本文整理汇总了Python中neuralnet.NeuralNet.excite方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.excite方法的具体用法？Python NeuralNet.excite怎么用？Python NeuralNet.excite使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类neuralnet.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.excite方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: MineSweeper
# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import excite [as 别名]
class MineSweeper(object):
'''
The minesweeper class.
'''
def __init__(self):
self.brain = NeuralNet(settings.NUM_INPUTS,
settings.NUM_OUTPUTS,
settings.NUM_HIDDEN,
settings.NEURONS_PER_HIDDEN)
self.position = Vector2D(random() * settings.WINDOW_WIDTH,
random() * settings.WINDOW_HEIGHT)
self.look_at = Vector2D()
self.rotation = random() * 2 * math.pi
self.ltrack = 0.16
self.rtrack = 0.16
self.fitness = 0.0
self.scale = settings.SWEEPER_SCALE
self.closest_mine = 0
self.speed = 0.0
def reset(self):
self.position = Vector2D(random() * settings.WINDOW_WIDTH,
random() * settings.WINDOW_HEIGHT)
self.fitness = 0.0
self.rotation = random() * 2 * math.pi
def get_closest_mine(self, all_mines):
'''
finds and returns a Vector2D that is the position of the mine closest to
this minesweeper. Expects an iterable of `Mine`s as the parameter
`all_mines`.
'''
closest_so_far = INFINITY
closest_object = Vector2D(0, 0)
ctr = 0
for mine in all_mines:
l = (mine.position - self.position).length()
if l < closest_so_far:
closest_so_far = l
closest_object = self.position - mine.position
self.closest_mine = ctr
ctr += 1
return closest_object
def update(self, all_mines):
'''
This is the real brains function. Takes an iterable of mines. It first
takes sensor readings and feed these to the ANN of our minesweeper.
The inputs are:
1) A vector (Vector2D) to the closest mine,
2) The "look at" vector (also a Vector2D).
The brain(ANN) returns 2 outputs, ltrack and rtrack - which are forces
applied on left and right tracks, respectively. Depending on these, the
acceleration and/or the rotation is calculated and the position vector
is updated accordingly.
'''
# Inputs to the brain.
inputs = []
# First input: vector to the closest mine.
closest_mine = self.get_closest_mine(all_mines)
closest_mine.normalize()
# Place the inputs on the input list
inputs.append(closest_mine.x)
inputs.append(closest_mine.y)
inputs.append(self.look_at.x)
inputs.append(self.look_at.y)
# Now, excite the brain and get the feedback
output = self.brain.excite(inputs, settings.BIAS, filter_sigmoid=True)
# Make sure we get back the expected number of outputs
if len(output) != settings.NUM_OUTPUTS:
raise Exception( 'An error occurred: The number of outputs from '
+'the ANN is not what was expected.')
self.ltrack, self.rtrack = output
rot_force = self.ltrack - self.rtrack
rot_force = clamp(rot_force,
-settings.MAX_TURN_RATE,
settings.MAX_TURN_RATE)
# New rotation and speed:
self.rotation += rot_force
self.speed = self.ltrack + self.rtrack
# Get the new look at:
self.look_at.x = -math.sin(self.rotation)
self.look_at.y = math.cos(self.rotation)
# Get the new position:
self.position += (self.look_at * self.speed)
# Wrap around the screen
if self.position.x > settings.WINDOW_WIDTH:
self.position.x = 0
if self.position.x < 0:
self.position.x = settings.WINDOW_WIDTH
#.........这里部分代码省略.........