def parse_args(struct_engine_mode: namedtuple):
'''
@function parse_args
@date Sun, 10 May 2020 12:21:57 +0530
@brief function to parse the arguments provided for engine
@param [IN] struct_engine_mode - namedtuple containing the fields to be
parsed from the arguments and populated
'''
if struct_engine_mode is None:
raise ValueError(ERR_VALUE.format("Engine mode structure"))
argsp = ArgumentParser()
# elements handled in engine_mode_t -> struct_engine_mode
# gameconf, build_mode
# optional arguments
build_mode_info = "Engine mode has to be in debug(1)"
argsp.add_argument("--build-mode", help=build_mode_info, type=int)
gameconf_info = "Custom configuration filepath [absolute]"
argsp.add_argument("--config", help=gameconf_info)
pr = argsp.parse_args()
struct_engine_mode.build_mode = True if (pr.build_mode == 1) else False
struct_engine_mode.gameconf = pr.config
def move(row:int, column:int, game_state: namedtuple) -> namedtuple:
"""
This function will perform the given move the for the current player
:rtype : othello namedtuple
:param row: int
:param column: int
:param game_state: othello namedtuple
"""
#print('move {},{} = {}'.format(row, column, game_state.game_board[row - 1][column - 1]))
if row > len(game_state.game_board) or row < 1:
raise InvalidMove
if column > len(game_state.game_board[0]) or column < 1:
raise InvalidMove
if not _check_move(row, column, game_state):
raise InvalidMove
for number in range(_find_north(row, column, game_state)):
game_state.game_board[(row - 1) - (number + 1)][(column - 1)] = game_state.current_player
for number in range(_find_south(row, column, game_state)):
game_state.game_board[(row - 1) + (number + 1)][(column - 1)] = game_state.current_player
for number in range(_find_west(row, column, game_state)):
game_state.game_board[(row - 1)][(column - 1) - (number + 1)] = game_state.current_player
for number in range(_find_east(row, column, game_state)):
game_state.game_board[(row - 1)][(column - 1) + (number + 1)] = game_state.current_player
for number in range(_find_northwest(row, column, game_state)):
game_state.game_board[(row - 1) - (number + 1)][(column - 1) - (number + 1)] = game_state.current_player
for number in range(_find_northeast(row, column, game_state)):
game_state.game_board[(row - 1) - (number + 1)][(column - 1) + (number + 1)] = game_state.current_player
for number in range(_find_southwest(row, column, game_state)):
game_state.game_board[(row - 1) + (number + 1)][(column - 1) - (number + 1)] = game_state.current_player
for number in range(_find_southeast(row, column, game_state)):
game_state.game_board[(row - 1) + (number + 1)][(column - 1) + (number + 1)] = game_state.current_player
game_state.game_board[row - 1][column - 1] = game_state.current_player
game_state = count_disk(game_state)
game_state = _change_player(game_state)
if not _check_player(game_state):
game_state = _change_player(game_state)
if _check_win(game_state):
game_state = game_state._replace(win_result=_find_winner(game_state))
return game_state
return game_state
def __init__(self, conf: namedtuple):
self.size = conf.size
# inhibitory neurons
isi_size = int((1 - conf.splitSize) * self.size)
self.neurons = [conf.neuron(conf.isiConfig) for _ in range(isi_size)]
# exicitory neurons
self.neurons.extend(
[conf.neuron(conf.iseConfig) for _ in range(self.size - isi_size)])
self.α = conf.traceAlpha
self.activities = []
def _change_player(game_state: namedtuple) -> namedtuple:
"""
This function changes the current player in the game state and returns it.
:rtype : othello namedtuple
:param game_state: othello namedtuple
"""
#print('_change_player)')
if game_state.current_player == BLACK:
game_state = game_state._replace(current_player=WHITE)
else:
game_state = game_state._replace(current_player=BLACK)
return game_state
def __init__(self, ipaddr=None, device=None, community='Public',
retries=3, timeout=9):
self.device = device
self.ipaddr = ipaddr
self.community = community
self.SNMPObject = NT('SNMPObject', ['modName', 'datetime', 'symName',
'index', 'value'])
self.SNMPIndexed = NT('SNMPIndexed', ['modName', 'datetime', 'symName',
'index', 'value'])
self.query_timeout = float(timeout)/int(retries)
self.query_retries = int(retries)
self._index = None
self.cmdGen = cmdgen.CommandGenerator()
def _change_player(game_state: namedtuple) -> namedtuple:
"""
This function changes the current player in the game state and returns it.
