Discrete EBM#
Concrete discrete energy-based model implementations with spin and categorical factor types.
hamon.models.DiscreteEBMInteraction
#
An interaction that shows up when sampling from discrete-variable EBMs.
Attributes:
n_spin: the number of spin states involved in the interaction.weights: the weight tensor associated with this interaction.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__init__(n_spin: int, weights: Array) -> None
#
Initialize self. See help(type(self)) for accurate signature.
hamon.models.DiscreteEBMFactor
#
Implements batches of energy function terms of the form s_1 * ... * s_M * W[c_1, ..., c_N], where the s_i are spin variables and the c_i are categorical variables.
No variable should show up twice in any given interaction. If this happens, the result of sampling from a model that includes the bad factor might not agree with the Boltzmann distribution. For example, the interaction w * s_1 * s_1 * s_2 would violate this rule because s_1 shows up twice. To allow you to do something weird if you want to, this condition has not been enforced in the code.
Attributes:
spin_node_groups: the node groups involved in the batch of factors that represent spin-valued random variables.categorical_node_groups: the node groups involved in the batch of factors that represent categorical-valued random variables.weights: the batch of weight tensors W associated with the factors we are implementing.weightsshould have leading dimension b, where b is number of nodes in each element ofspin_node_groupsandcategorical_node_groups. This tensor has shape [b, x_1, ..., x_N] where b is the number of nodes in each block and N is the length ofcategorical_node_groups.is_spin: a map that indicates if a given node type represents a spin-valued random variable or not.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__init__(spin_node_groups: list[hamon.block_management.Block], categorical_node_groups: list[hamon.block_management.Block], weights: Array)
#
energy(global_state: list[Array], block_spec: BlockSpec)
#
Compute the energy associated with this factor.
In this case, that is the sum of terms like s_1 * ... * s_M * W[c_1, ..., c_N].
to_interaction_groups() -> list[hamon.interaction.InteractionGroup]
#
Produce interaction groups that implement this factor.
hamon.models.SquareDiscreteEBMFactor
#
A discrete factor with a square interaction weight tensor (shape [b, x, x, ..., x]).
If a discrete factor is square, the interaction groups corresponding to different choices of the head node blocks can be merged. This could yield smaller XLA programs and improved runtime performance via more efficient use of accelerators.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
energy(global_state: list[Array], block_spec: BlockSpec)
#
Compute the energy associated with this factor.
In this case, that is the sum of terms like s_1 * ... * s_M * W[c_1, ..., c_N].
__init__(spin_node_groups: list[hamon.block_management.Block], categorical_node_groups: list[hamon.block_management.Block], weights: Array)
#
Enforce that the weights are actually square.
to_interaction_groups() -> list[hamon.interaction.InteractionGroup]
#
hamon.models.SpinEBMFactor
#
A DiscreteEBMFactor that involves only spin variables.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
to_interaction_groups() -> list[hamon.interaction.InteractionGroup]
#
energy(global_state: list[Array], block_spec: BlockSpec)
#
Compute the energy associated with this factor.
In this case, that is the sum of terms like s_1 * ... * s_M * W[c_1, ..., c_N].
__init__(node_groups: list[hamon.block_management.Block], weights: Array)
#
hamon.models.CategoricalEBMFactor
#
A DiscreteEBMFactor that involves only categorical variables.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
to_interaction_groups() -> list[hamon.interaction.InteractionGroup]
#
Produce interaction groups that implement this factor.
energy(global_state: list[Array], block_spec: BlockSpec)
#
Compute the energy associated with this factor.
In this case, that is the sum of terms like s_1 * ... * s_M * W[c_1, ..., c_N].
__init__(node_groups: list[hamon.block_management.Block], weights: Array)
#
hamon.models.SquareCategoricalEBMFactor
#
A DiscreteEBMFactor that involves only categorical variables that also has a square weight tensor.
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
to_interaction_groups() -> list[hamon.interaction.InteractionGroup]
#
energy(global_state: list[Array], block_spec: BlockSpec)
#
Compute the energy associated with this factor.
In this case, that is the sum of terms like s_1 * ... * s_M * W[c_1, ..., c_N].
__init__(node_groups: list[hamon.block_management.Block], weights: Array)
#
Conditional samplers#
The exact single-site conditionals for discrete states, vectorized over a color
class. SpinGibbsConditional is Bernoulli; CategoricalGibbsConditional is
softmax over n_categories.
hamon.models.SpinGibbsConditional
#
A conditional update for spin-valued random variables that will perform a Gibbs sampling update given one or
more DiscreteEBMInteractions.
