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https://github.com/Findus23/collision-analyisis-and-interpolation.git
synced 2024-09-19 15:13:50 +02:00
94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
from statistics import mean
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from keras.engine.saving import load_model
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from CustomScaler import CustomScaler
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from interpolators.griddata import GriddataInterpolator
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from interpolators.rbf import RbfInterpolator
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from simulation import Simulation
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from simulation_list import SimulationList
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import numpy as np
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simulations = SimulationList.jsonlines_load()
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scaler = CustomScaler()
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scaler.fit(simulations.X)
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model = load_model("model.hd5")
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def squared_error(inter: float, correct: float) -> float:
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return (inter - correct) ** 2
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def absolute_error(inter: float, correct: float) -> float:
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return abs(inter - correct)
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def neural_network_test(scaled_input) -> float:
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nn_input = np.asarray([scaled_input])
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testoutput = model.predict(nn_input)[0][0]
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return testoutput
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def rbf_test(scaled_parameters) -> float:
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scaled_data = scaler.transform_data(simulations.X)
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interpolator = RbfInterpolator(scaled_data, simulations.Y)
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result = interpolator.interpolate(*scaled_parameters)
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return result
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def grid_test(scaled_parameters) -> float:
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scaled_data = scaler.transform_data(simulations.X)
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interpolator = GriddataInterpolator(scaled_data, simulations.Y)
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result = interpolator.interpolate(*scaled_parameters)
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return result
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nn_squared_errors = []
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nn_errors = []
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rbf_squared_errors = []
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rbf_errors = []
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grid_squared_errors = []
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grid_errors = []
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sim: Simulation
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a = 0
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for sim in simulations.simlist:
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if not sim.testcase:
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continue
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a += 1
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continue
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testinput = [sim.alpha, sim.v, sim.projectile_mass, sim.gamma,
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sim.target_water_fraction, sim.projectile_water_fraction]
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scaled_input = list(scaler.transform_parameters(testinput))
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nn_output = neural_network_test(scaled_input)
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nn_squared_errors.append(squared_error(nn_output, sim.water_retention_both))
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nn_errors.append(absolute_error(nn_output, sim.water_retention_both))
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rbf_output = rbf_test(scaled_input)
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rbf_squared_errors.append(squared_error(rbf_output, sim.water_retention_both))
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rbf_errors.append(absolute_error(rbf_output, sim.water_retention_both))
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# grid_output = grid_test(scaled_input)
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grid_output = 1 # dummy to speed up calculation
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grid_squared_errors.append(squared_error(grid_output, sim.water_retention_both))
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grid_errors.append(absolute_error(grid_output, sim.water_retention_both))
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print(nn_output, rbf_output, grid_output, sim.water_retention_both)
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print(a)
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print()
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# print(nn_squared_errors)
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print(mean(nn_squared_errors))
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print(mean(nn_errors))
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print()
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# print(rbf_squared_errors)
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print(mean(rbf_squared_errors))
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print(mean(rbf_errors))
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print()
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# print(grid_squared_errors)
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print(mean(grid_squared_errors))
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print(mean(grid_errors))
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