:rtype : othello namedtuple
:param game_state: othello namedtuple
"""
#print('_change_player)')
if game_state.current_player == BLACK:
game_state = game_state._replace(current_player=WHITE)
else:
game_state = game_state._replace(current_player=BLACK)
return game_state
def set_variable(var: namedtuple) -> namedtuple:
"""Sets the variable from the user input
Args:
var (namedtuple): namedtuple("Variable", ["name", "string", "content"])
Returns:
namedtuple: namedtuple with input set correcly.
"""
return var._replace(content=input(f"Set variable {var.name}: "))
def FillNC(root_grp_ptr, scene_location):
retGps = NT("returnGroups", "calGrp, productsGrp, navGrp, slaGrp, periodGrp")
root_grp_ptr.createDimension('samples', 512)
root_grp_ptr.createDimension('scan_lines', 2000)
root_grp_ptr.createDimension('bands', 128)
root_grp_ptr.instrument = 'HICO'
root_grp_ptr.institution = 'NASA Goddard Space Flight Center'
root_grp_ptr.resolution = '100m'
root_grp_ptr.location_description = scene_location
root_grp_ptr.license = 'http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/'
root_grp_ptr.naming_authority = 'gov.nasa.gsfc.sci.oceandata'
root_grp_ptr.date_created = DT.strftime(DT.utcnow(), '%Y-%m-%dT%H:%M:%SZ')
root_grp_ptr.creator_name = 'NASA/GSFC'
root_grp_ptr.creator_email = '*****@*****.**'
root_grp_ptr.publisher_name = 'NASA/GSFC'
root_grp_ptr.publisher_url = 'http_oceancolor.gsfc.nasa.gov'
root_grp_ptr.publisher_email = '*****@*****.**'
root_grp_ptr.processing_level = 'L1B'
nav_grp = root_grp_ptr.createGroup('navigation')
nav_vars = list()
nav_vars.append(nav_grp.createVariable('sensor_zenith', 'f4', ('scan_lines', 'samples',)))
nav_vars.append(nav_grp.createVariable('solar_zenith', 'f4', ('scan_lines', 'samples',)))
nav_vars.append(nav_grp.createVariable('sensor_azimuth', 'f4', ('scan_lines', 'samples',)))
nav_vars.append(nav_grp.createVariable('solar_azimuth', 'f4', ('scan_lines', 'samples',)))
nav_vars.append(nav_grp.createVariable('longitudes', 'f4', ('scan_lines', 'samples',)))
nav_vars.append(nav_grp.createVariable('latitudes', 'f4', ('scan_lines', 'samples',)))
for var in nav_vars:
var.units = 'degrees'
var.valid_min = -180
var.valid_max = 180
var.long_name = var.name.replace('_', ' ').rstrip('s')
retGps.navGrp = nav_grp
retGps.productsGrp = root_grp_ptr.createGroup('products')
lt = retGps.productsGrp.createVariable('Lt', 'u2', ('scan_lines',
'samples', 'bands'))
lt.scale_factor = float32([0.02])
lt.add_offset = float32(0)
lt.units = "W/m^2/micrometer/sr"
# lt.valid_range = nparray([0, 16384], dtype='u2')
lt.long_name = "HICO Top of Atmosphere"
lt.wavelength_units = "nanometers"
# lt.createVariable('fwhm', 'f4', ('bands',))
lt.fwhm = npones((128,), dtype='f4') * -1
# wv = lt.createVariable('wavelengths', 'f4', ('bands',))
lt.wavelengths = npones((128,), dtype='f4')
lt.wavelength_units = "nanometers"
retGps.slaGrp = root_grp_ptr.createGroup('scan_line_attributes')
retGps.slaGrp.createVariable('scan_quality_flags', 'u1', ('scan_lines',
'samples'))
# Create metadata group and sub-groups
meta_grp = root_grp_ptr.createGroup('metadata')
pl_info_grp = meta_grp.createGroup("FGDC/Identification_Information/Platform_and_Instrument_Identification")
pl_info_grp.Instrument_Short_Name = "hico"
prc_lvl_grp = meta_grp.createGroup("FGDC/Identification_Information/Processing_Level")
prc_lvl_grp.Processing_Level_Identifier = "Level-1B"
retGps.periodGrp = meta_grp.createGroup("FGDC/Identification_Information/Time_Period_of_Content")
# fill HICO group
retGps.calGrp = meta_grp.createGroup("HICO/Calibration")
return retGps
def create_dict_named(obj: namedtuple) -> dict:
""" Create a dict out of a namedtuple.
