// ta_Dump File v3.0 -- code v6.3.3.0 rev6558 LeabraProject .projects["scalar_val_test"] { taBase_Group @.templates = [0] { }; Doc_Group @.docs = [1] { taDoc @["ScalarValDocs"] { }; }; Wizard_Group @.wizards = [1] { LeabraWizard @["LeabraWizard_1"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NO_CLIP"] { }; }; }; }; SelectEdit_Group @.edits = [2] { SelectEdit @["ControlPanel"] { EditMbrItem_Group @.mbrs = [23] { EditMbrItem @[0] { }; EditMbrItem @[1] { }; EditMbrItem @[2] { }; EditMbrItem @[3] { }; EditMbrItem @[4] { }; EditMbrItem @[5] { }; EditMbrItem @[6] { }; EditMbrItem @[7] { }; EditMbrItem @[8] { }; EditMbrItem @[9] { }; EditMbrItem @[10] { }; EditMbrItem @[11] { }; EditMbrItem @[12] { }; EditMbrItem @[13] { }; EditMbrItem @[14] { }; EditMbrItem @[15] { }; EditMbrItem @[16] { }; EditMbrItem @[17] { }; EditMbrItem @[18] { }; EditMbrItem @[19] { }; EditMbrItem @[20] { }; EditMbrItem @[21] { }; EditMbrItem @[22] { }; }; EditMthItem_Group @.mths = [8] { EditMthItem @[0] { }; EditMthItem @[1] { }; EditMthItem @[2] { }; EditMthItem @[3] { }; EditMthItem @[4] { }; EditMthItem @[5] { }; EditMthItem @[6] { }; EditMthItem @[7] { }; }; }; ClusterRun @["ClusterRun"] { EditMbrItem_Group @.mbrs = [0] { }; EditMthItem_Group @.mths = [0] { }; DataTableCols @.jobs_running.data = [26] { String_Data @["tag"] { }; String_Data @["notes"] { }; String_Data @["params"] { }; String_Data @["status"] { }; String_Data @["status_info"] { }; String_Data @["submit_time"] { }; String_Data @["start_time"] { }; String_Data @["end_time"] { }; String_Data @["job_no"] { }; String_Data @["job_out"] { }; String_Data @["job_out_file"] { }; String_Data @["dat_files"] { }; String_Data @["other_files"] { }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["command"] { }; String_Data @["repo_url"] { }; String_Data @["cluster"] { }; String_Data @["queue"] { }; String_Data @["run_time"] { }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["submit_svn"] { }; String_Data @["submit_job"] { }; }; DataTableCols @.jobs_done.data = [26] { String_Data @["tag"] { }; String_Data @["notes"] { }; String_Data @["params"] { }; String_Data @["status"] { }; String_Data @["status_info"] { }; String_Data @["submit_time"] { }; String_Data @["start_time"] { }; String_Data @["end_time"] { }; String_Data @["job_no"] { }; String_Data @["job_out"] { }; String_Data @["job_out_file"] { }; String_Data @["dat_files"] { }; String_Data @["other_files"] { }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["command"] { }; String_Data @["repo_url"] { }; String_Data @["cluster"] { }; String_Data @["queue"] { }; String_Data @["run_time"] { }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["submit_svn"] { }; String_Data @["submit_job"] { }; }; DataTableCols @.jobs_archive.data = [26] { String_Data @["tag"] { }; String_Data @["notes"] { }; String_Data @["params"] { }; String_Data @["status"] { }; String_Data @["status_info"] { }; String_Data @["submit_time"] { }; String_Data @["start_time"] { }; String_Data @["end_time"] { }; String_Data @["job_no"] { }; String_Data @["job_out"] { }; String_Data @["job_out_file"] { }; String_Data @["dat_files"] { }; String_Data @["other_files"] { }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["command"] { }; String_Data @["repo_url"] { }; String_Data @["cluster"] { }; String_Data @["queue"] { }; String_Data @["run_time"] { }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["submit_svn"] { }; String_Data @["submit_job"] { }; }; DataTableCols @.file_list.data = [9] { String_Data @["file_name"] { }; String_Data @["tag"] { }; String_Data @["size"] { }; String_Data @["kind"] { }; String_Data @["date_modified"] { }; String_Data @["date_created"] { }; String_Data @["svn_file_path"] { }; String_Data @["proj_file_path"] { }; String_Data @["file_path"] { }; }; DataTableCols @.cluster_info.data = [6] { String_Data @["queue"] { }; String_Data @["job_no"] { }; String_Data @["user"] { }; String_Data @["state"] { }; String_Data @["procs"] { }; String_Data @["start_time"] { }; }; ParamSearchAlgo_List @.search_algos = [0] { }; }; }; DataTable_Group @.data = [0] { DataTable_Group @.gp["InputData"] = [3] { DataTable @["FrequencyInputs"] { DataTableCols @.data = [6] { String_Data @["Name"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["Input"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV1"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV2"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV3"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["frequency"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; }; }; DataTable @["FrequencyRendering"] { DataTableCols @.data = [6] { String_Data @["Name"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["Input"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV1"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV2"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["SV3"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; float_Data @["frequency"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_wd"] { }; }; }; }; }; DataTable @["ValueEnv"] { DataTableCols @.data = [5] { String_Data @["Name"] { }; float_Data @["LeabraLayer_0"] { }; float_Data @["SV1"] { }; float_Data @["SV2"] { }; float_Data @["frequency"] { }; }; }; }; DataTable_Group @.gp["OutputData"] = [2] { DataTable @["TrialOutputData"] { DataTableCols @.data = [14] { int_Data @["batch"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["epoch"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["trial"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; String_Data @["trial_name"] { }; String_Data @["group_name"] { }; int_Data @["phase_no"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; float_Data @["minus_cycles"] { }; float_Data @["sse"] { }; float_Data @["ext_rew"] { }; float_Data @["sv1"] { }; float_Data @["sv2"] { }; float_Data @["sv3"] { }; double_Data @["prob1"] { }; double_Data @["prob2"] { }; }; }; DataTable @["EpochOutputData"] { DataTableCols @.data = [13] { int_Data @["batch"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; int_Data @["epoch"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NARROW"] { }; }; }; float_Data @["avg_sse"] { }; float_Data @["cnt_err"] { }; float_Data @["avg_ext_rew"] { }; float_Data @["avg_cycles"] { }; float_Data @["epoch_time_tot"] { }; float_Data @["epoch_time_usr"] { }; float_Data @["avg_sv1"] { }; float_Data @["avg_sv2"] { }; float_Data @["avg_sv3"] { }; double_Data @["prob1"] { }; double_Data @["prob2"] { }; }; }; }; DataTable_Group @.gp["AnalysisData"] { }; }; taBase_Group @.data_proc = [4] { taDataProc @["data_base"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NO_CLIP"] { }; }; }; taDataAnal @["data_anal"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NO_CLIP"] { }; }; }; taDataGen @["data_gen"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NO_CLIP"] { }; }; }; taImageProc @["image_proc"] { UserDataItem_List @*(.user_data_) { UserDataItem @["NO_CLIP"] { }; }; }; }; Program_Group @.programs = [0] { Program_Group @.gp["LeabraAll_Std"] = [11] { Program @["LeabraBatch"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [2] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; }; ProgVar_List @.vars = [3] { ProgVar @["batch"] { }; ProgVar @["init_batch"] { }; ProgVar @["max_batch"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [1] { NetCounterInit @[0] { }; }; ProgEl_List @.prog_code = [2] { IfElse @[0] { ProgEl_List @.true_code = [1] { NetCounterInit @[0] { }; }; ProgEl_List @.false_code = [1] { MemberAssign @[0] { }; }; }; WhileLoop @[1] { ProgEl_List @.loop_code = [2] { ProgramCall @[0] { ProgArg_List @.prog_args = [3] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; }; }; NetCounterIncr @[1] { }; }; }; }; }; Program @["LeabraTrain"] { ProgObjList @.objs = [2] { RndSeed_List @["rnd_seeds"] { taList @.seeds = [25] { RndSeed @["RndSeed_1"] { }; RndSeed @["RndSeed_2"] { }; RndSeed @["RndSeed_3"] { }; RndSeed @["RndSeed_4"] { }; RndSeed @["RndSeed_5"] { }; RndSeed @["RndSeed_6"] { }; RndSeed @["RndSeed_7"] { }; RndSeed @["RndSeed_8"] { }; RndSeed @["RndSeed_9"] { }; RndSeed @["RndSeed_10"] { }; RndSeed @["RndSeed_11"] { }; RndSeed @["RndSeed_12"] { }; RndSeed @["RndSeed_13"] { }; RndSeed @["RndSeed_14"] { }; RndSeed @["RndSeed_15"] { }; RndSeed @["RndSeed_16"] { }; RndSeed @["RndSeed_17"] { }; RndSeed @["RndSeed_18"] { }; RndSeed @["RndSeed_19"] { }; RndSeed @["RndSeed_20"] { }; RndSeed @["RndSeed_21"] { }; RndSeed @["RndSeed_22"] { }; RndSeed @["RndSeed_23"] { }; RndSeed @["RndSeed_24"] { }; RndSeed @["RndSeed_25"] { }; }; }; RndSeed @["rnd_seed"] { }; }; ProgType_List @.types = [1] { DynEnumType @["RndInitType"] { DynEnumItem_List @.enums = [3] { DynEnumItem @["OLD_SEED"] { }; DynEnumItem @["NEW_SEED"] { }; DynEnumItem @["BATCH_SEEDS"] { }; }; }; }; ProgVar_List @.args = [3] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; ProgVar @["no_prompts"] { }; }; ProgVar_List @.vars = [12] { ProgVar @["max_epoch"] { }; ProgVar @["epoch"] { }; ProgVar @["train_mode"] { }; ProgVar @["rnd_init"] { }; ProgVar @["err_stopcrit"] { }; ProgVar @["n_under_thr"] { }; ProgVar @["save_final_wts"] { }; ProgVar @["save_wts_interval"] { }; ProgVar @["n_under_cnt"] { }; ProgVar @["rnd_seeds"] { }; ProgVar @["train_timer"] { }; ProgVar @["rnd_seed"] { }; }; Function_List @.functions = [1] { Function @["GetRandomSeed"] { ProgVar_List @.args = [0] { }; ProgEl_List @.fun_code = [2] { LocalVars @[0] { ProgVar_List @.local_vars = [0] { }; }; Switch @[1] { ProgEl_List @.cases = [3] { CaseBlock @[0] { ProgEl_List @.prog_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; }; CaseBlock @[1] { ProgEl_List @.prog_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; }; CaseBlock @[2] { ProgEl_List @.prog_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; }; }; }; }; }; }; }; ProgEl_List @.init_code = [7] { AssignExpr @[0] { }; MethodCall @[1] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; AssignExpr @[2] { }; AssignExpr @[3] { }; FunctionCall @[4] { ProgArg_List @.fun_args = [0] { }; }; WtInitPrompt @[5] { ProgEl_List @.yes_code = [3] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; PrintExpr @[2] { }; }; }; AssignExpr @[6] { }; }; ProgEl_List @.prog_code = [10] { AssignExpr @[0] { }; MethodCall @[1] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; AssignExpr @[2] { }; MemberAssign @[3] { }; FunctionCall @[4] { ProgArg_List @.fun_args = [0] { }; }; IfElse @[5] { ProgEl_List @.true_code = [2] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; PrintExpr @[1] { }; }; ProgEl_List @.false_code = [0] { }; }; AssignExpr @[6] { }; WhileLoop @[7] { ProgEl_List @.loop_code = [4] { ProgramCall @[0] { ProgArg_List @.prog_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; NetCounterIncr @[1] { }; IfElse @[2] { ProgEl_List @.true_code = [2] { VarIncr @[0] { }; IfBreak @[1] { }; }; ProgEl_List @.false_code = [1] { AssignExpr @[0] { }; }; }; IfElse @[3] { ProgEl_List @.true_code = [1] { IfElse @[0] { ProgEl_List @.true_code = [1] { ProgramCall @[0] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.false_code = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; }; }; IfElse @[8] { ProgEl_List @.true_code = [1] { ProgramCall @[0] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.false_code = [0] { }; }; MethodCall @[9] { ProgArg_List @.meth_args = [0] { }; }; }; }; Program @["LeabraEpoch"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [2] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; }; ProgVar_List @.vars = [10] { ProgVar @["n_replications"] { }; ProgVar @["prob_switch_interval"] { }; ProgVar @["data_loop_order"] { }; ProgVar @["trial"] { }; ProgVar @["trial_mon_data"] { }; ProgVar @["epoch_timer"] { }; ProgVar @["data_loop_index"] { }; ProgVar @["compute_rel_netin"] { }; ProgVar @["adapt_rel_abs_netin"] { }; ProgVar @["FrequencyInputs"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [2] { NetCounterInit @[0] { }; AssignExpr @[1] { }; }; ProgEl_List @.prog_code = [12] { NetCounterInit @[0] { }; AssignExpr @[1] { }; MethodCall @[2] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; MethodCall @[3] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[4] { ProgEl_List @.true_code = [1] { ProgramCall @[0] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.false_code = [0] { }; }; DataGenCall @[5] { ProgArg_List @.meth_args = [5] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; ProgArg @[3] { }; ProgArg @[4] { }; }; }; NetDataLoop @[6] { ProgEl_List @.loop_code = [1] { ProgramCall @[0] { ProgArg_List @.prog_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; }; }; IfElse @[7] { ProgEl_List @.true_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; MethodCall @[8] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[9] { ProgEl_List @.true_code = [2] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[1] { ProgEl_List @.true_code = [2] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; MethodCall @[10] { ProgArg_List @.meth_args = [0] { }; }; ProgramCall @[11] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; }; }; Program @["LeabraTrial"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [2] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; }; ProgVar_List @.vars = [4] { ProgVar @["phase_no"] { }; ProgVar @["update_net_view"] { }; ProgVar @["trial"] { }; ProgVar @["compute_rel_netin"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [1] { NetCounterInit @[0] { }; }; ProgEl_List @.prog_code = [8] { NetCounterInit @[0] { }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; WhileLoop @[2] { ProgEl_List @.loop_code = [3] { ProgramCall @[0] { ProgArg_List @.prog_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; NetCounterIncr @[1] { }; MethodCall @[2] { ProgArg_List @.meth_args = [0] { }; }; }; }; MethodCall @[3] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[4] { ProgEl_List @.true_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; IfElse @[5] { ProgEl_List @.true_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; ProgramCall @[6] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; NetUpdateView @[7] { }; }; }; Program @["LeabraSettle"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [2] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; }; ProgVar_List @.vars = [3] { ProgVar @["cycle"] { }; ProgVar @["min_cycles"] { }; ProgVar @["update_net_view"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [1] { NetCounterInit @[0] { }; }; ProgEl_List @.prog_code = [10] { NetCounterInit @[0] { }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; ProgramCall @[2] { ProgArg_List @.prog_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MethodCall @[3] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[4] { ProgEl_List @.true_code = [1] { AssignExpr @[0] { }; }; ProgEl_List @.false_code = [1] { AssignExpr @[0] { }; }; }; WhileLoop @[5] { ProgEl_List @.loop_code = [4] { ProgramCall @[0] { ProgArg_List @.prog_args = [1] { ProgArg @[0] { }; }; }; NetCounterIncr @[1] { }; IfContinue @[2] { }; IfBreak @[3] { }; }; }; MethodCall @[6] { ProgArg_List @.meth_args = [0] { }; }; IfElse @[7] { ProgEl_List @.true_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; IfElse @[8] { ProgEl_List @.true_code = [1] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; }; ProgEl_List @.false_code = [0] { }; }; NetUpdateView @[9] { }; }; }; Program @["LeabraCycle"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [1] { ProgVar @["network"] { }; }; ProgVar_List @.vars = [1] { ProgVar @["update_net_view"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [0] { }; ProgEl_List @.prog_code = [2] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; NetUpdateView @[1] { }; }; }; Program @["ApplyInputs"] { ProgObjList @.objs = [1] { LayerWriter @["LayerWriter_0"] { LayerDataEl_List @.layer_data = [5] { LayerWriterEl @[0] { }; LayerWriterEl @[1] { }; LayerWriterEl @[2] { }; LayerWriterEl @[3] { }; LayerWriterEl @[4] { }; }; }; }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [2] { ProgVar @["network"] { }; ProgVar @["input_data"] { }; }; ProgVar_List @.vars = [1] { ProgVar @["LayerWriter_0"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [3] { MethodCall @[0] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MethodCall @[1] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; MethodCall @[2] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.prog_code = [2] { MethodCall @[0] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; }; }; Program @["LeabraTrialMonitor"] { ProgObjList @.objs = [1] { NetMonitor @["trial_netmon"] { NetMonItem_List @.items = [14] { NetMonItem @["batch"] { }; NetMonItem @["epoch"] { }; NetMonItem @["trial"] { }; NetMonItem @["trial_name"] { }; NetMonItem @["group_name"] { }; NetMonItem @["phase_no"] { }; NetMonItem @["minus_cycles"] { }; NetMonItem @["sse"] { }; NetMonItem @["ext_rew"] { }; NetMonItem @["sv1"] { }; NetMonItem @["sv2"] { }; NetMonItem @["sv3"] { }; NetMonItem @["prob1"] { }; NetMonItem @["prob2"] { }; }; }; }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [1] { ProgVar @["network"] { }; }; ProgVar_List @.vars = [5] { ProgVar @["trial_mon_data"] { }; ProgVar @["trial_netmon"] { }; ProgVar @["sv1"] { }; ProgVar @["sv2"] { }; ProgVar @["sv3"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [3] { MethodCall @[0] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MethodCall @[1] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; MethodCall @[2] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.prog_code = [8] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; AssignExpr @[2] { }; AssignExpr @[3] { }; AssignExpr @[4] { }; DataVarProg @[5] { }; MethodCall @[6] { ProgArg_List @.meth_args = [0] { }; }; MethodCall @[7] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; }; }; Program @["LeabraEpochMonitor"] { ProgObjList @.objs = [1] { NetMonitor @["epoch_netmon"] { NetMonItem_List @.items = [13] { NetMonItem @["batch"] { }; NetMonItem @["epoch"] { }; NetMonItem @["avg_sse"] { }; NetMonItem @["cnt_err"] { }; NetMonItem @["avg_ext_rew"] { }; NetMonItem @["avg_cycles"] { }; NetMonItem @["epoch_time_tot"] { }; NetMonItem @["epoch_time_usr"] { }; NetMonItem @["avg_sv1"] { }; NetMonItem @["avg_sv2"] { }; NetMonItem @["avg_sv3"] { }; NetMonItem @["prob1"] { }; NetMonItem @["prob2"] { }; }; }; }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [1] { ProgVar @["network"] { }; }; ProgVar_List @.vars = [3] { ProgVar @["epoch_mon_data"] { }; ProgVar @["epoch_netmon"] { }; ProgVar @["epoch_timer"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [4] { AssignExpr @[0] { }; MethodCall @[1] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MethodCall @[2] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; MethodCall @[3] { ProgArg_List @.meth_args = [1] { ProgArg @[0] { }; }; }; }; ProgEl_List @.prog_code = [7] { MethodCall @[0] { ProgArg_List @.meth_args = [0] { }; }; MethodCall @[1] { ProgArg_List @.meth_args = [0] { }; }; AssignExpr @[2] { }; MethodCall @[3] { ProgArg_List @.meth_args = [4] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; ProgArg @[3] { }; }; }; MethodCall @[4] { ProgArg_List @.meth_args = [4] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; ProgArg @[3] { }; }; }; MethodCall @[5] { ProgArg_List @.meth_args = [0] { }; }; Comment @[6] { }; }; }; Program @["SaveWeights"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [1] { ProgVar @["network"] { }; }; ProgVar_List @.vars = [6] { ProgVar @["tag"] { }; ProgVar @["wts_subdir"] { }; ProgVar @["fname"] { }; ProgVar @["epoch_str"] { }; ProgVar @["batch_str"] { }; ProgVar @["final_tag"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [0] { }; ProgEl_List @.prog_code = [6] { IfReturn @[0] { }; MiscCall @[1] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; MiscCall @[2] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; AssignExpr @[3] { }; MethodCall @[4] { ProgArg_List @.meth_args = [4] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; ProgArg @[3] { }; }; }; MethodCall @[5] { ProgArg_List @.meth_args = [2] { ProgArg @[0] { }; ProgArg @[1] { }; }; }; }; }; Program @["RandomProbabilities"] { ProgObjList @.objs = [0] { }; ProgType_List @.types = [0] { }; ProgVar_List @.args = [1] { ProgVar @["FrequencyInputs"] { }; }; ProgVar_List @.vars = [2] { ProgVar @["prob1"] { }; ProgVar @["prob2"] { }; }; Function_List @.functions = [0] { }; ProgEl_List @.init_code = [0] { }; ProgEl_List @.prog_code = [6] { RandomCall @[0] { ProgArg_List @.meth_args = [0] { }; }; AssignExpr @[1] { }; MethodCall @[2] { ProgArg_List @.meth_args = [3] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; }; }; MethodCall @[3] { ProgArg_List @.meth_args = [3] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; }; }; MethodCall @[4] { ProgArg_List @.meth_args = [3] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; }; }; MethodCall @[5] { ProgArg_List @.meth_args = [3] { ProgArg @[0] { }; ProgArg @[1] { }; ProgArg @[2] { }; }; }; }; }; }; }; taViewer_List @.viewers = [1] { MainWindowViewer @["Browser3"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_win_lft"] { }; UserDataItem @["view_win_top"] { }; UserDataItem @["view_win_wd"] { }; UserDataItem @["view_win_ht"] { }; UserDataItem @["view_win_iconified"] { }; UserDataItem @["view_splitter_state"] { }; }; ToolBar_List @.toolbars = [1] { ToolBar @["Application"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_win_visible"] { }; }; }; }; FrameViewer_List @.frames = [3] { BrowseViewerTaBase @["Tree"] { }; PanelViewer @["Panels"] { }; T3PanelViewer @["T3Frames"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_panel_selected"] { }; }; T3Panel_List @.panels = [2] { T3Panel @["ScalarValTestNet"] { T3DataView_List @.root_view.children = [1] { NetView @["ScalarValTestNet_View"] { T3Annotation_List @.annotations = [0] { }; T3DataView_List @.annote_children = [0] { }; ScaleRange_List @.scale_ranges = [6] { ScaleRange @[0] { }; ScaleRange @[1] { }; ScaleRange @[2] { }; ScaleRange @[3] { }; ScaleRange @[4] { }; ScaleRange @[5] { }; }; }; }; T3SavedView_List @.saved_views = [10] { T3SavedView @["Vw_0"] { }; T3SavedView @["Vw_1"] { }; T3SavedView @["Vw_2"] { }; T3SavedView @["Vw_3"] { }; T3SavedView @["Vw_4"] { }; T3SavedView @["Vw_5"] { }; T3SavedView @["Vw_6"] { }; T3SavedView @["Vw_7"] { }; T3SavedView @["Vw_8"] { }; T3SavedView @["Vw_9"] { }; }; }; T3Panel @["EpochOutputData"] { T3DataView_List @.root_view.children = [1] { GraphTableView @["EpochOutputData_Graph"] { T3DataView_List @.children = [13] { GraphColView @["batch"] { }; GraphColView @["epoch"] { }; GraphColView @["avg_sse"] { }; GraphColView @["cnt_err"] { }; GraphColView @["avg_ext_rew"] { }; GraphColView @["avg_cycles"] { }; GraphColView @["epoch_time_tot"] { }; GraphColView @["epoch_time_usr"] { }; GraphColView @["avg_sv1"] { }; GraphColView @["avg_sv2"] { }; GraphColView @["avg_sv3"] { }; GraphColView @["prob1"] { }; GraphColView @["prob2"] { }; }; T3Annotation_List @.annotations = [0] { }; T3DataView_List @.annote_children = [0] { }; }; }; T3SavedView_List @.saved_views = [10] { T3SavedView @["Vw_0"] { }; T3SavedView @["Vw_1"] { }; T3SavedView @["Vw_2"] { }; T3SavedView @["Vw_3"] { }; T3SavedView @["Vw_4"] { }; T3SavedView @["Vw_5"] { }; T3SavedView @["Vw_6"] { }; T3SavedView @["Vw_7"] { }; T3SavedView @["Vw_8"] { }; T3SavedView @["Vw_9"] { }; }; }; }; }; }; DockViewer_List @.docks = [1] { ToolBoxDockViewer @["Tools"] { UserDataItem_List @*(.user_data_) { UserDataItem @["view_win_lft"] { }; UserDataItem @["view_win_top"] { }; UserDataItem @["view_win_wd"] { }; UserDataItem @["view_win_ht"] { }; UserDataItem @["view_win_iconified"] { }; UserDataItem @["view_visible"] { }; }; }; }; }; }; Network_Group @.networks = [1] { LeabraNetwork @["ScalarValTestNet"] { UserDataItem_List @*(.user_data_) { UserDataItem @["cos_diff"] { }; UserDataItem @["cos_err"] { }; UserDataItem @["norm_err"] { }; UserDataItem @["ext_rew"] { }; UserDataItem @["maxda"] { }; UserDataItem @["minus_output_name"] { }; UserDataItem @["minus_cycles"] { }; UserDataItem @["ct_cycle"] { }; UserDataItem @["phase_no"] { }; UserDataItem @["phase"] { }; UserDataItem @["sse"] { }; UserDataItem @["output_name"] { }; UserDataItem @["trial_name"] { }; UserDataItem @["group_name"] { }; UserDataItem @["time"] { }; UserDataItem @["cycle"] { }; UserDataItem @["tick"] { }; UserDataItem @["trial"] { }; UserDataItem @["group"] { }; UserDataItem @["epoch"] { }; UserDataItem @["batch"] { }; }; BaseSpec_Group @.specs = [4] { LeabraUnitSpec @["LeabraUnitSpec_0"] { BaseSpec_Group @.children = [3] { LeabraUnitSpec @["SV1"] { BaseSpec_Group @.children = [0] { }; Schedule @.noise_sched = [0] { }; }; LeabraUnitSpec @["SV2"] { BaseSpec_Group @.children = [0] { }; Schedule @.noise_sched = [0] { }; }; LeabraUnitSpec @["SV3"] { BaseSpec_Group @.children = [0] { }; Schedule @.noise_sched = [0] { }; }; }; Schedule @.noise_sched = [0] { }; }; LeabraLayerSpec @["LeabraLayerSpec_0"] { BaseSpec_Group @.children = [3] { ScalarValLayerSpec @["SV1"] { BaseSpec_Group @.children = [0] { }; }; ScalarValLayerSpec @["SV2"] { BaseSpec_Group @.children = [0] { }; }; LeabraLayerSpec @["SV3"] { BaseSpec_Group @.children = [0] { }; }; }; }; LeabraConSpec @["LeabraConSpec_0"] { BaseSpec_Group @.children = [4] { LeabraBiasSpec @["LeabraBiasSpec_0"] { BaseSpec_Group @.children = [0] { }; Schedule @.lrate_sched = [0] { }; }; LeabraConSpec @["SV1Cons"] { BaseSpec_Group @.children = [0] { }; Schedule @.lrate_sched = [0] { }; }; LeabraConSpec @["SV2Cons"] { BaseSpec_Group @.children = [0] { }; Schedule @.lrate_sched = [0] { }; }; LeabraConSpec @["SV3Cons"] { BaseSpec_Group @.children = [0] { }; Schedule @.lrate_sched = [0] { }; }; }; Schedule @.lrate_sched = [0] { }; }; FullPrjnSpec @["FullPrjnSpec_0"] { BaseSpec_Group @.children = [0] { }; }; }; Layer_Group @.layers = [4] { LeabraLayer @["Input"] { Projection_Group @.projections = [0] { }; Unit_Group @.units = [16] { }; }; LeabraLayer @["SV1"] { Projection_Group @.projections = [1] { LeabraPrjn @["Fm_Input"] { }; }; Unit_Group @.units = [4] { }; }; LeabraLayer @["SV2"] { Projection_Group @.projections = [1] { LeabraPrjn @["Fm_Input"] { }; }; Unit_Group @.units = [12] { }; }; LeabraLayer @["SV3"] { Projection_Group @.projections = [1] { LeabraPrjn @["Fm_Input"] { }; }; Unit_Group @.units = [1] { }; }; }; }; }; }; LeabraProject .projects["scalar_val_test"] { name="scalar_val_test"; desc=" "; tags=; version { major=0; minor=0; step=0; }; license { owner="Regents of the University of Colorado"; license=NO_LIC; org="CCNLab at the University of Colorado Boulder"; year="2011"; custom=; }; wiki_url { sync=0; wiki=; url=; }; templates { name="taBase_Group_0"; el_typ=taBase; el_def=0; }; docs { name="Doc_Group_2"; el_typ=taDoc; el_def=0; taDoc @["ScalarValDocs"] { name="ScalarValDocs"; desc=; auto_open=1; web_doc=1; wiki="emergent"; url="ScalarValLayerSpec"; full_url="http://grey.colorado.edu/emergent/index.php/ScalarValLayerSpec"; text_size=1; text=" == Enter Title Here == "; html_text=" ScalarValLayerSpec - Emergent

