def __init__(self, p):
Solution.__init__(self, p)
#self.base_solution = GlobalCompensatedUW(p)
voltage = self.voltage
#correct scale factor at 250V, 17 MHz should be about .19
voltage('Q', -9 , 0.010 , -.19, .85)
voltage('Q', -8 , 0.019 , -.19, .85)
voltage('Q', -7 , 0.037 , -.19, .85)
voltage('Q', -6 , 0.089 , -.19, .85)
voltage('Q', -5 , 0.266 , -.19, .85)
voltage('Q', -4 , 0.928 , -.19, .85)
voltage('Q', -3 , 1.587 , -.19, .85)
voltage('Q', -2 , -2.109, -.19, .85)
voltage('Q', -1 , -2.109, -.19, .85)
voltage('Q', 0 , -2.109, -.19, .85)
voltage('Q', 1 , -2.109, -.19, .85)
voltage('Q', 2 , -2.109, -.19, .85)
voltage('Q', 3 , 1.588, -.19, .85)
voltage('Q', 4 , 0.93 , -.19, .85)
voltage('Q', 5 , 0.266 , -.19, .85)
voltage('Q', 6 , 0.090 , -.19, .85)
voltage('Q', 7 , 0.037 , -.19, .85)
voltage('Q', 8 , 0.019 , -.19, .85)
voltage('Q', 9 , 0.010 , -.19, .85)
def __init__(self, p):
Solution.__init__(self, p)
#self.base_solution = GlobalCompensatedUW(p)
voltage = self.voltage
#correct scale factor at 250V, 17 MHz should be about .19
voltage('Q', -9, 0.010, -.19, .85)
voltage('Q', -8, 0.019, -.19, .85)
voltage('Q', -7, 0.037, -.19, .85)
voltage('Q', -6, 0.089, -.19, .85)
voltage('Q', -5, 0.266, -.19, .85)
voltage('Q', -4, 0.928, -.19, .85)
voltage('Q', -3, 1.587, -.19, .85)
voltage('Q', -2, -2.109, -.19, .85)
voltage('Q', -1, -2.109, -.19, .85)
voltage('Q', 0, -2.109, -.19, .85)
voltage('Q', 1, -2.109, -.19, .85)
voltage('Q', 2, -2.109, -.19, .85)
voltage('Q', 3, 1.588, -.19, .85)
voltage('Q', 4, 0.93, -.19, .85)
voltage('Q', 5, 0.266, -.19, .85)
voltage('Q', 6, 0.090, -.19, .85)
voltage('Q', 7, 0.037, -.19, .85)
voltage('Q', 8, 0.019, -.19, .85)
voltage('Q', 9, 0.010, -.19, .85)
def __init__(self, p):
Solution.__init__(self, p)
self.base_solution = UWQuant(p)
self.laser('Q', 0 , [.1] )
self.laser('Q', 19 , [3.1] )
def __init__(self, p):
Solution.__init__(self, p)
self.base_solution = UWQuant(p)
self.laser('Q', 0, [.1])
self.laser('Q', 19, [3.1])
def __init__(self, graph, omega, phi_particle, phi_global):
Solution.__init__(self, graph)
self.omega = omega
self.phi_particle = phi_particle
self.phi_global = phi_global
self.velocity = np.random.rand(self.dimension) * 2 - 1
self.best_position = self.position
self.best_fitness = self.fitness
def __init__(self, chro_size):
# Constructor.
Solution.__init__(self, num_objs)
