nd2py.core.transform package

Submodules

nd2py.core.transform.bfgs_fit module

nd2py.core.transform.bfgs_fit.collect_numbers(expression)

class nd2py.core.transform.bfgs_fit.BFGSFit(*args: Any, **kwargs: Any)

Bases: BaseEstimator, RegressorMixin

__init__(expression: Symbol, edge_list=None, num_nodes=None, use_eps=1e-08, method='BFGS', tol=1e-06, options=None, fold_constant=False)

fit(X, y=None)

predict(X)

用拟合好的 expression 去计算新的 X 上的输出。

nd2py.core.transform.fix_nettype module

class nd2py.core.transform.fix_nettype.FixNetType

Bases: Visitor

generic_visit(node, *args, **kwargs) → _Type

direction = ‘top-down’: 每个 node 的 nettype 由 kwargs[‘nettype’] 决定。 direction = ‘bottom-up’: 每个 node 的 nettype 由其 operands 决定。只保证每个 node 运算不会出错即可，不需要对 kwargs[‘nettype’] 负责

visit_Number(node: Number, *args, **kwargs) → _Type

visit_Variable(node: Variable, *args, **kwargs) → _Type

visit_BinaryOp(node, *args, **kwargs) → _Type

visit_Add(node, *args, **kwargs) → _Type

visit_Sub(node, *args, **kwargs) → _Type

visit_Mul(node, *args, **kwargs) → _Type

visit_Div(node, *args, **kwargs) → _Type

visit_Pow(node, *args, **kwargs) → _Type

visit_Max(node, *args, **kwargs) → _Type

visit_Min(node, *args, **kwargs) → _Type

visit_Aggr(node, *args, **kwargs) → _Type

visit_Rgga(node, *args, **kwargs) → _Type

visit_Sour(node, *args, **kwargs) → _Type

visit_Targ(node, *args, **kwargs) → _Type

visit_Readout(node, *args, **kwargs) → _Type

fix_nettype(node: Symbol, *args, **kwargs) → Symbol

edge_to_node(node: Symbol, *args, **kwargs) → Symbol

node_to_edge(node: Symbol, *args, **kwargs) → Symbol

edge_to_scalar(node: Symbol, *args, **kwargs) → Symbol

node_to_scalar(node: Symbol, *args, **kwargs) → Symbol

scalar_to_node(node: Symbol, *args, **kwargs) → Symbol

scalar_to_edge(node: Symbol, *args, **kwargs) → Symbol

nd2py.core.transform.fold_constant module

class nd2py.core.transform.fold_constant.FoldConstant(fold_fitable: bool = True, fold_constant: bool = True)

Bases: Visitor

访问器，用于将表达式中不含 Number 的子表达式折叠为 Constant。

__init__(fold_fitable: bool = True, fold_constant: bool = True)

generic_visit(node, *args, **kwargs)

visit_Empty(node: Symbol, *args, **kwargs)

visit_Number(node: Number, *args, **kwargs)

visit_Variable(node: Variable, *args, **kwargs)

nd2py.core.transform.reduce module

class nd2py.core.transform.reduce.ReduceRule(source: Symbol, target: Symbol)

Bases: object

__init__(source: Symbol, target: Symbol)

class nd2py.core.transform.reduce.Reduce(n_variables=4, constants=[0, 1, -1, 3.141592653589793, 2.718281828459045], binary=None, unary=None, max_online_iterations: int = 10, load_cache: bool = True, num_anchors=100)

Bases: object

__init__(n_variables=4, constants=[0, 1, -1, 3.141592653589793, 2.718281828459045], binary=None, unary=None, max_online_iterations: int = 10, load_cache: bool = True, num_anchors=100)

prepare_rule_dict(l_max: int = 8, force_rebuild: bool = False, save_cache: bool = True, show_progress: bool = True)

离线阶段：基于 Kruskal 最小生成森林算法的变体发现化简规则并构建 reduce_rules 字典。

这是单进程版本，按顺序处理每个表达式。

Parameters:

• l_max – 最大表达式长度

• force_rebuild – 是否强制重新构建（忽略缓存）

• save_cache – 是否将构建结果缓存到文件

• show_progress – 是否显示进度条

prepare_rule_dict_parallel(l_max: int = 8, n_jobs: int = -1, batch_size: int = 10000, force_rebuild: bool = False, save_cache: bool = True, show_progress: bool = True)