ScalarValLayerSpec

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The ScalarValLayerSpec encodes and decodes scalar, real-numbered values based on a coarse coded distributed representation (e.g., a Gaussian bump) across multiple units. This provides a very efficient and effective way of representing scalar values -- individual Leabra units do not do a very good job of that, as they have a strong binary bias.

See also TwoDValLayerSpec

Demo Project

Load the Demo project demo/leabra/scalar_val_test.proj for a test project that does localist and the 12 unit Gaussian bump, trained with different probabilities of a 0 or 1, showing that they accurately represent these probabilities as a graded scalar value.

Generally Good Parameters

The following are sets of parameters that have been tested and found to work well at learning a scalar value representation based on probabilistic binary inputs and continuous-valued inputs.

Gaussian Bump, 12 Units for 0-1 Value Rep

  • Layer:
    • 12 units, 1 = value and 11 are value-coding
    • unit range is -0.5 to 1.5 (margin around target value range of 0-1)
    • unit values are: -.5, -.3, -.1 .1 .3 .5 .7 .9 1.1 1.3 1.5
  • LeabraLayerSpec: k = 3; KWTA_AVG_INHIB, pt = .6 --or-- KWTA_INHIB, pt = .25
  • Std Unit Spec
  • ConSpec: for probabilistic encoding, using wt_sig = 1, 1 instead of 6, 1.25 is key; also err_sb off is more fidelitous.

Gaussian Bump, 22 Units for 0-1 Value Rep

  • Layer:
    • 22 units, 1 = value and 21 value coding
    • unit_range -0.5 to 1.5
    • unit values are -.5 to 1.5 in .1 increments
  • LeabraLayerSpec: k = 6; KWTA_AVG_INHIB, pt = .6 --or-- KWTA_INHIB, pt = .25
  • Std Unit Spec
  • ConSpec: for probabilistic encoding, using wt_sig = 1, 1 instead of 6, 1.25 is key; also err_sb off is more fidelitous.

Localist, 4 units for 0-1 Value Rep (used in PVLV)

  • Layer:
    • 4 units, 1 = value and 3 value coding
    • unit_range = 0-1
    • unit values are 0, .5, 1
  • LeabraLayerSpec: k = 1, KWTA_AVG_INHIB, pt = .9
  • UnitSpec:
    • act.thr = .17, gain = 220, nvar = 0.01 (softer gain overall)
    • act_fun = NOISY_LINEAR (linear response!)

Special Setup for ScalarValLayers

There are two things you need to do different from standard layers:

InputData (when the layer receives input)

  • See InputData for general information about this
  • The key thing is that ScalarVals only need the 1st unit to receive external input, where this input is the scalar value to be represented on the layer (unless the clamp_pat flag is selected, in which case you can clamp the whole pattern if you want).
  • To achieve this, just change the geometry of your input data table column that goes to this layer: select the column, and do Column/Change Col Cell Geom menu action, and change to 1x1 geom with 2 dims (NOTE: do NOT make it a scalar -- the system that applies input to the network requires that this be at least a 2d matrix).

