class AnnRunner(object):
"""Wraps up the gross reality of running a ``print'' using the printer simulation (controlled by a neural network)"""
camera_size = 3
def __init__(self, ideal_grid_path, cell_size, units_per_cell=10):
"""Sets up all the pieces needed to perform a print with the simulated 3d printer (controlled by the neural network). Takes in a path to an a ``goal'' or ``ideal'' grid, and constructs the GridWorld based on the dimensions of that goal grid. Understands both a ``camera'', which observes the actual world (around the print head) and an ``ideal camera'' which observes the same location but based on the ``goal grid''
"""
ideal_grid = Grid(path=ideal_grid_path, scale=cell_size)
self.ideal_grid = ideal_grid
self.gridworld = GridWorld(ideal_grid.width, ideal_grid.height, cell_size)
self.gridworld.set_ideal_grid(ideal_grid)
self.printer = Printer(10, 10, 9, self.gridworld, units_per_cell) #TODO: shouldn't be giving location values here when it's determined somewhere else. that smells a lot
self.camera = Camera(self.gridworld.grid, self.printer, self.camera_size)
self.ideal_camera = Camera(self.gridworld.ideal_grid, self.printer, self.camera_size)
def run(self, n, iterations=10000):
"""Runs a simulated print run with the printer simulation (controlled by an ANN. Starts the printer in the location provided by the ideal grid spec
"""
#set the printer location to the starting postition as defined by the ideal_grid spec
self.printer.set_position_on_grid(*self.gridworld.get_starting_position())
for i in xrange(iterations):
self.printer.setPenDown()
actual = self.camera.all_cell_values()
ideal = self.ideal_camera.all_cell_values()
pattern = [i - a for i,a in zip(actual, ideal)]
result = n.propagate(pattern)
result = [int(round(x)) for x in result]
result = ''.join(map(str, result))
self.printer.set_printer_direction(self.get_velocity(result[:2]), self.get_velocity(result[2:]))
self.printer.simulate()
self.update()
return (self.ideal_grid, self.gridworld.grid)
def update(self):
return
def get_velocity(self, instruction):
"""Translates between the output of the neural network and direction instructions for the printer. leftright and updown are translated separately"""
if instruction == "10":
return -1
elif instruction == "01":
return 1
else:
return 0
class AnnRunner(object):
camera_size = 3
def __init__(self, ideal_grid, units_per_cell=10):
self.gridworld = GridWorld(ideal_grid.width, ideal_grid.height, ideal_grid.gridsize)
self.gridworld.set_ideal_grid(ideal_grid)
self.printer = Printer(10, 10, 9, 1, self.gridworld, units_per_cell) #TODO: shouldn't be giving location values here when it's determined somewhere else. that smells a lot
self.camera = Camera(self.gridworld.grid, self.printer, self.camera_size)
self.ideal_grid = self.gridworld.ideal_grid
self.ideal_camera = Camera(self.gridworld.ideal_grid, self.printer, self.camera_size)
width = self.gridworld.width() * self.gridworld.gridsize()
height = self.gridworld.height() * self.gridworld.gridsize()
def run(self, n, iterations=10000):
self.printer.set_position_on_grid(self.ideal_grid.starting_point[0], self.ideal_grid.starting_point[1])
for i in xrange(iterations):
self.printer.setPenDown()
actual = self.camera.all_cell_values()
ideal = self.ideal_camera.all_cell_values()
pattern = [i - a for i,a in zip(actual, ideal)]
result = n.propagate(pattern)
result = [int(round(x)) for x in result]
result = ''.join(map(str, result))
self.printer.v = Vector(self.get_velocity(result[:2]), self.get_velocity(result[2:]))
self.printer.simulate()
self.update()
return (self.ideal_grid, self.gridworld.grid)
def update(self):
return
def get_velocity(self, instruction):
if instruction == "10":
return -1
elif instruction == "01":
return 1
else:
return 0