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def get_agent_keys ( logger = None ) : paramiko_agent = paramiko . Agent ( ) agent_keys = paramiko_agent . get_keys ( ) if logger : logger . info ( '{0} keys loaded from agent' . format ( len ( agent_keys ) ) ) return list ( agent_keys )
Load public keys from any available SSH agent
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def get_keys ( logger = None , host_pkey_directories = None , allow_agent = False ) : keys = SSHTunnelForwarder . get_agent_keys ( logger = logger ) if allow_agent else [ ] if host_pkey_directories is not None : paramiko_key_types = { 'rsa' : paramiko . RSAKey , 'dsa' : paramiko . DSSKey , 'ecdsa' : paramiko . ECDSAKey , 'ed25519' : paramiko . Ed25519Key } for directory in host_pkey_directories or [ DEFAULT_SSH_DIRECTORY ] : for keytype in paramiko_key_types . keys ( ) : ssh_pkey_expanded = os . path . expanduser ( os . path . join ( directory , 'id_{}' . format ( keytype ) ) ) if os . path . isfile ( ssh_pkey_expanded ) : ssh_pkey = SSHTunnelForwarder . read_private_key_file ( pkey_file = ssh_pkey_expanded , logger = logger , key_type = paramiko_key_types [ keytype ] ) if ssh_pkey : keys . append ( ssh_pkey ) if logger : logger . info ( '{0} keys loaded from host directory' . format ( len ( keys ) ) ) return keys
Load public keys from any available SSH agent or local . ssh directory .
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def _get_transport ( self ) : if self . ssh_proxy : if isinstance ( self . ssh_proxy , paramiko . proxy . ProxyCommand ) : proxy_repr = repr ( self . ssh_proxy . cmd [ 1 ] ) else : proxy_repr = repr ( self . ssh_proxy ) self . logger . debug ( 'Connecting via proxy: {0}' . format ( proxy_repr ) ) _socket = self . ssh_proxy else : _socket = socket . socket ( socket . AF_INET , socket . SOCK_STREAM ) if isinstance ( _socket , socket . socket ) : _socket . settimeout ( SSH_TIMEOUT ) _socket . connect ( ( self . ssh_host , self . ssh_port ) ) transport = paramiko . Transport ( _socket ) transport . set_keepalive ( self . set_keepalive ) transport . use_compression ( compress = self . compression ) transport . daemon = self . daemon_transport return transport
Return the SSH transport to the remote gateway
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def _create_tunnels ( self ) : if not self . is_active : try : self . _connect_to_gateway ( ) except socket . gaierror : # raised by paramiko.Transport msg = 'Could not resolve IP address for {0}, aborting!' . format ( self . ssh_host ) self . logger . error ( msg ) return except ( paramiko . SSHException , socket . error ) as e : template = 'Could not connect to gateway {0}:{1} : {2}' msg = template . format ( self . ssh_host , self . ssh_port , e . args [ 0 ] ) self . logger . error ( msg ) return for ( rem , loc ) in zip ( self . _remote_binds , self . _local_binds ) : try : self . _make_ssh_forward_server ( rem , loc ) except BaseSSHTunnelForwarderError as e : msg = 'Problem setting SSH Forwarder up: {0}' . format ( e . value ) self . logger . error ( msg )
Create SSH tunnels on top of a transport to the remote gateway
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def _process_deprecated ( attrib , deprecated_attrib , kwargs ) : if deprecated_attrib not in DEPRECATIONS : raise ValueError ( '{0} not included in deprecations list' . format ( deprecated_attrib ) ) if deprecated_attrib in kwargs : warnings . warn ( "'{0}' is DEPRECATED use '{1}' instead" . format ( deprecated_attrib , DEPRECATIONS [ deprecated_attrib ] ) , DeprecationWarning ) if attrib : raise ValueError ( "You can't use both '{0}' and '{1}'. " "Please only use one of them" . format ( deprecated_attrib , DEPRECATIONS [ deprecated_attrib ] ) ) else : return kwargs . pop ( deprecated_attrib ) return attrib
Processes optional deprecate arguments
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def read_private_key_file ( pkey_file , pkey_password = None , key_type = None , logger = None ) : ssh_pkey = None for pkey_class in ( key_type , ) if key_type else ( paramiko . RSAKey , paramiko . DSSKey , paramiko . ECDSAKey , paramiko . Ed25519Key ) : try : ssh_pkey = pkey_class . from_private_key_file ( pkey_file , password = pkey_password ) if logger : logger . debug ( 'Private key file ({0}, {1}) successfully ' 'loaded' . format ( pkey_file , pkey_class ) ) break except paramiko . PasswordRequiredException : if logger : logger . error ( 'Password is required for key {0}' . format ( pkey_file ) ) break except paramiko . SSHException : if logger : logger . debug ( 'Private key file ({0}) could not be loaded ' 'as type {1} or bad password' . format ( pkey_file , pkey_class ) ) return ssh_pkey
Get SSH Public key from a private key file given an optional password
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def _check_tunnel ( self , _srv ) : if self . skip_tunnel_checkup : self . tunnel_is_up [ _srv . local_address ] = True return self . logger . info ( 'Checking tunnel to: {0}' . format ( _srv . remote_address ) ) if isinstance ( _srv . local_address , string_types ) : # UNIX stream s = socket . socket ( socket . AF_UNIX , socket . SOCK_STREAM ) else : s = socket . socket ( socket . AF_INET , socket . SOCK_STREAM ) s . settimeout ( TUNNEL_TIMEOUT ) try : # Windows raises WinError 10049 if trying to connect to 0.0.0.0 connect_to = ( '127.0.0.1' , _srv . local_port ) if _srv . local_host == '0.0.0.0' else _srv . local_address s . connect ( connect_to ) self . tunnel_is_up [ _srv . local_address ] = _srv . tunnel_ok . get ( timeout = TUNNEL_TIMEOUT * 1.1 ) self . logger . debug ( 'Tunnel to {0} is DOWN' . format ( _srv . remote_address ) ) except socket . error : self . logger . debug ( 'Tunnel to {0} is DOWN' . format ( _srv . remote_address ) ) self . tunnel_is_up [ _srv . local_address ] = False except queue . Empty : self . logger . debug ( 'Tunnel to {0} is UP' . format ( _srv . remote_address ) ) self . tunnel_is_up [ _srv . local_address ] = True finally : s . close ( )
Check if tunnel is already established
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def start ( self ) : if self . is_alive : self . logger . warning ( 'Already started!' ) return self . _create_tunnels ( ) if not self . is_active : self . _raise ( BaseSSHTunnelForwarderError , reason = 'Could not establish session to SSH gateway' ) for _srv in self . _server_list : thread = threading . Thread ( target = self . _serve_forever_wrapper , args = ( _srv , ) , name = 'Srv-{0}' . format ( address_to_str ( _srv . local_port ) ) ) thread . daemon = self . daemon_forward_servers thread . start ( ) self . _check_tunnel ( _srv ) self . is_alive = any ( self . tunnel_is_up . values ( ) ) if not self . is_alive : self . _raise ( HandlerSSHTunnelForwarderError , 'An error occurred while opening tunnels.' )
Start the SSH tunnels
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def stop ( self ) : self . logger . info ( 'Closing all open connections...' ) opened_address_text = ', ' . join ( ( address_to_str ( k . local_address ) for k in self . _server_list ) ) or 'None' self . logger . debug ( 'Listening tunnels: ' + opened_address_text ) self . _stop_transport ( ) self . _server_list = [ ] # reset server list self . tunnel_is_up = { }
Shut the tunnel down .