Monitoring layer values

  • See Monitor Data for general info about this
  • As with input data, the key issue is recording only from the 1st unit that encodes the scalar value
  • As of 4.0.17, the monitor system allows you to use the following syntax for the variable to monitor on a layer: units[0].act_eq (note: must use eq and not plain act, which is always zero -- can also monitor act_m or act_p) -- this will grab the value from the first unit in the layer, which is what you want.
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"; }; }; wizards { name="Wizard_Group_2"; el_typ=LeabraWizard; el_def=0; LeabraWizard @["LeabraWizard_1"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_12"; el_typ=UserDataItem; el_def=0; UserDataItem @["NO_CLIP"] { name="NO_CLIP"; value 1 0=1; val_type_fixed=0; }; }; name="LeabraWizard_1"; auto_open=0; }; }; edits { name="SelectEdit_Group_1"; el_typ=SelectEdit; el_def=0; SelectEdit @["ControlPanel"] { name="ControlPanel"; auto_edit=1; running_updt=0; desc=; mbrs { name="EditMbrItem_Group_1"; el_typ=EditMbrItem; el_def=0; EditMbrItem @[0] { label="bias_lrate"; desc="[Default: 0.01;0.02] [0.01 for std Leabra, .02 for CtLeabra] learning rate -- how fast do the weights change per experience"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["LeabraBiasSpec_0"]$$; mbr=LeabraConSpec::lrate; is_numeric=1; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[1] { label="sv1_kwta"; desc=" desired activity level over entire layer (NOTE: used to set target activity for UNIT_INHIB, AVG_MAX_PT_INHIB, but not used for actually computing inhib for these cases)
k from: how is the active_k determined: directly by k, by pct, or by no. of units where ext > pat_q
* USE K: use the k specified directly
* USE PCT: use the percentage pct to compute the k as a function of layer size
* USE PAT K: use the activity level of the current event pattern (k = of units > pat_q)
k: desired number of active units in the layer
pct: desired proportion of activity (used to compute a k value based on layer size, .25 std)
pat q: [Default: 0.2;0.5] threshold for pat_k based activity level: add to k if ext > pat_q
diff act pct: [Default: false] if true, use different actual percent activity for expected overall layer activation -- the expected layer activation contributes to the normalization of net input scaling -- lower activity = stronger connections and vice-versa, so that different inputs with different activity levels are equated in their relative contribution to net input, by default. Read the Leabra NetinScaling section of the online wiki docs for full details
act pct: actual percent activity to put in kwta.pct field of layer -- see diff_act_pct for implications for netinput scaling, and read the Leabra NetinScaling section of the online wiki docs for full details"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$$; mbr=LeabraLayerSpec::kwta; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[2] { label="sv2_kwta"; desc=" desired activity level over entire layer (NOTE: used to set target activity for UNIT_INHIB, AVG_MAX_PT_INHIB, but not used for actually computing inhib for these cases)
k from: how is the active_k determined: directly by k, by pct, or by no. of units where ext > pat_q
* USE K: use the k specified directly
* USE PCT: use the percentage pct to compute the k as a function of layer size
* USE PAT K: use the activity level of the current event pattern (k = of units > pat_q)
k: desired number of active units in the layer
pct: desired proportion of activity (used to compute a k value based on layer size, .25 std)
pat q: [Default: 0.2;0.5] threshold for pat_k based activity level: add to k if ext > pat_q
diff act pct: [Default: false] if true, use different actual percent activity for expected overall layer activation -- the expected layer activation contributes to the normalization of net input scaling -- lower activity = stronger connections and vice-versa, so that different inputs with different activity levels are equated in their relative contribution to net input, by default. Read the Leabra NetinScaling section of the online wiki docs for full details
act pct: actual percent activity to put in kwta.pct field of layer -- see diff_act_pct for implications for netinput scaling, and read the Leabra NetinScaling section of the online wiki docs for full details"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$$; mbr=LeabraLayerSpec::kwta; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[3] { label="sv1_inhib"; desc=" how to compute inhibition -- for kwta modes, a single global inhibition value is computed for the entire layer
type: how to compute inhibition (g_i)
* KWTA INHIB: between thresholds of k and k+1th most activated units (sets precise k value, should use i_kwta_pt = .2 std for gelin, .25 otherwise)
* KWTA AVG INHIB: average of top k vs avg of rest (provides more flexibility in actual k value, should use i_kwta_pt = .5 std for gelin, .6 otherwise)
* FF FB INHIB: simulated feedforward and feedback inhibitory interneuron inhibition, with supra-linear gain on the feedback inhibition to prevent runaway positive feedback, and to produce pop-out effects potentially
* UNIT INHIB: unit-based inhibition (g_i from netinput -- requires connections with inhib flag set to provide inhibition)
kwta pt: [Default: 0.2;0.5] [Defaults: .2 for KWTA_INHIB, .5 for KWTA_AVG]
gi: overall gain on ff & fb inhibition -- this is main paramter to adjust to change overall activation levels -- typically between 1-2
ff: [Default: 1] overall inhibitory contribution from feedforward inhibition -- computed from average netinput
fb: [Default: 0.5;1] overall inhibitory contribution from feedback inhibition -- computed from average activation
fbx: [Default: 0;0.1] extra feedback inhibition to add above the inflection point -- 0 means nothing extra, + = greater slope, - = lower slope -- not apparently needed for robust inhibition, but might be useful for some effects
infl: [Default: 0.3] inflection point in feedback inhibition curve (in terms of average activation), at which point the slope changes, by increment of fbx
dt: [Default: 0.7] time constant for integrating inhibitory values
ff0: [Default: 0.1] feedforward zero point in terms of average netinput -- below this level, no FF inhibition is computed -- the 0.1 default should be good for most cases.
min i: [Default: 0] minimum inhibition value -- set this higher than zero to prevent units from getting active even if there is not much overall excitation"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mbr=LeabraLayerSpec::inhib; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1; }; }; EditMbrItem @[4] { label="sv2_inhib"; desc=" how to compute inhibition -- for kwta modes, a single global inhibition value is computed for the entire layer
type: how to compute inhibition (g_i)
* KWTA INHIB: between thresholds of k and k+1th most activated units (sets precise k value, should use i_kwta_pt = .2 std for gelin, .25 otherwise)
* KWTA AVG INHIB: average of top k vs avg of rest (provides more flexibility in actual k value, should use i_kwta_pt = .5 std for gelin, .6 otherwise)
* FF FB INHIB: simulated feedforward and feedback inhibitory interneuron inhibition, with supra-linear gain on the feedback inhibition to prevent runaway positive feedback, and to produce pop-out effects potentially
* UNIT INHIB: unit-based inhibition (g_i from netinput -- requires connections with inhib flag set to provide inhibition)
kwta pt: [Default: 0.2;0.5] [Defaults: .2 for KWTA_INHIB, .5 for KWTA_AVG]
gi: overall gain on ff & fb inhibition -- this is main paramter to adjust to change overall activation levels -- typically between 1-2
ff: [Default: 1] overall inhibitory contribution from feedforward inhibition -- computed from average netinput
fb: [Default: 0.5;1] overall inhibitory contribution from feedback inhibition -- computed from average activation
fbx: [Default: 0;0.1] extra feedback inhibition to add above the inflection point -- 0 means nothing extra, + = greater slope, - = lower slope -- not apparently needed for robust inhibition, but might be useful for some effects
infl: [Default: 0.3] inflection point in feedback inhibition curve (in terms of average activation), at which point the slope changes, by increment of fbx
dt: [Default: 0.7] time constant for integrating inhibitory values
ff0: [Default: 0.1] feedforward zero point in terms of average netinput -- below this level, no FF inhibition is computed -- the 0.1 default should be good for most cases.
min i: [Default: 0] minimum inhibition value -- set this higher than zero to prevent units from getting active even if there is not much overall excitation"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mbr=LeabraLayerSpec::inhib; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1; }; }; EditMbrItem @[5] { label="sv3_inhib"; desc=" how to compute inhibition -- for kwta modes, a single global inhibition value is computed for the entire layer
type: how to compute inhibition (g_i)
* KWTA INHIB: between thresholds of k and k+1th most activated units (sets precise k value, should use i_kwta_pt = .2 std for gelin, .25 otherwise)
* KWTA AVG INHIB: average of top k vs avg of rest (provides more flexibility in actual k value, should use i_kwta_pt = .5 std for gelin, .6 otherwise)
* FF FB INHIB: simulated feedforward and feedback inhibitory interneuron inhibition, with supra-linear gain on the feedback inhibition to prevent runaway positive feedback, and to produce pop-out effects potentially
* UNIT INHIB: unit-based inhibition (g_i from netinput -- requires connections with inhib flag set to provide inhibition)
kwta pt: [Default: 0.2;0.5] [Defaults: .2 for KWTA_INHIB, .5 for KWTA_AVG]
gi: overall gain on ff & fb inhibition -- this is main paramter to adjust to change overall activation levels -- typically between 1-2
ff: [Default: 1] overall inhibitory contribution from feedforward inhibition -- computed from average netinput
fb: [Default: 0.5;1] overall inhibitory contribution from feedback inhibition -- computed from average activation
fbx: [Default: 0;0.1] extra feedback inhibition to add above the inflection point -- 0 means nothing extra, + = greater slope, - = lower slope -- not apparently needed for robust inhibition, but might be useful for some effects
infl: [Default: 0.3] inflection point in feedback inhibition curve (in terms of average activation), at which point the slope changes, by increment of fbx
dt: [Default: 0.7] time constant for integrating inhibitory values
ff0: [Default: 0.1] feedforward zero point in terms of average netinput -- below this level, no FF inhibition is computed -- the 0.1 default should be good for most cases.
min i: [Default: 0] minimum inhibition value -- set this higher than zero to prevent units from getting active even if there is not much overall excitation"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV3"]$$; mbr=LeabraLayerSpec::inhib; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1; }; }; EditMbrItem @[6] { label="sv1_scalar"; desc=" specifies how values are represented in terms of distributed patterns of activation across the layer
rep: type of representation of scalar value to use
* GAUSSIAN: gaussian bump, with value = weighted average of tuned unit values
* LOCALIST: each unit represents a distinct value; intermediate values represented by graded activity of neighbors; overall activity is weighted-average across all units
un width: sigma parameter of a gaussian specifying the tuning width of the coarse-coded units (in unit_range min-max units, unless norm_width is true, meaning use normalized 0-1 proportion of unit range)
norm width: un_width is specified in normalized 0-1 proportion of unit range
clamp pat: [Default: false] if true, environment provides full set of values to clamp over entire layer (instead of providing single scalar value to clamp on 1st unit, which then generates a corresponding distributed pattern)
min sum act: [Default: 0.2] minimum total activity of all the units representing a value: when computing weighted average value, this is used as a minimum for the sum that you divide by
clip val: ensure that value remains within specified range
send thr: use unitspec.opt_thresh.send threshold to cut off small activation contributions to overall average value (i.e., if unit's activation is below this threshold, it doesn't contribute to weighted average computation)
init nms: initialize unit names when weights are initialized"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mbr=ScalarValLayerSpec::scalar; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[7] { label="sv2_scalar"; desc=" specifies how values are represented in terms of distributed patterns of activation across the layer
rep: type of representation of scalar value to use
* GAUSSIAN: gaussian bump, with value = weighted average of tuned unit values
* LOCALIST: each unit represents a distinct value; intermediate values represented by graded activity of neighbors; overall activity is weighted-average across all units
un width: sigma parameter of a gaussian specifying the tuning width of the coarse-coded units (in unit_range min-max units, unless norm_width is true, meaning use normalized 0-1 proportion of unit range)
norm width: un_width is specified in normalized 0-1 proportion of unit range
clamp pat: [Default: false] if true, environment provides full set of values to clamp over entire layer (instead of providing single scalar value to clamp on 1st unit, which then generates a corresponding distributed pattern)
min sum act: [Default: 0.2] minimum total activity of all the units representing a value: when computing weighted average value, this is used as a minimum for the sum that you divide by
clip val: ensure that value remains within specified range
send thr: use unitspec.opt_thresh.send threshold to cut off small activation contributions to overall average value (i.e., if unit's activation is below this threshold, it doesn't contribute to weighted average computation)
init nms: initialize unit names when weights are initialized"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mbr=ScalarValLayerSpec::scalar; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[8] { label="sv1_unit_range"; desc=" range of values represented across the units; for GAUSSIAN, add extra values above and below true useful range to prevent edge effects.
min: minimum value
max: maximum value"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mbr=ScalarValLayerSpec::unit_range; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[9] { label="sv2_unit_range"; desc=" range of values represented across the units; for GAUSSIAN, add extra values above and below true useful range to prevent edge effects.
min: minimum value
max: maximum value"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mbr=ScalarValLayerSpec::unit_range; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[10] { label="sv1_bias_val"; desc=" specifies bias for given value (as gaussian bump)
un: bias on individual units
* NO UN: no unit bias
* GC: bias value enters as a conductance in gc.h or gc.a
* BWT: bias value enters as a bias.wt
un shp: shape of unit bias
* VAL: bias as a particular value representation
* NEG SLP: bias as an increasingly negative slope (gc.a or -bwt) as unit values increase
* POS SLP: bias as an increasingly positive slope (gc.h or +bwt) as unit values increase
un gain: [Default: 1] gain multiplier (strength) of bias to apply for units. WT = .03 as basic weight multiplier
wt: bias on weights: always uses a val-shaped bias
* NO WT: no weight bias
* WT: input weights
wt gain: [Default: 1] gain multiplier (strength) of bias to apply for weights (gain 1 = .03 wt value)
val: value location (center of gaussian bump)"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mbr=ScalarValLayerSpec::bias_val; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[11] { label="sv2_bias_val"; desc=" specifies bias for given value (as gaussian bump)
un: bias on individual units
* NO UN: no unit bias
* GC: bias value enters as a conductance in gc.h or gc.a
* BWT: bias value enters as a bias.wt
un shp: shape of unit bias
* VAL: bias as a particular value representation
* NEG SLP: bias as an increasingly negative slope (gc.a or -bwt) as unit values increase
* POS SLP: bias as an increasingly positive slope (gc.h or +bwt) as unit values increase
un gain: [Default: 1] gain multiplier (strength) of bias to apply for units. WT = .03 as basic weight multiplier
wt: bias on weights: always uses a val-shaped bias
* NO WT: no weight bias
* WT: input weights
wt gain: [Default: 1] gain multiplier (strength) of bias to apply for weights (gain 1 = .03 wt value)
val: value location (center of gaussian bump)"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mbr=ScalarValLayerSpec::bias_val; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[12] { label="sv1_act_fun"; desc=" activation function to use -- typically NOISY_XX1 or SPIKE -- others are for special purposes or testing
* NOISY XX1: x over x plus 1 convolved with Gaussian noise (noise is nvar)
* SPIKE: discrete spiking activations (spike when > thr) -- default params produce adaptive exponential (AdEx) model
* NOISY LINEAR: simple linear output function (still thesholded) convolved with Gaussian noise (noise is nvar)
* XX1: x over x plus 1, hard threshold, no noise convolution
* LINEAR: simple linear output function (still thesholded)"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV1"]$$; mbr=LeabraUnitSpec::act_fun; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[13] { label="sv2_act_fun"; desc=" activation function to use -- typically NOISY_XX1 or SPIKE -- others are for special purposes or testing
* NOISY XX1: x over x plus 1 convolved with Gaussian noise (noise is nvar)
* SPIKE: discrete spiking activations (spike when > thr) -- default params produce adaptive exponential (AdEx) model
* NOISY LINEAR: simple linear output function (still thesholded) convolved with Gaussian noise (noise is nvar)
* XX1: x over x plus 1, hard threshold, no noise convolution
* LINEAR: simple linear output function (still thesholded)"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV2"]$$; mbr=LeabraUnitSpec::act_fun; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[14] { label="sv1_act"; desc=" activation function parameters -- very important for determining the shape of the selected act_fun
gelin: [Default: true] IMPORTANT: Use BioParams button with all default settings if turning this on in an old project to set other important params to match. Computes rate-code activations directly off of the g_e excitatory conductance (i.e., net = netinput) compared to the g_e value that would put the unit right at its firing threshold (g_e_thr) -- this reproduces the empirical rate-code behavior of a discrete spiking network much better than computing from the v_m - thr value. other conductances (g_i, g_l, g_a, g_h) enter via their effects on the effective threshold (g_e_thr). the activation dynamics update over time using the dt.vm time constant, only after v_m itself is over threshold -- if v_m is under threshold, driving act is zero
thr: [Default: 0.5] threshold value Theta (Q) for firing output activation (.5 is more accurate value based on AdEx biological parameters and normalization -- see BioParams button -- use this for gelin)
gain: [Default: 100;40] gain (gamma) of the rate-coded activation functions -- 100 is default for gelin = true with NOISY_XX1, but 40 is closer to the actual spiking behavior of the AdEx model -- use lower values for more graded signals, generaly in lower input/sensory layers of the network
nvar: [Default: 0.005;0.01] variance of the Gaussian noise kernel for convolving with XX1 in NOISY_XX1 and NOISY_LINEAR -- determines the level of curvature of the activation function near the threshold -- increase for more graded responding there -- note that this is not actual stochastic noise, just constant convolved gaussian smoothness to the activation function
avg dt: [Default: 0.005] time constant for integrating activation average (act_avg -- computed across trials) -- used mostly for visualization purposes
avg init: [Default: 0.15] initial activation average value
i thr: [STD or NO_AH for da mod units] how to compute the inhibitory threshold for kWTA functions (what currents to include or exclude in determining what amount of inhibition would keep the unit just at threshold firing) -- for units with dopamine-like modulation using the a and h currents, NO_AH makes learning much more reliable because otherwise kwta partially compensates for the da modulation
* STD: include all currents (except bias weights) in inhibitory threshold computation
* NO A: exclude gc.a current: allows accommodation to knock out units from kwta active list, without other units coming in to take their place
* NO H: exclude gc.h current: prevent hysteresis current from affecting inhibitory threshold computation
* NO AH: exclude gc.a and gc.h currents: prevent both accommodation and hysteresis currents from affecting inhibitory threshold computation