Args:
obj: Namedtuple.
Returns:
A dict containing the data.
"""
data = {
'__type__': type(obj).__name__,
'__data__': create_dict(obj._asdict())
}
return data
def genotype_to_dict(genotype: namedtuple):
"""Converts the given genotype to a dictionary that can be serialized.
Inverse operation to dict_to_genotype().
Args:
genotype (namedtuple): The genotype that should be converted.
Returns:
dict: The converted genotype.
"""
genotype_dict = genotype._asdict()
for key, val in genotype_dict.items():
if type(val) == range:
genotype_dict[key] = [node for node in val]
return genotype_dict
def prepare_log(log: namedtuple):
try:
LogModel = log_model_map[log.__class__.__name__]
except KeyError:
raise
sensitive_fields = sorted(LogModel.sensitive_fields())
log_dic_python = log._asdict()
log_dic_python['hmac1'] = encrypt_log(log_dic_python, sensitive_fields)
table = LogModel._meta.table_name
log_dic_db = {
field.column_name: field.db_value(value)
for field, value in LogModel._normalize_data(None, log_dic_python).items()
}
fields = log._fields
return log_dic_db, table, fields
def results_to_csv(config: namedtuple, val_metrics: dict, attn_metrics: dict, output_csv=None):
keys = set(val_metrics.keys()) | set(attn_metrics.keys())
keys.add('epoch')
overlapping_keys = val_metrics.keys() & attn_metrics.keys()
if len(overlapping_keys) > 0:
raise ValueError("Found overlapping keys {} in training and attention metrics".format(overlapping_keys))
def find_keys_with_epoch(metrics_dict: dict) -> set:
return set(list(filter(lambda x: type(metrics_dict[x]) is list, metrics_dict.keys())))
val_keys_with_epoch = find_keys_with_epoch(val_metrics)
val_keys_without_epoch = val_metrics.keys() - val_keys_with_epoch
attn_keys_with_epoch = find_keys_with_epoch(attn_metrics)
attn_metrics_without_epoch = attn_metrics.keys() - attn_keys_with_epoch
keys_without_epochs = (val_metrics.keys() - val_keys_with_epoch) | (attn_metrics.keys() - attn_keys_with_epoch)
model_parameters = dict(config._asdict())
df = pd.DataFrame(columns=(list(model_parameters.keys()) + list(keys)))
def add_all_keys_with_epoch(df_, keys_with_epoch, undef_keys, metrics):
(max_epoch,) = set(len(metrics[x]) for x in keys_with_epoch)
for epoch in range(max_epoch):
update_dict_ = dict(model_parameters)
update_dict_['epoch'] = epoch
for k in keys_with_epoch:
update_dict_[k] = metrics[k][epoch]
for k in undef_keys:
update_dict_[k] = ""
df_ = df_.append(update_dict_, ignore_index=True)
return df_
df = add_all_keys_with_epoch(df, val_keys_with_epoch, keys_without_epochs | attn_keys_with_epoch, val_metrics)
df = add_all_keys_with_epoch(df, attn_keys_with_epoch, keys_without_epochs | val_keys_with_epoch, attn_metrics)
update_dict = dict(model_parameters)
for k in attn_metrics_without_epoch:
update_dict[k] = attn_metrics[k]
for k in val_keys_without_epoch:
update_dict[k] = val_metrics[k]
for k in val_keys_with_epoch | attn_keys_with_epoch:
update_dict[k] = ""
update_dict['epoch'] = ""
df = df.append(update_dict, ignore_index=True)
if output_csv:
os.makedirs(os.path.dirname(output_csv), exist_ok=True)
df.to_csv(output_csv, index=False, compression=None)
return df
def namedtuple_to_xml(item: namedtuple):
elem = Element(type(item).__name__)
asdict = item._asdict().items()
for key, val in asdict:
if val is None:
continue
if type(val) is namedtuple:
child = namedtuple_to_xml(val)
elif type(val) is list:
child = Element(key)
for item in val:
child.append(namedtuple_to_xml(item))
else:
child = Element(key)
child.text = str(val)
elem.append(child)
return elem
def search_variable(var: namedtuple, vars: List[namedtuple], i: int,
session: sessionmaker):
while True:
show_variable(None)
print("Enter variable name:")
inp = input()
if inp == "exit":
break
db_var = session.query(Variable).filter(Variable.name == inp).first()
if db_var:
print()
print(f"Set {var.name} to {db_var.content}")
vars[i] = var._replace(content=db_var.content)
break
else:
print()
print("Variable name not found")
print("Enter new name or enter exit to leave")
print()
return vars
def count_disk(game_state: namedtuple) -> namedtuple:
"""
This function counts the disks on the game board and updates the game state and returns it.
