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• nhibernate fatal error

  - by Afif Lamloumi

  i have an error ( System.InvalidCastException: Unable to cast object of type 'AccountProxy' to type 'System.String'.) when i did this code i mapped the tables( Account,AccountString,EventData,...) of the the database opengts ( open source) i have this error when i called a function from EventData.cs IQuery query = session.CreateQuery("FROM Eventdata"); IList pets = query.List(); return pets; the Stack Trace: [InvalidCastException: Impossible d'effectuer un cast d'un objet de type 'AccountProxy' en type 'System.String'.] (Object , Object[] , SetterCallback ) +431 NHibernate.Bytecode.Lightweight.AccessOptimizer.SetPropertyValues(Object target, Object[] values) +20 NHibernate.Tuple.Component.PocoComponentTuplizer.SetPropertyValues(Object component, Object[] values) +49 NHibernate.Type.ComponentType.SetPropertyValues(Object component, Object[] values, EntityMode entityMode) +34 NHibernate.Type.ComponentType.ResolveIdentifier(Object value, ISessionImplementor session, Object owner) +150 NHibernate.Type.ComponentType.NullSafeGet(IDataReader rs, String[] names, ISessionImplementor session, Object owner) +42 NHibernate.Loader.Loader.GetKeyFromResultSet(Int32 i, IEntityPersister persister, Object id, IDataReader rs, ISessionImplementor session) +93 NHibernate.Loader.Loader.GetRowFromResultSet(IDataReader resultSet, ISessionImplementor session, QueryParameters queryParameters, LockMode[] lockModeArray, EntityKey optionalObjectKey, IList hydratedObjects, EntityKey[] keys, Boolean returnProxies) +92 NHibernate.Loader.Loader.DoQuery(ISessionImplementor session, QueryParameters queryParameters, Boolean returnProxies) +675 NHibernate.Loader.Loader.DoQueryAndInitializeNonLazyCollections(ISessionImplementor session, QueryParameters queryParameters, Boolean returnProxies) +129 NHibernate.Loader.Loader.DoList(ISessionImplementor session, QueryParameters queryParameters) +116 [GenericADOException: could not execute query [ select eventdata0_.deviceID as deviceID5_, eventdata0_.timestamp as timestamp5_, eventdata0_.statusCode as statusCode5_, eventdata0_.accountID as accountID5_, eventdata0_.latitude as latitude5_, eventdata0_.longitude as longitude5_, eventdata0_.gpsAge as gpsAge5_, eventdata0_.speedKPH as speedKPH5_, eventdata0_.heading as heading5_, eventdata0_.altitude as altitude5_, eventdata0_.transportID as transpo11_5_, eventdata0_.inputMask as inputMask5_, eventdata0_.outputMask as outputMask5_, eventdata0_.address as address5_, eventdata0_.DataSource as DataSource5_, eventdata0_.rawdata as rawdata5_, eventdata0_.distanceKM as distanceKM5_, eventdata0_.odometerKM as odometerKM5_, eventdata0_.geozoneIndex as geozone19_5_, eventdata0_.geozoneID as geozoneID5_, eventdata0_.creationTime as creatio21_5_ from eventdata eventdata0_ ] [SQL: select eventdata0_.deviceID as deviceID5_, eventdata0_.timestamp as timestamp5_, eventdata0_.statusCode as statusCode5_, eventdata0_.accountID as accountID5_, eventdata0_.latitude as latitude5_, eventdata0_.longitude as longitude5_, eventdata0_.gpsAge as gpsAge5_, eventdata0_.speedKPH as speedKPH5_, eventdata0_.heading as heading5_, eventdata0_.altitude as altitude5_, eventdata0_.transportID as transpo11_5_, eventdata0_.inputMask as inputMask5_, eventdata0_.outputMask as outputMask5_, eventdata0_.address as address5_, eventdata0_.DataSource as DataSource5_, eventdata0_.rawdata as rawdata5_, eventdata0_.distanceKM as distanceKM5_, eventdata0_.odometerKM as odometerKM5_, eventdata0_.geozoneIndex as geozone19_5_, eventdata0_.geozoneID as geozoneID5_, eventdata0_.creationTime as creatio21_5_ from eventdata eventdata0_]] NHibernate.Loader.Loader.DoList(ISessionImplementor session, QueryParameters