formatting for tests
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@ -54,7 +54,7 @@ def test_turckheim(client, request): # pylint: disable=redefined-outer-name
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assert len(landmarks) > 2 # check that there is something to visit
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assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
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# assert 2==3
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'''
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def test_bellecour(client, request) : # pylint: disable=redefined-outer-name
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"""
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Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
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@ -214,7 +214,7 @@ def test_shopping(client, request) : # pylint: disable=redefined-outer-name
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assert response.status_code == 200 # check for successful planning
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assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
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assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
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'''
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# def test_new_trip_single_prefs(client):
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# response = client.post(
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@ -466,18 +466,27 @@ class Optimizer:
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# SET CONSTRAINTS FOR EQUALITY
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A_eq, b_eq = self.init_eq_not_stay(L) # Force solution not to stay in same place
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A, b = self.respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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A, b = self.respect_user_must_avoid(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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A, b = self.respect_start_finish(L) # Force start and finish positions
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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A, b = self.respect_order(L) # Respect order of visit (only works when max_time is limiting factor)
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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A, b = self.respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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A, b = self.respect_user_must_avoid(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
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A_eq = np.vstack((A_eq, A), dtype=np.int8)
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b_eq += b
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print(A_ub)
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print('\n\n')
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print(b_ub)
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print('\n\n')
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print(A_eq)
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print('\n\n')
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print(b_eq)
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# SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
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x_bounds = [(0, 1)]*L*L
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