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def _serve_forever_wrapper ( self , _srv , poll_interval = 0.1 ) : self . logger . info ( 'Opening tunnel: {0} <> {1}' . format ( address_to_str ( _srv . local_address ) , address_to_str ( _srv . remote_address ) ) ) _srv . serve_forever ( poll_interval ) # blocks until finished self . logger . info ( 'Tunnel: {0} <> {1} released' . format ( address_to_str ( _srv . local_address ) , address_to_str ( _srv . remote_address ) ) )
Wrapper for the server created for a SSH forward
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def _stop_transport ( self ) : try : self . _check_is_started ( ) except ( BaseSSHTunnelForwarderError , HandlerSSHTunnelForwarderError ) as e : self . logger . warning ( e ) for _srv in self . _server_list : tunnel = _srv . local_address if self . tunnel_is_up [ tunnel ] : self . logger . info ( 'Shutting down tunnel {0}' . format ( tunnel ) ) _srv . shutdown ( ) _srv . server_close ( ) # clean up the UNIX domain socket if we're using one if isinstance ( _srv , _UnixStreamForwardServer ) : try : os . unlink ( _srv . local_address ) except Exception as e : self . logger . error ( 'Unable to unlink socket {0}: {1}' . format ( self . local_address , repr ( e ) ) ) self . is_alive = False if self . is_active : self . _transport . close ( ) self . _transport . stop_thread ( ) self . logger . debug ( 'Transport is closed' )
Close the underlying transport when nothing more is needed
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def local_bind_ports ( self ) : self . _check_is_started ( ) return [ _server . local_port for _server in self . _server_list if _server . local_port is not None ]
Return a list containing the ports of local side of the TCP tunnels
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def local_bind_hosts ( self ) : self . _check_is_started ( ) return [ _server . local_host for _server in self . _server_list if _server . local_host is not None ]
Return a list containing the IP addresses listening for the tunnels
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def get_submodules ( module ) : if not inspect . ismodule ( module ) : raise RuntimeError ( 'Can only extract submodules from a module object, ' 'for example imported via importlib.import_module' ) submodules = get_members ( module , inspect . ismodule ) module_path = list ( getattr ( module , '__path__' , [ None ] ) ) [ 0 ] if module_path is not None : for item in listdir ( module_path ) : module_name = extract_module_name ( osp . join ( module_path , item ) ) if module_name is not None : try : submodules [ module_name ] = importlib . import_module ( '.{}' . format ( module_name ) , package = module . __name__ ) except ImportError : # This is necessary in case random directories are in the path or things can # just not be imported due to other ImportErrors. pass return submodules
This function imports all sub - modules of the supplied module and returns a dictionary with module names as keys and the sub - module objects as values . If the supplied parameter is not a module object a RuntimeError is raised .
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def extract_module_name ( absolute_path ) : base_name = osp . basename ( osp . normpath ( absolute_path ) ) # If the basename starts with _ it's probably __init__.py or __pycache__ or something internal. # At the moment there seems to be no use case for those if base_name [ 0 ] in ( '.' , '_' ) : return None # If it's a directory, there's nothing else to check, so it can be returned directly if osp . isdir ( absolute_path ) : return base_name module_name , extension = osp . splitext ( base_name ) # If it's a file, it must have a .py ending if extension == '.py' : return module_name return None
This function tries to extract a valid module name from the basename of the supplied path . If it s a directory the directory name is returned if it s a file the file name without extension is returned . If the basename starts with _ or . or it s a file with an ending different from . py the function returns None
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def dict_strict_update ( base_dict , update_dict ) : additional_keys = set ( update_dict . keys ( ) ) - set ( base_dict . keys ( ) ) if len ( additional_keys ) > 0 : raise RuntimeError ( 'The update dictionary contains keys that are not part of ' 'the base dictionary: {}' . format ( str ( additional_keys ) ) , additional_keys ) base_dict . update ( update_dict )
This function updates base_dict with update_dict if and only if update_dict does not contain keys that are not already in base_dict . It is essentially a more strict interpretation of the term updating the dict .
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def format_doc_text ( text ) : return '\n' . join ( textwrap . fill ( line , width = 99 , initial_indent = ' ' , subsequent_indent = ' ' ) for line in inspect . cleandoc ( text ) . splitlines ( ) )
A very thin wrapper around textwrap . fill to consistently wrap documentation text for display in a command line environment . The text is wrapped to 99 characters with an indentation depth of 4 spaces . Each line is wrapped independently in order to preserve manually added line breaks .