queryParameters) +213 NHibernate.Loader.Loader.ListIgnoreQueryCache(ISessionImplementor session, QueryParameters queryParameters) +18 NHibernate.Loader.Loader.List(ISessionImplementor session, QueryParameters queryParameters, ISet`1 querySpaces, IType[] resultTypes) +79 NHibernate.Hql.Ast.ANTLR.Loader.QueryLoader.List(ISessionImplementor session, QueryParameters queryParameters) +51 NHibernate.Hql.Ast.ANTLR.QueryTranslatorImpl.List(ISessionImplementor session, QueryParameters queryParameters) +231 NHibernate.Engine.Query.HQLQueryPlan.PerformList(QueryParameters queryParameters, ISessionImplementor session, IList results) +369 NHibernate.Impl.SessionImpl.List(String query, QueryParameters queryParameters, IList results) +317 NHibernate.Impl.SessionImpl.List(String query, QueryParameters parameters) +282 NHibernate.Impl.QueryImpl.List() +163 DATA1.EventdataExtensions.GetEventdata() in C:\Users\HP\Desktop\our_project\DATA1\Queries\Eventdata.cs:33 MvcApplication7.Controllers.HistoriqueController.Index() in C:\Users\HP\Desktop\our_project\MvcApplication7\Controllers\HistoriqueController.cs:17 lambda_method(Closure , ControllerBase , Object[] ) +62 System.Web.Mvc.ActionMethodDispatcher.Execute(ControllerBase controller, Object[] parameters) +17 System.Web.Mvc.ReflectedActionDescriptor.Execute(ControllerContext controllerContext, IDictionary`2 parameters) +208 System.Web.Mvc.ControllerActionInvoker.InvokeActionMethod(ControllerContext controllerContext, ActionDescriptor actionDescriptor, IDictionary`2 parameters) +27 System.Web.Mvc.<>c__DisplayClass15.<InvokeActionMethodWithFilters>b__12() +55 System.Web.Mvc.ControllerActionInvoker.InvokeActionMethodFilter(IActionFilter filter, ActionExecutingContext preContext, Func`1 continuation) +263 System.Web.Mvc.<>c__DisplayClass17.<InvokeActionMethodWithFilters>b__14() +19 System.Web.Mvc.ControllerActionInvoker.InvokeActionMethodWithFilters(ControllerContext controllerContext, IList`1 filters, ActionDescriptor actionDescriptor, IDictionary`2 parameters) +191 System.Web.Mvc.ControllerActionInvoker.InvokeAction(ControllerContext controllerContext, String actionName) +343 System.Web.Mvc.Controller.ExecuteCore() +116 System.Web.Mvc.ControllerBase.Execute(RequestContext requestContext) +97 System.Web.Mvc.ControllerBase.System.Web.Mvc.IController.Execute(RequestContext requestContext) +10 System.Web.Mvc.<>c__DisplayClassb.<BeginProcessRequest>b__5() +37 System.Web.Mvc.Async.<>c__DisplayClass1.<MakeVoidDelegate>b__0() +21 System.Web.Mvc.Async.<>c__DisplayClass8`1.<BeginSynchronous>b__7(IAsyncResult _) +12 System.Web.Mvc.Async.WrappedAsyncResult`1.End() +62 System.Web.Mvc.<>c__DisplayClasse.<EndProcessRequest>b__d() +50 System.Web.Mvc.SecurityUtil.<GetCallInAppTrustThunk>b__0(Action f) +7 System.Web.Mvc.SecurityUtil.ProcessInApplicationTrust(Action action) +22 System.Web.Mvc.MvcHandler.EndProcessRequest(IAsyncResult asyncResult) +60 System.Web.Mvc.MvcHandler.System.Web.IHttpAsyncHandler.EndProcessRequest(IAsyncResult result) +9 System.Web.CallHandlerExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() +8841105 System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) +184 Any suggestions? how can correct this error Data entity class (outtake from comment): public class MyClass { public virtual string DeviceID { get; set; } public virtual int Timestamp { get; set; } public virtual string Account { get; set; } public virtual int StatusCode { get; set; } public virtual double Latitude { get; set; } public virtual double Longitude { get; set; } public virtual int GpsAge { get; set; } public virtual double SpeedKPH { get; set; } public virtual double Heading { get; set; } public override bool Equals(object obj) { return true; } public override int GetHashCode() { return 0; } }

