def __init__(self):
super().__init__()
self.model = Hopper()
self.__setOptimizationParams__(total_duration=0.4, epsilon=0.)
self.opti = ca.Opti()
self.var_dt = False
self.__setVariables__()
self.__setConstraints__()
self.__setCosts__()
def main():
theCompressor = Compresser() # create a Compressor object
theHopper = Hopper() # create a Hopper object
theHopper.fill_coal() # fill out the Hopper object with coal
# loop through and have the Compressor object take coal out of the Hopper
# and compress it.
for i in range(10):
print(theHopper)
theCompressor.get_coal(theHopper)
theCompressor.compress()
print(theCompressor)
def __init__(self,
db,
interface,
related_interface,
sniffer_name,
enable_hopping,
use_24=True,
use_pop5=False):
self.db = db
self.sniffer_name = sniffer_name
self.interface = interface
self.enable_hopping = enable_hopping
# Check interface existance
if not self._iface_exists(interface):
print "Exiting: Interface %s doesn't exist" % interface
sys.exit(1)
if related_interface and not self._iface_exists(related_interface):
print "Exiting: Related interface %s doesn't exist" % interface
sys.exit(1)
# Logging
header = 'SNIFF'
if sniffer_name:
header += '_' + sniffer_name
self.log = Log(self.db, use_stdout=True, header=header)
# Submodules
self.packet_parser = PacketParser(self.log)
self.hopper = Hopper(self.log, interface, related_interface)
ret = self.hopper.configure(use_24=use_24, use_pop5=use_pop5)
if ret is False:
sys.exit(1)
config.conf.sniff_promisc = 0
self.log.info("Promiscuous mode disabled")
self.watchdog = WatchDog(interval=20)
# local endpoint when running trained brain locally in docker container
url = "http://localhost:5000/v1/prediction"
response = requests.get(url, json=state)
action = response.json()
return action
if __name__ == '__main__':
logging.basicConfig()
log = logging.getLogger("hopper")
log.setLevel(level='DEBUG')
# we will use our environment (wrapper of OpenAI env)
hopper = Hopper(iteration_limit=1000)
# setting initial camera position
hopper.initialize_camera(distance=2, yaw=10, pitch=-20)
<<<<<<< HEAD
=======
# setting initial camera position
hopper.initialize_camera(distance=2, yaw=10, pitch=-20)
>>>>>>> a7a19c68ba9116d31ce4ce6aa41b78dcd515d250
# specify which agent you want to use,
# BonsaiAgent that uses trained Brain or
# RandomAgent that randomly selects next action
agent = BonsaiAgent()
import sys
sys.path.append('../libs')
import numpy as np
from hopper import Hopper
#raw_traj = np.array([[0,1,1.5,2,3,5,6,7,9,10], [0, 0.18559, 0.1813, 0.1921, 0.273, 0.567, 0.784, 0.674, 0.9373, 0.8389], [1.483, 2.485, 0.792, 1.13, 4.21, 11.78, 18.292, 8.11, 9.32, 12.31], [10.483, 21.485, 9.792, 21.13, 24.21, 31.78, 38.292, 28.11, 19.32, 112.31]])
test_hopper = Hopper()
from math import sqrt
from hopper import Hopper
from hopperUtil import *
desiredPrecision = 2
N = 25
tf = 2*1.6
legLength = 0.16
r0 = [0, legLength/2]
rf = [1.0, legLength]
v0 = [0, 0]
w0 = 0
hipOffset = {'front': {'x': 0.5, 'z': -0.25}, 'hind': {'x': -0.5, 'z': -0.25}}
matlab_hopper = eng.Hopper(legLength, hipOffset)
hop = Hopper(N, eng, matlab_hopper)
# hop.mdt_precision = int(ceil(-np.log2(desiredPrecision)))
hop.dtBounds = tuple((1/sqrt(legLength/9.81))*np.array([0.05, 0.2]))
hop.dtNom = 0.04*(1/sqrt(legLength/9.81))
hop.rotationMax = np.pi/8
hop.nOrientationSectors = 1 #int(floor(np.pi/8/desiredPrecision))
print 'hop.nOrientationSectors = %d' % hop.nOrientationSectors
hop.velocityMax = 3.
hop.positionMax = 1.5*rf[0]/legLength
hop.forceMax = 3.
hop.angularVelocityMax = 5.
addThreePlatfomWorld(hop, legLength, 0.3*legLength)
#addFlatWorld(hop, legLength)
hop.constructVisualizer()
m_nlp = hop.constructPyomoModel()
def normL2(m, var):