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def do_import ( self , * names ) : try : module_object = importlib . import_module ( self . _module ) objects = tuple ( getattr ( module_object , name ) for name in names ) except ImportError : def failing_init ( obj , * args , * * kwargs ) : raise self . _exception objects = tuple ( type ( name , ( object , ) , { '__init__' : failing_init } ) for name in names ) return objects if len ( objects ) != 1 else objects [ 0 ]
Tries to import names from the module specified on initialization of the FromOptionalDependency - object . In case an ImportError occurs the requested names are replaced with stub objects .
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def get_api ( self ) : return { 'class' : type ( self . _object ) . __name__ , 'methods' : list ( self . _function_map . keys ( ) ) }
This method returns the class name and a list of exposed methods . It is exposed to RPC - clients by an instance of ExposedObjectCollection .
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def remove_object ( self , name ) : if name not in self . _object_map : raise RuntimeError ( 'No object with name {} is registered.' . format ( name ) ) for fn_name in list ( self . _function_map . keys ( ) ) : if fn_name . startswith ( name + '.' ) or fn_name . startswith ( name + ':' ) : self . _remove_function ( fn_name ) del self . _object_map [ name ]
Remove the object exposed under that name . If no object is registered under the supplied name a RuntimeError is raised .
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def set_set_point ( self , param ) : if self . temperature_low_limit <= param <= self . temperature_high_limit : self . set_point_temperature = param return ""
Sets the target temperature .
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def set_circulating ( self , param ) : if param == 0 : self . is_circulating = param self . circulate_commanded = False elif param == 1 : self . is_circulating = param self . circulate_commanded = True return ""
Sets whether to circulate - in effect whether the heater is on .
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def get_protocols ( self , device ) : return self . _reg . device_builder ( device , self . _rv ) . protocols
Returns a list of available protocols for the specified device .
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def linear ( current , target , rate , dt ) : sign = ( target > current ) - ( target < current ) if not sign : return current new_value = current + sign * rate * dt if sign * new_value > sign * target : return target return new_value
This function returns the new value after moving towards target at the given speed constantly for the time dt .
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def meta ( self ) : if not self . _pv . meta_data_property or not self . _meta_target : return { } return getattr ( self . _meta_target , self . _pv . meta_data_property )
Value of the bound meta - property on the target .
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def doc ( self ) : return self . _pv . doc or inspect . getdoc ( getattr ( type ( self . _target ) , self . _pv . property , None ) ) or ''
Docstring of property on target or override specified on PV - object .
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def bind ( self , * targets ) : self . property = 'value' self . meta_data_property = 'meta' return BoundPV ( self , self . _get_target ( self . property , * targets ) , self . _get_target ( self . meta_data_property , * targets ) )
Tries to bind the PV to one of the supplied targets . Targets are inspected according to the order in which they are supplied .
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def _get_callable ( self , func , * targets ) : if not callable ( func ) : func_name = func func = next ( ( getattr ( obj , func , None ) for obj in targets if func in dir ( obj ) ) , None ) if not func : raise AttributeError ( 'No method with the name \'{}\' could be found on any of the target objects ' '(device, interface). Please check the spelling.' . format ( func_name ) ) return func
If func is already a callable it is returned directly . If it s a string it is assumed to be a method on one of the objects supplied in targets and that is returned . If no method with the specified name is found an AttributeError is raised .
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def _function_has_n_args ( self , func , n ) : if inspect . ismethod ( func ) : n += 1 argspec = inspect . getargspec ( func ) defaults = argspec . defaults or ( ) return len ( argspec . args ) - len ( defaults ) == n
Returns true if func has n arguments . Arguments with default and self for methods are not considered .
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def start_server ( self ) : if self . _server is None : self . _server = SimpleServer ( ) self . _server . createPV ( prefix = self . _options . prefix , pvdb = { k : v . config for k , v in self . interface . bound_pvs . items ( ) } ) self . _driver = PropertyExposingDriver ( interface = self . interface , device_lock = self . device_lock ) self . _driver . process_pv_updates ( force = True ) self . log . info ( 'Started serving PVs: %s' , ', ' . join ( ( self . _options . prefix + pv for pv in self . interface . bound_pvs . keys ( ) ) ) )
Creates a pcaspy - server .
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def handle ( self , cycle_delay = 0.1 ) : if self . _server is not None : self . _server . process ( cycle_delay ) self . _driver . process_pv_updates ( )
Call this method to spend about cycle_delay seconds processing requests in the pcaspy server . Under load for example when running caget at a high frequency the actual time spent in the method may be much shorter . This effect is not corrected for .
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def _make_request ( self , method , * args ) : response , request_id = self . _connection . json_rpc ( self . _prefix + method , * args ) if 'id' not in response : raise ProtocolException ( 'JSON-RPC response does not contain ID field.' ) if response [ 'id' ] != request_id : raise ProtocolException ( 'ID of JSON-RPC request ({}) did not match response ({}).' . format ( request_id , response [ 'id' ] ) ) if 'result' in response : return response [ 'result' ] if 'error' in response : if 'data' in response [ 'error' ] : exception_type = response [ 'error' ] [ 'data' ] [ 'type' ] exception_message = response [ 'error' ] [ 'data' ] [ 'message' ] if not hasattr ( exceptions , exception_type ) : raise RemoteException ( exception_type , exception_message ) else : exception = getattr ( exceptions , exception_type ) raise exception ( exception_message ) else : raise ProtocolException ( response [ 'error' ] [ 'message' ] )
This method performs a JSON - RPC request via the object s ZMQ socket . If successful the result is returned otherwise exceptions are raised . Server side exceptions are raised using the same type as on the server if they are part of the exceptions - module . Otherwise a RemoteException is raised .
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def get_status ( self ) : # "The first command sent must be a 'T' command" from T95 manual self . device . serial_command_mode = True Tarray = [ 0x80 ] * 10 # Status byte (SB1) Tarray [ 0 ] = { 'stopped' : 0x01 , 'heat' : 0x10 , 'cool' : 0x20 , 'hold' : 0x30 , } . get ( self . device . _csm . state , 0x01 ) if Tarray [ 0 ] == 0x30 and self . device . hold_commanded : Tarray [ 0 ] = 0x50 # Error status byte (EB1) if self . device . pump_overspeed : Tarray [ 1 ] |= 0x01 # TODO: Add support for other error conditions? # Pump status byte (PB1) Tarray [ 2 ] = 0x80 + self . device . pump_speed # Temperature Tarray [ 6 : 10 ] = [ ord ( x ) for x in "%04x" % ( int ( self . device . temperature * 10 ) & 0xFFFF ) ] return '' . join ( chr ( c ) for c in Tarray )
Models T Command functionality of device .