:rtype : othello namedtuple
:param game_state: othello namedtuple
"""
#print('_count_disk')
black_count = 0
white_count = 0
for row in game_state.game_board:
for cell in row:
if cell == BLACK:
black_count += 1
if cell == WHITE:
white_count += 1
game_state = game_state._replace(black_score=black_count)
game_state = game_state._replace(white_score=white_count)
return game_state
def count_disk(game_state: namedtuple) -> namedtuple:
"""
This function counts the disks on the game board and updates the game state and returns it.
:rtype : othello namedtuple
:param game_state: othello namedtuple
"""
#print('_count_disk')
black_count = 0
white_count = 0
for row in game_state.game_board:
for cell in row:
if cell == BLACK:
black_count += 1
if cell == WHITE:
white_count += 1
game_state = game_state._replace(black_score=black_count)
game_state = game_state._replace(white_score=white_count)
return game_state
def _get_by_entity(self, entity: namedtuple) -> namedtuple:
if self.n >= 5:
self.stop()
self.start()
self.n += 1
# noinspection PyProtectedMember
result = entity._asdict()
self.browser.get(entity.url)
table = self.browser.find_element_by_xpath("//div[@class='portlet']//table[@align='center'][2]")
if table:
result["content"] = table.text
if '不开展' not in table.text:
span_tag = table.find_elements_by_xpath("//table[@border>'0']//tr[1]//td")
data_tag = table.find_elements_by_xpath("//table[@border>'0']//tr[2]//td")
span = [tag.text for tag in span_tag]
data = [tag.text for tag in data_tag]
try:
result["days"] = data[["期限" in w for w in span].index(True)]
result["amount"] = data[["量" in w for w in span].index(True)]
result["rate"] = data[["利率" in w for w in span].index(True)]
except ValueError:
pass
return self.entity(**result)
def save_link(self, link: namedtuple) -> None:
# noinspection PyProtectedMember
self.update("link", {
"head": link.head,
"tail": link.tail
}, link._asdict(), True)
class v2c(object):
"""Build an SNMPv2c manager object"""
def __init__(self, ipaddr=None, device=None, community='Public',
retries=3, timeout=9):
self.device = device
self.ipaddr = ipaddr
self.community = community
self.SNMPObject = NT('SNMPObject', ['modName', 'datetime', 'symName',
'index', 'value'])
self.SNMPIndexed = NT('SNMPIndexed', ['modName', 'datetime', 'symName',
'index', 'value'])
self.query_timeout = float(timeout)/int(retries)
self.query_retries = int(retries)
self._index = None
self.cmdGen = cmdgen.CommandGenerator()
#mibBuilder = builder.MibBuilder()
#mibPath = mibBuilder.getMibPath()+('/opt/python/Models/Network/MIBs',)
#mibBuilder.setMibPath(*mibPath)
#mibBuilder.loadModules(
# 'RFC-1213',
# )
#mibView = view.MibViewController(mibBuilder)
def index(self, oid=None):
"""Build an SNMP Manager index to reference in get or walk operations. First v2c.index('ifName'). Then, v2c.get_index('ifHCInOctets', 'eth0') or v2c.walk_index('ifHCInOctets'). Instead of referencing a numerical index, the index will refer to the value that was indexed."""
