elif tags: for tag in tags: if not isinstance(tag, str) and not isinstance(tag, int) and not isinstance(tag, float): raise TypeError("A tags must be string, integer, or float") if tag in self.tags: for vector_name in self.tags[tag]: vectors.add(self.space[vector_name]) return list(vectors) def insert(self, vector: Vector) -> None: """Add a Vector object to the space. Parameters ---------- vector : Vector The input Vector object that must be added to the Space Raises ------ Exception - if the vector size or type is not compatible with the space; - if a vector with the same name of the input one is already in the space. Examples -------- >>> from hdlib.space import Space, Vector >>> vector = Vector() >>> space = Space() >>> space.insert(vector) It creates a random bipolar vector with size 10,000 and adds it to a space that by default can host bipolar vectors with size 10,000. >>> vector = Vector(size=15000) >>> space = Space() >>> space.insert(vector) Exception: Space and vectors with different size are not compatible By default, the space can host bipolar vectors with size 10,000, while here we explicitly created a Vector object with size 15,000 which is not compatible with the space. """ if self.size != vector.size: raise Exception("Space and vectors with different size are not compatible") if self.vtype != vector.vtype: raise Exception("Attempting to insert a {} vector into a {} space: failed".format(vector.vtype, self.vtype)) if vector.name in self.space: raise Exception("Vector \"{}\" already in space".format(vector.name)) self.space[vector.name] = vector for tag in vector.tags: if tag not in self.tags: self.tags[tag] = set() self.tags[tag].add(vector.name) def bulk_insert( self, names: List[str], tags: Optional[List[List[Union[str, int, float]]]]=None, ignore_existing: bool=False ) -> None: """Add vectors to the space in bulk. Parameters ---------- names : list A list with vector names. tags : list, optional An optional list of lists with vector tags. ignore_existing : bool, default False If True, do not raise an exception in case the space contains a vector with the same name specified in `names`. Raises ------ TypeError - if `names` or `tags` are not instance of list; - if the elements of the `names` list are not instance of a primitive. Exception - if the number of elements in `names` doesn't match with the number of elements in `tags`; - if there already a vector in the space with the same name in `names`. Examples -------- >>> from hdlib.space import Space >>> space = Space() >>> space.bulk_insert(names=['my_vector_1', 'my_vector_2']) >>> space.memory() ['my_vector_1', 'my_vector_2'] Create two random bipolar vectors with size 10,000 just by specifying a list with vector names. The vector type and size is inherited by the space that by default can host bipolar vectors with size 10,000. >>> space.bulk_insert(names=['my_vector_3', 'my_vector_4'], tags=[['tag1'], ['tag1', 'tag2']]) >>> vectors = space.get(tags=['tag1']) >>> for vector in vectors: ... print(vector.name) my_vector_3 my_vector_4 Add other two vectors and assigned them a few tags, then retrieve the vectors with tag 'tag1'. Both 'my_vector_3' and 'my_vector_4' contain 'tag1' in their set of tags. """ if not isinstance(names, list): raise TypeError("Input must be a list of strings") if tags and not isinstance(tags, list): raise TypeError("tags must be a list of lists of strings") if tags and len(names) != len(tags): raise Exception("The number of vector IDs must match the size of the tags list") names = set(names) for pos, name in enumerate(names): if not isinstance(name, (bool, str, int, float, None)): raise TypeError("Entries in input list must be instances of primitives") name = str(name) if name in self.space: if not ignore_existing: raise Exception("Vector \"{}\" already exists in the space".format(name)) else: continue vector_tags = set(tags[pos]) if tags else set() vector = Vector(name=name, size=self.size, tags=vector_tags, vtype=self.vtype) self.insert(vector) def remove(self, name: str) -> Vector: """Remove a vector from the space by its name. Parameters ---------- name : str The name of the vector that must be removed from the space. Returns ------- Vector Returns the Vector object. Raises ------ TypeError If the vector name is not an instance of a primitive. Exception If there is not a vector with that specific name in the space. Examples -------- >>> form hdlib.space import Space, Vector >>> vector = Vector(name='my_vector') >>> space = Space() >>> space.insert(vector) >>> space.remove('my_vector') >>> len(space) 0 Create a vector called 'my_vector', add it to the space and then remove it. Finally check how many vectors are in the space. """ try: name = str(name) except: raise TypeError("Vector name must be instance of a primitive") if name not in self.space: raise Exception("Vector not in space") vector = self.space[name] del self.space[name] for tag in vector.tags: self.tags[tag].remove(vector.name) if not self.tags[tag]: del self.tags[tag] return vector def add_tag(self, name: str, tag: Union[str, int, float]) -> None: """Tag a vector. Parameters ---------- name : str The vector name or ID. tag : str, int, float The tag must be a primitive. Raises ------ TypeError If the name or tag are not instance of primitives. Exception If there is not a vector in the space with that specific name or ID. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> my_vector = Vector(name='my_vector') >>> space.insert(my_vector) >>> space.add_tag('my_vector', 'tag') >>> for vector in space.get(tags['tag']): ... print(vector.name) my_vector This creates a Vector object add it to a Space. It then assigns a tag to the vector and searches for vector with that specific tag within the space. It finally prints the vector names. """ try: name = str(name) except: raise TypeError("Vector name must be instance of a primitive") if name not in self.space: raise Exception("Vector not in space") if not isinstance(tag, str) and not isinstance(tag, int) and not isinstance(tag, float): raise TypeError("Tags must be string, integer, or float") self.space[name].tags.add(tag) if tag not in self.tags: self.tags[tag] = set() self.tags[tag].add(name) def remove_tag(self, name: str, tag: Union[str, int, float]) -> None: """Untag a vector. Parameters ---------- name : str The vector name or ID. tag : str, int, float The tag must be a primitive. Raises ------ TypeError If the name or tag are not instance of primitives. Exception If there is not a vector in the space with that specific name or ID. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> my_vector = Vector(name='my_vector', tags={'tag'}) >>> space.insert(my_vector) >>> space.remove_tag('my_vector', 'tag') >>> len(space.get(tags['tag'])) 0 This initializes a space, inserts a vector with a tag into the space, then untags the vector, and finally searches for vectors with that specific tag. No vectors are returned since there was only one vector with that tag that has been untagged. """ try: name = str(name) except: raise TypeError("Vector name must be instance of a primitive") if name not in self.space: raise Exception("Vector not in space") if not isinstance(tag, str) and not isinstance(tag, int) and not isinstance(tag, float): raise TypeError("Tags must be string, integer, or float") if tag in self.tags: self.space[name].tags.remove(tag) self.tags[tag].remove(name) if not self.tags[tag]: del self.tags[tag] def link(self, name1: str, name2: str) -> None: """Link two vectors in the space through by their names. Links are directed edges. Parameters ---------- name1 : str Name or ID of the first vector. name2 : str Name or ID of the second vector. Raises ------ TypeError If vectors names are not instance of a primitive. Exception If there are no vectors in space named `name1` and `name2`. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1') >>> vector2 = Vector(name='vector2') >>> space.insert(vector1) >>> space.insert(vector2) >>> space.link('vector2', 'vector1') >>> vector2 = space.get(names=['vector2'])[0] >>> 'vector1' in vector2.children True Define a space with two vectors 'vector1' and 'vector2'. Link 'vector2' with 'vector1'. Retrieve 'vector2' from the space and check whether 'vector1' is in its set of linked nodes. """ try: name1 = str(name1) name2 = str(name2) except: raise TypeError("Vector name must be instance of a primitive") if name1 not in self.space: raise Exception("Vector \"{}\" not in space".format(name1)) if name2 not in self.space: raise Exception("Vector \"{}\" not in space".format(name2)) self.space[name1].children.add(name2) self.space[name2].parents.add(name1) def set_root(self, name: str) -> None: """Vector links can be used to define a tree structure. Set a specific vector as root. Parameters ---------- name : str Name or ID of vector in space. Raises ------ TypeError If the vector name or ID is not instance of a primitive. Exception If there are no vectors in the space with the specified name. Examples -------- >>> from hdlib.space import Space >>> space = Space() >>> space.bulk_insert(names=['vector1', 'vector2', 'vector3']) >>> space.link('vector1', 'vector2') >>> space.link('vector1', 'vector3') >>> space.set_root('vector1') >>> vector1 = space.get(names=['vector1'])[0] >>> for vector in vector1.children: ... print(vector) vector2 vector3 Create a space and add three vectors in bulk. Link 'vector1' to 'vector2' and 'vector3', and set 'vector1' as root. Finally, print the name of the nodes linked to the root. """ try: name = str(name) except: raise TypeError("Vector name must be instance of a primitive") if name not in self.space: raise Exception("Vector \"{}\" not in space".format(name)) self.root = name def find(self, vector: Vector, threshold: float=np.Inf, method: str="cosine") -> Tuple[str, float]: """Search for the closest vector in space. Parameters ---------- vector : Vector Input Vector object. Search for the closest vector to this Vector in the space. threshold : float, default numpy.Inf Threshold on distance between vectors. method : {'cosine', 'euclidean', 'hamming'}, default 'cosine' Distance metric. Returns ------- tuple A tuple with the name of the closest vector in space and its distance with the input vector. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1') >>> vector2 = Vector(name='vector2') >>> vector3 = Vector(name='vector3') >>> space.insert(vector1) >>> space.insert(vector2) >>> space.insert(vector3) >>> space.find(vector1) ('vector1', 0.0) Create a space with three vectors 'vector1', 'vector2', and 'vector3', and search for the closest vector to 'vector1'. The result is obviously itself, 'vector1', with a cosine distance of 0.0. """ # Exploit self.find_all() to seach for the best match # It will take care of raising exceptions in case of problems with input arguments distances, best = self.find_all(vector, threshold=threshold, method=method) return best, distances[best] def find_all(self, vector: Vector, threshold: float=np.Inf, method: str="cosine") -> Tuple[dict, str]: """Compute distance of the input vector against all vectors in space. Parameters ---------- vector : Vector