* ALL: include all currents INCLUDING bias weights"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV1"]$; mbr=LeabraUnitSpec::act; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[15] { label="sv2_act"; desc=" activation function parameters -- very important for determining the shape of the selected act_fun
gelin: [Default: true] IMPORTANT: Use BioParams button with all default settings if turning this on in an old project to set other important params to match. Computes rate-code activations directly off of the g_e excitatory conductance (i.e., net = netinput) compared to the g_e value that would put the unit right at its firing threshold (g_e_thr) -- this reproduces the empirical rate-code behavior of a discrete spiking network much better than computing from the v_m - thr value. other conductances (g_i, g_l, g_a, g_h) enter via their effects on the effective threshold (g_e_thr). the activation dynamics update over time using the dt.vm time constant, only after v_m itself is over threshold -- if v_m is under threshold, driving act is zero
thr: [Default: 0.5] threshold value Theta (Q) for firing output activation (.5 is more accurate value based on AdEx biological parameters and normalization -- see BioParams button -- use this for gelin)
gain: [Default: 100;40] gain (gamma) of the rate-coded activation functions -- 100 is default for gelin = true with NOISY_XX1, but 40 is closer to the actual spiking behavior of the AdEx model -- use lower values for more graded signals, generaly in lower input/sensory layers of the network
nvar: [Default: 0.005;0.01] variance of the Gaussian noise kernel for convolving with XX1 in NOISY_XX1 and NOISY_LINEAR -- determines the level of curvature of the activation function near the threshold -- increase for more graded responding there -- note that this is not actual stochastic noise, just constant convolved gaussian smoothness to the activation function
avg dt: [Default: 0.005] time constant for integrating activation average (act_avg -- computed across trials) -- used mostly for visualization purposes
avg init: [Default: 0.15] initial activation average value
i thr: [STD or NO_AH for da mod units] how to compute the inhibitory threshold for kWTA functions (what currents to include or exclude in determining what amount of inhibition would keep the unit just at threshold firing) -- for units with dopamine-like modulation using the a and h currents, NO_AH makes learning much more reliable because otherwise kwta partially compensates for the da modulation
* STD: include all currents (except bias weights) in inhibitory threshold computation
* NO A: exclude gc.a current: allows accommodation to knock out units from kwta active list, without other units coming in to take their place
* NO H: exclude gc.h current: prevent hysteresis current from affecting inhibitory threshold computation
* NO AH: exclude gc.a and gc.h currents: prevent both accommodation and hysteresis currents from affecting inhibitory threshold computation
* ALL: include all currents INCLUDING bias weights"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV2"]$; mbr=LeabraUnitSpec::act; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[16] { label="sv3_act"; desc=" activation function parameters -- very important for determining the shape of the selected act_fun
gelin: [Default: true] IMPORTANT: Use BioParams button with all default settings if turning this on in an old project to set other important params to match. Computes rate-code activations directly off of the g_e excitatory conductance (i.e., net = netinput) compared to the g_e value that would put the unit right at its firing threshold (g_e_thr) -- this reproduces the empirical rate-code behavior of a discrete spiking network much better than computing from the v_m - thr value. other conductances (g_i, g_l, g_a, g_h) enter via their effects on the effective threshold (g_e_thr). the activation dynamics update over time using the dt.vm time constant, only after v_m itself is over threshold -- if v_m is under threshold, driving act is zero
thr: [Default: 0.5] threshold value Theta (Q) for firing output activation (.5 is more accurate value based on AdEx biological parameters and normalization -- see BioParams button -- use this for gelin)
gain: [Default: 100;40] gain (gamma) of the rate-coded activation functions -- 100 is default for gelin = true with NOISY_XX1, but 40 is closer to the actual spiking behavior of the AdEx model -- use lower values for more graded signals, generaly in lower input/sensory layers of the network
nvar: [Default: 0.005;0.01] variance of the Gaussian noise kernel for convolving with XX1 in NOISY_XX1 and NOISY_LINEAR -- determines the level of curvature of the activation function near the threshold -- increase for more graded responding there -- note that this is not actual stochastic noise, just constant convolved gaussian smoothness to the activation function
avg dt: [Default: 0.005] time constant for integrating activation average (act_avg -- computed across trials) -- used mostly for visualization purposes
avg init: [Default: 0.15] initial activation average value
i thr: [STD or NO_AH for da mod units] how to compute the inhibitory threshold for kWTA functions (what currents to include or exclude in determining what amount of inhibition would keep the unit just at threshold firing) -- for units with dopamine-like modulation using the a and h currents, NO_AH makes learning much more reliable because otherwise kwta partially compensates for the da modulation
* STD: include all currents (except bias weights) in inhibitory threshold computation
* NO A: exclude gc.a current: allows accommodation to knock out units from kwta active list, without other units coming in to take their place
* NO H: exclude gc.h current: prevent hysteresis current from affecting inhibitory threshold computation
* NO AH: exclude gc.a and gc.h currents: prevent both accommodation and hysteresis currents from affecting inhibitory threshold computation
* ALL: include all currents INCLUDING bias weights"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV3"]$$; mbr=LeabraUnitSpec::act; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[17] { label="sv1_rnd"; desc=" Weight randomization specification. Note that NONE means no value at all, not the mean, and should be used if some other source is setting the weights, e.g., from a projectionspec or loading from a file etc
type: type of random variable to generate
* UNIFORM: uniform with var = range on either side of the mean
* BINOMIAL: binomial with var = p, par = n
* POISSON: poisson with lambda = var
* GAMMA: gamma with var and par = stages
* GAUSSIAN: normal with var
* NONE: just the mean
mean: mean of random distribution
var: 'varibility' parameter for the random numbers (gauss = standard deviation, not variance; uniform = half-range)
par: extra parameter for distribution (depends on each one)"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV1Cons"]$$; mbr=ConSpec::rnd; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[18] { label="sv2_rnd"; desc=" Weight randomization specification. Note that NONE means no value at all, not the mean, and should be used if some other source is setting the weights, e.g., from a projectionspec or loading from a file etc
type: type of random variable to generate
* UNIFORM: uniform with var = range on either side of the mean
* BINOMIAL: binomial with var = p, par = n
* POISSON: poisson with lambda = var
* GAMMA: gamma with var and par = stages
* GAUSSIAN: normal with var
* NONE: just the mean
mean: mean of random distribution
var: 'varibility' parameter for the random numbers (gauss = standard deviation, not variance; uniform = half-range)
par: extra parameter for distribution (depends on each one)"; cust_desc=0; base=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV2Cons"]$$; mbr=ConSpec::rnd; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[19] { label="sv1_wt_sig"; desc=" sigmoidal weight function for contrast enhancement: high gain makes weights more binary & discriminative
gain: [Default: 1;6] gain (contrast, sharpness) of the weight contrast function (1 = linear)
off: [Default: 1:1.25] offset of the function (1=centered at .5, >1=higher, <1=lower) -- 1.25 is standard for Leabra CHL, 1.0 for XCAL
dwt norm: [Default: false] normalize weight changes -- this adds a significant amount of computational cost, and generally makes learning more robust, but a well-tuned network should not require it, and it causes some interference with prior learning that may not be very biologically plausible or desirable -- dwt -= (act_p / sum act_p) (sum dwt) over receiving projection"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV1Cons"]$; mbr=LeabraConSpec::wt_sig; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[20] { label="sv2_wt_sig"; desc=" sigmoidal weight function for contrast enhancement: high gain makes weights more binary & discriminative
gain: [Default: 1;6] gain (contrast, sharpness) of the weight contrast function (1 = linear)
off: [Default: 1:1.25] offset of the function (1=centered at .5, >1=higher, <1=lower) -- 1.25 is standard for Leabra CHL, 1.0 for XCAL
dwt norm: [Default: false] normalize weight changes -- this adds a significant amount of computational cost, and generally makes learning more robust, but a well-tuned network should not require it, and it causes some interference with prior learning that may not be very biologically plausible or desirable -- dwt -= (act_p / sum act_p) (sum dwt) over receiving projection"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV2Cons"]$; mbr=LeabraConSpec::wt_sig; is_numeric=0; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[21] { label="sv1_lrate"; desc="[Default: 0.01;0.02] [0.01 for std Leabra, .02 for CtLeabra] learning rate -- how fast do the weights change per experience"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV1Cons"]$; mbr=LeabraConSpec::lrate; is_numeric=1; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; EditMbrItem @[22] { label="sv2_lrate"; desc="[Default: 0.01;0.02] [0.01 for std Leabra, .02 for CtLeabra] learning rate -- how fast do the weights change per experience"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV2Cons"]$; mbr=LeabraConSpec::lrate; is_numeric=1; param_search { search=0; min_val=0; max_val=1; next_val=0; incr=0.1000000014901161; }; }; }; mths { name="EditMthItem_Group_1"; el_typ=EditMthItem; el_def=0; group_type=GT_BUTTONS; EditMthItem @[0] { label="Init"; desc=" set the program state back to the beginning"; cust_desc=0; base=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"]$$; mth=Program::Init; }; EditMthItem @[1] { label="Run"; desc=" run the program -- if not yet Init, will run Init first"; cust_desc=0; base=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"]$; mth=Program::Run_Gui; }; EditMthItem @[2] { label="Step"; desc=" step the program at the level of the given program -- if NULL then step_prog default value will be used"; cust_desc=0; base=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"]$; mth=Program::Step_Gui; }; EditMthItem @[3] { label="Stop"; desc=" stop the current program at its next natural stopping point (i.e., cleanly stopping when appropriate chunks of computation have completed)"; cust_desc=0; base=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"]$; mth=Program::Stop; }; EditMthItem @[4] { label="sv1_LabelUnitsNet"; desc=" label all layers in given network using this spec"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mth=ScalarValLayerSpec::LabelUnitsNet; }; EditMthItem @[5] { label="sv2_LabelUnitsNet"; desc=" label all layers in given network using this spec"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mth=ScalarValLayerSpec::LabelUnitsNet; }; EditMthItem @[6] { label="sv1_ReConfig"; desc=" reconfigure layer and associated specs for current scalar.rep type; if n_units > 0, changes number of units in layer to specified value"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$; mth=ScalarValLayerSpec::ReConfig; }; EditMthItem @[7] { label="sv2_ReConfig"; desc=" reconfigure layer and associated specs for current scalar.rep type; if n_units > 0, changes number of units in layer to specified value"; cust_desc=0; base=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$; mth=ScalarValLayerSpec::ReConfig; }; }; }; ClusterRun @["ClusterRun"] { name="ClusterRun"; auto_edit=1; running_updt=0; desc=; mbrs { name=; el_typ=EditMbrItem; el_def=0; }; mths { name=; el_typ=EditMthItem; el_def=0; group_type=GT_BUTTONS; }; jobs_running { name="jobs_running"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["tag"] { name="tag"; desc="unique tag id for this job -- all files etc are named according to this tag"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["notes"] { name="notes"; desc="notes for the job -- describe any specific information about the model configuration etc -- can use this for searching and sorting results"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["params"] { name="params"; desc="emergent parameters based on currently selected items in the ClusterRun"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status"] { name="status"; desc="status of job: REQUESTED, CANCELLED, SUBMITTED, QUEUED, RUNNING, DONE, KILLED"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status_info"] { name="status_info"; desc="more detailed information about status"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_time"] { name="submit_time"; desc="when was the job submitted (tracks time from emergent client submission)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["start_time"] { name="start_time"; desc="when did the job actually start running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["end_time"] { name="end_time"; desc="when did the job finish running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_no"] { name="job_no"; desc="job number on cluster -- assigned once the job is submitted to the cluster"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out"] { name="job_out"; desc="job output information -- contains (top of) the job standard output and standard error output as the job is running (truncated to top 2048 characters if longer than that) -- full information available in job_out_file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out_file"] { name="job_out_file"; desc="job output file -- file name containing full job output information -- file name should be tag.out"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["dat_files"] { name="dat_files"; desc="list of data table output (results) files generated by model (space separated) -- these files have the tag in their name, and end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["other_files"] { name="other_files"; desc="list of other output (results) files generated by model (space separated) -- these files have the tag in their name but do not end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="command_id"; desc="id for this command, assigned by the search algorithm in an algorithm-specific manner (optional)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["command"] { name="command"; desc="emergent command line, up to point of parameters"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["repo_url"] { name="repo_url"; desc="name of repository to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["cluster"] { name="cluster"; desc="name of cluster to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["queue"] { name="queue"; desc="if specified, indicate a particular queue on the computing resource"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["run_time"] { name="run_time"; desc="how long will the jobs take to run -- syntax is number followed by unit indicator -- m=minutes, h=hours, d=days -- e.g., 30m, 12h, or 2d -- typically the job will be killed if it exceeds this amount of time, so be sure to not underestimate"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="ram_gb"; desc="how many gigabytes of ram is required? 0 means do not specify this parameter for the job submission -- for large memory jobs, it can be important to specify this to ensure proper allocation of resources"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="n_threads"; desc="number of parallel threads to use for running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="mpi_nodes"; desc="number of nodes to use for mpi run -- 0 or -1 means not to use mpi"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_batches"; desc="if > 0, use parallel batch mode with this number of batches"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_nodes"; desc="if doing parallel batch mode, and cluster has by_node policy, then this is the number of nodes to allocate to the overall job"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_svn"] { name="submit_svn"; desc="svn revision for the original job submission"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_job"] { name="submit_job"; desc="index of job number within a given submission -- equal to the row number of the original set of jobs submitted in submit_svn jobs"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; }; data_flags=AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; jobs_done { name="jobs_done"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["tag"] { name="tag"; desc="unique tag id for this job -- all files etc are named according to this tag"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["notes"] { name="notes"; desc="notes for the job -- describe any specific information about the model configuration etc -- can use this for searching and sorting results"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["params"] { name="params"; desc="emergent parameters based on currently selected items in the ClusterRun"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status"] { name="status"; desc="status of job: REQUESTED, CANCELLED, SUBMITTED, QUEUED, RUNNING, DONE, KILLED"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status_info"] { name="status_info"; desc="more detailed information about status"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_time"] { name="submit_time"; desc="when was the job submitted (tracks time from emergent client submission)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["start_time"] { name="start_time"; desc="when did the job actually start running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["end_time"] { name="end_time"; desc="when did the job finish running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_no"] { name="job_no"; desc="job number on cluster -- assigned once the job is submitted to the cluster"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out"] { name="job_out"; desc="job output information -- contains (top of) the job standard output and standard error output as the job is running (truncated to top 2048 characters if longer than that) -- full information available in job_out_file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out_file"] { name="job_out_file"; desc="job output file -- file name containing full job output information -- file name should be tag.out"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["dat_files"] { name="dat_files"; desc="list of data table output (results) files generated by model (space separated) -- these files have the tag in their name, and end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["other_files"] { name="other_files"; desc="list of other output (results) files generated by model (space separated) -- these files have the tag in their name but do not end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="command_id"; desc="id for this command, assigned by the search algorithm in an algorithm-specific manner (optional)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["command"] { name="command"; desc="emergent command line, up to point of parameters"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["repo_url"] { name="repo_url"; desc="name of repository to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["cluster"] { name="cluster"; desc="name of cluster to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["queue"] { name="queue"; desc="if specified, indicate a particular queue on the computing resource"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["run_time"] { name="run_time"; desc="how long will the jobs take to run -- syntax is number followed by unit indicator -- m=minutes, h=hours, d=days -- e.g., 30m, 12h, or 2d -- typically the job will be killed if it exceeds this amount of time, so be sure to not underestimate"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="ram_gb"; desc="how many gigabytes of ram is required? 