Utils

This section contains the API documentation for utility functions and helpers.

Parser Utilities

The parser module provides tools for converting mathematical expressions to callable functions.

class skagent.parser.ControlToken(args)

Represents a parsed Control variable.

skagent.parser.math_text_to_lambda(text)

Returns a function represented by the given mathematical text.

skagent.parser.skagent_loader()

A PyYAML loader that supports tags for scikit-agent, such as random variables and model tags.

Core Parser Functions

skagent.parser.math_text_to_lambda(text)

Returns a function represented by the given mathematical text.

Parser Classes

class skagent.parser.ControlToken(args)

Bases: object

Represents a parsed Control variable.

class skagent.parser.Expression(text)

Bases: object

func()

General Utilities

The utils module contains general-purpose utility functions.

skagent.utils.apply_fun_to_vals(fun, vals)

Applies a function to the arguments defined in vals. This is equivalent to fun(**vals), except that vals may contain keys that are not named arguments of fun.

Parameters:

• fun (callable)

• vals (dict)

skagent.utils.compute_parameter_difference(params1, params2)

Compute the L2 norm of the difference between two parameter vectors.

skagent.utils.create_vectorized_function_wrapper_with_mapping(lambda_func, param_to_column)

Create a vectorized wrapper that automatically maps lambda parameters to correct tensor columns based on a parameter-to-column mapping.

Parameters:

• lambda_func – Original lambda function

• param_to_column – A mapping from parameter names to columns of a tensor

skagent.utils.extract_parameters(network)

Extract all parameters from a PyTorch network into a flat tensor.

skagent.utils.reconcile(vec_a, vec_b)

Returns a new vector with the values of vec_b but with the object type and shape of vec_a.

Example Usage

Parsing Mathematical Expressions

from skagent.parser import math_text_to_lambda

# Convert string expression to callable function
utility_func = math_text_to_lambda("c**(1-gamma)/(1-gamma)")

# Use the function
gamma = 2.0
c = 1.5
utility = utility_func(c, gamma)

Working with Grids

import skagent as ska

# Create a grid configuration
config = {
    "wealth": {"min": 0.0, "max": 10.0, "count": 50},
    "income": {"min": 0.5, "max": 2.0, "count": 20},
}

# Create grid
grid = ska.Grid.from_config(config)

# Access grid points
wealth_points = grid["wealth"]
income_points = grid["income"]

# Convert to dictionary
grid_dict = grid.to_dict()

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This page is under construction. Content will be added as the API develops.