Input Vector object. Search for the closest vector to this Vector in the space. threshold : float, default numpy.Inf Threshold on distance between vectors. method : {'cosine', 'euclidean', 'hamming'}, default 'cosine' Distance metric. Returns ------- dict A dictionary the distances between the input vector and all the other vectors in the space, in addition to the name of the closest vector. Raises ------ ValueError If the threshold is lower than 0.0. Exception If the size of the input vector is not compatible with the size of vectors in the space. Examples -------- >>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1', seed=1) >>> vector2 = Vector(name='vector2', seed=2) >>> vector3 = Vector(name='vector3', seed=3) >>> space.insert(vector1) >>> space.insert(vector2) >>> space.insert(vector3) >>> space.find_all(vector1) ({'vector1': 0.0, 'vector2': 0.996, 'vector3': 0.985}, 'vector1') Create a space with three vectors 'vector1', 'vector2', and 'vector3', and compute the cosine distance between 'vector1' and all the other vectors in space (including itseld). The closest vector is obviously itself, 'vector1', with a cosine distance of 0.0. Use a seed for reproducing the same distances. """ if self.size != vector.size: raise Exception("Space and vectors with different size are not compatible") if threshold < 0.0: raise ValueError("Threshold cannot be lower than 0.0") distances = dict() distance = np.Inf best = None for v in self.space: # Compute distance dist = self.space[v].dist(vector, method=method) if dist <= threshold: distances[v] = dist if distances[v] < distance: best = v distance = distances[v] return distances, best def dump(self, to_file: Optional[os.path.abspath]=None) -> None: """Dump the Space object to a pickle file. Parameters ---------- to_file Path to the file used to dump the Space object to. Raises ------ Exception If the `to_file` file already exists. Examples -------- >>> import os >>> from hdlib.space import Space >>> space = Space() >>> space.dump(to_file='~/my_space.pkl') >>> os.path.isfile('~/my_space.pkl') True Create a Space object and dump it to a pickle file under the home directory. """ if not to_file: # Dump the space to a pickle file in the current working directory if not file path is provided to_file = os.path.join(os.getcwd(), "space.pkl") if os.path.isfile(to_file): raise Exception("The output file already exists!\n{}".format(to_file)) with open(to_file, "wb") as pkl: pickle.dump(self, pkl)Methods
def add_tag(self, name: str, tag: Union[str, int, float]) ‑> None-
Tag a vector.
Parameters
name:str- The vector name or ID.
tag:str, int, float- The tag must be a primitive.
Raises
TypeError- If the name or tag are not instance of primitives.
Exception- If there is not a vector in the space with that specific name or ID.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> my_vector = Vector(name='my_vector') >>> space.insert(my_vector) >>> space.add_tag('my_vector', 'tag') >>> for vector in space.get(tags['tag']): ... print(vector.name) my_vectorThis creates a Vector object add it to a Space. It then assigns a tag to the vector and searches for vector with that specific tag within the space. It finally prints the vector names.
def bulk_insert(self, names: List[str], tags: Optional[List[List[Union[str, int, float]]]] = None, ignore_existing: bool = False) ‑> None-
Add vectors to the space in bulk.
Parameters
names:list- A list with vector names.
tags:list, optional- An optional list of lists with vector tags.
ignore_existing:bool, defaultFalse- If True, do not raise an exception in case the space contains a vector with the same name specified in
names.
Raises
TypeError-
- if
namesortagsare not instance of list; - if the elements of the
nameslist are not instance of a primitive.
- if
Exception-
- if the number of elements in
namesdoesn't match with the number of elements intags; - if there already a vector in the space with the same name in
names.
- if the number of elements in
Examples
>>> from hdlib.space import Space >>> space = Space() >>> space.bulk_insert(names=['my_vector_1', 'my_vector_2']) >>> space.memory() ['my_vector_1', 'my_vector_2']Create two random bipolar vectors with size 10,000 just by specifying a list with vector names. The vector type and size is inherited by the space that by default can host bipolar vectors with size 10,000.
>>> space.bulk_insert(names=['my_vector_3', 'my_vector_4'], tags=[['tag1'], ['tag1', 'tag2']]) >>> vectors = space.get(tags=['tag1']) >>> for vector in vectors: ... print(vector.name) my_vector_3 my_vector_4Add other two vectors and assigned them a few tags, then retrieve the vectors with tag 'tag1'. Both 'my_vector_3' and 'my_vector_4' contain 'tag1' in their set of tags.
def dump(self, to_file: Optional[-
Dump the Space object to a pickle file.
Parameters
to_file- Path to the file used to dump the Space object to.
Raises
Exception- If the
to_filefile already exists.
Examples
>>> import os >>> from hdlib.space import Space >>> space = Space() >>> space.dump(to_file='~/my_space.pkl') >>> os.path.isfile('~/my_space.pkl') TrueCreate a Space object and dump it to a pickle file under the home directory.
def find(self, vector: Vector, threshold: float = inf, method: str = 'cosine') ‑> Tuple[str, float]-
Search for the closest vector in space.
Parameters
vector:Vector- Input Vector object. Search for the closest vector to this Vector in the space.
threshold:float, defaultnumpy.Inf- Threshold on distance between vectors.
method:{'cosine', 'euclidean', 'hamming'}, default'cosine'- Distance metric.