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• Using a boost::fusion::map in boost::spirit::karma

  - by user1097105

  I am using boost spirit to parse some text files into a data structure and now I am beginning to generate text from this data structure (using spirit karma). One attempt at a data structure is a boost::fusion::map (as suggested in an answer to this question). But although I can use boost::spirit::qi::parse() and get data in it easily, when I tried to generate text from it using karma, I failed. Below is my attempt (look especially at the "map_data" type). After some reading and playing around with other fusion types, I found boost::fusion::vector and BOOST_FUSION_DEFINE_ASSOC_STRUCT. I succeeded to generate output with both of them, but they don't seem ideal: in vector you cannot access a member using a name (it is like a tuple) -- and in the other solution, I don't think I need both ways (member name and key type) to access the members. #include <iostream> #include <string> #include <boost/spirit/include/karma.hpp> #include <boost/fusion/include/map.hpp> #include <boost/fusion/include/make_map.hpp> #include <boost/fusion/include/vector.hpp> #include <boost/fusion/include/as_vector.hpp> #include <boost/fusion/include/transform.hpp> struct sb_key; struct id_key; using boost::fusion::pair; typedef boost::fusion::map < pair<sb_key, int> , pair<id_key, unsigned long> > map_data; typedef boost::fusion::vector < int, unsigned long > vector_data; #include <boost/fusion/include/define_assoc_struct.hpp> BOOST_FUSION_DEFINE_ASSOC_STRUCT( (), assocstruct_data, (int, a, sb_key) (unsigned long, b, id_key)) namespace karma = boost::spirit::karma; template <typename X> std::string to_string ( const X& data ) { std::string generated; std::back_insert_iterator<std::string> sink(generated); karma::generate_delimited ( sink, karma::int_ << karma::ulong_, karma::space, data ); return generated; } int main() { map_data d1(boost::fusion::make_map<sb_key, id_key>(234, 35314988526ul)); vector_data d2(boost::fusion::make_vector(234, 35314988526ul)); assocstruct_data d3(234,35314988526ul); std::cout << "map_data as_vector: " << boost::fusion::as_vector(d1) << std::endl; //std::cout << "map_data to_string: " << to_string(d1) << std::endl; //*FAIL No 1* std::cout << "at_key (sb_key): " << boost::fusion::at_key<sb_key>(d1) << boost::fusion::at_c<0>(d1) << std::endl << std::endl; std::cout << "vector_data: " << d2 << std::endl; std::cout << "vector_data to_string: " << to_string(d2) << std::endl << std::endl; std::cout << "assoc_struct as_vector: " << boost::fusion::as_vector(d3) << std::endl; std::cout << "assoc_struct to_string: " << to_string(d3) << std::endl; std::cout << "at_key (sb_key): " << boost::fusion::at_key<sb_key>(d3) << d3.a << boost::fusion::at_c<0>(d3) << std::endl; return 0; } Including the commented line gives lots of pages of compilation errors, among which notably something like: no known conversion for argument 1 from ‘boost::fusion::pair’ to ‘double’ no known conversion for argument 1 from ‘boost::fusion::pair’ to ‘float’ Might it be that to_string needs the values of the map_data, and not the pairs? Though I am not good with templates, I tried to get a vector from a map using transform in the following way template <typename P> struct take_second { typename P::second_type operator() (P p) { return p.second; } }; // ... inside main() pair <char, int> ff(32); std::cout << "take_second (expect 32): " << take_second<pair<char,int>>()(ff) << std::endl; std::cout << "transform map_data and to_string: " << to_string(boost::fusion::transform(d1, take_second<>())); //*FAIL No 2* But I don't know what types am I supposed to give when instantiating take_second and anyway I think there must be an easier way to get (iterate over) the values of a map (is there?) If you answer this question, please also give your opinion on whether using an ASSOC_STRUCT or a map is better.