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def set_rate ( self , param ) : # TODO: Is not having leading zeroes / 4 digits an error? rate = int ( param ) if 1 <= rate <= 15000 : self . device . temperature_rate = rate / 100.0 return ""
Models Rate Command functionality of device .
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def set_limit ( self , param ) : # TODO: Is not having leading zeroes / 4 digits an error? limit = int ( param ) if - 2000 <= limit <= 6000 : self . device . temperature_limit = limit / 10.0 return ""
Models Limit Command functionality of device .
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def pump_command ( self , param ) : lookup = [ c for c in "0123456789:;<=>?@ABCDEFGHIJKLMN" ] if param == "a0" : self . device . pump_manual_mode = False elif param == "m0" : self . device . pump_manual_mode = True elif param in lookup : self . device . manual_target_speed = lookup . index ( param ) return ""
Models LNP Pump Commands functionality of device .
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def set_context ( self , new_context ) : self . _context = new_context if hasattr ( self , '_set_logging_context' ) : self . _set_logging_context ( self . _context )
Assigns the new context to the member variable _context .
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def _initialize_data ( self ) : self . serial_command_mode = False self . pump_overspeed = False self . start_commanded = False self . stop_commanded = False self . hold_commanded = False # Real device remembers values from last run, we use arbitrary defaults self . temperature_rate = 5.0 # Rate of change of temperature in C/min self . temperature_limit = 0.0 # Target temperature in C self . pump_speed = 0 # Pump speed in arbitrary unit, ranging 0 to 30 self . temperature = 24.0 # Current temperature in C self . pump_manual_mode = False self . manual_target_speed = 0
This method is called once on construction . After that it may be manually called again to reset the device to its default state .
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def has_log ( target ) : logger_name = target . __name__ def get_logger_name ( context = None ) : log_names = [ root_logger_name , logger_name ] if context is not None : log_names . insert ( 1 , context if isinstance ( context , string_types ) else context . __class__ . __name__ ) return '.' . join ( log_names ) def _set_logging_context ( obj , context ) : """ Changes the logger name of this class using the supplied context according to the rules described in the documentation of :func:`has_log`. To clear the context of a class logger, supply ``None`` as the argument. :param context: String or object, ``None`` to clear context. """ obj . log . name = get_logger_name ( context ) target . log = logging . getLogger ( get_logger_name ( ) ) target . _set_logging_context = _set_logging_context return target
This is a decorator to add logging functionality to a class or function .
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def get_usage_text ( parser , indent = None ) : usage_text = StringIO ( ) parser . print_help ( usage_text ) usage_string = usage_text . getvalue ( ) if indent is None : return usage_string return '\n' . join ( [ ' ' * indent + line for line in usage_string . split ( '\n' ) ] )
This small helper function extracts the help information from an ArgumentParser instance and indents the text by the number of spaces supplied in the indent - argument .
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def stop ( self ) : self . _target = self . position self . log . info ( 'Stopping movement after user request.' ) return self . target , self . position
Stops the motor and returns the new target and position which are equal
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def run_simulation ( argument_list = None ) : # noqa: C901 try : arguments = parser . parse_args ( argument_list or sys . argv [ 1 : ] ) if arguments . version : print ( __version__ ) return if arguments . relaxed_versions : print ( 'Unknown option --relaxed-versions. Did you mean --ignore-versions?' ) return loglevel = 'debug' if arguments . verify else arguments . output_level if loglevel != 'none' : logging . basicConfig ( level = getattr ( logging , loglevel . upper ( ) ) , format = default_log_format ) if arguments . add_path is not None : additional_path = os . path . abspath ( arguments . add_path ) logging . getLogger ( ) . debug ( 'Extending path with: %s' , additional_path ) sys . path . append ( additional_path ) strict_versions = use_strict_versions ( arguments . strict_versions , arguments . ignore_versions ) simulation_factory = SimulationFactory ( arguments . device_package , strict_versions ) if not arguments . device : devices = [ 'Please specify a device to simulate. The following devices are available:' ] for dev in simulation_factory . devices : devices . append ( ' ' + dev ) print ( '\n' . join ( devices ) ) return if arguments . list_protocols : print ( '\n' . join ( simulation_factory . get_protocols ( arguments . device ) ) ) return protocols = parse_adapter_options ( arguments . adapter_options ) if not arguments . no_interface else { } simulation = simulation_factory . create ( arguments . device , arguments . setup , protocols , arguments . rpc_host ) if arguments . show_interface : print ( simulation . _adapters . documentation ( ) ) return if arguments . list_adapter_options : configurations = simulation . _adapters . configuration ( ) for protocol , options in configurations . items ( ) : print ( '{}:' . format ( protocol ) ) for opt , val in options . items ( ) : print ( ' {} = {}' . format ( opt , val ) ) return simulation . cycle_delay = arguments . cycle_delay simulation . speed = arguments . speed if not arguments . verify : try : simulation . start ( ) except KeyboardInterrupt : print ( '\nInterrupt received; shutting down. Goodbye, cruel world!' ) simulation . log . critical ( 'Simulation aborted by user interaction' ) finally : simulation . stop ( ) except LewisException as e : print ( '\n' . join ( ( 'An error occurred:' , str ( e ) ) ) )
This is effectively the main function of a typical simulation run . Arguments passed in are parsed and used to construct and run the simulation .
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def map_arguments ( self , arguments ) : if self . argument_mappings is None : return arguments return [ f ( a ) for f , a in zip ( self . argument_mappings , arguments ) ]
Returns the mapped function arguments . If no mapping functions are defined the arguments are returned as they were supplied .
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def map_return_value ( self , return_value ) : if callable ( self . return_mapping ) : return self . return_mapping ( return_value ) if self . return_mapping is not None : return self . return_mapping return return_value
Returns the mapped return_value of a processed request . If no return_mapping has been defined the value is returned as is . If return_mapping is a static value that value is returned ignoring return_value completely .
