def eval_regression_model(model: NeuralNetwork, X_test: np.ndarray,
y_test: np.ndarray):
'''
Compute mae and rmse for a neural network.
'''
preds = model.forward(X_test)
preds = preds.reshape(-1, 1)
print("Mean absolute error: {:.2f}".format(mae(preds, y_test)))
print()
print("Root mean squared error {:.2f}".format(rmse(preds, y_test)))
import numpy as np
from neuralnetwork import NeuralNetwork
from layers.linear import LinearLayer
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
""" Results for the AND function for each line from the inputs array
The first column is False, the second Column is for True
"""
labels = np.array([[0, 1], [1, 0], [1, 0], [0, 1]])
nn = NeuralNetwork()
nn.add_layer(LinearLayer(2, 2))
nn.train(inputs, labels, epochs=5000)
for index, row in enumerate(inputs):
prediction = nn.forward(row)
print row, prediction, labels[index]
class VariablePendulum():
def __init__(self, space, configFile, numActions=500):
self.num_actions = numActions
self.config = configFile
self.space = space
self.colour = (np.random.uniform(0, 255), np.random.uniform(0, 255),
np.random.uniform(0, 255), 255)
self.objects = self.generateModel()
self.initial_amplitude = abs(self.angle())
self.maximum_amplitude = abs(self.angle())
self.fitness = 0
self.action_step = 0
self.num_reversals = 0
self.last_action = 0
self.prev_angle = self.angle()
self.prev_dir = 2
self.steps_to_done = 0
self.done = False
self.reached_max = False
self.reached_max_step = 0
self.movable = True
self.energydata = []
self.phasedata = []
self.pumpdata = []
self.neuralnetwork = NeuralNetwork(4, 2, *self.config['hiddenLayers'])
def update(self):
if not self.done:
self.dtheta = self.angle() - self.prev_angle
self.ddtheta = self.angle() - self.dtheta
action = self.neuralnetwork.forward([
abs(self.angle()),
abs(self.dtheta),
abs(self.ddtheta), self.maximum_amplitude
])
self.step(action)
self.checkMovable()
self.energydata.append(
(self.action_step, self.body.kinetic_energy))
self.phasedata.append((self.angle(), self.dtheta))
self.pumpdata.append(
(self.action_step, self.angle(), self.prev_dir))
def resist(self):
fx = self.dtheta * self.config['dampingCoefficient']
fy = self.dtheta * self.config['dampingCoefficient']
self.body.apply_impulse_at_local_point((fx, fy))
def generateModel(self):
# Create objects
moment_of_inertia = 0.25 * self.config["flywheelMass"] * self.config[
"flywheelRadius"]**2
self.body = pymunk.Body(mass=self.config["flywheelMass"],
moment=moment_of_inertia)
self.body.position = self.config["flywheelInitialPosition"]
self.circle = pymunk.Circle(self.body,
radius=self.config["flywheelRadius"])
self.circle.filter = pymunk.ShapeFilter(
categories=0b1, mask=pymunk.ShapeFilter.ALL_MASKS() ^ 0b1)
self.circle.friction = 90000
# Create joints
self.joint = pymunk.PinJoint(self.space.static_body, self.body,
self.config["pivotPosition"])
self.top = self.body.position[1] + self.config["flywheelRadius"]
self.space.add(self.body, self.circle, self.joint)
def angle(self):
y = self.config['pivotPosition'][1] - self.body.position.y
x = self.body.position.x - self.config['pivotPosition'][0]
angle = np.arctan(2 * x / y)
return angle
def extendRope(self, direction):
if direction != self.prev_dir:
self.num_reversals += 1
self.prev_dir = direction
if direction == 0:
if self.movable:
self.joint.distance = self.config["minPendulumLength"]
self.last_action = self.action_step
self.movable = False
elif direction == 1:
if self.movable:
self.joint.distance = self.config["maxPendulumLength"]
self.last_action = self.action_step
self.movable = False
def checkMovable(self):
if self.action_step - self.last_action > self.config['actionDelay']:
self.movable = True
def step(self, action):
self.action_step += 1
self.prev_angle = self.angle()
amplitude = abs(self.angle())
if amplitude > self.maximum_amplitude:
self.maximum_amplitude = amplitude