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• Java AD Authentication across Trusted Domains

  - by benjiisnotcool

  I am trying to implement Active Directory authentication in Java which will be ran from a Linux machine. Our AD set-up will consist of multiple servers that share trust relationships with one another so for our test environment we have two domain controllers: test1.ad1.foo.com who trusts test2.ad2.bar.com. Using the code below I can successfully authenticate a user from test1 but not on test2: public class ADDetailsProvider implements ResultSetProvider { private String domain; private String user; private String password; public ADDetailsProvider(String user, String password) { //extract domain name if (user.contains("\\")) { this.user = user.substring((user.lastIndexOf("\\") + 1), user.length()); this.domain = user.substring(0, user.lastIndexOf("\\")); } else { this.user = user; this.domain = ""; } this.password = password; } /* Test from the command line */ public static void main (String[] argv) throws SQLException { ResultSetProvider res = processADLogin(argv[0], argv[1]); ResultSet results = null; res.assignRowValues(results, 0); System.out.println(argv[0] + " " + argv[1]); } public boolean assignRowValues(ResultSet results, int currentRow) throws SQLException { // Only want a single row if (currentRow >= 1) return false; try { ADAuthenticator adAuth = new ADAuthenticator(); LdapContext ldapCtx = adAuth.authenticate(this.domain, this.user, this.password); NamingEnumeration userDetails = adAuth.getUserDetails(ldapCtx, this.user); // Fill the result set (throws SQLException). while (userDetails.hasMoreElements()) { Attribute attr = (Attribute)userDetails.next(); results.updateString(attr.getID(), attr.get().toString()); } results.updateInt("authenticated", 1); return true; } catch (FileNotFoundException fnf) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught File Not Found Exception trying to read cris_authentication.properties"); results.updateInt("authenticated", 0); return false; } catch (IOException ioe) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught IO Excpetion processing login"); results.updateInt("authenticated", 0); return false; } catch (AuthenticationException aex) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught Authentication Exception attempting to bind to LDAP for [{0}]", this.user); results.updateInt("authenticated", 0); return true; } catch (NamingException ne) { Logger.getAnonymousLogger().log(Level.WARNING, "Caught Naming Exception performing user search or LDAP bind for [{0}]", this.user); results.updateInt("authenticated", 0); return true; } } public void close() { // nothing needed here } /** * This method is called via a Postgres function binding to access the * functionality provided by this class. */ public static ResultSetProvider processADLogin(String user, String password) { return new ADDetailsProvider(user, password); } } public class ADAuthenticator { public ADAuthenticator() throws FileNotFoundException, IOException { Properties props = new Properties(); InputStream inStream = this.getClass().getClassLoader(). getResourceAsStream("com/bar/foo/ad/authentication.properties"); props.load(inStream); this.domain = props.getProperty("ldap.domain"); inStream.close(); } public LdapContext authenticate(String domain, String user, String pass) throws AuthenticationException, NamingException, IOException { Hashtable env = new Hashtable(); this.domain = domain; env.put(Context.INITIAL_CONTEXT_FACTORY, com.sun.jndi.ldap.LdapCtxFactory); env.put(Context.PROVIDER_URL, "ldap://" + test1.ad1.foo.com + ":" + 3268); env.put(Context.SECURITY_AUTHENTICATION, simple); env.put(Context.REFERRAL, follow); env.put(Context.SECURITY_PRINCIPAL, (domain + "\\" + user)); env.put(Context.SECURITY_CREDENTIALS, pass); // Bind using specified username and password LdapContext ldapCtx = new InitialLdapContext(env, null); return ldapCtx; } public NamingEnumeration getUserDetails(LdapContext ldapCtx, String user) throws NamingException { // List of attributes to return from LDAP query String returnAttributes[] = {"ou", "sAMAccountName", "givenName", "sn", "memberOf"}; //Create the search controls SearchControls searchCtls = new SearchControls(); searchCtls.setReturningAttributes(returnAttributes); //Specify the search scope searchCtls.setSearchScope(SearchControls.SUBTREE_SCOPE); // Specify the user to search against String searchFilter = "(&(objectClass=*)(sAMAccountName=" + user + "))"; //Perform the search NamingEnumeration answer = ldapCtx.search("dc=dev4,dc=dbt,dc=ukhealth,dc=local", searchFilter, searchCtls); // Only care about the first tuple Attributes userAttributes = ((SearchResult)answer.next()).getAttributes(); if (userAttributes.size() <= 0) throw new NamingException(); return (NamingEnumeration) userAttributes.getAll(); } From what I understand of the trust relationship, if trust1 receives a login attempt for a user in trust2, then it should forward the login attempt on to it and it works this out from the user's domain name. Is this correct or am I missing something or is this not possible using the method above? --EDIT-- The stack trace from the LDAP bind is {java.naming.provider.url=ldap://test1.ad1.foo.com:3268, java.naming.factory.initial=com.sun.jndi.ldap.LdapCtxFactory, java.naming.security.authentication=simple, java.naming.referral=follow} 30-Oct-2012 13:16:02 ADDetailsProvider assignRowValues WARNING: Caught Authentication Exception attempting to bind to LDAP for [trusttest] Auth error is [LDAP: error code 49 - 80090308: LdapErr: DSID-0C0903A9, comment: AcceptSecurityContext error, data 52e, v1db0]