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def start_server ( self ) : if self . _server is None : if self . _options . telnet_mode : self . interface . in_terminator = '\r\n' self . interface . out_terminator = '\r\n' self . _server = StreamServer ( self . _options . bind_address , self . _options . port , self . interface , self . device_lock )
Starts the TCP stream server binding to the configured host and port . Host and port are configured via the command line arguments .
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def handle ( self , cycle_delay = 0.1 ) : asyncore . loop ( cycle_delay , count = 1 ) self . _server . process ( int ( cycle_delay * 1000 ) )
Spend approximately cycle_delay seconds to process requests to the server .
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def _override_data ( self , overrides ) : if overrides is not None : for name , val in overrides . items ( ) : self . log . debug ( 'Trying to override initial data (%s=%s)' , name , val ) if name not in dir ( self ) : raise AttributeError ( 'Can not override non-existing attribute' '\'{}\' of class \'{}\'.' . format ( name , type ( self ) . __name__ ) ) setattr ( self , name , val )
This method overrides data members of the class but does not allow for adding new members .
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def get ( self , addr , count ) : addr -= self . _start_addr data = self . _data [ addr : addr + count ] if len ( data ) != count : addr += self . _start_addr raise IndexError ( "Invalid address range [{:#06x} - {:#06x}]" . format ( addr , addr + count ) ) return data
Read list of count values at addr memory location in DataBank .
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def set ( self , addr , values ) : addr -= self . _start_addr end = addr + len ( values ) if not 0 <= addr <= end <= len ( self . _data ) : addr += self . _start_addr raise IndexError ( "Invalid address range [{:#06x} - {:#06x}]" . format ( addr , addr + len ( values ) ) ) self . _data [ addr : end ] = values
Write list values to addr memory location in DataBank .
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def from_bytearray ( self , stream ) : fmt = '>HHHBB' size_header = struct . calcsize ( fmt ) if len ( stream ) < size_header : raise EOFError ( self . transaction_id , self . protocol_id , self . length , self . unit_id , self . fcode ) = struct . unpack ( fmt , bytes ( stream [ : size_header ] ) ) size_total = size_header + self . length - 2 if len ( stream ) < size_total : raise EOFError self . data = stream [ size_header : size_total ] del stream [ : size_total ]
Constructs this frame from input data stream consuming as many bytes as necessary from the beginning of the stream .
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def to_bytearray ( self ) : header = bytearray ( struct . pack ( '>HHHBB' , self . transaction_id , self . protocol_id , self . length , self . unit_id , self . fcode ) ) return header + self . data
Convert this frame into its bytearray representation .
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def is_valid ( self ) : conditions = [ self . protocol_id == 0 , # Modbus always uses protocol 0 2 <= self . length <= 260 , # Absolute length limits len ( self . data ) == self . length - 2 , # Total length matches data length ] return all ( conditions )
Check integrity and validity of this frame .
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def create_exception ( self , code ) : frame = deepcopy ( self ) frame . length = 3 frame . fcode += 0x80 frame . data = bytearray ( chr ( code ) ) return frame
Create an exception frame based on this frame .
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def create_response ( self , data = None ) : frame = deepcopy ( self ) if data is not None : frame . data = data frame . length = 2 + len ( frame . data ) return frame
Create a response frame based on this frame .
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def _all_expander ( fringe , iteration , viewer ) : expanded_neighbors = [ node . expand ( local_search = True ) for node in fringe ] if viewer : viewer . event ( 'expanded' , list ( fringe ) , expanded_neighbors ) list ( map ( fringe . extend , expanded_neighbors ) )
Expander that expands all nodes on the fringe .
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def beam ( problem , beam_size = 100 , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _all_expander , iterations_limit = iterations_limit , fringe_size = beam_size , random_initial_states = True , stop_when_no_better = iterations_limit == 0 , viewer = viewer )
Beam search .
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def _first_expander ( fringe , iteration , viewer ) : current = fringe [ 0 ] neighbors = current . expand ( local_search = True ) if viewer : viewer . event ( 'expanded' , [ current ] , [ neighbors ] ) fringe . extend ( neighbors )
Expander that expands only the first node on the fringe .
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def beam_best_first ( problem , beam_size = 100 , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _first_expander , iterations_limit = iterations_limit , fringe_size = beam_size , random_initial_states = True , stop_when_no_better = iterations_limit == 0 , viewer = viewer )
Beam search best first .
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def hill_climbing ( problem , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _first_expander , iterations_limit = iterations_limit , fringe_size = 1 , stop_when_no_better = True , viewer = viewer )
Hill climbing search .
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def hill_climbing_stochastic ( problem , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _random_best_expander , iterations_limit = iterations_limit , fringe_size = 1 , stop_when_no_better = iterations_limit == 0 , viewer = viewer )
Stochastic hill climbing .
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def hill_climbing_random_restarts ( problem , restarts_limit , iterations_limit = 0 , viewer = None ) : restarts = 0 best = None while restarts < restarts_limit : new = _local_search ( problem , _first_expander , iterations_limit = iterations_limit , fringe_size = 1 , random_initial_states = True , stop_when_no_better = True , viewer = viewer ) if not best or best . value < new . value : best = new restarts += 1 if viewer : viewer . event ( 'no_more_runs' , best , 'returned after %i runs' % restarts_limit ) return best
Hill climbing with random restarts .
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def _exp_schedule ( iteration , k = 20 , lam = 0.005 , limit = 100 ) : return k * math . exp ( - lam * iteration )
Possible scheduler for simulated_annealing based on the aima example .
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def simulated_annealing ( problem , schedule = _exp_schedule , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _create_simulated_annealing_expander ( schedule ) , iterations_limit = iterations_limit , fringe_size = 1 , stop_when_no_better = iterations_limit == 0 , viewer = viewer )
Simulated annealing .