This function can be extended to handle a broader class of interactions via inheritance. Specifically, a
child class can override the compute_parameters method defined here, compute contributions to \(\gamma\)
from other types of interactions, and then call this method to take into account the contributions from
DiscreteEBMInteractions.
n_keys
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
init() -> None
#
Initialize the sampler state before sampling begins.
This is called before the first iteration of block sampling, after which the return of this method is
superseded by the return from sample.
Returns:
| Type | Description |
|---|---|
None
|
the initial sampler state to use for the first iteration of block sampling. |
sample(key: Key, interactions: list[PyTree], active_flags: list[Array], states: list[list[PyTree[Shaped[Array, 'nodes ?*state'], State]]], sampler_state: ~_SamplerState, output_sd: PyTree[jax.ShapeDtypeStruct]) -> tuple[PyTree[Shaped[Array, 'nodes ?*state'], State], ~_SamplerState]
#
Sample from the distribution by first computing the parameters and then generating a sample based off of them.
key is either a single key — split here, as before — or the
n_keys-long stack the block runner batch-split on this sampler's
behalf. The two are the same keys either way.
sample_given_parameters(key: Key, parameters: PyTree, sampler_state: None, output_sd: PyTree[jax.ShapeDtypeStruct]) -> tuple[PyTree[Shaped[Array, 'nodes ?*state'], State], None]
#
Sample from a spin-valued bernoulli distribution given the parameter \(\gamma\). In hamon,
1 is represented by the boolean value True and -1 is represented by False.
__init__() -> None
#
Initialize self. See help(type(self)) for accurate signature.
compute_parameters(key: Key, interactions: list[PyTree], active_flags: list[Array], states: list[list[PyTree[Shaped[Array, 'nodes ?*state'], _State]]], sampler_state: None, output_sd: PyTree[jax.ShapeDtypeStruct]) -> PyTree
#
Compute the parameter \(\gamma\) of a spin-valued Bernoulli distribution given DiscreteEBMInteractions:
where the sum over \(i\) is over all the DiscreteEBMInteractions seen by this function.
hamon.models.CategoricalGibbsConditional
#
A conditional update for categorical random variables that will perform a Gibbs sampling update given one or
more DiscreteEBMInteractions.
This function can be extended to handle other interactions in the same way as hamon.models.SpinGibbsConditional.
Attributes:
n_categories: how many categories are involved in the softmax distribution this sampler will sample from.
n_keys
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__abstractclassvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractmethods__
class-attribute
#
Build an immutable unordered collection of unique elements.
__abstractvars__
class-attribute
#
Build an immutable unordered collection of unique elements.
__annotations__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_fields__
class-attribute
#
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__dataclass_params__
class-attribute
#
__doc__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__
class-attribute
#
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer iteral.
int('0b100', base=0) 4
__match_args__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__
class-attribute
#
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__static_attributes__
class-attribute
#
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
init() -> None
#
Initialize the sampler state before sampling begins.
This is called before the first iteration of block sampling, after which the return of this method is
superseded by the return from sample.
Returns:
| Type | Description |
|---|---|
None
|
the initial sampler state to use for the first iteration of block sampling. |
sample(key: Key, interactions: list[PyTree], active_flags: list[Array], states: list[list[PyTree[Shaped[Array, 'nodes ?*state'], State]]], sampler_state: ~_SamplerState, output_sd: PyTree[jax.ShapeDtypeStruct]) -> tuple[PyTree[Shaped[Array, 'nodes ?*state'], State], ~_SamplerState]
#
Sample from the distribution by first computing the parameters and then generating a sample based off of them.
key is either a single key — split here, as before — or the
n_keys-long stack the block runner batch-split on this sampler's
behalf. The two are the same keys either way.
sample_given_parameters(key: Key, parameters: PyTree, sampler_state: None, output_sd: PyTree[jax.ShapeDtypeStruct]) -> tuple[PyTree[Shaped[Array, 'nodes ?*state'], State], None]
#
Sample from a softmax distribution given the parameter vector $ heta$.
__init__(n_categories: int) -> None
#
Initialize self. See help(type(self)) for accurate signature.
compute_parameters(key: Key, interactions: list[PyTree], active_flags: list[Array], states: list[list[PyTree[Shaped[Array, 'nodes ?*state'], _State]]], sampler_state: None, output_sd: PyTree[jax.ShapeDtypeStruct]) -> PyTree
#
Compute the parameter \(\theta\) of a softmax distribution given DiscreteEBMInteractions:
where the sum over \(i\) is over all the DiscreteEBMInteractions seen by this function.