itk-numerics

import itk import numpy as np # Define a simple cost function: f(x) = (x-5)^2 # This minimizes to x=5 class MyCostFunction(itk.SingleValuedCostFunction): def __init__(self): super().__init__() self.SetNumberOfParameters(1) def GetValue(self, parameters): x = parameters.GetElement(0) return (x - 5.0)**2 def GetDerivative(self, parameters, derivative): x = parameters.GetElement(0) derivative.SetElement(0, 2.0 * (x - 5.0)) # Instantiate the cost function and optimizer cost_function = MyCostFunction.New() optimizer = itk.GradientDescentOptimizer.New() # Configure the optimizer optimizer.SetCostFunction(cost_function) optimizer.SetLearningRate(0.1) optimizer.SetNumberOfIterations(100) # Set initial position for optimization initial_position = itk.OptimizerParameters[itk.D].New() initial_position.SetSize(1) initial_position.SetElement(0, 0.0) # Start optimization at x=0 optimizer.SetInitialPosition(initial_position) # Run the optimization print(f"Initial position: {initial_position.GetElement(0):.2f}") optimizer.StartOptimization() final_position = optimizer.GetCurrentPosition() print(f"Optimized position: {final_position.GetElement(0):.2f}")