self._index = dict()
self._intfobj = dict()
snmpidx = self.walk(oid=oid)
for ii in snmpidx:
## the dicts below are keyed by the SNMP index number
# value below is the text string of the intf name
self._index[ii.index] = ii.value
# value below is the intf object
if not (self.device is None):
self._intfobj[ii.index] = self.device.find_match_intf(ii.value,
enforce_format=False)
def walk_index(self, oid=None):
"""Example usage, first index with v2c.index('ifName'), then v2c.get_index('ifHCInOctets', 'eth0')"""
if not (self._index is None):
tmp = list()
snmpvals = self.walk(oid=oid)
for idx, ii in enumerate(snmpvals):
tmp.append([ii.modName, datetime.now(), ii.symName,
self._index[ii.index], ii.value])
return map(self.SNMPIndexed._make, tmp)
else:
raise ValueError, "Must populate with SNMP.v2c.index() first"
def walk(self, oid=None):
if isinstance(self._format(oid), tuple):
errorIndication, errorStatus, errorIndex, \
varBindTable = self.cmdGen.nextCmd(
cmdgen.CommunityData('test-agent', self.community),
cmdgen.UdpTransportTarget((self.ipaddr, 161),
retries=self.query_retries,
timeout=self.query_timeout),
self._format(oid),
)
# Parsing only for now... no return value...
self._parse(errorIndication, errorStatus, errorIndex, varBindTable)
elif isinstance(oid, str):
errorIndication, errorStatus, errorIndex, \
varBindTable = self.cmdGen.nextCmd(
# SNMP v2
cmdgen.CommunityData('test-agent', self.community),
# Transport
cmdgen.UdpTransportTarget((self.ipaddr, 161)),
(('', oid),),
#cmdgen.MibVariable(oid).loadMibs(),
)
return self._parse_resolve(errorIndication, errorStatus,
errorIndex, varBindTable)
else:
raise ValueError, "Unknown oid format: %s" % oid
def get_index(self, oid=None, index=None):
"""In this case, index should be similar to the values you indexed from... i.e. if you index with ifName, get_index('ifHCInOctets', 'eth0')"""
if not (self._index is None) and isinstance(index, str):
# Map the interface name provided in index to an ifName index...
snmpvals = None
for idx, value in self._index.items():
if index == value:
# if there is an exact match between the text index and the
# snmp index value...
snmpvals = self.get(oid=oid, index=idx)
break
else:
# TRY mapping the provided text index into an interface obj
_intfobj = self.device.find_match_intf(index)
if not (_intfobj is None):
for key, val in self._intfobj.items():
if (val==_intfobj):
snmpvals = self.get(oid=oid, index=key)
break
# Ensure we only parse a valid response...
if not (snmpvals is None):
tmp = [snmpvals.modName, datetime.now(), snmpvals.symName,
self._index[snmpvals.index], snmpvals.value]
return self.SNMPIndexed._make(tmp)
elif not isinstance(index, str):
raise ValueError, "index must be a string value"
else:
raise ValueError, "Must populate with SNMP.v2c.index() first"
def get(self, oid=None, index=None):
if isinstance(self._format(oid), tuple):
errorIndication, errorStatus, errorIndex, \
varBindTable = self.cmdGen.getCmd(
cmdgen.CommunityData('test-agent', self.community),
cmdgen.UdpTransportTarget((self.ipaddr, 161),
retries=self.query_retries,
timeout=self.query_timeout),
self._format(oid),
)
