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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)
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