# self.min = 0.
# self.max = 1.
self.num_objectives = num_objs
self.size = chro_size
for _ in range(self.size):
self.attributes.append(random.randint(1, 5))
self.evaluate_solution()
def __init__(self, p):
Solution.__init__(self, p)
#voltage = self._voltage
voltage = self.voltage
voltage('Q', -5, .981, 1.0)
voltage('Q', -4, .981, 1.0)
voltage('Q', -3, .981, 1.0)
voltage('Q', -2, .981, 1.0)
voltage('Q', -1, -.857, .378)
voltage('Q', 0, -1, .378)
voltage('Q', 1, -.857, 1.0)
voltage('Q', 2, .981, 1.0)
voltage('Q', 3, .981, 1.0)
voltage('Q', 4, .981, 1.0)
voltage('Q', 5, .981, 1.0)
def __init__(self, p):
Solution.__init__(self, p)
#voltage = self._voltage
voltage = self.voltage
voltage('Q', -5, .981, 1.0)
voltage('Q', -4, .981, 1.0)
voltage('Q', -3, .981, 1.0)
voltage('Q', -2, .981, 1.0)
voltage('Q', -1, -.857, .378)
voltage('Q', 0, -1, .378)
voltage('Q', 1, -.857, 1.0)
voltage('Q', 2, .981, 1.0)
voltage('Q', 3, .981, 1.0)
voltage('Q', 4, .981, 1.0)
voltage('Q', 5, .981, 1.0)
def __init__(self, p):
Solution.__init__(self, p)
voltage = self.voltage
# correct scale factor at 250V, 17 MHz should be about .19
voltage('Q', -9, 0.004, -.19, .85)
voltage('Q', -8, 0.006, -.19, .85)
voltage('Q', -7, 0.010, -.19, .85)
voltage('Q', -6, 0.019, -.19, .85)
voltage('Q', -5, 0.037, -.19, .85)
voltage('Q', -4, 0.089, -.19, .85)
voltage('Q', -3, 0.266, -.19, .85)
voltage('Q', -2, 0.928, -.19, .85)
voltage('Q', -1, 1.587, -.19, .85)
voltage('Q', 0, -2.109, -.19, .85)
voltage('Q', 1, 1.588, -.19, .85)
voltage('Q', 2, 0.930, -.19, .85)
voltage('Q', 3, 0.266, -.19, .85)
voltage('Q', 4, 0.090, -.19, .85)
voltage('Q', 5, 0.037, -.19, .85)
voltage('Q', 6, 0.019, -.19, .85)
voltage('Q', 7, 0.010, -.19, .85)
voltage('Q', 8, 0.007, -.19, .85)
voltage('Q', 9, 0.004, -.19, .85)
def __init__(self, p):
Solution.__init__(self, p)
voltage = self.voltage
# correct scale factor at 250V, 17 MHz should be about .19
voltage('Q', -9 , 0.004 , 0)
voltage('Q', -8 , 0.006 , 0)
voltage('Q', -7 , 0.010 , 0)
voltage('Q', -6 , 0.019 , 0)
voltage('Q', -5 , 0.037 , 0)
voltage('Q', -4 , 0.089 , 0)
voltage('Q', -3 , 0.266 , .001)
voltage('Q', -2 , 0.928 , .005)
voltage('Q', -1 , 1.587 , .01)
voltage('Q', 0 , -2.109, -.014)
voltage('Q', 1 , 1.588 , .009)
voltage('Q', 2 , 0.930 , .003)
voltage('Q', 3 , 0.266 , .001)
voltage('Q', 4 , 0.090 , 0)
voltage('Q', 5 , 0.037 , 0)
voltage('Q', 6 , 0.019 , 0)
voltage('Q', 7 , 0.010 , 0)
voltage('Q', 8 , 0.007 , 0)
voltage('Q', 9 , 0.004 , 0)
def __init__(self, game_state_as_array):
self.method_name = "A*"
self.heuristic_estimate = "H3() (Manhattan Distance + 2 * Number Of Linear Conflict)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(AStar(H3()))
def __init__(self, game_state_as_array):
self.method_name = "Descent Hill Climbing"
self.heuristic_estimate = "H3() (Manhattan Distance + 2 * Number Of Linear Conflict)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(DescentHillClimbing(H3()))
def __init__(self, game_state_as_array):
self.method_name = "Descent Hill Climbing"