# Parsing only for now... no return value...
self._parse(errorIndication, errorStatus, errorIndex, varBindTable)
elif isinstance(oid, str) and isinstance(index, int):
errorIndication, errorStatus, errorIndex, \
varBindTable = self.cmdGen.getCmd(
# SNMP v2
cmdgen.CommunityData('test-agent', self.community),
# Transport
cmdgen.UdpTransportTarget((self.ipaddr, 161)),
(('', oid), index),
#cmdgen.MibVariable(oid).loadMibs(),
)
return self._parse_resolve(errorIndication, errorStatus,
errorIndex, [varBindTable])[0]
else:
raise ValueError, "Unknown oid format: %s" % oid
def bulkwalk(self, oid=None):
"""SNMP bulkwalk a device. NOTE: This often is faster, but does not work as well as a simple SNMP walk"""
if isinstance(self._format(oid), tuple):
errorIndication, errorStatus, errorIndex, varBindTable = self.cmdGen.bulkCmd(
cmdgen.CommunityData('test-agent', self.community),
cmdgen.UdpTransportTarget((self.ipaddr, 161),
retries=self.query_retries,
timeout=self.query_timeout),
0,
25,
self._format(oid),
)
return self._parse(errorIndication, errorStatus,
errorIndex, varBindTable)
elif isinstance(oid, str):
errorIndication, errorStatus, errorIndex, varBindTable = self.cmdGen.bulkCmd(
cmdgen.CommunityData('test-agent', self.community),
cmdgen.UdpTransportTarget((self.ipaddr, 161),
retries=self.query_retries,
timeout=self.query_timeout),
0,
25,
(('', oid),),
#cmdgen.MibVariable(oid).loadMibs(),
)
return self._parse_resolve(errorIndication, errorStatus,
errorIndex, varBindTable)
else:
raise ValueError, "Unknown oid format: %s" % oid
def _parse_resolve(self, errorIndication=None, errorStatus=None,
errorIndex=None, varBindTable=None):
"""Parse MIB walks and resolve into MIB names"""
retval = list()
if errorIndication:
print errorIndication
else:
if errorStatus:
print '%s at %s\n' % (
errorStatus.prettyPrint(),
varBindTable[-1][int(errorIndex)-1]
)
else:
for varBindTableRow in varBindTable:
for oid, val in varBindTableRow:
(symName, modName), indices = cmdgen.mibvar.oidToMibName(
self.cmdGen.mibViewController, oid
)
val = cmdgen.mibvar.cloneFromMibValue(
self.cmdGen.mibViewController, modName, symName,
val)
# Try to parse the index as an int first,
# then as a string
try:
index = int(string.join(map(lambda v: v.prettyPrint(), indices), '.'))
except ValueError:
index = str(string.join(map(lambda v: v.prettyPrint(), indices), '.'))
# Re-format values as float or integer, if possible...
tmp = val.prettyPrint()
if re.search(r"""^\s*\d+\s*$""", tmp):
value = int64(tmp)
elif re.search(r"""^\s*\d+\.\d+\s*$""", tmp):
value = float64(tmp)
else:
value = tmp
retval.append(self.SNMPObject._make([modName,
datetime.now(), symName, index, value]))
return retval
def _parse(self, errorIndication, errorStatus, errorIndex,
varBindTable):
if errorIndication:
print errorIndication
else:
if errorStatus:
print '%s at %s\n' % (
errorStatus.prettyPrint(),
errorIndex and varBindTable[-1][int(errorIndex)-1] or '?'
)
else:
for varBindTableRow in varBindTable:
for name, val in varBindTableRow:
print '%s = %s' % (name.prettyPrint(), val.prettyPrint())
def _format(self, oid):
"""Format a numerical OID in the form of 1.3.4.1.2.1 into a tuple"""
if isinstance(oid, str):
if re.search('(\d+\.)+\d+', oid):
tmp = list()
for ii in oid.split('.'):
tmp.append(int(ii))
return tuple(tmp)
else:
return oid
def __reader(struct_definition: Struct, output_tuple: namedtuple,
data) -> namedtuple:
"""Helper function for building out struct reader functions"""
return output_tuple._make(struct_definition.unpack(data))
def data(self, cls: namedtuple):
self._data = cls
for name, value in cls._asdict().items():
self.__setattr__(name, value)
def avg_profit(company: namedtuple) -> namedtuple:
summ = company.quarter_profit_1 + company.quarter_profit_2 + company.quarter_profit_3 + company.quarter_profit_4
company = company._replace(avg=summ / 10)
return company
def _compute_meaning_atom(self, atom: namedtuple, debug=False):
''' Computes the meaning of an atom for all models.
The meaning of integer constants are model invariant:
its meaning is the same in each model: namely, that integer
itself. The meaning of set variables are model dependent: it
provides, given an enumeration of model, the information of,
given a model in that enumeration, which elements of that
model are present in that set.