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def _create_genetic_expander ( problem , mutation_chance ) : def _expander ( fringe , iteration , viewer ) : fitness = [ x . value for x in fringe ] sampler = InverseTransformSampler ( fitness , fringe ) new_generation = [ ] expanded_nodes = [ ] expanded_neighbors = [ ] for _ in fringe : node1 = sampler . sample ( ) node2 = sampler . sample ( ) child = problem . crossover ( node1 . state , node2 . state ) action = 'crossover' if random . random ( ) < mutation_chance : # Noooouuu! she is... he is... *IT* is a mutant! child = problem . mutate ( child ) action += '+mutation' child_node = SearchNodeValueOrdered ( state = child , problem = problem , action = action ) new_generation . append ( child_node ) expanded_nodes . append ( node1 ) expanded_neighbors . append ( [ child_node ] ) expanded_nodes . append ( node2 ) expanded_neighbors . append ( [ child_node ] ) if viewer : viewer . event ( 'expanded' , expanded_nodes , expanded_neighbors ) fringe . clear ( ) for node in new_generation : fringe . append ( node ) return _expander
Creates an expander that expands the bests nodes of the population crossing over them .
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def genetic ( problem , population_size = 100 , mutation_chance = 0.1 , iterations_limit = 0 , viewer = None ) : return _local_search ( problem , _create_genetic_expander ( problem , mutation_chance ) , iterations_limit = iterations_limit , fringe_size = population_size , random_initial_states = True , stop_when_no_better = iterations_limit == 0 , viewer = viewer )
Genetic search .
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def _local_search ( problem , fringe_expander , iterations_limit = 0 , fringe_size = 1 , random_initial_states = False , stop_when_no_better = True , viewer = None ) : if viewer : viewer . event ( 'started' ) fringe = BoundedPriorityQueue ( fringe_size ) if random_initial_states : for _ in range ( fringe_size ) : s = problem . generate_random_state ( ) fringe . append ( SearchNodeValueOrdered ( state = s , problem = problem ) ) else : fringe . append ( SearchNodeValueOrdered ( state = problem . initial_state , problem = problem ) ) finish_reason = '' iteration = 0 run = True best = None while run : if viewer : viewer . event ( 'new_iteration' , list ( fringe ) ) old_best = fringe [ 0 ] fringe_expander ( fringe , iteration , viewer ) best = fringe [ 0 ] iteration += 1 if iterations_limit and iteration >= iterations_limit : run = False finish_reason = 'reaching iteration limit' elif old_best . value >= best . value and stop_when_no_better : run = False finish_reason = 'not being able to improve solution' if viewer : viewer . event ( 'finished' , fringe , best , 'returned after %s' % finish_reason ) return best
Basic algorithm for all local search algorithms .
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def actions ( self , state ) : rows = string_to_list ( state ) row_e , col_e = find_location ( rows , 'e' ) actions = [ ] if row_e > 0 : actions . append ( rows [ row_e - 1 ] [ col_e ] ) if row_e < 2 : actions . append ( rows [ row_e + 1 ] [ col_e ] ) if col_e > 0 : actions . append ( rows [ row_e ] [ col_e - 1 ] ) if col_e < 2 : actions . append ( rows [ row_e ] [ col_e + 1 ] ) return actions
Returns a list of the pieces we can move to the empty space .
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def is_attribute ( method , name = None ) : if name is None : name = method . __name__ method . is_attribute = True method . name = name return method
Decorator for methods that are attributes .
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def load ( cls , filepath ) : with open ( filepath , "rb" ) as filehandler : classifier = pickle . load ( filehandler ) if not isinstance ( classifier , Classifier ) : raise ValueError ( "Pickled object is not a Classifier" ) return classifier
Loads a pickled version of the classifier saved in filepath
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def tree_to_str ( root ) : xs = [ ] for value , node , depth in iter_tree ( root ) : template = "{indent}" if node is not root : template += "case={value}\t" if node . attribute is None : template += "result={result} -- P={prob:.2}" else : template += "split by {split}:\t" + "(partial result={result} -- P={prob:.2})" line = template . format ( indent = " " * depth , value = value , result = node . result [ 0 ] , prob = node . result [ 1 ] , split = str ( node . attribute ) ) xs . append ( line ) return "\n" . join ( xs )
Returns a string representation of a decision tree with root node root .
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def save ( self , filepath ) : if not filepath or not isinstance ( filepath , str ) : raise ValueError ( "Invalid filepath" ) with open ( filepath , "wb" ) as filehandler : pickle . dump ( self , filehandler )
Saves the classifier to filepath . Because this classifier needs to save the dataset it must be something that can be pickled and not something like an iterator .
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def _max_gain_split ( self , examples ) : gains = self . _new_set_of_gain_counters ( ) for example in examples : for gain in gains : gain . add ( example ) winner = max ( gains , key = lambda gain : gain . get_gain ( ) ) if not winner . get_target_class_counts ( ) : raise ValueError ( "Dataset is empty" ) return winner
Returns an OnlineInformationGain of the attribute with max gain based on examples .
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def backtrack ( problem , variable_heuristic = '' , value_heuristic = '' , inference = True ) : assignment = { } domains = deepcopy ( problem . domains ) if variable_heuristic == MOST_CONSTRAINED_VARIABLE : variable_chooser = _most_constrained_variable_chooser elif variable_heuristic == HIGHEST_DEGREE_VARIABLE : variable_chooser = _highest_degree_variable_chooser else : variable_chooser = _basic_variable_chooser if value_heuristic == LEAST_CONSTRAINING_VALUE : values_sorter = _least_constraining_values_sorter else : values_sorter = _basic_values_sorter return _backtracking ( problem , assignment , domains , variable_chooser , values_sorter , inference = inference )
Backtracking search .
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def _most_constrained_variable_chooser ( problem , variables , domains ) : # the variable with fewer values available return sorted ( variables , key = lambda v : len ( domains [ v ] ) ) [ 0 ]
Choose the variable that has less available values .
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def _highest_degree_variable_chooser ( problem , variables , domains ) : # the variable involved in more constraints return sorted ( variables , key = lambda v : problem . var_degrees [ v ] , reverse = True ) [ 0 ]
Choose the variable that is involved on more constraints .
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def _count_conflicts ( problem , assignment , variable = None , value = None ) : return len ( _find_conflicts ( problem , assignment , variable , value ) )
Count the number of violated constraints on a given assignment .