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• Solving embarassingly parallel problems using Python multiprocessing

  - by gotgenes

  How does one use multiprocessing to tackle embarrassingly parallel problems? Embarassingly parallel problems typically consist of three basic parts: Read input data (from a file, database, tcp connection, etc.). Run calculations on the input data, where each calculation is independent of any other calculation. Write results of calculations (to a file, database, tcp connection, etc.). We can parallelize the program in two dimensions: Part 2 can run on multiple cores, since each calculation is independent; order of processing doesn't matter. Each part can run independently. Part 1 can place data on an input queue, part 2 can pull data off the input queue and put results onto an output queue, and part 3 can pull results off the output queue and write them out. This seems a most basic pattern in concurrent programming, but I am still lost in trying to solve it, so let's write a canonical example to illustrate how this is done using multiprocessing. Here is the example problem: Given a CSV file with rows of integers as input, compute their sums. Separate the problem into three parts, which can all run in parallel: Process the input file into raw data (lists/iterables of integers) Calculate the sums of the data, in parallel Output the sums Below is traditional, single-process bound Python program which solves these three tasks: #!/usr/bin/env python # -*- coding: UTF-8 -*- # basicsums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file. """ import csv import optparse import sys def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) return cli_parser def parse_input_csv(csvfile): """Parses the input CSV and yields tuples with the index of the row as the first element, and the integers of the row as the second element. The index is zero-index based. :Parameters: - `csvfile`: a `csv.reader` instance """ for i, row in enumerate(csvfile): row = [int(entry) for entry in row] yield i, row def sum_rows(rows): """Yields a tuple with the index of each input list of integers as the first element, and the sum of the list of integers as the second element. The index is zero-index based. :Parameters: - `rows`: an iterable of tuples, with the index of the original row as the first element, and a list of integers as the second element """ for i, row in rows: yield i, sum(row) def write_results(csvfile, results): """Writes a series of results to an outfile, where the first column is the index of the original row of data, and the second column is the result of the calculation. The index is zero-index based. :Parameters: - `csvfile`: a `csv.writer` instance to which to write results - `results`: an iterable of tuples, with the index (zero-based) of the original row as the first element, and the calculated result from that row as the second element """ for result_row in results: csvfile.writerow(result_row) def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # gets an iterable of rows that's not yet evaluated input_rows = parse_input_csv(in_csvfile) # sends the rows iterable to sum_rows() for results iterable, but # still not evaluated result_rows = sum_rows(input_rows) # finally evaluation takes place as a chain in write_results() write_results(out_csvfile, result_rows) infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) Let's take this program and rewrite it to use multiprocessing to parallelize the three parts outlined above. Below is a skeleton of this new, parallelized program, that needs to be fleshed out to address the parts in the comments: #!/usr/bin/env python # -*- coding: UTF-8 -*- # multiproc_sums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file, using multiple processes if desired. """ import csv import multiprocessing import optparse import sys NUM_PROCS = multiprocessing.cpu_count() def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) cli_parser.add_option('-n', '--numprocs', type='int', default=NUM_PROCS, help="Number of processes to launch [DEFAULT: %default]") return cli_parser def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # Parse the input file and add the parsed data to a queue for # processing, possibly chunking to decrease communication between # processes. # Process the parsed data as soon as any (chunks) appear on the # queue, using as many processes as allotted by the user # (opts.numprocs); place results on a queue for output. # # Terminate processes when the parser stops putting data in the # input queue. # Write the results to disk as soon as they appear on the output # queue. # Ensure all child processes have terminated. # Clean up files. infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) These pieces of code, as well as another piece of code that can generate example CSV files for testing purposes, can be found on github. I would appreciate any insight here as to how you concurrency gurus would approach this problem. Here are some questions I had when thinking about this problem. Bonus points for addressing any/all: Should I have child processes for reading in the data and placing it into the queue, or can the main process do this without blocking until all input is read? Likewise, should I have a child process for writing the results out from the processed queue, or can the main process do this without having to wait for all the results? Should I use a processes pool for the sum operations? If yes, what method do I call on the pool to get it to start processing the results coming into the input queue, without blocking the input and output processes, too? apply_async()? map_async()? imap()? imap_unordered()? Suppose we didn't need to siphon off the input and output queues as data entered them, but could wait until all input was parsed and all results were calculated (e.g., because we know all the input and output will fit in system memory). Should we change the algorithm in any way (e.g., not run any processes concurrently with I/O)?