self.heuristic_estimate = "H2() (Manhattan Distance Of Tiles Out Of Place)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(DescentHillClimbing(H2()))
def __init__(self, game_state_as_array):
self.method_name = "Descent Hill Climbing"
self.heuristic_estimate = "H1() (Counting Out Of Placed Tiles)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(DescentHillClimbing(H1()))
def __init__(self, game_state_as_array): self.method_name = "A*" self.heuristic_estimate = "H1() (Counting Out Of Placed Tiles)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(AStar(H1()))
def __init__(self, game_state_as_array):
self.method_name = "A*"
self.heuristic_estimate = "H1() (Counting Out Of Placed Tiles)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(AStar(H1()))
def __init__(self, game_state_as_array): self.method_name = "Descent Hill Climbing" self.heuristic_estimate = "H2() (Manhattan Distance Of Tiles Out Of Place)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(DescentHillClimbing(H2()))
def __init__(self, game_state_as_array): self.method_name = "Descent Hill Climbing" self.heuristic_estimate = "H1() (Counting Out Of Placed Tiles)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(DescentHillClimbing(H1()))
def __init__(self, graph, alpha, beta, gamma):
Solution.__init__(self, graph)
self.alpha = alpha # control of randomness
self.beta = beta # attractiveness
self.gamma = gamma # light absorption coefficient
self.intensity = None # Intensity of individual firefly
def __init__(self, p):
Solution.__init__(self, p)
voltage = self.voltage
laser = self.laser
self.add_negatives = True
voltage('Q', -4 , 0.0 , 0)
voltage('Q', -3 , 0.266 , 0)
voltage('Q', -2 , 0.930 , 0)
voltage('Q', -1 , 1.587 , 0)
voltage('Q', 0 , -2.109 , 0)
voltage('Q', 1 , 1.587 , 0)
voltage('Q', 2 , 0.930 , 0)
voltage('Q', 3 , 0.266 , 0)
voltage('Q', 4 , 0 , 0)
voltage('Q', 5 , 0 , 0)
voltage('Q', -4 , 0.0 , 0 , offset=.0833)
voltage('Q', -3, 0.718929 , 0 , offset=.0833)
voltage('Q', -2, 0.778137 , 0 , offset=.0833)
voltage('Q', -1, 1.58724 , 0 , offset=.0833)
voltage('Q', 0, -2.09125 , 0 , offset=.0833)
voltage('Q', 1, 1.56385 , 0 , offset=.0833)
voltage('Q', 2, 0.90748 , 0 , offset=.0833)
voltage('Q', 3, 0.463796 , 0 , offset=.0833)
voltage('Q', 4, 0 , 0 , offset=.0833)
voltage('Q', 5, 0 , 0 , offset=.0833)
voltage('Q', -4 , 0.0 , 0 , offset=.166 )
voltage('Q', -3, 0.528491 , 0 , offset=.166 )
voltage('Q', -2, 1.8765 , 0 , offset=.166 )
voltage('Q', -1, 1.64933 , 0 , offset=.166 )
voltage('Q', 0, -2.02426 , 0 , offset=.166 )
voltage('Q', 1, 1.09393 , 0 , offset=.166 )
voltage('Q', 2, 1.05327 , 0 , offset=.166 )
voltage('Q', 3, 0.322058 , 0 , offset=.166 )
voltage('Q', 4, 0 , 0 , offset=.166 )
voltage('Q', 5, 0 , 0 , offset=.166 )
voltage('Q', -4 , 0.0 , 0 , offset=.25 )
voltage('Q', -3, -0.212357 , 0 , offset=.25 )
voltage('Q', -2, 2.23654 , 0 , offset=.25 )
voltage('Q', -1, 1.95669 , 0 , offset=.25 )
voltage('Q', 0, -1.94595 , 0 , offset=.25 )
voltage('Q', 1, 0.547035 , 0 , offset=.25 )