Returns
tuple- A tuple with the name of the closest vector in space and its distance with the input vector.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1') >>> vector2 = Vector(name='vector2') >>> vector3 = Vector(name='vector3') >>> space.insert(vector1) >>> space.insert(vector2) >>> space.insert(vector3) >>> space.find(vector1) ('vector1', 0.0)Create a space with three vectors 'vector1', 'vector2', and 'vector3', and search for the closest vector to 'vector1'. The result is obviously itself, 'vector1', with a cosine distance of 0.0.
def find_all(self, vector: Vector, threshold: float = inf, method: str = 'cosine') ‑> Tuple[dict, str]-
Compute distance of the input vector against all vectors in space.
Parameters
vector:Vector- Input Vector object. Search for the closest vector to this Vector in the space.
threshold:float, defaultnumpy.Inf- Threshold on distance between vectors.
method:{'cosine', 'euclidean', 'hamming'}, default'cosine'- Distance metric.
Returns
dict- A dictionary the distances between the input vector and all the other vectors in the space, in addition to the name of the closest vector.
Raises
ValueError- If the threshold is lower than 0.0.
Exception- If the size of the input vector is not compatible with the size of vectors in the space.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1', seed=1) >>> vector2 = Vector(name='vector2', seed=2) >>> vector3 = Vector(name='vector3', seed=3) >>> space.insert(vector1) >>> space.insert(vector2) >>> space.insert(vector3) >>> space.find_all(vector1) ({'vector1': 0.0, 'vector2': 0.996, 'vector3': 0.985}, 'vector1')Create a space with three vectors 'vector1', 'vector2', and 'vector3', and compute the cosine distance between 'vector1' and all the other vectors in space (including itseld). The closest vector is obviously itself, 'vector1', with a cosine distance of 0.0. Use a seed for reproducing the same distances.
def get(self, names: Optional[List[str]] = None, tags: Optional[List[Union[str, int, float]]] = None) ‑> List[Vector]-
Get vectors by names or tags.
Parameters
names:list, optional- A list with vector names. It is required in case no tags are specified.
tags:list, optional- A list with vector tags. It is required in case no names are specified.
Returns
list- A list of Vector objects in the space according to the specified names or tags.
Raises
Exception-
- if no
namesortagsare provided in input; - if both
namesandtagsare provided in input.
- if no
TypeError- If names or tags in the input lists are not instance of primitives.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='my_vector_1', tags={'tag1', 'tag2'}) >>> vector2 = Vector(name='my_vector_2', tags={'tag2', 'tag3', 'tag4'}) >>> space.insert(vector1) >>> space.insert(vector2) >>> vectors = space.get(tags=['tag2']) >>> for vector in vectors: ... print(vector.name) my_vector_1 my_vector_2This creates two Vector objects with a few tags and add them to a Space. It then retrieves a list of vectors by searching for a specific tag which is in common between the two vectors in this case. It finally prints the vector names.
def insert(self, vector: Vector) ‑> None-
Add a Vector object to the space.
Parameters
vector:Vector- The input Vector object that must be added to the Space
Raises
Exception-
- if the vector size or type is not compatible with the space;
- if a vector with the same name of the input one is already in the space.
Examples
>>> from hdlib.space import Space, Vector >>> vector = Vector() >>> space = Space() >>> space.insert(vector)It creates a random bipolar vector with size 10,000 and adds it to a space that by default can host bipolar vectors with size 10,000.
>>> vector = Vector(size=15000) >>> space = Space() >>> space.insert(vector) Exception: Space and vectors with different size are not compatibleBy default, the space can host bipolar vectors with size 10,000, while here we explicitly created a Vector object with size 15,000 which is not compatible with the space.
def link(self, name1: str, name2: str) ‑> None-
Link two vectors in the space through by their names. Links are directed edges.
Parameters
name1:str- Name or ID of the first vector.
name2:str- Name or ID of the second vector.
Raises
TypeError- If vectors names are not instance of a primitive.
Exception- If there are no vectors in space named
name1andname2.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> vector1 = Vector(name='vector1') >>> vector2 = Vector(name='vector2') >>> space.insert(vector1) >>> space.insert(vector2) >>> space.link('vector2', 'vector1') >>> vector2 = space.get(names=['vector2'])[0] >>> 'vector1' in vector2.children TrueDefine a space with two vectors 'vector1' and 'vector2'. Link 'vector2' with 'vector1'. Retrieve 'vector2' from the space and check whether 'vector1' is in its set of linked nodes.
def memory(self) ‑> List[str]-
Return names or IDs of vectors in space.
Returns
list- A list with vectors names or IDs
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> vector = Vector(name='my_vector') >>> space.insert(vector) >>> space.memory() ['my_vector']Create a Space and add a Vector called 'my_vector'. The memory function returns the list of vector names. In this case a list with one element only 'my_vector'.
def remove(self, name: str) ‑> Vector-
Remove a vector from the space by its name.
Parameters
name:str- The name of the vector that must be removed from the space.
Returns
Vector- Returns the Vector object.
Raises
TypeError- If the vector name is not an instance of a primitive.
Exception- If there is not a vector with that specific name in the space.