离线阶段：基于 Kruskal 最小生成森林算法的变体发现化简规则并构建 reduce_rules 字典。

这是多进程版本，使用 joblib.Parallel 加速同一长度内的表达式处理。

Parameters:

• l_max – 最大表达式长度

• n_jobs – 并行进程数，-1 表示使用所有 CPU 核心

• force_rebuild – 是否强制重新构建（忽略缓存）

• save_cache – 是否将构建结果缓存到文件

• show_progress – 是否显示进度条

static has_variable(tau: Symbol) → bool

检查表达式是否包含变量

static get_start_length(hash_dict: Dict, reduce_rules: List[ReduceRule]) → int

获取应该从哪个长度开始处理（跳过已缓存的）

static get_array_hash(val, n=8)

static process_tau(tau: Symbol, anchors: Dict[str, np.ndarray], hash_dict: Dict[str, List[Tuple['Symbol', np.ndarray]]])

static process_tau_batch(tau_list: List['Symbol'], anchors: Dict[str, np.ndarray], hash_dict: Dict[str, List[Tuple['Symbol', np.ndarray]]])

nd2py.core.transform.simplify module

class nd2py.core.transform.simplify.Simplify

Bases: Visitor

generic_visit(node: Symbol, *args, **kwargs) → _Type

remove_nested_unary(node: Symbol, *args, **kwargs) → _Type

visit_Sin(node: Symbol, *args, **kwargs) → _Type

visit_Cos(node: Symbol, *args, **kwargs) → _Type

visit_Tanh(node: Symbol, *args, **kwargs) → _Type

visit_Sigmoid(node: Symbol, *args, **kwargs) → _Type

visit_Sqrt(node: Symbol, *args, **kwargs) → _Type

visit_SqrtAbs(node: Symbol, *args, **kwargs) → _Type

visit_Exp(node: Symbol, *args, **kwargs) → _Type

visit_Log(node: Symbol, *args, **kwargs) → _Type

visit_LogAbs(node: Symbol, *args, **kwargs) → _Type

visit_Readout(node: Readout, *args, **kwargs) → _Type

visit_Number(node: Number, *args, **kwargs) → _Type

visit_Variable(node: Variable, *args, **kwargs) → _Type

visit_Add(node: Add, *args, **kwargs) → _Type

visit_Sub(node: Add, *args, **kwargs) → _Type

visit_Mul(node: Mul, *args, **kwargs) → _Type

visit_Div(node: Mul, *args, **kwargs) → _Type

visit_Neg(node: Neg, *args, **kwargs) → _Type

visit_Inv(node: Inv, *args, **kwargs) → _Type

visit_Aggr(node: Aggr, *args, **kwargs) → _Type

nd2py.core.transform.split_by_add module

class nd2py.core.transform.split_by_add.SplitByAdd

Bases: Visitor

generic_visit(node: Symbol, *args, **kwargs) → _Type

visit_Add(node: Add, *args, **kwargs) → _Type

visit_Sub(node: Sub, *args, **kwargs) → _Type

visit_Mul(node: Mul, *args, **kwargs) → _Type

visit_Div(node: Div, *args, **kwargs) → _Type

visit_Neg(node: Neg, *args, **kwargs) → _Type

visit_Sour(node: Sour, *args, **kwargs) → _Type

visit_Targ(node: Targ, *args, **kwargs) → _Type

visit_Aggr(node: Aggr, *args, **kwargs) → _Type

visit_Rgga(node: Rgga, *args, **kwargs) → _Type

visit_Readout(node: Readout, *args, **kwargs) → _Type

merge_bias(items: List[Symbol], *args, **kwargs) → List[Symbol]

Merge bias terms in the node.

nd2py.core.transform.split_by_mul module

class nd2py.core.transform.split_by_mul.SplitByMul

Bases: Visitor

generic_visit(node: Symbol, *args, **kwargs) → _Type

visit_Mul(node: Mul, *args, **kwargs) → _Type

visit_Div(node: Div, *args, **kwargs) → _Type

merge_coefficients(items: List[Symbol], *args, **kwargs) → List[Symbol]

Merge coefficients from the symbols.