ytm.simple.RegressionTM#

class ytm.simple.RegressionTM(n_clauses: int, T: float, s: float, dim: int | tuple[int, ...], y_range: tuple[float, float], **opt)#

Bases: BaseTM

TM for regression: one real-valued target per sample (Regression TM).

Targets in y_range are scaled to class sum targets in [0, T], and predictions are scaled back. Defaults differ from BaseTM: negative_clauses=False.

Parameters:

n_clausesint: Number of clauses.
Tfloat: Vote threshold. Class sums are clipped to [0, T].
sfloat: Specificity, at least 1.0.
dimint or tuple of int: Input shape: D, (H, W) or (H, W, C). Inputs are binary.
y_rangetuple of (float, float): Range (lo, hi) of the targets.
**opt: Other options of BaseTM.

calc_class_sums(clause_outputs: ndarray, clip: bool) → ndarray#: Weighted vote of the active clauses per class. clause_outputs has shape (N, n_banks, n_clauses).

clause_outputs(X: ndarray) → ndarray#: Clause outputs, the OR over patches. Returns shape (N, n_banks, n_clauses), bool.

feedback(class_id: int, X: ndarray, co: ndarray, dropped: ndarray, target: int, prob: float) → None#

Give feedback for one class.

Parameters:

class_idint: Class to give feedback for.
Xndarray of shape (n_clauses, n_literals), bool: Literals of the patch each clause is contrasted against.
condarray of shape (n_clauses,), bool: Clause outputs.
droppedndarray of shape (n_clauses,), bool: Clauses dropped for this epoch.
targetint: +1 to push the class sum up to T_max, -1 to push it down to T_min.
probfloat: Probability of selecting each clause for feedback.

fit(X: ndarray, Y: ndarray, shuffle: bool = True, clause_drop_p: float = 0.0) → None#

Train for one epoch.

Parameters:

Xarray-like of bool, shape (N, *dim) or (N, prod(dim)): Binary inputs.
Yarray-like: Targets, see the subclass.
shufflebool, default=True: Visit the samples in a random order.
clause_drop_pfloat, default=0.0: Probability of dropping each clause for this epoch (Drop Clause). Dropped clauses output 0 and get no feedback.

predict(X: ndarray, clip_class_sums: bool = True)#

Predict the target of each sample: the class sum scaled from [0, T] back to y_range.

Parameters:

Xarray-like of bool, shape (N, *dim) or (N, prod(dim)): Binary inputs.
clip_class_sumsbool, default=True: Clip class sums to [0, T] before scaling, so predictions stay within y_range.

Returns:

predsndarray of shape (N,): Predicted targets.
class_sumsndarray of shape (N, 1): Weighted vote sum.

score(X: ndarray) → ndarray#: Class sums for X, not clipped. Returns shape (N, n_classes), float32.

t1a(bank: int, clause_filter: ndarray, X: ndarray) → None#: Type Ia feedback: reinforce include for true literals, exclude for false literals.

t1b(bank: int, clause_filter: ndarray) → None#: Type Ib feedback: reinforce exclude for every literal with probability 1/s.

t2(bank: int, clause_filter: ndarray, X: ndarray) → None#: Type II feedback: penalize the exclusion of false literals, making the clause output 0 for the input.