Examples
>>> form hdlib.space import Space, Vector >>> vector = Vector(name='my_vector') >>> space = Space() >>> space.insert(vector) >>> space.remove('my_vector') >>> len(space) 0Create a vector called 'my_vector', add it to the space and then remove it. Finally check how many vectors are in the space.
def remove_tag(self, name: str, tag: Union[str, int, float]) ‑> None-
Untag a vector.
Parameters
name:str- The vector name or ID.
tag:str, int, float- The tag must be a primitive.
Raises
TypeError- If the name or tag are not instance of primitives.
Exception- If there is not a vector in the space with that specific name or ID.
Examples
>>> from hdlib.space import Space, Vector >>> space = Space() >>> my_vector = Vector(name='my_vector', tags={'tag'}) >>> space.insert(my_vector) >>> space.remove_tag('my_vector', 'tag') >>> len(space.get(tags['tag'])) 0This initializes a space, inserts a vector with a tag into the space, then untags the vector, and finally searches for vectors with that specific tag. No vectors are returned since there was only one vector with that tag that has been untagged.
def set_root(self, name: str) ‑> None-
Vector links can be used to define a tree structure. Set a specific vector as root.
Parameters
name:str- Name or ID of vector in space.
Raises
TypeError- If the vector name or ID is not instance of a primitive.
Exception- If there are no vectors in the space with the specified name.
Examples
>>> from hdlib.space import Space >>> space = Space() >>> space.bulk_insert(names=['vector1', 'vector2', 'vector3']) >>> space.link('vector1', 'vector2') >>> space.link('vector1', 'vector3') >>> space.set_root('vector1') >>> vector1 = space.get(names=['vector1'])[0] >>> for vector in vector1.children: ... print(vector) vector2 vector3Create a space and add three vectors in bulk. Link 'vector1' to 'vector2' and 'vector3', and set 'vector1' as root. Finally, print the name of the nodes linked to the root.
class Vector (name: Optional[str] = None, size: int = 10000, vector: Optional[numpy.ndarray] = None, vtype: str = 'bipolar', tags: Optional[Set[Union[str, int, float]]] = None, seed: Optional[int] = None, warning: bool = False, from_file: Optional[-
Vector object.
Initialize a Vector object.
Parameters
name:str, optional- The unique identifier of the Vector object. A random UUID v4 is generated if not specified.
size:int, optional, default10000- The size of the vector. It is 10,000 by default and cannot be less than that.
vector:numpy.ndarray, optional, defaultNone- The actual vector. A random vector is created if not specified.
vtype:{'binary', 'bipolar'}, default'bipolar'- The vector type.
tags:set, defaultNone- An optional set of vector tags. Tags can be str, int, and float.
seed:int, defaultNone- An optional seed for reproducibly generating the vector numpy.ndarray randomly.
warning:bool, defaultFalse- Print warning messages if True.
from_file:str, defaultNone- Path to a pickle file. Used to load a Vector object from file.
Returns
Vector- A new Vector object.
Raises
Exception- If the pickle object in
from_fileis not instance of Vector. FileNotFoundError- If
from_fileis not None but the file does not exist. TypeError-
- if the vector name is not instance of a primitive;
- if
tagsis not an instance of set; - if
vectoris not an instance of numpy.ndarray; - if
sizeis not an integer number.
ValueError-
- if
vtypeis different than 'binary' or 'bipolar'; - if
sizeis lower than 10,000.
- if
Examples
>>> from hdlib.space import Vector >>> vector = Vector() >>> type(vector) <class 'hdlib.space.Vector'>A new bipolar vector with a size of 10,000 is created by default.
>>> vector = Vector(size=10) ValueError: Vector size must be greater than or equal to 10000This throws a ValueError since the vector size cannot be less than 10,000.