Args:
atom: A namedtuple created by "Atom". Either set variable
A or B, or an integer constant from 0 to
max_model_size.
Returns:
For integer constants: an immutable np.array of shape
(1,N_OF_MODELS), for set variables: a list of
self.max_model_size many immutable np.arrays of shape
(model_size, number_of_subsets**model_size) for model_size
from 1 to max_model_size.
'''
# Model-invariant atom case: integer constants.
# An array with that same integer for each model in the
# universe.
if atom.is_constant:
atom_meaning = np.array([atom.func()] * self.N_OF_MODELS)
atom_meaning.flags.writeable = False
# Model-dependent atom case: set variables.
# A set representation shows which elements are in a set
# accross the whole universe of models.
else:
atom_meaning = []
cur_model_size = 1
# Make matrix template of the correct shape.
# Columns represent models, rows represent objects.
# Entry (i,j) represents whether element i in model j
# is in A (1) or not in A (0).
# For model_size 0 to max_model_size there will be a matrix
# of shape (model_size, number_of_subsets ** model_size)
# in set_repr.
number_of_models_of_cur_model_size = (
self.number_of_subsets**cur_model_size)
set_repr = np.zeros(
(cur_model_size, number_of_models_of_cur_model_size),
dtype=np.uint8)
# Offset = number of models of previous model sizes.
offset = 0
# Fil in the matrix template, per model.
start_of_new_model_block = (self.number_of_subsets**cur_model_size)
for model_idx, model in enumerate(self.generate_universe()):
# When all models of cur_model_size have been treated,
# go to the next block of models: the models of size
# cur_model_size + 1. Store the results of current
# block of models.
if debug:
print()
print("start_of_new_model_block = ", end="")
print(start_of_new_model_block)
print("model_idx, offset, model = ", end="")
print(model_idx, offset, utils.tuple_format(model))
if model_idx == start_of_new_model_block:
if debug:
print()
print()
cur_model_size += 1
offset = start_of_new_model_block
start_of_new_model_block += (
self.number_of_subsets**cur_model_size)
set_repr.flags.writeable = False
atom_meaning.append(set_repr)
# Reset matrix template: make new matrix template
# of the correct shape.
set_repr = np.zeros(
(cur_model_size, self.number_of_subsets**
cur_model_size),
dtype=np.uint8,
)
# For object 1 to object current_model_size in the
# current model, fill in set-inclusion for this
# set-variable. Fill in collumn i = model_index - offset
# (the i"th model of current_model_size).
if debug:
print("model =", model)
print("atom.func(model) =", atom, atom.func(model))
print("set_repr =", set_repr)
set_repr[:, model_idx - offset] = atom.func(model)
if debug:
print("set_repr =", set_repr)
# Finish.
set_repr.flags.writeable = False
atom_meaning.append(set_repr)
return atom_meaning
def unpack_named(data: bytes, packing: str, nt: namedtuple):
return [nt._make(x) for x in struct.iter_unpack(packing, data)]
def nt2json(nt: namedtuple):
return json.dumps(nt._asdict(), default=str)
def __init__(self, Prime=0, RunID=0):
TH.Thread.__init__(self)
PrimeBase.THL.acquire()
if True:
self._PR = Prime # current prrime
self._ID = RunID # 0 : dispatcher --- 1..p : worker threads
# print('init thread: ', Prime, RunID, flush=True)
#
tn = self._MakeName(Prime, RunID)
self.setName(tn)
# easy identifier over all threads
#
nt = NT('Info', [
'Prime', 'RunID', 'TObject', 'Alive', 'I_AmReady', 'PauseCnt',
'WhereItIs', 'InitTime', 'StartTime', 'ReadyTime', 'FinitTime'
])
nt.Prime = Prime # current prrime
nt.RunID = RunID # 0 : dispatcher --- 1..p : worker threads
nt.TObject = self
nt.alive = self.is_alive()
nt.I_AmReady = False
nt.InitTime = DT.datetime.now()
nt.StartTime = None
nt.ReadyTime = None
nt.FinitTime = None
nt.PauseCnt = 0 # counter for thread sleep phases
nt.WhereItIs = 0
#
self.AllThreads.setdefault(tn, nt)
PrimeBase.THL.release()
def parse_namedtuple(losses: namedtuple, prefix: str):
log = {'{}/{}'.format(prefix, k): v for k, v in losses._asdict().items()}
return log
def save_node(self, node: namedtuple) -> None:
# noinspection PyProtectedMember
self.update("node", {
"name": node.name,
"source": node.source
}, node._asdict(), True)
def message_from_tuple(payload_tuple: namedtuple, attributes: dict = None):
tuple_as_json = json.dumps(payload_tuple._asdict())
return mock_message(tuple_as_json, attributes)
def _prefixed(nt: namedtuple, prefix):
"""Convert a named tuple into a dict with prefixed names."""
