40 lines
914 B
Python
40 lines
914 B
Python
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import numpy as np
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from scipy.optimize import minimize
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# example model
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def model(params, x):
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return params[0] * x + params[1]
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# Cost function
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def cost_function(params, x_data, y_data):
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return np.sum((model(params, x_data) - y_data) ** 2)
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# Global cost function
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def global_cost_function(params, experiments):
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total_cost = 0
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for x_data, y_data in experiments:
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total_cost += cost_function(params, x_data, y_data)
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return total_cost
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# Example data for two experiments
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x_data_1 = np.array([0, 1, 2, 3])
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y_data_1 = np.array([1, 3, 5, 7])
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x_data_2 = np.array([0, 1, 2, 3])
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y_data_2 = np.array([2, 4, 6, 8])
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experiments = [(x_data_1, y_data_1), (x_data_2, y_data_2)]
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# Initial guess/parameters
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initial_params = [1, 0]
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# Run the optimization
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result = minimize(global_cost_function, initial_params, args=(experiments,))
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# Optimized parameters
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print("Optimized parameters:", result.x)
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