voltage('Q', 2, 1.32921 , 0 , offset=.25 )
voltage('Q', 3, 0.532968 , 0 , offset=.25 )
voltage('Q', 4, 0 , 0 , offset=.25 )
voltage('Q', 5, 0 , 0 , offset=.25 )
voltage('Q', -4 , 0.0 , 0 , offset=.333 )
voltage('Q', -3, 1.06518 , 0 , offset=.333 )
voltage('Q', -2, 1.7089 , 0 , offset=.333 )
voltage('Q', -1, 2.3241 , 0 , offset=.333 )
voltage('Q', 0, -1.94588 , 0 , offset=.333 )
voltage('Q', 1, 0.305831 , 0 , offset=.333 )
voltage('Q', 2, 1.15094 , 0 , offset=.333 )
voltage('Q', 3, -0.0490063 , 0 , offset=.333 )
voltage('Q', 4, 0 , 0 , offset=.333 )
voltage('Q', 5, 0 , 0 , offset=.333 )
voltage('Q', -4 , 0.0 , 0 , offset=.416 )
voltage('Q', -3, 0.572676 , 0 , offset=.416 )
voltage('Q', -2, -0.548656 , 0 , offset=.416 )
voltage('Q', -1, 2.51127 , 0 , offset=.416 )
voltage('Q', 0, -1.38724 , 0 , offset=.416 )
voltage('Q', 1, -0.783095 , 0 , offset=.416 )
voltage('Q', 2, 2.22243 , 0 , offset=.416 )
voltage('Q', 3, 0.487476 , 0 , offset=.416 )
voltage('Q', 4, 0 , 0 , offset=.416 )
voltage('Q', 5, 0 , 0 , offset=.416 )
voltage('Q', -4 , 0.0 , 0 , offset=.5 )
voltage('Q', -3, 0.3305371 , 0 , offset=.5 )
voltage('Q', -2, 0.574142 , 0 , offset=.5 )
voltage('Q', -1, 2.426975 , 0 , offset=.5 )
voltage('Q', 0, -1.1787785 , 0 , offset=.5 )
voltage('Q', 1, -1.1787785 , 0 , offset=.5 )
voltage('Q', 2, 2.426975 , 0 , offset=.5 )
voltage('Q', 3, 0.574142 , 0 , offset=.5 )
voltage('Q', 4, 0.3305371 , 0 , offset=.5 )
voltage('Q', 5, 0 , 0 , offset=.5 )
laser('Q', 0 , [.1] )
laser('Q', 19 , [3.1] )
def __init__(self, p):
Solution.__init__(self, p)
def __init__(self, p):
Solution.__init__(self, p)
voltage = self.voltage
laser = self.laser
self.add_negatives = True
voltage('Q', -4, 0.0, 0)
voltage('Q', -3, 0.266, 0)
voltage('Q', -2, 0.930, 0)
voltage('Q', -1, 1.587, 0)
voltage('Q', 0, -2.109, 0)
voltage('Q', 1, 1.587, 0)
voltage('Q', 2, 0.930, 0)
voltage('Q', 3, 0.266, 0)
voltage('Q', 4, 0, 0)
voltage('Q', 5, 0, 0)
voltage('Q', -4, 0.0, 0, offset=.0833)
voltage('Q', -3, 0.718929, 0, offset=.0833)
voltage('Q', -2, 0.778137, 0, offset=.0833)
voltage('Q', -1, 1.58724, 0, offset=.0833)
voltage('Q', 0, -2.09125, 0, offset=.0833)
voltage('Q', 1, 1.56385, 0, offset=.0833)
voltage('Q', 2, 0.90748, 0, offset=.0833)
voltage('Q', 3, 0.463796, 0, offset=.0833)
voltage('Q', 4, 0, 0, offset=.0833)
voltage('Q', 5, 0, 0, offset=.0833)
voltage('Q', -4, 0.0, 0, offset=.166)
voltage('Q', -3, 0.528491, 0, offset=.166)
voltage('Q', -2, 1.8765, 0, offset=.166)
voltage('Q', -1, 1.64933, 0, offset=.166)
voltage('Q', 0, -2.02426, 0, offset=.166)
voltage('Q', 1, 1.09393, 0, offset=.166)
voltage('Q', 2, 1.05327, 0, offset=.166)
voltage('Q', 3, 0.322058, 0, offset=.166)
voltage('Q', 4, 0, 0, offset=.166)
voltage('Q', 5, 0, 0, offset=.166)
voltage('Q', -4, 0.0, 0, offset=.25)
voltage('Q', -3, -0.212357, 0, offset=.25)
voltage('Q', -2, 2.23654, 0, offset=.25)
voltage('Q', -1, 1.95669, 0, offset=.25)
voltage('Q', 0, -1.94595, 0, offset=.25)
voltage('Q', 1, 0.547035, 0, offset=.25)
voltage('Q', 2, 1.32921, 0, offset=.25)