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def _find_conflicts ( problem , assignment , variable = None , value = None ) : if variable is not None and value is not None : assignment = deepcopy ( assignment ) assignment [ variable ] = value conflicts = [ ] for neighbors , constraint in problem . constraints : # if all the neighbors on the constraint have values, check if conflict if all ( n in assignment for n in neighbors ) : if not _call_constraint ( assignment , neighbors , constraint ) : conflicts . append ( ( neighbors , constraint ) ) return conflicts
Find violated constraints on a given assignment with the possibility of specifying a new variable and value to add to the assignment before checking .
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def _least_constraining_values_sorter ( problem , assignment , variable , domains ) : # the value that generates less conflicts def update_assignment ( value ) : new_assignment = deepcopy ( assignment ) new_assignment [ variable ] = value return new_assignment values = sorted ( domains [ variable ] [ : ] , key = lambda v : _count_conflicts ( problem , assignment , variable , v ) ) return values
Sort values based on how many conflicts they generate if assigned .
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def _backtracking ( problem , assignment , domains , variable_chooser , values_sorter , inference = True ) : from simpleai . search . arc import arc_consistency_3 if len ( assignment ) == len ( problem . variables ) : return assignment pending = [ v for v in problem . variables if v not in assignment ] variable = variable_chooser ( problem , pending , domains ) values = values_sorter ( problem , assignment , variable , domains ) for value in values : new_assignment = deepcopy ( assignment ) new_assignment [ variable ] = value if not _count_conflicts ( problem , new_assignment ) : # TODO on aima also checks if using fc new_domains = deepcopy ( domains ) new_domains [ variable ] = [ value ] if not inference or arc_consistency_3 ( new_domains , problem . constraints ) : result = _backtracking ( problem , new_assignment , new_domains , variable_chooser , values_sorter , inference = inference ) if result : return result return None
Internal recursive backtracking algorithm .
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def _min_conflicts_value ( problem , assignment , variable ) : return argmin ( problem . domains [ variable ] , lambda x : _count_conflicts ( problem , assignment , variable , x ) )
Return the value generate the less number of conflicts . In case of tie a random value is selected among this values subset .
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def min_conflicts ( problem , initial_assignment = None , iterations_limit = 0 ) : assignment = { } if initial_assignment : assignment . update ( initial_assignment ) else : for variable in problem . variables : value = _min_conflicts_value ( problem , assignment , variable ) assignment [ variable ] = value iteration = 0 run = True while run : conflicts = _find_conflicts ( problem , assignment ) conflict_variables = [ v for v in problem . variables if any ( v in conflict [ 0 ] for conflict in conflicts ) ] if conflict_variables : variable = random . choice ( conflict_variables ) value = _min_conflicts_value ( problem , assignment , variable ) assignment [ variable ] = value iteration += 1 if iterations_limit and iteration >= iterations_limit : run = False elif not _count_conflicts ( problem , assignment ) : run = False return assignment
Min conflicts search .
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def convert_to_binary ( variables , domains , constraints ) : def wdiff ( vars_ ) : def diff ( variables , values ) : hidden , other = variables if hidden . startswith ( 'hidden' ) : idx = vars_ . index ( other ) return values [ 1 ] == values [ 0 ] [ idx ] else : idx = vars_ . index ( hidden ) return values [ 0 ] == values [ 1 ] [ idx ] diff . no_wrap = True # so it's not wrapped to swap values return diff new_constraints = [ ] new_domains = copy ( domains ) new_variables = list ( variables ) last = 0 for vars_ , const in constraints : if len ( vars_ ) == 2 : new_constraints . append ( ( vars_ , const ) ) continue hidden = 'hidden%d' % last new_variables . append ( hidden ) last += 1 new_domains [ hidden ] = [ t for t in product ( * map ( domains . get , vars_ ) ) if const ( vars_ , t ) ] for var in vars_ : new_constraints . append ( ( ( hidden , var ) , wdiff ( vars_ ) ) ) return new_variables , new_domains , new_constraints
Returns new constraint list all binary using hidden variables .
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def boltzmann_exploration ( actions , utilities , temperature , action_counter ) : utilities = [ utilities [ x ] for x in actions ] temperature = max ( temperature , 0.01 ) _max = max ( utilities ) _min = min ( utilities ) if _max == _min : return random . choice ( actions ) utilities = [ math . exp ( ( ( u - _min ) / ( _max - _min ) ) / temperature ) for u in utilities ] probs = [ u / sum ( utilities ) for u in utilities ] i = 0 tot = probs [ i ] r = random . random ( ) while i < len ( actions ) and r >= tot : i += 1 tot += probs [ i ] return actions [ i ]
returns an action with a probability depending on utilities and temperature
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def mkconstraints ( ) : constraints = [ ] for j in range ( 1 , 10 ) : vars = [ "%s%d" % ( i , j ) for i in uppercase [ : 9 ] ] constraints . extend ( ( c , const_different ) for c in combinations ( vars , 2 ) ) for i in uppercase [ : 9 ] : vars = [ "%s%d" % ( i , j ) for j in range ( 1 , 10 ) ] constraints . extend ( ( c , const_different ) for c in combinations ( vars , 2 ) ) for b0 in [ 'ABC' , 'DEF' , 'GHI' ] : for b1 in [ [ 1 , 2 , 3 ] , [ 4 , 5 , 6 ] , [ 7 , 8 , 9 ] ] : vars = [ "%s%d" % ( i , j ) for i in b0 for j in b1 ] l = list ( ( c , const_different ) for c in combinations ( vars , 2 ) ) constraints . extend ( l ) return constraints
Make constraint list for binary constraint problem .
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def precision ( classifier , testset ) : hit = 0 total = 0 for example in testset : if classifier . classify ( example ) [ 0 ] == classifier . target ( example ) : hit += 1 total += 1 if total == 0 : raise ValueError ( "Empty testset!" ) return hit / float ( total )
Runs the classifier for each example in testset and verifies that the classification is correct using the target .