0 means do not specify this parameter for the job submission -- for large memory jobs, it can be important to specify this to ensure proper allocation of resources"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="n_threads"; desc="number of parallel threads to use for running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="mpi_nodes"; desc="number of nodes to use for mpi run -- 0 or -1 means not to use mpi"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_batches"; desc="if > 0, use parallel batch mode with this number of batches"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_nodes"; desc="if doing parallel batch mode, and cluster has by_node policy, then this is the number of nodes to allocate to the overall job"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_svn"] { name="submit_svn"; desc="svn revision for the original job submission"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_job"] { name="submit_job"; desc="index of job number within a given submission -- equal to the row number of the original set of jobs submitted in submit_svn jobs"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; }; data_flags=AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; jobs_archive { name="jobs_archive"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["tag"] { name="tag"; desc="unique tag id for this job -- all files etc are named according to this tag"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["notes"] { name="notes"; desc="notes for the job -- describe any specific information about the model configuration etc -- can use this for searching and sorting results"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["params"] { name="params"; desc="emergent parameters based on currently selected items in the ClusterRun"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status"] { name="status"; desc="status of job: REQUESTED, CANCELLED, SUBMITTED, QUEUED, RUNNING, DONE, KILLED"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["status_info"] { name="status_info"; desc="more detailed information about status"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_time"] { name="submit_time"; desc="when was the job submitted (tracks time from emergent client submission)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["start_time"] { name="start_time"; desc="when did the job actually start running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["end_time"] { name="end_time"; desc="when did the job finish running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_no"] { name="job_no"; desc="job number on cluster -- assigned once the job is submitted to the cluster"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out"] { name="job_out"; desc="job output information -- contains (top of) the job standard output and standard error output as the job is running (truncated to top 2048 characters if longer than that) -- full information available in job_out_file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_out_file"] { name="job_out_file"; desc="job output file -- file name containing full job output information -- file name should be tag.out"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["dat_files"] { name="dat_files"; desc="list of data table output (results) files generated by model (space separated) -- these files have the tag in their name, and end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["other_files"] { name="other_files"; desc="list of other output (results) files generated by model (space separated) -- these files have the tag in their name but do not end in .dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["command_id"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="command_id"; desc="id for this command, assigned by the search algorithm in an algorithm-specific manner (optional)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["command"] { name="command"; desc="emergent command line, up to point of parameters"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["repo_url"] { name="repo_url"; desc="name of repository to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["cluster"] { name="cluster"; desc="name of cluster to run job on"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["queue"] { name="queue"; desc="if specified, indicate a particular queue on the computing resource"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["run_time"] { name="run_time"; desc="how long will the jobs take to run -- syntax is number followed by unit indicator -- m=minutes, h=hours, d=days -- e.g., 30m, 12h, or 2d -- typically the job will be killed if it exceeds this amount of time, so be sure to not underestimate"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["ram_gb"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="ram_gb"; desc="how many gigabytes of ram is required? 0 means do not specify this parameter for the job submission -- for large memory jobs, it can be important to specify this to ensure proper allocation of resources"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["n_threads"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="n_threads"; desc="number of parallel threads to use for running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["mpi_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="mpi_nodes"; desc="number of nodes to use for mpi run -- 0 or -1 means not to use mpi"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_batches"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_batches"; desc="if > 0, use parallel batch mode with this number of batches"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; int_Data @["pb_nodes"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="pb_nodes"; desc="if doing parallel batch mode, and cluster has by_node policy, then this is the number of nodes to allocate to the overall job"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_svn"] { name="submit_svn"; desc="svn revision for the original job submission"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["submit_job"] { name="submit_job"; desc="index of job number within a given submission -- equal to the row number of the original set of jobs submitted in submit_svn jobs"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; }; data_flags=AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; file_list { name="file_list"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["file_name"] { name="file_name"; desc="name of file -- does not include any path information"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["tag"] { name="tag"; desc="job tag associated with this file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["size"] { name="size"; desc="size of file -- with typical suffixes (K = kilobytes, M = megabytes, G = gigabytes)"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["kind"] { name="kind"; desc="type of file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["date_modified"] { name="date_modified"; desc="timestamp for when the file was last modified"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["date_created"] { name="date_created"; desc="timestamp for when the file was first created"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["svn_file_path"] { name="svn_file_path"; desc="path to file in SVN repository, relative to root of svn_repo repository"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["proj_file_path"] { name="proj_file_path"; desc="path to file relative to the parent project directory -- e.g., results/filename.dat"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["file_path"] { name="file_path"; desc="full path to file on local file system, including all parent directories and name of file -- takes you directly to the file"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; }; data_flags=AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; cluster_info { name="cluster_info"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["queue"] { name="queue"; desc="queue that this info relates to"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["job_no"] { name="job_no"; desc="job number or total number of actve jobs"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["user"] { name="user"; desc="user name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["state"] { name="state"; desc="current scheduler state -- or description of global state info"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["procs"] { name="procs"; desc="number of processors for this job or for global state info"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; String_Data @["start_time"] { name="start_time"; desc="timestamp for when the job was submitted or started running"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; }; }; data_flags=AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; search_algos { name=; el_typ=ParamSearchAlgo; el_def=0; }; cur_search_algo=NULL; last_submit_time=; notes=; extra_files=; svn_repo=; repo_url=; cluster=; queue=; run_time=; ram_gb=0; n_threads=1; use_mpi=0; mpi_nodes=10; parallel_batch=0; pb_batches=10; pb_nodes=0; nowin_x=0; }; }; data { name="DataTable_Group_4"; el_typ=DataTable; el_def=0; DataTable_Group @.gp["InputData"] { name="InputData"; el_typ=DataTable; el_def=0; DataTable @["FrequencyInputs"] { name="FrequencyInputs"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["Name"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_57"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="Name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [2] "1.1";"1.0"; }; }; float_Data @["Input"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_58"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="Input"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 4;4; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [4 4 2] 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0; }; }; float_Data @["SV1"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_59"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="SV1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 2] 1;0; }; }; float_Data @["SV2"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_60"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="SV2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 2] 1;0; }; }; float_Data @["SV3"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="SV3"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 2] 1;0; }; }; float_Data @["frequency"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_61"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="frequency"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [2] 0.33499423;0.66500574; }; }; }; data_flags=SAVE_ROWS|AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [2] 0;1; }; }; DataTable @["FrequencyRendering"] { name="FrequencyRendering"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["Name"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_63"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="Name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [100] "1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1"; "1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1"; "1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1";"1.1"; "1.1";"1.1";"1.1";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; "1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0";"1.0"; }; }; float_Data @["Input"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_64"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="Input"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 4;4; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [4 4 100] 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 0;0;1;0;0;0;1;0;0;0; 1;0;0;0;1;0;0;0;1;0; 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0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; }; }; float_Data @["SV2"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_66"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="SV2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 100] 1;1;1;1;1;1;1;1;1;1; 1;1;1;1;1;1;1;1;1;1; 1;1;1;1;1;1;1;1;1;1; 1;1;1;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; }; }; float_Data @["SV3"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_67"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="SV3"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 100] 1;1;1;1;1;1;1;1;1;1; 1;1;1;1;1;1;1;1;1;1; 1;1;1;1;1;1;1;1;1;1; 1;1;1;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0; }; }; float_Data @["frequency"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_68"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_wd"] { name="view_panel_wd"; value 6 0=0.1572327017784119; val_type_fixed=0; }; }; name="frequency"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [100] 0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423; 0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423; 0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423;0.33499423; 0.33499423;0.33499423;0.33499423;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; 0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574;0.66500574; }; }; }; data_flags=SAVE_ROWS|AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [100] 0;1;2;3;4;5;6;7;8;9; 10;11;12;13;14;15;16;17;18;19; 20;21;22;23;24;25;26;27;28;29; 30;31;32;33;34;35;36;37;38;39; 40;41;42;43;44;45;46;47;48;49; 50;51;52;53;54;55;56;57;58;59; 60;61;62;63;64;65;66;67;68;69; 70;71;72;73;74;75;76;77;78;79; 80;81;82;83;84;85;86;87;88;89; 90;91;92;93;94;95;96;97;98;99; }; }; DataTable @["ValueEnv"] { name="ValueEnv"; desc=; data { name="data"; el_typ=String_Data; el_def=0; String_Data @["Name"] { name="Name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [2] "1.1";"2.1"; }; }; float_Data @["LeabraLayer_0"] { name="LeabraLayer_0"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 4;4; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [4 4 2] 0;0;0;0;0;0;0;0;1;0; 0;0;1;0;0;0;0;0;0;0; 0;0;0;0;1;0;0;0;0;1; 0;0; }; }; float_Data @["SV1"] { name="SV1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 2] 0.89999998;0.1; }; }; float_Data @["SV2"] { name="SV2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=1; cell_geom{ 1;1; }; calc_expr { expr=; }; dim_names { name=; [2] ;; }; width=0; ar { name=; [1 1 2] 0.89999998;0.1; }; }; float_Data @["frequency"] { name="frequency"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [2] 0.5;0.5; }; }; }; data_flags=SAVE_ROWS|AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [2] 0;1; }; }; }; DataTable_Group @.gp["OutputData"] { name="OutputData"; el_typ=DataTable; el_def=0; DataTable @["TrialOutputData"] { name="TrialOutputData"; desc=; data { name="data"; el_typ=int_Data; el_def=0; int_Data @["batch"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_65"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="batch"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; int_Data @["epoch"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_66"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="epoch"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; int_Data @["trial"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_67"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="trial"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; String_Data @["trial_name"] { name="trial_name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; String_Data @["group_name"] { name="group_name"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; int_Data @["phase_no"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_68"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="phase_no"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["minus_cycles"] { name="minus_cycles"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["sse"] { name="sse"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["ext_rew"] { name="ext_rew"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["sv1"] { name="sv1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["sv2"] { name="sv2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["sv3"] { name="sv3"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; double_Data @["prob1"] { name="prob1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; double_Data @["prob2"] { name="prob2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; }; data_flags=SAVE_ROWS|AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; DataTable @["EpochOutputData"] { name="EpochOutputData"; desc=; data { name="data"; el_typ=int_Data; el_def=0; int_Data @["batch"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_67"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="batch"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; int_Data @["epoch"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_68"; el_typ=UserDataItem; el_def=0; UserDataItem @["NARROW"] { name="NARROW"; value 1 0=1; val_type_fixed=0; }; }; name="epoch"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_sse"] { name="avg_sse"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["cnt_err"] { name="cnt_err"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_ext_rew"] { name="avg_ext_rew"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_cycles"] { name="avg_cycles"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["epoch_time_tot"] { name="epoch_time_tot"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["epoch_time_usr"] { name="epoch_time_usr"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_sv1"] { name="avg_sv1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_sv2"] { name="avg_sv2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; float_Data @["avg_sv3"] { name="avg_sv3"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; double_Data @["prob1"] { name="prob1"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; double_Data @["prob2"] { name="prob2"; col_flags=SAVE_ROWS|SAVE_DATA; is_matrix=0; cell_geom{ 1; }; calc_expr { expr=; }; dim_names { name=; [0] }; width=0; ar { name=; [0] }; }; }; data_flags=SAVE_ROWS|AUTO_CALC; auto_load=NO_AUTO_LOAD; auto_load_file=; keygen 4 0=0; row_indexes { name=; [0] }; }; }; DataTable_Group @.gp["AnalysisData"] { name="AnalysisData"; el_typ=DataTable; el_def=0; }; }; data_proc { name="taBase_Group_0"; el_typ=taDataProc; el_def=0; taDataProc @["data_base"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_8"; el_typ=UserDataItem; el_def=0; UserDataItem @["NO_CLIP"] { name="NO_CLIP"; value 1 0=1; val_type_fixed=0; }; }; name="data_base"; }; taDataAnal @["data_anal"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_9"; el_typ=UserDataItem; el_def=0; UserDataItem @["NO_CLIP"] { name="NO_CLIP"; value 1 0=1; val_type_fixed=0; }; }; name="data_anal"; }; taDataGen @["data_gen"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_10"; el_typ=UserDataItem; el_def=0; UserDataItem @["NO_CLIP"] { name="NO_CLIP"; value 1 0=1; val_type_fixed=0; }; }; name="data_gen"; }; taImageProc @["image_proc"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_11"; el_typ=UserDataItem; el_def=0; UserDataItem @["NO_CLIP"] { name="NO_CLIP"; value 1 0=1; val_type_fixed=0; }; }; name="image_proc"; }; }; programs { name="Program_Group_1"; el_typ=Program; el_def=0; tags=; desc=; Program_Group @.gp["LeabraAll_Std"] { name="LeabraAll_Std"; el_typ=Program; el_def=0; tags="Leabra, Std, All"; desc="The full set of programs for training a standard Leabra network"; Program @["LeabraBatch"] { name="LeabraBatch"; short_nm="Batch"; tags="Leabra, Std"; desc="Iterate over training runs (a batch of training runs) -- just a simple loop that calls training program"; flags=0; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=.projects["scalar_val_test"].networks["ScalarValTestNet"]$$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to operate on -- updates batch counter on network and passes it to train program"; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="datatable with training patterns -- not used by this program, but passed to train program"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["batch"] { name="batch"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="batch counter"; init_from=NULL; }; ProgVar @["init_batch"] { name="init_batch"; var_type=T_Bool; bool_val=1; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="initialize batch counter at start of run -- turn off if using \"parallel batch\" mode to run different batches on different processes"; init_from=NULL; }; ProgVar @["max_batch"] { name="max_batch"; var_type=T_Int; int_val=10; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="maximum number of batch runs to perform"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initializes local batch counter and batch field on network"; flags=0; orig_prog_code=; network_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].args["network"]$$; local_ctr_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].vars["batch"]$$; counter=Network::batch; update_after=0; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; IfElse @[0] { desc=; flags=0; orig_prog_code=; cond { expr="init_batch"; }; show_false_code=1; true_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initializes local batch counter and batch field on network"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].vars["batch"]$; counter=Network::batch; update_after=0; }; }; false_code { name=; el_typ=ProgCode; el_def=0; MemberAssign @[0] { desc="just set directly to current batch value"; flags=0; orig_prog_code=; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].args["network"]$; path="batch"; expr { expr="batch"; }; update_after=0; }; }; }; WhileLoop @[1] { desc="the main loop over training runs"; flags=0; orig_prog_code=; loop_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc="run the training program -- sets the network and input_data args"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=LeabraNetwork; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; ProgArg @[1] { arg_type=DataTable; type="DataTable*"; name="input_data"; required=1; def_val=; expr { expr="input_data"; }; }; ProgArg @[2] { arg_type=bool; type="bool"; name="no_prompts"; required=1; def_val=; expr { expr="true"; }; }; }; target=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"]$; targ_ld_init="*LeabraTrain*"; }; NetCounterIncr @[1] { desc="increment the local batch counter and copy to network"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraBatch"].vars["batch"]$; counter=Network::batch; update_after=0; }; }; test { expr="batch < max_batch"; }; }; }; step_prog=NULL; step_n=1; }; Program @["LeabraTrain"] { name="LeabraTrain"; short_nm="Train"; tags="Leabra, Train, RndSeedList"; desc="A complete training run of a Leabra network: iterating over epochs until the network has learned the task. Also has a list of random seeds that are tied to the network batch counter, providing consistent runs regardless of what happens in the training of each network, or where it is run (e.g., on different processors)"; flags=0; objs { name=; el_typ=RndSeed; el_def=0; RndSeed_List @["rnd_seeds"] { name="rnd_seeds"; seeds { name=; el_typ=RndSeed; el_def=0; RndSeed @["RndSeed_1"] { name="RndSeed_1"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_2"] { name="RndSeed_2"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_3"] { name="RndSeed_3"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_4"] { name="RndSeed_4"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_5"] { name="RndSeed_5"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_6"] { name="RndSeed_6"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_7"] { name="RndSeed_7"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_8"] { name="RndSeed_8"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_9"] { name="RndSeed_9"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_10"] { name="RndSeed_10"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_11"] { name="RndSeed_11"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_12"] { name="RndSeed_12"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_13"] { name="RndSeed_13"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_14"] { name="RndSeed_14"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_15"] { name="RndSeed_15"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_16"] { name="RndSeed_16"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_17"] { name="RndSeed_17"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_18"] { name="RndSeed_18"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_19"] { name="RndSeed_19"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_20"] { name="RndSeed_20"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_21"] { name="RndSeed_21"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_22"] { name="RndSeed_22"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_23"] { name="RndSeed_23"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_24"] { name="RndSeed_24"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; RndSeed @["RndSeed_25"] { name="RndSeed_25"; seed{ 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}; mti=624; is_gauss_double_cached=0; cached_gauss_double=0; }; }; }; RndSeed @["rnd_seed"] { name="rnd_seed"; seed{ 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}; mti=156; is_gauss_double_cached=0; cached_gauss_double=0; }; }; types { name=; el_typ=DynEnumType; el_def=0; DynEnumType @["RndInitType"] { name="RndInitType"; desc=; enums { name=; el_typ=DynEnumItem; el_def=0; DynEnumItem @["OLD_SEED"] { name="OLD_SEED"; value=0; desc="use stored random seed value (recreates same sequence every time)"; }; DynEnumItem @["NEW_SEED"] { name="NEW_SEED"; value=1; desc="generate new random seed (new sequence of random numbers)"; }; DynEnumItem @["BATCH_SEEDS"] { name="BATCH_SEEDS"; value=2; desc="use a fixed list of random seeds with the current batch number selecting the seed to use -- provides a consistent set of initial conditions for each batch regardless.."; }; }; bits=0; }; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["no_prompts"] { name="no_prompts"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="If train is called by other programs (e.g., Batch), they should set this to true -- otherwise it is reset to false in Init"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["max_epoch"] { name="max_epoch"; var_type=T_Int; int_val=500; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["epoch"] { name="epoch"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["train_mode"] { name="train_mode"; var_type=T_HardEnum; int_val=1; hard_enum_type=Network::TrainMode; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["rnd_init"] { name="rnd_init"; var_type=T_DynEnum; dyn_enum_val { enum_type=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].types["RndInitType"]$$; value=2; }; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="how to initialize the random numbers when the Init button is pressed"; init_from=NULL; }; ProgVar @["err_stopcrit"] { name="err_stopcrit"; var_type=T_Real; real_val=-1; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="stopping criterion for training, in terms of count of trials with errors in the epoch (nework cnt_err)"; init_from=NULL; }; ProgVar @["n_under_thr"] { name="n_under_thr"; var_type=T_Int; int_val=2; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="number of times network needs to be under stopcrit threshold to actually stop"; init_from=NULL; }; ProgVar @["save_final_wts"] { name="save_final_wts"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="save the network's weights at the end of training? "; init_from=NULL; }; ProgVar @["save_wts_interval"] { name="save_wts_interval"; var_type=T_Int; int_val=-1; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="save the weights every this many epochs, if number is >= 1 (otherwise do not save weights)"; init_from=NULL; }; ProgVar @["n_under_cnt"] { name="n_under_cnt"; var_type=T_Int; int_val=0; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="current under threshold counter"; init_from=NULL; }; ProgVar @["rnd_seeds"] { name="rnd_seeds"; var_type=T_Object; object_type=RndSeed_List; object_val=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].objs["rnd_seeds"]$$; objs_ptr=1; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["train_timer"] { name="train_timer"; var_type=T_Object; object_type=TimeUsed; object_val=.projects["scalar_val_test"].networks["ScalarValTestNet"].train_time$$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["rnd_seed"] { name="rnd_seed"; var_type=T_Object; object_type=RndSeed; object_val=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].objs["rnd_seed"]$$; objs_ptr=1; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; Function @["GetRandomSeed"] { desc=; flags=0; orig_prog_code=; name="GetRandomSeed"; return_type=T_Int; object_type=taOBase; args { name=; el_typ=ProgVar; el_def=0; }; fun_code { name=; el_typ=ProgCode; el_def=0; LocalVars @[0] { desc=; flags=0; orig_prog_code=; local_vars { name=; el_typ=ProgVar; el_def=0; }; }; Switch @[1] { desc=; flags=0; orig_prog_code=; switch_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["rnd_init"]$$; cases { name=; el_typ=CaseBlock; el_def=0; CaseBlock @[0] { desc=; flags=0; orig_prog_code=; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="restore previous random seed (all runs produce same results) -- click on rnd_seed object to generate a new seed to use for this if you want"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["rnd_seed"]$$; method=RndSeed::OldSeed; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; case_val { expr="OLD_SEED"; }; }; CaseBlock @[1] { desc=; flags=0; orig_prog_code=; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="new random numbers each time"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["rnd_seed"]$; method=RndSeed::NewSeed; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; case_val { expr="NEW_SEED"; }; }; CaseBlock @[2] { desc=; flags=0; orig_prog_code=; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="restore previous random seed (all runs produce same results); change to NewSeed to have new random numbers each time"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["rnd_seeds"]$$; method=RndSeed_List::UseSeed; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=int; type="int"; name="idx"; required=1; def_val=; expr { expr="network.batch % rnd_seeds.seeds.size"; }; }; }; }; }; case_val { expr="BATCH_SEEDS"; }; }; }; }; }; }; }; init_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["no_prompts"]$$; expr { expr="false"; }; }; MethodCall @[1] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$$; method=taBase::CheckConfig; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr="false"; }; }; }; }; AssignExpr @[2] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["train_timer"]$$; expr { expr="network.train_time"; }; }; AssignExpr @[3] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["n_under_cnt"]$$; expr { expr="0"; }; }; FunctionCall @[4] { desc="initialize random seed"; flags=0; orig_prog_code=; result_var=NULL; fun=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].functions["GetRandomSeed"]$$; fun_args { name=; el_typ=ProgArg; el_def=0; }; }; WtInitPrompt @[5] { desc="don't initialize weights without checking"; flags=0; orig_prog_code=; prompt="Do you want to Initialize Network Weights"; yes_label="Yes"; no_label="No"; yes_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="need to have built network by now in any case, so do it here to make double sure -- prevents errs when loading project with startup program"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$; method=Network::Build; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; MethodCall @[1] { desc="initialize network weights: could also load pre-set weights or something else here"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$; method=Network::Init_Weights; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; PrintExpr @[2] { desc=; flags=0; orig_prog_code=; expr { expr="network.name << \" Weights Initialized\""; }; my_mask=NULL; debug_level=NULL; }; }; }; AssignExpr @[6] { desc="grab the official network epoch counter: will be initialized if needed by now"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["epoch"]$$; expr { expr="network.epoch"; }; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc=; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["train_timer"]$; expr { expr="network.train_time"; }; }; MethodCall @[1] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["train_timer"]$; method=TimeUsed::StartTimer; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="reset_used"; required=0; def_val="true"; expr { expr="true"; }; }; }; }; AssignExpr @[2] { desc=; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["n_under_cnt"]$; expr { expr="0"; }; }; MemberAssign @[3] { desc="set network's training mode to our local value"; flags=0; orig_prog_code=; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$; path="train_mode"; expr { expr="train_mode"; }; update_after=0; }; FunctionCall @[4] { desc="initialize random seed"; flags=0; orig_prog_code=; result_var=NULL; fun=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].functions["GetRandomSeed"]$; fun_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[5] { desc=; flags=0; orig_prog_code=; cond { expr="no_prompts"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="initialize network weights: could also load pre-set weights or something else here"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$; method=Network::Init_Weights; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; PrintExpr @[1] { desc=; flags=0; orig_prog_code=; expr { expr="network.name << \" Weights Initialized\""; }; my_mask=NULL; debug_level=NULL; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; AssignExpr @[6] { desc="grab the official network epoch counter: will be initialized if needed by now"; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["epoch"]$; expr { expr="network.epoch"; }; }; WhileLoop @[7] { desc=; flags=0; orig_prog_code=; loop_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc=; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=LeabraNetwork; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; ProgArg @[1] { arg_type=DataTable; type="DataTable*"; name="input_data"; required=1; def_val=; expr { expr="input_data"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"]$$; targ_ld_init="*LeabraEpoch*"; }; NetCounterIncr @[1] { desc="increment the epoch counter"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["epoch"]$; counter=Network::epoch; update_after=0; }; IfElse @[2] { desc=; flags=0; orig_prog_code=; cond { expr="network.cnt_err <= err_stopcrit"; }; show_false_code=1; true_code { name=; el_typ=ProgCode; el_def=0; VarIncr @[0] { desc=; flags=0; orig_prog_code=; var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["n_under_cnt"]$; expr { expr="1"; }; }; IfBreak @[1] { desc="stop if errors go below stopping criterion (note: could use sse or avg_sse here too)"; flags=0; orig_prog_code=; cond { expr="n_under_cnt >= n_under_thr"; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc=; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["n_under_cnt"]$; expr { expr="0"; }; }; }; }; IfElse @[3] { desc=; flags=0; orig_prog_code=; cond { expr="save_wts_interval >= 1"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; IfElse @[0] { desc=; flags=0; orig_prog_code=; cond { expr="epoch % save_wts_interval == 0"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc=; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=Network; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"]$$; targ_ld_init="*SaveWeights*"; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; }; test { expr="epoch < max_epoch"; }; }; IfElse @[8] { desc=; flags=0; orig_prog_code=; cond { expr="save_final_wts"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc=; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=Network; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; }; target=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"]$; targ_ld_init="*SaveWeights*"; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; MethodCall @[9] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrain"].vars["train_timer"]$; method=TimeUsed::EndTimer; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; step_prog=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"]$$; step_n=1; }; Program @["LeabraEpoch"] { name="LeabraEpoch"; short_nm="Epoch"; tags="Leabra, Std"; desc="iterates over all of the items in a data table and calls LeabraTrial process on them"; flags=0; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to operate on"; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="table of patterns to present to the network, one row at a time"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["n_replications"] { name="n_replications"; var_type=T_Int; int_val=100; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["prob_switch_interval"] { name="prob_switch_interval"; var_type=T_Int; int_val=20; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["data_loop_order"] { name="data_loop_order"; var_type=T_HardEnum; int_val=1; hard_enum_type=DataLoop::Order; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="controls the order in which events (rows of the input data datatable) are presented to the network (SEQUENTIAL, PERMUTED, RANDOM)"; init_from=NULL; }; ProgVar @["trial"] { name="trial"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="current trial (event) within the epoch -- increments automatically"; init_from=NULL; }; ProgVar @["trial_mon_data"] { name="trial_mon_data"; var_type=T_Object; object_type=DataTable; object_val=.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="trial-level output data -- we reset it every epoch by default, so it just contains last epoch of data"; init_from=NULL; }; ProgVar @["epoch_timer"] { name="epoch_timer"; var_type=T_Object; object_type=TimeUsed; object_val=.projects["scalar_val_test"].networks["ScalarValTestNet"].epoch_time$$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="timer to record time required to perform one epoch of processing (object is on network -- this is a pointer to it)"; init_from=NULL; }; ProgVar @["data_loop_index"] { name="data_loop_index"; var_type=T_Int; int_val=87; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="index counter for the looping over items in the input_data datatable (not always the same as trial counter, depending on distributed memory computation)"; init_from=NULL; }; ProgVar @["compute_rel_netin"] { name="compute_rel_netin"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="call Compute_AvgAbsRelNetin() function to collect relative net input information telling how much each layer receives from each of its receiving projections"; init_from=NULL; }; ProgVar @["adapt_rel_abs_netin"] { name="adapt_rel_abs_netin"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="use the computed rel and abs netin values to adapt the wt_scale.abs and wt_scale.rel params of network conspecs to match target values that are set on the projections -- see conspec rel_net_adapt and layerspec abs_net_adapt parmeters and comments for more information"; init_from=NULL; }; ProgVar @["FrequencyInputs"] { name="FrequencyInputs"; var_type=T_Object; object_type=DataTable; object_val=.projects["scalar_val_test"].data.gp["InputData"]["FrequencyInputs"]$$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize trial counter (local variable and in the network)"; flags=0; orig_prog_code=; network_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$$; local_ctr_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["trial"]$$; counter=Network::trial; update_after=0; }; AssignExpr @[1] { desc="get pointer to epoch timer object on network"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["epoch_timer"]$$; expr { expr="network.epoch_time"; }; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize trial counter (local variable and in the network)"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["trial"]$; counter=Network::trial; update_after=0; }; AssignExpr @[1] { desc="get pointer to epoch timer object on network"; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["epoch_timer"]$; expr { expr="network.epoch_time"; }; }; MethodCall @[2] { desc="start the epoch timer to record computation time per epoch"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["epoch_timer"]$; method=TimeUsed::StartTimer; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="reset_used"; required=0; def_val="true"; expr { expr="true"; }; }; }; }; MethodCall @[3] { desc="reset trial-level monitor data every epoch, so it reflects only the most recent epoch's worth of data (turn flags OFF to accumulate trial data across entire training run)"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["trial_mon_data"]$$; method=DataTable::ResetData; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[4] { desc=; flags=0; orig_prog_code=; cond { expr="network.epoch % prob_switch_interval == 0"; }; show_false_code=0; true_code { name="ProgEl_List_102"; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc=; flags=0; orig_prog_code=; prog_args { name="ProgArg_List_0"; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataTable; type="DataTable*"; name="FrequencyInputs"; required=1; def_val=; expr { expr="FrequencyInputs"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"]$$; targ_ld_init="*RandomProbabilities*"; }; }; false_code { name="ProgEl_List_103"; el_typ=ProgCode; el_def=0; }; }; DataGenCall @[5] { desc=; flags=0; orig_prog_code=; result_var=NULL; object_type=taDataGen; method=taDataGen::ReplicateByFrequency; meth_args { name="ProgArg_List_0"; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataTable_ptr; type="DataTable*"; name="repl_output"; required=1; def_val=; expr { expr="input_data"; }; }; ProgArg @[1] { arg_type=const_DataTable_ptr; type="DataTable*"; name="data_list_in"; required=1; def_val=; expr { expr="FrequencyInputs"; }; }; ProgArg @[2] { arg_type=int; type="int"; name="total_number"; required=1; def_val=; expr { expr="n_replications"; }; }; ProgArg @[3] { arg_type=const_taString_ref; type="taString&"; name="freq_col_nm"; required=0; def_val="frequency"; expr { expr="\"frequency\""; }; }; ProgArg @[4] { arg_type=bool; type="bool"; name="renorm_freqs"; required=0; def_val="true"; expr { expr="true"; }; }; }; }; NetDataLoop @[6] { desc="iterates over the events/rows of input_data, according to data_loop_order variable"; flags=0; orig_prog_code=; loop_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc="run the trial program, passing network and