>>> vector1 = Vector() >>> vector1.dump(to_file='~/my_vector.pkl') >>> vector2 = Vector(from_file='~/my_vector.pkl') >>> type(vector2) <class 'hdlib.space.Vector'>This creates a random bipolar vector
vector1, dumps the object to a pickle file under the home directory, and finally create a new vector objectvector2from the pickle file.Expand source code
class Vector(object): """Vector object.""" def __init__( self, name: Optional[str]=None, size: int=10000, vector: Optional[np.ndarray]=None, vtype: str="bipolar", tags: Optional[Set[Union[str, int, float]]]=None, seed: Optional[int]=None, warning: bool=False, from_file: Optional[os.path.abspath]=None, ) -> "Vector": """Initialize a Vector object. Parameters ---------- name : str, optional The unique identifier of the Vector object. A random UUID v4 is generated if not specified. size : int, optional, default 10000 The size of the vector. It is 10,000 by default and cannot be less than that. vector : numpy.ndarray, optional, default None The actual vector. A random vector is created if not specified. vtype : {'binary', 'bipolar'}, default 'bipolar' The vector type. tags : set, default None An optional set of vector tags. Tags can be str, int, and float. seed : int, default None An optional seed for reproducibly generating the vector numpy.ndarray randomly. warning : bool, default False Print warning messages if True. from_file : str, default None Path to a pickle file. Used to load a Vector object from file. Returns ------- Vector A new Vector object. Raises ------ Exception If the pickle object in `from_file` is not instance of Vector. FileNotFoundError If `from_file` is not None but the file does not exist. TypeError - if the vector name is not instance of a primitive; - if `tags` is not an instance of set; - if `vector` is not an instance of numpy.ndarray; - if `size` is not an integer number. ValueError - if `vtype` is different than 'binary' or 'bipolar'; - if `size` is lower than 10,000. Examples -------- >>> from hdlib.space import Vector >>> vector = Vector() >>> type(vector) <class 'hdlib.space.Vector'> A new bipolar vector with a size of 10,000 is created by default. >>> vector = Vector(size=10) ValueError: Vector size must be greater than or equal to 10000 This throws a ValueError since the vector size cannot be less than 10,000. >>> vector1 = Vector() >>> vector1.dump(to_file='~/my_vector.pkl') >>> vector2 = Vector(from_file='~/my_vector.pkl') >>> type(vector2) <class 'hdlib.space.Vector'> This creates a random bipolar vector `vector1`, dumps the object to a pickle file under the home directory, and finally create a new vector object `vector2` from the pickle file. """ # Conditions on vector name or ID # Vector name is casted to string. For this reason, only Python primitives are allowed # A random name is assigned if not specified try: if name is None: name = str(uuid.uuid4()) else: name = str(name) self.name = name except: raise TypeError("Vector name must be instance of a primitive") # Register random seed for reproducibility self.seed = seed # Take track of the hdlib version self.version = __version__ if tags and not isinstance(tags, set): raise TypeError("Tags must be a set") # Add tags self.tags = tags if tags else set() # Add links # Used to link Vectors by their names or IDs self.parents = set() self.children = set() # Conditions on vector # It must be a numpy.ndarray # A random vector is generated if not specified if vector is not None: if not isinstance(vector, np.ndarray): raise TypeError("Vector must be instance of numpy.ndarray") self.vector = vector self.size = len(self.vector) if self.size < 10000: raise ValueError("Vector size must be greater than or equal to 10000") self.vtype = vtype # Try to infer the vector type from the content of the vector itself # only in the case where the elements are not all 1s if not (self.vector == 1).all(): if ((self.vector == 0) | (self.vector == 1)).all(): self.vtype = "binary" elif ((self.vector == -1) | (self.vector == 1)).all(): self.vtype = "bipolar" else: if warning: print("Vector type can be binary or bipolar only") elif from_file: if not os.path.isfile(from_file): raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), from_file) else: # Load vector from pickle file with open(from_file, "rb") as pkl: from_file_obj = pickle.load(pkl) if not isinstance(from_file_obj, type(self)): raise Exception("Pickle object is not instance of {}".format(type(self))) self.__dict__.update(from_file_obj.__dict__) if self.version != __version__: print("Warning: the specified Space has been created with a different version of hdlib") else: # Conditions on vector size # It must be an integer number greater than or equal to 10000 # This size makes sure that vectors are quasi-orthogonal in space if not isinstance(size, int): raise TypeError("Vector size must be an integer number") if size < 10000: raise ValueError("Vector size must be greater than or equal to 10000") self.size = size if vtype not in ("bipolar", "binary"): raise ValueError("Vector type can be binary or bipolar only") # Add vector type self.vtype = vtype.lower() if seed is None: rand = np.random.default_rng() else: # Conditions on random seed for reproducibility # numpy allows integers as random seeds if not isinstance(seed, int): raise TypeError("Seed must be an integer number") rand = np.random.default_rng(seed=self.seed) # Build a random binary vector self.vector = rand.integers(2, size=size) if vtype == "bipolar": # Build a random bipolar vector self.vector = 2 * self.vector - 1 def __len__(self) -> int: """Get the vector size. Returns ------- int The vector size. Examples -------- >>> from hdlib.space import Vector >>> vector = Vector() >>> len(vector) 10000 Return the vector size, which is 10,000 by default here """ return self.size def __str__(self) -> None: """Print the Vector object properties. Returns ------- str A description of the Vector object. It reports the name, seed, size, vector type, tags, and the actual vector. Examples -------- >>> from hdlib.space import Vector >>> vector = Vector() >>> print(vector) Class: hdlib.space.Vector Version: 0.1.17 Name: 89ea628b-3d29-47e1-9d10-34bdbfce8d40 Seed: None Size: 10000 Type: bipolar Tags: [] Vector: [ 1 -1 -1 ... -1 1 -1] Print the Vector object properties. The name has been generated as a UUID v4, while the vector size and type are 10,000 and 'bipolar' by default. No tags have been specified. Thus, the set of vector tags is empty. """ return """ Class: hdlib.space.Vector Version: {} Name: {} Seed: {} Size: {} Type: {} Tags: {} Vector: {} """.format( self.version, self.name, self.seed, self.size, self.vtype, np.array(list(self.tags)), self.vector ) def __add__(self, vector: "Vector") -> "Vector": """Implement the addition operator between two Vector objects as bundle. Returns ------- Vector A new vector object as the result of the bundle operator on the two input vectors. Raises ------ TypeError If the input `vector` is not instance of the Vector class. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector3 = vector1 + vector2 >>> type(vector3) <class 'hdlib.space.Vector'> The bundle function returns a new Vector object whose content is computed as the element-wise sum of the two input vectors. """ if not isinstance(vector, type(self)): raise TypeError("Cannot apply the bundle operator to non-Vector objects") # Import arithmetic.bundle here to avoid circular imports from hdlib.arithmetic import bundle as bundle_operator return bundle_operator(self, vector) def __sub__(self, vector: "Vector") -> "Vector": """Implement the subtraction operator between two Vector objects. Returns ------- Vector A new vector object as the result of the subtraction operator on the two input vectors. Raises ------ TypeError If the input `vector` is not instance of the Vector class. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector3 = vector1 - vector2 >>> type(vector3) <class 'hdlib.space.Vector'> The subtraction operation returns a new Vector object whose content is computed as the element-wise subtraction of the two input vectors. """ if not isinstance(vector, type(self)): raise TypeError("Cannot apply the subtraction operator to non-Vector objects") # Import arithmetic.bind here to avoid circular imports from hdlib.arithmetic import subtraction as subtraction_operator return subtraction_operator(self, vector) def __mul__(self, vector: "Vector") -> "Vector": """Implement the multiplication operator between two Vector objects as bind. Returns ------- Vector A new vector object as the result of the bind operator on the two input vectors. Raises ------ TypeError If the input `vector` is not instance of the Vector class. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector3 = vector1 * vector2 >>> type(vector3) <class 'hdlib.space.Vector'> The bind function returns a new Vector object whose content is computed as the element-wise multiplication of the two input vectors. """ if not isinstance(vector, type(self)): raise TypeError("Cannot apply the bind operator to non-Vector objects") # Import arithmetic.bind here to avoid circular imports from hdlib.arithmetic import bind as bind_operator return bind_operator(self, vector) def dist(self, vector: "Vector", method: str="cosine") -> float: """Compute distance between vectors. Parameters ---------- vector : Vector A Vector object from which the distance must be computed. method : {'cosine', 'euclidean', 'hamming'}, optional, default 'cosine' The distance method. Returns ------- float The distance between the current Vector object and the input `vector`. Raises ------ Exception If the current vector has a different size or vector type than the input vector. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector(seed=1) >>> vector2 = Vector(seed=2) >>> vector1.dist(vector2, method='cosine') 0.996 Generate two random bipolar vectors and compute the distance between them. """ if self.size != vector.size: raise Exception("Vectors must have the same size") if self.vtype != vector.vtype: raise Exception("Vectors must be of the same type") if method.lower() == "cosine": return 1 - np.dot(self.vector, vector.vector) / (np.linalg.norm(self.vector) * np.linalg.norm(vector.vector)) elif method.lower() == "hamming": return np.count_nonzero(self.vector != vector.vector) elif method.lower() == "euclidean": return np.linalg.norm(self.vector - vector.vector) else: raise ValueError("Distance method \"{}\" is not supported".format(method)) def normalize(self) -> None: """Normalize a vector after a binding or bundling with another vector. Raises ------ Exception If the vector type is not supported (i.e., is different from binary and bipolar). Examples -------- >>> from hdlib.space import Vector >>> from hdlib.arithmetic import bind >>> vector1 = Vector() >>> vector2 = Vector() >>> vector3 = bind(vector1, vector2) >>> vector3.normalize() >>> ((vector3.vector == -1) | (vector3.vector == 1)).all() True Binding or bundling two vectors can produce a new vector whose vtype is different from the one of the two input vector. This function normalizes the vector content in accordance to its vector type. """ if self.vtype not in ("bipolar", "binary"): raise Exception("Vector type is not supported") self.vector[self.vector > 0] = 1 self.vector[self.vector <= 0] = 0 if self.vtype == "binary" else -1 def bind(self, vector: "Vector") -> None: """Bind the current vector with another vector object inplace. Parameters ---------- vector : Vector The input Vector object. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.bind(vector2) It overrides the actual vector content of `vector1` with the result of the binding with `vector2`. Refers to hdlib.arithmetic.bind for additional information. """ # Import arithmetic.bind here to avoid circular imports from hdlib.arithmetic import bind as bind_operator self.__override_object(bind_operator(self, vector)) def bundle(self, vector: "Vector") -> None: """Bundle the current vector with another vector object inplace. Parameters ---------- vector : Vector The input Vector object. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.bundle(vector2) It overrides the actual vector content of `vector1` with the result of the bundling with `vector2`. Refers to hdlib.arithmetic.bundle for additional information. """ # Import arithmetic.bundle here to avoid circular imports from hdlib.arithmetic import bundle as bundle_operator self.