if self.body.position.y < self.config['pivotPosition'][1]:
self.reached_max = True
self.reached_max_step = self.action_step
self.done = True
self.extendRope(np.argmax(action))
if self.action_step >= self.num_actions:
self.done = True
def calculateFitness(self):
return ((self.maximum_amplitude - self.initial_amplitude) * 180 /
np.pi)**2 / (self.num_reversals)**0.5
def destroyModel(self):
self.space.remove(self.body, self.circle, self.joint)
class SmoothVariablePendulum():
def __init__(self, space, configFile, numActions=500):
self.num_actions = numActions
self.config = configFile
self.space = space
self.colour = (np.random.uniform(0, 255), np.random.uniform(0, 255),
np.random.uniform(0, 255), 255)
self.objects = self.generateModel()
self.initial_amplitude = abs(self.angle())
self.maximum_amplitude = abs(self.angle())
self.fitness = 0
self.action_step = 0
self.num_reversals = 0
self.last_action = 0
self.prev_angle = 0
self.prev_dir = 2
self.steps_to_done = 0
self.done = False
self.complete = False
self.movable = True
self.neuralnetwork = NeuralNetwork(6, 5, *self.config['hiddenLayers'])
def update(self):
if not self.done:
dtheta = self.angle() - self.prev_angle
ddtheta = self.angle() - dtheta
action = self.neuralnetwork.forward([
abs(self.angle()),
abs(dtheta),
abs(ddtheta), self.maximum_amplitude,
self.body.angular_velocity, self.joint.distance
])
self.step(action)
self.checkMovable()
def generateModel(self):
#Create objects
moment_of_inertia = 0.25 * self.config["flywheelMass"] * self.config[
"flywheelRadius"]**2
self.body = pymunk.Body(mass=self.config["flywheelMass"],
moment=moment_of_inertia)
self.body.position = self.config["flywheelInitialPosition"]
self.circle = pymunk.Circle(self.body,
radius=self.config["flywheelRadius"])
self.circle.filter = pymunk.ShapeFilter(
categories=0b1, mask=pymunk.ShapeFilter.ALL_MASKS() ^ 0b1)
self.circle.friction = 90000
self.circle.color = self.colour
#Create joints
self.joint = pymunk.PinJoint(self.space.static_body, self.body,
self.config["pivotPosition"])
self.top = self.body.position[1] + self.config["flywheelRadius"]
self.space.add(self.body, self.circle, self.joint)
def angle(self):
y = self.config['pivotPosition'][1] - self.body.position.y
x = self.body.position.x - self.config['pivotPosition'][0]
angle = np.arctan(x / y)
return angle
def extendRope(self, direction):
if direction != self.prev_dir and direction != 2:
self.num_reversals += 1
self.prev_dir = direction
if direction == 0:
if self.movable:
self.joint.distance -= self.config['squattingSpeed']
if self.joint.distance < self.config['minPendulumLength']:
self.joint.distance = self.config["minPendulumLength"]
elif direction == 1:
if self.movable:
self.joint.distance += self.config['squattingSpeed']
if self.joint.distance > self.config['maxPendulumLength']:
self.joint.distance = self.config["maxPendulumLength"]
elif direction == 2:
self.body.angular_velocity += 1
if self.body.angular_velocity > 5:
self.body.angular_velocity = 5
elif direction == 3:
self.body.angular_velocity -= 1
if self.body.angular_velocity < -5:
self.body.angular_velocity = -5
else:
pass
def checkMovable(self):
if self.action_step - self.last_action > self.config['actionDelay']:
self.movable = True
def step(self, action):
self.action_step += 1
self.prev_angle = self.angle()
amplitude = abs(self.angle())
if amplitude > self.maximum_amplitude:
self.maximum_amplitude = amplitude
self.extendRope(np.argmax(action))
if self.action_step >= self.num_actions:
self.done = True
if self.body.position.y < self.config['pivotPosition'][1]:
self.done = True
self.complete = True
def calculateFitness(self):
if self.complete:
return (self.maximum_amplitude -
self.initial_amplitude) + 200 / self.num_reversals
else:
return (self.maximum_amplitude - self.initial_amplitude)
def destroyModel(self):
self.space.remove(self.body, self.circle, self.joint)