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• C++/boost generator module, feedback/critic please

  - by aaa

  hello. I wrote this generator, and I think to submit to boost people. Can you give me some feedback about it it basically allows to collapse multidimensional loops to flat multi-index queue. Loop can be boost lambda expressions. Main reason for doing this is to make parallel loops easier and separate algorithm from controlling structure (my fieldwork is computational chemistry where deep loops are common) 1 #ifndef _GENERATOR_HPP_ 2 #define _GENERATOR_HPP_ 3 4 #include <boost/array.hpp> 5 #include <boost/lambda/lambda.hpp> 6 #include <boost/noncopyable.hpp> 7 8 #include <boost/mpl/bool.hpp> 9 #include <boost/mpl/int.hpp> 10 #include <boost/mpl/for_each.hpp> 11 #include <boost/mpl/range_c.hpp> 12 #include <boost/mpl/vector.hpp> 13 #include <boost/mpl/transform.hpp> 14 #include <boost/mpl/erase.hpp> 15 16 #include <boost/fusion/include/vector.hpp> 17 #include <boost/fusion/include/for_each.hpp> 18 #include <boost/fusion/include/at_c.hpp> 19 #include <boost/fusion/mpl.hpp> 20 #include <boost/fusion/include/as_vector.hpp> 21 22 #include <memory> 23 24 /** 25 for loop generator which can use lambda expressions. 26 27 For example: 28 @code 29 using namespace generator; 30 using namespace boost::lambda; 31 make_for(N, N, range(bind(std::max<int>, _1, _2), N), range(_2, _3+1)); 32 // equivalent to pseudocode 33 // for l=0,N: for k=0,N: for j=max(l,k),N: for i=k,j 34 @endcode 35 36 If range is given as upper bound only, 37 lower bound is assumed to be default constructed 38 Lambda placeholders may only reference first three indices. 39 */ 40 41 namespace generator { 42 namespace detail { 43 44 using boost::lambda::constant_type; 45 using boost::lambda::constant; 46 47 /// lambda expression identity 48 template<class E, class enable = void> 49 struct lambda { 50 typedef E type; 51 }; 52 53 /// transform/construct constant lambda expression from non-lambda 54 template<class E> 55 struct lambda<E, typename boost::disable_if< 56 boost::lambda::is_lambda_functor<E> >::type> 57 { 58 struct constant : boost::lambda::constant_type<E>::type { 59 typedef typename boost::lambda::constant_type<E>::type base_type; 60 constant() : base_type(boost::lambda::constant(E())) {} 61 constant(const E &e) : base_type(boost::lambda::constant(e)) {} 62 }; 63 typedef constant type; 64 }; 65 66 /// range functor 67 template<class L, class U> 68 struct range_ { 69 typedef boost::array<int,4> index_type; 70 range_(U upper) : bounds_(typename lambda<L>::type(), upper) {} 71 range_(L lower, U upper) : bounds_(lower, upper) {} 72 73 template< typename T, size_t N> 74 T lower(const boost::array<T,N> &index) { 75 return bound<0>(index); 76 } 77 78 template< typename T, size_t N> 79 T upper(const boost::array<T,N> &index) { 80 return bound<1>(index); 81 } 82 83 private: 84 template<bool b, typename T> 85 T bound(const boost::array<T,1> &index) { 86 return (boost::fusion::at_c<b>(bounds_))(index[0]); 87 } 88 89 template<bool b, typename T> 90 T bound(const boost::array<T,2> &index) { 91 return (boost::fusion::at_c<b>(bounds_))(index[0], index[1]); 92 } 93 94 template<bool