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0.14.32
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‎notebooks/00_spotPython_tests.ipynb‎

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"print(next(iter(data_module.train_dataloader()))[0].numpy())\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n",
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spotPython tuning: 0.0 [########--] 80.00% \n",
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"spotPython tuning: 0.0 [#########-] 86.67% \n",
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"spotPython tuning: 0.0 [#########-] 93.33% \n",
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"spotPython tuning: 0.0 [##########] 100.00% Done...\n",
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"\n",
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"S.X: [[ 0.00000000e+00 0.00000000e+00]\n",
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" [ 0.00000000e+00 1.00000000e+00]\n",
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" [ 1.00000000e+00 0.00000000e+00]\n",
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" [ 1.00000000e+00 1.00000000e+00]\n",
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" [-9.09243389e-01 -1.58234577e-01]\n",
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" [-2.05817107e-01 -4.81249089e-01]\n",
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" [ 9.49741171e-01 -9.46312716e-01]\n",
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" [-1.20955714e-01 6.38358863e-02]\n",
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" [-6.62787018e-01 1.74316373e-01]\n",
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" [ 2.82008441e-01 9.30010114e-01]\n",
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" [ 4.78788115e-01 6.53210582e-01]\n",
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" [ 2.64764215e-04 4.00803185e-03]\n",
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" [-1.66363820e-05 4.65001027e-03]\n",
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" [-2.60995680e-04 5.46114194e-03]\n",
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" [ 3.74504308e-03 1.86731890e-02]]\n",
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"S.y: [0.00000000e+00 1.00000000e+00 1.00000000e+00 2.00000000e+00\n",
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" 8.51761723e-01 2.73961367e-01 1.79751605e+00 1.87053051e-02\n",
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" 4.69672829e-01 9.44447573e-01 6.55922124e-01 1.61344194e-05\n",
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" 2.16228723e-05 2.98921900e-05 3.62713334e-04]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from spotPython.fun.objectivefunctions import analytical\n",
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"from spotPython.spot import spot\n",
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"from spotPython.utils.init import (\n",
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" fun_control_init, optimizer_control_init, surrogate_control_init, design_control_init\n",
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" )\n",
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"# number of initial points:\n",
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"ni = 7\n",
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"# start point X_0\n",
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"X_start = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
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"fun = analytical().fun_sphere\n",
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"fun_control = fun_control_init(\n",
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" lower = np.array([-1, -1]),\n",
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" upper = np.array([1, 1]))\n",
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"design_control=design_control_init(init_size=ni)\n",
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"S = spot.Spot(fun=fun,\n",
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" fun_control=fun_control,\n",
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" design_control=design_control,)\n",
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"S.run(X_start=X_start)\n",
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"print(f\"S.X: {S.X}\")\n",
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"print(f\"S.y: {S.y}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎pyproject.toml‎

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@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotpython"
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version = "0.14.31"
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version = "0.14.32"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

‎src/spotPython/spot/spot.py‎

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print("No results to write.")
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def run(self, X_start=None) -> Spot:
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"""
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Run the surrogate based optimization.
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The optimization process is controlled by the following steps:
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1. Initialize design
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2. Update stats
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3. Fit surrogate
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4. Update design
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5. Update stats
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6. Update writer
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7. Fit surrogate
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8. Show progress if needed
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Args:
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self (object): Spot object
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X_start (numpy.ndarray, optional): initial design. Defaults to None.
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Returns:
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(object): Spot object
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Examples:
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>>> import numpy as np
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from spotPython.fun.objectivefunctions import analytical
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from spotPython.spot import spot
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from spotPython.utils.init import (
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fun_control_init, optimizer_control_init, surrogate_control_init, design_control_init
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)
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# number of initial points:
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ni = 7
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# start point X_0
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X_start = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
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fun = analytical().fun_sphere
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fun_control = fun_control_init(
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lower = np.array([-1, -1]),
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upper = np.array([1, 1]))
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design_control=design_control_init(init_size=ni)
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S = spot.Spot(fun=fun,
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fun_control=fun_control,
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design_control=design_control,)
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S.run(X_start=X_start)
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print(f"S.X: {S.X}")
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print(f"S.y: {S.y}")
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Seed set to 123
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Seed set to 123
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spotPython tuning: 0.0 [########--] 80.00%
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spotPython tuning: 0.0 [#########-] 86.67%
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spotPython tuning: 0.0 [#########-] 93.33%
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spotPython tuning: 0.0 [##########] 100.00% Done...
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S.X: [[ 0.00000000e+00 0.00000000e+00]
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[ 0.00000000e+00 1.00000000e+00]
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[ 1.00000000e+00 0.00000000e+00]
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[ 1.00000000e+00 1.00000000e+00]
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[-9.09243389e-01 -1.58234577e-01]
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[-2.05817107e-01 -4.81249089e-01]
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[ 9.49741171e-01 -9.46312716e-01]
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[-1.20955714e-01 6.38358863e-02]
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[-6.62787018e-01 1.74316373e-01]
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[ 2.82008441e-01 9.30010114e-01]
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[ 4.78788115e-01 6.53210582e-01]
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[ 2.64764215e-04 4.00803185e-03]
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[-1.66363820e-05 4.65001027e-03]
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[-2.60995680e-04 5.46114194e-03]
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[ 3.74504308e-03 1.86731890e-02]]
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S.y: [0.00000000e+00 1.00000000e+00 1.00000000e+00 2.00000000e+00
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8.51761723e-01 2.73961367e-01 1.79751605e+00 1.87053051e-02
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4.69672829e-01 9.44447573e-01 6.55922124e-01 1.61344194e-05
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2.16228723e-05 2.98921900e-05 3.62713334e-04]
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"""
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self.initialize_design(X_start)
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# New: self.update_stats() moved here:
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# changed in 0.5.9:

‎test/test_spot_run.py‎

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import numpy as np
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import pytest
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from spotPython.fun.objectivefunctions import analytical
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from spotPython.spot import spot
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from spotPython.utils.init import fun_control_init, design_control_init
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@pytest.fixture
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def setup_spot():
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"""
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PyTest Fixture for initializing Spot with given parameters.
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"""
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ni = 7 # number of initial points
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ne = 20 # number of evaluations
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X_start = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
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fun = analytical().fun_sphere
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fun_control = fun_control_init(lower=np.array([-1, -1]), upper=np.array([1, 1]), fun_evals=ne)
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design_control = design_control_init(init_size=ni)
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S = spot.Spot(
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fun=fun,
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fun_control=fun_control,
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design_control=design_control,
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)
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return S, X_start
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def test_spot_run_shapes(setup_spot):
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"""
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Test the shapes of S.X and S.y after running the Spot.run method.
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"""
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S, X_start = setup_spot
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ne = S.fun_control["fun_evals"]
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exp_X_shape = (ne, 2)
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exp_y_shape = (ne,)
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S.run(X_start=X_start)
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# Assert shapes
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assert S.X.shape == exp_X_shape, f"Optimized S.X shape {S.X.shape} does not match expected shape {exp_X_shape}."
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assert S.y.shape == exp_y_shape, f"Optimized S.y shape {S.y.shape} does not match expected shape {exp_y_shape}."

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