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| # This file acts as the base class for benchmarks in the Backend/Benchmarks directory. it has logic to define global variables and methods that can be used by all benchmarks. and minimizes code duplication. | |
| import numpy as np | |
| class BaseBenchmark: | |
| def __init__(self): | |
| self.global_min = None | |
| self.initial_guess = None | |
| self.path = [] | |
| self.loss_values = [] | |
| def set_global_min(self, point): | |
| self.global_min = point | |
| def set_initial_guess(self, guess): | |
| self.initial_guess = guess | |
| def add_to_path(self, point): | |
| self.path.append(point) | |
| def add_loss_value(self, value): | |
| self.loss_values.append(value) | |
| def reset(self): | |
| self.global_min = None | |
| self.initial_guess = None | |
| self.path.clear() | |
| self.loss_values.clear() | |
| def get_metrics(self): | |
| if self.global_min is None or not self.path or not self.loss_values: | |
| raise ValueError("Metrics cannot be calculated. Ensure global_min, path, and loss_values are set.") | |
| distance = np.linalg.norm(self.path[-1] - self.global_min) | |
| convergence_rate = len(self.path) if self.loss_values[-1] < 1e-5 else float('inf') | |
| return { | |
| 'distance': float(distance), | |
| 'final_loss': float(self.loss_values[-1]), | |
| 'convergence_rate': convergence_rate | |
| } | |
| def __str__(self): | |
| return f"BaseBenchmark(global_min={self.global_min}, initial_guess={self.initial_guess}, path_length={len(self.path)}, loss_values_length={len(self.loss_values)})" | |
| def __repr__(self): | |
| return f"BaseBenchmark(global_min={self.global_min}, initial_guess={self.initial_guess}, path_length={len(self.path)}, loss_values_length={len(self.loss_values)})" | |
| def __eq__(self, other): | |
| if not isinstance(other, BaseBenchmark): | |
| return False | |
| return (self.global_min == other.global_min and | |
| self.initial_guess == other.initial_guess and | |
| self.path == other.path and | |
| self.loss_values == other.loss_values) | |
| def __ne__(self, other): | |
| return not self.__eq__(other) | |
| def __hash__(self): | |
| return hash((self.global_min, tuple(self.initial_guess), tuple(self.path), tuple(self.loss_values))) | |
| def __len__(self): | |
| return len(self.path) | |