b, typename T, size_t N> 95 T bound(const boost::array<T,N> &index) { 96 using boost::fusion::at_c; 97 return (at_c<b>(bounds_))(index[0], index[1], index[2]); 98 } 99 100 boost::fusion::vector<typename lambda<L>::type, 101 typename lambda<U>::type> bounds_; 102 }; 103 104 template<typename T, size_t N> 105 struct for_base { 106 typedef boost::array<T,N> value_type; 107 virtual ~for_base() {} 108 virtual value_type next() = 0; 109 }; 110 111 /// N-index generator 112 template<typename T, size_t N, class R, class I> 113 struct for_ : for_base<T,N> { 114 typedef typename for_base<T,N>::value_type value_type; 115 typedef R range_tuple; 116 for_(const range_tuple &r) : r_(r), state_(true) { 117 boost::fusion::for_each(r_, initialize(index)); 118 } 119 /// @return new generator 120 for_* new_() { return new for_(r_); } 121 /// @return next index value and increment 122 value_type next() { 123 value_type next; 124 using namespace boost::lambda; 125 typename value_type::iterator n = next.begin(); 126 typename value_type::iterator i = index.begin(); 127 boost::mpl::for_each<I>(*(var(n))++ = var(i)[_1]); 128 129 state_ = advance<N>(r_, index); 130 return next; 131 } 132 /// @return false if out of bounds, true otherwise 133 operator bool() { return state_; } 134 135 private: 136 /// initialize indices 137 struct initialize { 138 value_type &index_; 139 mutable size_t i_; 140 initialize(value_type &index) : index_(index), i_(0) {} 141 template<class R_> void operator()(R_& r) const { 142 index_[i_++] = r.lower(index_); 143 } 144 }; 145 146 /// advance index[0:M) 147 template<size_t M> 148 struct advance { 149 /// stop recursion 150 struct stop { 151 stop(R r, value_type &index) {} 152 }; 153 /// advance index 154 /// @param r range tuple 155 /// @param index index array 156 advance(R &r, value_type &index) : index_(index), i_(0) { 157 namespace fusion = boost::fusion; 158 index[M-1] += 1; // increment index 159 fusion::for_each(r, *this); // update indices 160 state_ = index[M-1] >= fusion::at_c<M-1>(r).upper(index); 161 if (state_) { // out of bounds 162 typename boost::mpl::if_c<(M > 1), 163 advance<M-1>, stop>::type(r, index); 164 } 165 } 166 /// apply lower bound of range to index 167 template<typename R_> void operator()(R_& r) const { 168 if (i_ >= M) index_[i_] = r.lower(index_); 169 ++i_; 170 } 171 /// @return false if out of bounds, true otherwise 172 operator bool() { return state_; } 173 private: 174 value_type &index_; ///< index array reference 175 mutable size_t i_; ///< running index 176 bool state_; ///< out of bounds state 177 }; 178 179 value_type index; 180 range_tuple r_; 181 bool state_; 182 }; 183 184 185 /// polymorphic generator template base 186 template<typename T,size_t N> 187 struct For : boost::noncopyable { 188 typedef boost::array<T,N> value_type; 189 /// @return next index value and increment 190 value_type next() { return for_->next(); } 191 /// @return false if out of bounds, true otherwise 192 operator bool() const { return for_; } 193 protected: 194 /// reset smart pointer 195 void reset(for_base<T,N> *f) { for_.reset(f); } 196 std::auto_ptr<for_base<T,N> > for_; 197 }; 198 199 /// range [T,R) type 200 template<typename