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def kfold ( dataset , problem , method , k = 10 ) : if k <= 1 : raise ValueError ( "k argument must be at least 2" ) dataset = list ( dataset ) random . shuffle ( dataset ) trials = 0 positive = 0 for i in range ( k ) : train = [ x for j , x in enumerate ( dataset ) if j % k != i ] test = [ x for j , x in enumerate ( dataset ) if j % k == i ] classifier = method ( train , problem ) for data in test : trials += 1 result = classifier . classify ( data ) if result is not None and result [ 0 ] == problem . target ( data ) : positive += 1 return float ( positive ) / float ( trials )
Does a k - fold on dataset with method . This is it randomly creates k - partitions of the dataset and k - times trains the method with k - 1 parts and runs it with the partition left . After all this returns the overall success ratio .
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def actions ( self , state ) : actions = [ ] for index , char in enumerate ( state ) : if char == '_' : actions . append ( index ) return actions
actions are index where we can make a move
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def actions ( self , s ) : # we try to generate every possible state and then filter those # states that are valid return [ a for a in self . _actions if self . _is_valid ( self . result ( s , a ) ) ]
Possible actions from a state .
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def _is_valid ( self , s ) : # valid states: no more cannibals than missioners on each side, # and numbers between 0 and 3 return ( ( s [ 0 ] >= s [ 1 ] or s [ 0 ] == 0 ) ) and ( ( 3 - s [ 0 ] ) >= ( 3 - s [ 1 ] ) or s [ 0 ] == 3 ) and ( 0 <= s [ 0 ] <= 3 ) and ( 0 <= s [ 1 ] <= 3 )
Check if a state is valid .
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def result ( self , s , a ) : # result: boat on opposite side, and numbers of missioners and # cannibals updated according to the move if s [ 2 ] == 0 : return ( s [ 0 ] - a [ 1 ] [ 0 ] , s [ 1 ] - a [ 1 ] [ 1 ] , 1 ) else : return ( s [ 0 ] + a [ 1 ] [ 0 ] , s [ 1 ] + a [ 1 ] [ 1 ] , 0 )
Result of applying an action to a state .
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def arc_consistency_3 ( domains , constraints ) : arcs = list ( all_arcs ( constraints ) ) pending_arcs = set ( arcs ) while pending_arcs : x , y = pending_arcs . pop ( ) if revise ( domains , ( x , y ) , constraints ) : if len ( domains [ x ] ) == 0 : return False pending_arcs = pending_arcs . union ( ( x2 , y2 ) for x2 , y2 in arcs if y2 == x ) return True
Makes a CSP problem arc consistent .
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def expand ( self , local_search = False ) : new_nodes = [ ] for action in self . problem . actions ( self . state ) : new_state = self . problem . result ( self . state , action ) cost = self . problem . cost ( self . state , action , new_state ) nodefactory = self . __class__ new_nodes . append ( nodefactory ( state = new_state , parent = None if local_search else self , problem = self . problem , action = action , cost = self . cost + cost , depth = self . depth + 1 ) ) return new_nodes
Create successors .
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def step ( self , viewer = None ) : if not self . is_completed ( self . state ) : for agent in self . agents : action = agent . program ( self . percept ( agent , self . state ) ) next_state = self . do_action ( self . state , action , agent ) if viewer : viewer . event ( self . state , action , next_state , agent ) self . state = next_state if self . is_completed ( self . state ) : return
This method evolves one step in time
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def breadth_first ( problem , graph_search = False , viewer = None ) : return _search ( problem , FifoList ( ) , graph_search = graph_search , viewer = viewer )
Breadth first search .
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def depth_first ( problem , graph_search = False , viewer = None ) : return _search ( problem , LifoList ( ) , graph_search = graph_search , viewer = viewer )
Depth first search .
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def limited_depth_first ( problem , depth_limit , graph_search = False , viewer = None ) : return _search ( problem , LifoList ( ) , graph_search = graph_search , depth_limit = depth_limit , viewer = viewer )
Limited depth first search .
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def iterative_limited_depth_first ( problem , graph_search = False , viewer = None ) : solution = None limit = 0 while not solution : solution = limited_depth_first ( problem , depth_limit = limit , graph_search = graph_search , viewer = viewer ) limit += 1 if viewer : viewer . event ( 'no_more_runs' , solution , 'returned after %i runs' % limit ) return solution
Iterative limited depth first search .
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def uniform_cost ( problem , graph_search = False , viewer = None ) : return _search ( problem , BoundedPriorityQueue ( ) , graph_search = graph_search , node_factory = SearchNodeCostOrdered , graph_replace_when_better = True , viewer = viewer )
Uniform cost search .
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def greedy ( problem , graph_search = False , viewer = None ) : return _search ( problem , BoundedPriorityQueue ( ) , graph_search = graph_search , node_factory = SearchNodeHeuristicOrdered , graph_replace_when_better = True , viewer = viewer )
Greedy search .
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def _search ( problem , fringe , graph_search = False , depth_limit = None , node_factory = SearchNode , graph_replace_when_better = False , viewer = None ) : if viewer : viewer . event ( 'started' ) memory = set ( ) initial_node = node_factory ( state = problem . initial_state , problem = problem ) fringe . append ( initial_node ) while fringe : if viewer : viewer . event ( 'new_iteration' , fringe . sorted ( ) ) node = fringe . pop ( ) if problem . is_goal ( node . state ) : if viewer : viewer . event ( 'chosen_node' , node , True ) viewer . event ( 'finished' , fringe . sorted ( ) , node , 'goal found' ) return node else : if viewer : viewer . event ( 'chosen_node' , node , False ) memory . add ( node . state ) if depth_limit is None or node . depth < depth_limit : expanded = node . expand ( ) if viewer : viewer . event ( 'expanded' , [ node ] , [ expanded ] ) for n in expanded : if graph_search : others = [ x for x in fringe if x . state == n . state ] assert len ( others ) in ( 0 , 1 ) if n . state not in memory and len ( others ) == 0 : fringe . append ( n ) elif graph_replace_when_better and len ( others ) > 0 and n < others [ 0 ] : fringe . remove ( others [ 0 ] ) fringe . append ( n ) else : fringe . append ( n ) if viewer : viewer . event ( 'finished' , fringe . sorted ( ) , None , 'goal not found' )
Basic search algorithm base of all the other search algorithms .
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