__override_object(bundle_operator(self, vector)) def subtraction(self, vector: "Vector") -> None: """Subtract a vector from the current vector object inplace. Parameters ---------- vector : Vector The input Vector object. Examples -------- >>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.subtract(vector2) It overrides the actual vector content of `vector1` with the result of the subtraction with `vector2`. Refers to hdlib.arithmetic.subtraction for additional information. """ # Import arithmetic.subtraction here to avoid circular imports from hdlib.arithmetic import subtraction as subtraction_operator self.__override_object(subtraction_operator(self, vector)) def permute(self, rotate_by: int=1) -> None: """Permute the current vector inplace. Parameters ---------- rotate_by : int Rotate the input vector by `rotate_by` positions (the default is 1). Examples -------- >>> from hdlib.space import Vector >>> vector = Vector() >>> vector.permute(rotate_by=2) It overrides the actual vector content of `vector` with the result of applying the permute function inplace. Refers to hdlib.arithmetic.permute for additional information. """ # Import arithmetic.permute here to avoid circular imports from hdlib.arithmetic import permute as permute_operator self.__override_object(permute_operator(self, rotate_by=rotate_by)) def __override_object(self, vector: "Vector") -> None: """Override the Vector object with another Vector object. This is a private method. Parameters ---------- vector : Vector The input vector from which properties are inherited to the current vector. """ self.name = vector.name self.size = vector.size self.seed = vector.seed self.tags = vector.tags self.parents = vector.parents self.children = vector.children self.vtype = vector.vtype self.vector = vector.vector self.version = vector.version def dump(self, to_file: Optional[os.path.abspath]=None) -> None: """Dump the Vector object to a pickle file. Parameters ---------- to_file Path to the file used to dump the Vector object to. Raises ------ Exception If the `to_file` file already exists. Examples -------- >>> import os >>> from hdlib.space import Vector >>> vector = Vector() >>> vector.dump(to_file='~/my_vector.pkl') >>> os.path.isfile('~/my_vector.pkl') True Create a Vector object and dump it to a pickle file under the home directory. """ if not to_file: # Dump the vector to a pickle file in the current working directory if not file path is provided to_file = os.path.join(os.getcwd(), "{}.pkl".format(self.name)) if os.path.isfile(to_file): raise Exception("The output file already exists!\n{}".format(to_file)) with open(to_file, "wb") as pkl: pickle.dump(self, pkl)Methods
def bind(self, vector: Vector) ‑> None-
Bind the current vector with another vector object inplace.
Parameters
vector:Vector- The input Vector object.
Examples
>>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.bind(vector2)It overrides the actual vector content of
vector1with the result of the binding withvector2. Refers to hdlib.arithmetic.bind for additional information. def bundle(self, vector: Vector) ‑> None-
Bundle the current vector with another vector object inplace.
Parameters
vector:Vector- The input Vector object.
Examples
>>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.bundle(vector2)It overrides the actual vector content of
vector1with the result of the bundling withvector2. Refers to hdlib.arithmetic.bundle for additional information. def dist(self, vector: Vector, method: str = 'cosine') ‑> float-
Compute distance between vectors.
Parameters
vector:Vector- A Vector object from which the distance must be computed.
method:{'cosine', 'euclidean', 'hamming'}, optional, default'cosine'- The distance method.
Returns
float- The distance between the current Vector object and the input
vector.
Raises
Exception- If the current vector has a different size or vector type than the input vector.
Examples
>>> from hdlib.space import Vector >>> vector1 = Vector(seed=1) >>> vector2 = Vector(seed=2) >>> vector1.dist(vector2, method='cosine') 0.996Generate two random bipolar vectors and compute the distance between them.
def dump(self, to_file: Optional[-
Dump the Vector object to a pickle file.
Parameters
to_file- Path to the file used to dump the Vector object to.
Raises
Exception- If the
to_filefile already exists.
Examples
>>> import os >>> from hdlib.space import Vector >>> vector = Vector() >>> vector.dump(to_file='~/my_vector.pkl') >>> os.path.isfile('~/my_vector.pkl') TrueCreate a Vector object and dump it to a pickle file under the home directory.
def normalize(self) ‑> None-
Normalize a vector after a binding or bundling with another vector.
Raises
Exception- If the vector type is not supported (i.e., is different from binary and bipolar).
Examples
>>> from hdlib.space import Vector >>> from hdlib.arithmetic import bind >>> vector1 = Vector() >>> vector2 = Vector() >>> vector3 = bind(vector1, vector2) >>> vector3.normalize() >>> ((vector3.vector == -1) | (vector3.vector == 1)).all() TrueBinding or bundling two vectors can produce a new vector whose vtype is different from the one of the two input vector. This function normalizes the vector content in accordance to its vector type.
def permute(self, rotate_by: int = 1) ‑> None-
Permute the current vector inplace.
Parameters
rotate_by:int- Rotate the input vector by
rotate_bypositions (the default is 1).
Examples
>>> from hdlib.space import Vector >>> vector = Vector() >>> vector.permute(rotate_by=2)It overrides the actual vector content of
vectorwith the result of applying the permute function inplace. Refers to hdlib.arithmetic.permute for additional information. def subtraction(self, vector: Vector) ‑> None-
Subtract a vector from the current vector object inplace.
Parameters
vector:Vector- The input Vector object.
Examples
>>> from hdlib.space import Vector >>> vector1 = Vector() >>> vector2 = Vector() >>> vector1.subtract(vector2)It overrides the actual vector content of
vector1with the result of the subtraction withvector2. Refers to hdlib.arithmetic.subtraction for additional information.