T, typename R> 201 struct range_type { 202 typedef range_<T,R> type; 203 }; 204 205 /// range identity specialization 206 template<typename T, class L, class U> 207 struct range_type<T, range_<L,U> > { 208 typedef range_<L,U> type; 209 }; 210 211 namespace fusion = boost::fusion; 212 namespace mpl = boost::mpl; 213 214 template<typename T, size_t N, class R1, class R2, class R3, class R4> 215 struct range_tuple { 216 // full range vector 217 typedef typename mpl::vector<R1,R2,R3,R4> v; 218 typedef typename mpl::end<v>::type end; 219 typedef typename mpl::advance_c<typename mpl::begin<v>::type, N>::type pos; 220 // [0:N) range vector 221 typedef typename mpl::erase<v, pos, end>::type t; 222 // transform into proper range fusion::vector 223 typedef typename fusion::result_of::as_vector< 224 typename mpl::transform<t,range_type<T, mpl::_1> >::type 225 >::type type; 226 }; 227 228 229 template<typename T, size_t N, 230 class R1, class R2, class R3, class R4, 231 class O> 232 struct for_type { 233 typedef typename range_tuple<T,N,R1,R2,R3,R4>::type range_tuple; 234 typedef for_<T, N, range_tuple, O> type; 235 }; 236 237 } // namespace detail 238 239 240 /// default index order, [0:N) 241 template<size_t N> 242 struct order { 243 typedef boost::mpl::range_c<size_t,0, N> type; 244 }; 245 246 /// N-loop generator, 0 < N <= 5 247 /// @tparam T index type 248 /// @tparam N number of indices/loops 249 /// @tparam R1,... range types 250 /// @tparam O index order 251 template<typename T, size_t N, 252 class R1, class R2 = void, class R3 = void, class R4 = void, 253 class O = typename order<N>::type> 254 struct for_ : detail::for_type<T, N, R1, R2, R3, R4, O>::type { 255 typedef typename detail::for_type<T, N, R1, R2, R3, R4, O>::type base_type; 256 typedef typename base_type::range_tuple range_tuple; 257 for_(const range_tuple &range) : base_type(range) {} 258 }; 259 260 /// loop range [L:U) 261 /// @tparam L lower bound type 262 /// @tparam U upper bound type 263 /// @return range 264 template<class L, class U> 265 detail::range_<L,U> range(L lower, U upper) { 266 return detail::range_<L,U>(lower, upper); 267 } 268 269 /// make 4-loop generator with specified index ordering 270 template<typename T, class R1, class R2, class R3, class R4, class O> 271 for_<T, 4, R1, R2, R3, R4, O> 272 make_for(R1 r1, R2 r2, R3 r3, R4 r4, const O&) { 273 typedef for_<T, 4, R1, R2, R3, R4, O> F; 274 return F(F::range_tuple(r1, r2, r3, r4)); 275 } 276 277 /// polymorphic generator template forward declaration 278 template<typename T,size_t N> 279 struct For; 280 281 /// polymorphic 4-loop generator 282 template<typename T> 283 struct For<T,4> : detail::For<T,4> { 284 /// generator with default index ordering 285 template<class R1, class R2, class R3, class R4> 286 For(R1 r1, R2 r2, R3 r3, R4 r4) { 287 this->reset(make_for<T>(r1, r2, r3, r4).new_()); 288 } 289 /// generator with specified index ordering 290 template<class R1, class R2, class R3, class R4, class O> 291 For(R1 r1, R2 r2, R3 r3, R4 r4, O o) { 292 this->reset(make_for<T>(r1, r2, r3, r4, o).new_()); 293 } 294 }; 295 296 } 297 298 299 #endif /* _GENERATOR_HPP_ */

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