voltage('Q', 3, 0.532968, 0, offset=.25)
voltage('Q', 4, 0, 0, offset=.25)
voltage('Q', 5, 0, 0, offset=.25)
voltage('Q', -4, 0.0, 0, offset=.333)
voltage('Q', -3, 1.06518, 0, offset=.333)
voltage('Q', -2, 1.7089, 0, offset=.333)
voltage('Q', -1, 2.3241, 0, offset=.333)
voltage('Q', 0, -1.94588, 0, offset=.333)
voltage('Q', 1, 0.305831, 0, offset=.333)
voltage('Q', 2, 1.15094, 0, offset=.333)
voltage('Q', 3, -0.0490063, 0, offset=.333)
voltage('Q', 4, 0, 0, offset=.333)
voltage('Q', 5, 0, 0, offset=.333)
voltage('Q', -4, 0.0, 0, offset=.416)
voltage('Q', -3, 0.572676, 0, offset=.416)
voltage('Q', -2, -0.548656, 0, offset=.416)
voltage('Q', -1, 2.51127, 0, offset=.416)
voltage('Q', 0, -1.38724, 0, offset=.416)
voltage('Q', 1, -0.783095, 0, offset=.416)
voltage('Q', 2, 2.22243, 0, offset=.416)
voltage('Q', 3, 0.487476, 0, offset=.416)
voltage('Q', 4, 0, 0, offset=.416)
voltage('Q', 5, 0, 0, offset=.416)
voltage('Q', -4, 0.0, 0, offset=.5)
voltage('Q', -3, 0.3305371, 0, offset=.5)
voltage('Q', -2, 0.574142, 0, offset=.5)
voltage('Q', -1, 2.426975, 0, offset=.5)
voltage('Q', 0, -1.1787785, 0, offset=.5)
voltage('Q', 1, -1.1787785, 0, offset=.5)
voltage('Q', 2, 2.426975, 0, offset=.5)
voltage('Q', 3, 0.574142, 0, offset=.5)
voltage('Q', 4, 0.3305371, 0, offset=.5)
voltage('Q', 5, 0, 0, offset=.5)
laser('Q', 0, [.1])
laser('Q', 19, [3.1])
def __init__(self, game_state_as_array):
self.method_name = "A*"
self.heuristic_estimate = "H2() (Manhattan Distance Of Tiles Out Of Place)"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(AStar(H2()))
def __init__(self, game_state_as_array): self.method_name = "A*" self.heuristic_estimate = "H2() (Manhattan Distance Of Tiles Out Of Place)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(AStar(H2()))
def __init__(self, solution, objectives):
Solution.__init__(self, solution, objectives)
self.fitness = float(sys.maxint)
self.evaluation = self.evaluate()
def __init__(self, game_state_as_array): self.method_name = "A*" self.heuristic_estimate = "H3() (Manhattan Distance + 2 * Number Of Linear Conflict)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(AStar(H3()))
def __init__(self, game_state_as_array): self.method_name = "Descent Hill Climbing" self.heuristic_estimate = "H3() (Manhattan Distance + 2 * Number Of Linear Conflict)" Solution.__init__(self, game_state_as_array) self.setSearchMethod(DescentHillClimbing(H3()))
def __init__(self, p):
Solution.__init__(self, p)
self.base_solution = GlobalCompensatedUW(p)
def __init__(self, game_state_as_array):
self.method_name = "Breadth-First Search"
self.heuristic_estimate = "None"
Solution.__init__(self, game_state_as_array)
self.setSearchMethod(BFS())
def __init__(self, p):
Solution.__init__(self, p)
def __init__(self, p):
Solution.__init__(self, p)
self.base_solution = GlobalCompensatedUW(p)
def __init__(self, game_state_as_array): self.method_name = "Breadth-First Search" self.heuristic_estimate = "None" Solution.__init__(self, game_state_as_array) self.setSearchMethod(BFS())