result = {}
for key, value in nt._asdict().items():
result[prefix + key] = value
return result
def train_DQN(env: WrapIt, Q: DQN, Q_target: DQN, optimizer: namedtuple,
replay_buffer: ReplayBuffer, exploration: Schedule):
"""
@parameters
Q:
Q_target:
optimizer: torch.nn.optim.Optimizer with parameters
buffer: store the frame
@return
None
"""
assert type(env.observation_space) == gym.spaces.Box
assert type(env.action_space) == gym.spaces.Discrete
optimizer = optimizer.constructor(Q.parameters(), **optimizer.kwargs)
num_actions = env.action_space.n
num_param_updates = 0
mean_episode_reward = -float('nan')
best_mean_episode_reward = -float('inf')
LOG_EVERY_N_STEPS = 10000
last_obs = env.reset(passit=True)
# Q.getSummary()
out_count = 0
bar = tqdm(range(ARGS.timesteps))
for t in bar:
last_idx = replay_buffer.store_frame(last_obs)
recent_observations = replay_buffer.encode_recent_observation()
if t > ARGS.startepoch:
value = select_epsilon_greedy_action(Q, recent_observations,
exploration, t, num_actions)
action = value[0, 0]
else:
action = random.randrange(num_actions)
obs, reward, done, _ = env.step(action)
reward = max(-1.0, min(reward, 1.0))
replay_buffer.store_effect(last_idx, action, reward, done)
if done:
obs = env.reset()
last_obs = obs
# bar.set_description(f"{obs.shape} {obs.dtype}")
if (t > ARGS.startepoch and t % ARGS.dqn_freq == 0
and replay_buffer.can_sample(ARGS.batchsize)):
bar.set_description("backward")
(obs_batch, act_batch, rew_batch, next_obs_batch,
done_mask) = replay_buffer.sample(ARGS.batchsize)
(obs_batch, act_batch, rew_batch, next_obs_batch,
not_done_mask) = TENSOR(obs_batch, act_batch, rew_batch,
next_obs_batch, 1 - done_mask)
(obs_batch, act_batch, rew_batch, next_obs_batch,
not_done_mask) = TO(obs_batch, act_batch, rew_batch,
next_obs_batch, not_done_mask)
values = Q(obs_batch)
current_Q_values = values.gather(
1,
act_batch.unsqueeze(1).long()).squeeze()
# Compute next Q value based on which action gives max Q values
# Detach variable from the current graph since we don't want gradients for next Q to propagated
next_max_q = Q_target(next_obs_batch).detach().max(1)[0]
next_Q_values = not_done_mask * next_max_q
# Compute the target of the current Q values
Q_target_values = rew_batch + (ARGS.gamma * next_Q_values)
# Compute Bellman error
bellman_error = Q_target_values - current_Q_values
# clip the bellman error between [-1 , 1]
clipped_bellman_error = bellman_error.clamp(-1, 1)
# Note: clipped_bellman_delta * -1 will be right gradient
d_error = clipped_bellman_error * -1.0
# Clear previous gradients before backward pass
optimizer.zero_grad()
# run backward pass
# current_Q_values.backward(d_error.data.unsqueeze(1))
current_Q_values.backward(d_error.data)
# Perfom the update
optimizer.step()
num_param_updates += 1
if num_param_updates % ARGS.dqn_updatefreq == 0:
bar.set_description("update")
Q_target.load_state_dict(Q.state_dict())
def _asSortedList(scores: namedtuple, places: int = 4) -> List[str]:
"""Converts namedtuple of scores to a list of rounded values in name-
sorted order"""
tf = truncatedFloat(places)
return [tf % v for _, v in sorted(scores._asdict().items())]