input_data"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=LeabraNetwork; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; ProgArg @[1] { arg_type=DataTable; type="DataTable*"; name="input_data"; required=1; def_val=; expr { expr="input_data"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"]$$; targ_ld_init="*LeabraTrial*"; }; }; data_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["input_data"]$$; index_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["data_loop_index"]$$; order_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["data_loop_order"]$$; order=PERMUTED; update_after=0; dmem_nprocs=1; dmem_this_proc=0; grouped=0; group_col 9 0="Group"; group_index_var=NULL; group_order_var=NULL; group_order=PERMUTED; group_idx_list{ }; }; IfElse @[7] { desc="if full batch mode, update only at end of epoch"; flags=0; orig_prog_code=; cond { expr="network.wt_update == Network::BATCH"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="final update of weights based on accumulated changes"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; method=Network::Compute_Weights; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; MethodCall @[8] { desc="network accumulates some core statistics over the epoch -- this finalizes that process and computes summary stats"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; method=LeabraNetwork::Compute_EpochStats; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[9] { desc=; flags=NEW_EL; orig_prog_code=; cond { expr="compute_rel_netin"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; method=LeabraNetwork::Compute_AvgAbsRelNetin; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[1] { desc=; flags=0; orig_prog_code=; cond { expr="adapt_rel_abs_netin"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; method=LeabraNetwork::Compute_AdaptRelNetin; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; MethodCall @[1] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].args["network"]$; method=LeabraNetwork::Compute_AdaptAbsNetin; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; MethodCall @[10] { desc="done with the computation in the epoch -- record time it took"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpoch"].vars["epoch_timer"]$; method=TimeUsed::EndTimer; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; ProgramCall @[11] { desc="run program that records data from network and possibly other sources about the epoch"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=Network; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"]$$; targ_ld_init="*LeabraEpochMonitor*"; }; }; step_prog=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraCycle"]$$; step_n=1; }; Program @["LeabraTrial"] { name="LeabraTrial"; short_nm="Trial"; tags="Leabra, Std"; desc="Leabra processing of a single input/toutput event or external information: typically runs a minus and a plus phase, then learns (unless testing)"; flags=0; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to operate on -- typically set by higher-level calling programs"; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="datatable containing training input/output patterns"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["phase_no"] { name="phase_no"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="local phase counting variable (0 is typically minus phase, 1 is typically plus -- depends on network settings)"; init_from=NULL; }; ProgVar @["update_net_view"] { name="update_net_view"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="determines whether to update any network view displays after trial is completed"; init_from=NULL; }; ProgVar @["trial"] { name="trial"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|EDIT_VAL; reference=0; desc="current trial (event) within the epoch -- increments automatically"; init_from=NULL; }; ProgVar @["compute_rel_netin"] { name="compute_rel_netin"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="call ComputeAbsRelNetin() function to collect relative net input information telling how much each layer receives from each of its receiving projections"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize the local phase_no counter, and corresponding network one"; flags=0; orig_prog_code=; network_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$$; local_ctr_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].vars["phase_no"]$$; counter=LeabraNetwork::phase_no; update_after=0; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize the local phase_no counter, and corresponding network one"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].vars["phase_no"]$; counter=LeabraNetwork::phase_no; update_after=0; }; MethodCall @[1] { desc="initializes various counters at start of trial"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; method=LeabraNetwork::Trial_Init; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; WhileLoop @[2] { desc="loop over phases of settling in the network"; flags=0; orig_prog_code=; loop_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc="run the settle program (which iterates over cyles of network activation updating) for each phase"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=LeabraNetwork; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; ProgArg @[1] { arg_type=DataTable; type="DataTable*"; name="input_data"; required=1; def_val=; expr { expr="input_data"; }; }; }; target=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"]$; targ_ld_init="*LeabraSettle*"; }; NetCounterIncr @[1] { desc="increment the phase number (also on network)"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].vars["phase_no"]$; counter=LeabraNetwork::phase_no; update_after=0; }; MethodCall @[2] { desc="increments other phase state information to prepare for the next phase of settling"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; method=LeabraNetwork::Trial_UpdatePhase; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; test { expr="phase_no < network.phase_max"; }; }; MethodCall @[3] { desc="after the trial is over, do final computations: Compute_dWt (learn weights), compute stats"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; method=LeabraNetwork::Trial_Final; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[4] { desc=; flags=NEW_EL; orig_prog_code=; cond { expr="compute_rel_netin"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="special for adapting netin"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; method=LeabraNetwork::Compute_AbsRelNetin; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; IfElse @[5] { desc="test if it is time to update the weight values from delta weights (dWt) computed over trials"; flags=0; orig_prog_code=; cond { expr="network.Compute_Weights_Test(network.trial+1)"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="update the weight values based on changes computed by trial program"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; method=Network::Compute_Weights; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; ProgramCall @[6] { desc="records data about the trial-level processing to a datatable for graphing/processing"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=Network; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"]$$; targ_ld_init="*LeabraTrialMonitor*"; }; NetUpdateView @[7] { desc="update the network view(s) (only if update_net_view is true)"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].args["network"]$; update_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrial"].vars["update_net_view"]$$; }; }; step_prog=NULL; step_n=1; }; Program @["LeabraSettle"] { name="LeabraSettle"; short_nm="Settle"; tags="Leabra, Std"; desc="iterates over cycles of updating until network has settled into a stable state, or output activations have exceeded a threshold"; flags=0; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["cycle"] { name="cycle"; var_type=T_Int; int_val=0; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="current cycle of settling (local loop counter)"; init_from=NULL; }; ProgVar @["min_cycles"] { name="min_cycles"; var_type=T_Int; int_val=0; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="IMPORTANT: this value is obtained from the network min_cycles and min_cycles_phase2 -- change the value on the network object, not here in this program! sets the minimum number of cycles to settle for, regardless of network state changes, etc"; init_from=NULL; }; ProgVar @["update_net_view"] { name="update_net_view"; var_type=T_Bool; bool_val=1; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="if true, will update network views at end of settling"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize local cycle counter and corresponding counter on network"; flags=0; orig_prog_code=; network_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$$; local_ctr_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["cycle"]$$; counter=Network::cycle; update_after=0; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; NetCounterInit @[0] { desc="initialize local cycle counter and corresponding counter on network"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["cycle"]$; counter=Network::cycle; update_after=0; }; MethodCall @[1] { desc="resets input data, before getting new external inputs data from apply inputs call"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; method=Network::Init_InputData; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; ProgramCall @[2] { desc="apply external input activations from the input_data table to the network this program can be extended to do arbitrary things to generate data and apply it to network layers"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=Network; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; ProgArg @[1] { arg_type=DataTable; type="DataTable*"; name="input_data"; required=1; def_val=; expr { expr="input_data"; }; }; }; target=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"]$$; targ_ld_init="*ApplyInputs*"; }; MethodCall @[3] { desc="initializes various counters at start of settling"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; method=LeabraNetwork::Settle_Init; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[4] { desc="get appropriate min_cycles value depending on which phase we're in"; flags=0; orig_prog_code=; cond { expr="network.phase_no <= 1"; }; show_false_code=1; true_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc="get minimum number of cycles from parameter on network (which is where you should change this value!)"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["min_cycles"]$$; expr { expr="network.min_cycles"; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc="get minimum number of cycles from parameter on network (which is where you should change this value!)"; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["min_cycles"]$; expr { expr="network.min_cycles_phase2"; }; }; }; }; WhileLoop @[5] { desc="the main loop over cycles of updating"; flags=0; orig_prog_code=; loop_code { name=; el_typ=ProgCode; el_def=0; ProgramCall @[0] { desc="run the cycle program, which computes one cycle of activations"; flags=0; orig_prog_code=; prog_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=LeabraNetwork; type="LeabraNetwork*"; name="network"; required=1; def_val=; expr { expr="network"; }; }; }; target=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraCycle"]$; targ_ld_init="*LeabraCycle*"; }; NetCounterIncr @[1] { desc="increment cycle counter (also on network)"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; local_ctr_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["cycle"]$; counter=Network::cycle; update_after=0; }; IfContinue @[2] { desc="avoid subsequent stopping criteria if below min_cycles"; flags=0; orig_prog_code=; cond { expr="cycle < min_cycles"; }; }; IfBreak @[3] { desc="stopping criterion for settling: based either on maximum change in activation (maxda) or on the maximum activation value in the network getting over threshold (which ever comes first). Set either parmeter to values that are always false (e.g., trg_max_act_stopcrit = -1) to eliminate associated source of criterion for stopping settling."; flags=0; orig_prog_code=; cond { expr="(network.maxda < network.maxda_stopcrit) || (network.trg_max_act > network.trg_max_act_stopcrit)"; }; }; }; test { expr="cycle < network.cycle_max"; }; }; MethodCall @[6] { desc="perform final operations at end of settling (storing final activations, etc)"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; method=LeabraNetwork::Settle_Final; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; IfElse @[7] { desc="only run trial-level statistics in minus phase (otherwise network may have correct answer clamped on!). IMPORTANT: this assumes that you've got target activation values for output layers already presented in the minus phase -- if this is not the case (values are computed on the fly), you may want to run this instead at the start of the plus phase, after ApplyInputs"; flags=0; orig_prog_code=; cond { expr="network.Compute_TrialStats_Test()"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="compute trial-level statistics"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; method=LeabraNetwork::Compute_TrialStats; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; IfElse @[8] { desc="this stat must be called in plus phase when reward information is avail"; flags=0; orig_prog_code=; cond { expr="network.phase_no == 1"; }; show_false_code=0; true_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="get external reward information"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; method=LeabraNetwork::Compute_ExtRew; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; false_code { name=; el_typ=ProgCode; el_def=0; }; }; NetUpdateView @[9] { desc="update network views, if update_net_view == true"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].args["network"]$; update_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraSettle"].vars["update_net_view"]$$; }; }; step_prog=NULL; step_n=1; }; Program @["LeabraCycle"] { name="LeabraCycle"; short_nm="Cycle"; tags="Leabra, Std"; desc="runs one cycle of leabra processing (updating net inputs and activations)"; flags=0; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=LeabraNetwork; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["update_net_view"] { name="update_net_view"; var_type=T_Bool; bool_val=0; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="determines whether network views will be updated on a cycle-by-cycle basis (slow, but often quite useful for seeing how processing is proceeding)"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; }; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="this does all the standard leabra processing for one cycle of activation updating"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraCycle"].args["network"]$$; method=LeabraNetwork::Cycle_Run; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; NetUpdateView @[1] { desc="update network views if update_net_view == true"; flags=0; orig_prog_code=; network_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraCycle"].args["network"]$; update_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraCycle"].vars["update_net_view"]$$; }; }; step_prog=NULL; step_n=1; }; Program @["ApplyInputs"] { name="ApplyInputs"; short_nm="Inputs"; tags="Network, InputData, Apply"; desc="apply the current input data to the network as external input and target values"; flags=NO_STOP_STEP; objs { name=; el_typ=LayerWriter; el_def=0; LayerWriter @["LayerWriter_0"] { name="LayerWriter_0"; data=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; network=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; layer_data { name=; el_typ=LayerWriterEl; el_def=0; LayerWriterEl @[0] { chan_name="Input"; net_target=LAYER; layer_name="Input"; offset {x=0: y=0: }; use_layer_type=1; quiet=0; na_by_range=0; ext_flags=EXT; noise {name="RandomSpec_36": type=NONE: mean=0: var=0.5: par=1: }; }; LayerWriterEl @[1] { chan_name="SV1"; net_target=LAYER; layer_name="SV1"; offset {x=0: y=0: }; use_layer_type=1; quiet=0; na_by_range=0; ext_flags=TARG; noise {name="RandomSpec_37": type=NONE: mean=0: var=0.5: par=1: }; }; LayerWriterEl @[2] { chan_name="SV2"; net_target=LAYER; layer_name="SV2"; offset {x=0: y=0: }; use_layer_type=1; quiet=0; na_by_range=0; ext_flags=TARG; noise {name="RandomSpec_38": type=NONE: mean=0: var=0.5: par=1: }; }; LayerWriterEl @[3] { chan_name="SV3"; net_target=LAYER; layer_name="SV3"; offset {x=0: y=0: }; use_layer_type=1; quiet=0; na_by_range=0; ext_flags=TARG; noise {name="": type=NONE: mean=0: var=0.5: par=1: }; }; LayerWriterEl @[4] { chan_name="Name"; net_target=TRIAL_NAME; layer_name="Name"; offset {x=0: y=0: }; use_layer_type=1; quiet=0; na_by_range=0; ext_flags=0; noise {name="RandomSpec_39": type=NONE: mean=0: var=0.5: par=1: }; }; }; }; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=Network; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to apply inputs to -- typically set by calling program"; init_from=NULL; }; ProgVar @["input_data"] { name="input_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyRendering"]$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="input datatable containing input/output patterns"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["LayerWriter_0"] { name="LayerWriter_0"; var_type=T_Object; object_type=LayerWriter; object_val=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].objs["LayerWriter_0"]$$; objs_ptr=1; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="this is a pointer to the LayerWriter object in objs -- edit that object to determine how information is presented to the network"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="sets the datatable and network for the layer writer, so it knows what to write to"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].vars["LayerWriter_0"]$$; method=LayerWriter::SetDataNetwork; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataBlock_ptr; type="DataBlock*"; name="db"; required=1; def_val=; expr { expr="input_data"; }; }; ProgArg @[1] { arg_type=Network_ptr; type="Network*"; name="net"; required=1; def_val=; expr { expr="network"; }; }; }; }; MethodCall @[1] { desc="configure to fit the network and input data -- this may cause spurious warning messages and problems in some cases so just turn it OFF if necessary"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].vars["LayerWriter_0"]$; method=LayerWriter::AutoConfig; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="remove_unused"; required=0; def_val="true"; expr { expr=; }; }; }; }; MethodCall @[2] { desc="check the configuration of the layer writer -- will emit warnings and errors for missing or misconfigured items"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].vars["LayerWriter_0"]$; method=taList_impl::CheckConfig; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr="false"; }; }; }; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="sets the datatable and network for the layer writer, so it knows what to write to"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].vars["LayerWriter_0"]$; method=LayerWriter::SetDataNetwork; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataBlock_ptr; type="DataBlock*"; name="db"; required=1; def_val=; expr { expr="input_data"; }; }; ProgArg @[1] { arg_type=Network_ptr; type="Network*"; name="net"; required=1; def_val=; expr { expr="network"; }; }; }; }; MethodCall @[1] { desc="apply inputs to the network! layer writer has all the key specs"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["ApplyInputs"].vars["LayerWriter_0"]$; method=LayerWriter::ApplyInputData; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; }; step_prog=NULL; step_n=1; }; Program @["LeabraTrialMonitor"] { name="LeabraTrialMonitor"; short_nm="TrialMon"; tags="Leabra, Std, Monitor"; desc="monitor trial-level data from the network (and potentially other sources) -- stores results in datatable (TrialOutputData typically) that can be used for graph/grid views and further analysis "; flags=NO_STOP_STEP; objs { name=; el_typ=NetMonitor; el_def=0; NetMonitor @["trial_netmon"] { name="trial_netmon"; items { name=; el_typ=NetMonItem; el_def=0; NetMonItem @["batch"] { name="batch"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="batch"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["epoch"] { name="epoch"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="epoch"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["trial"] { name="trial"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="trial"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["trial_name"] { name="trial_name"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="trial_name"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["group_name"] { name="group_name"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="group_name"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["phase_no"] { name="phase_no"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="phase_no"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["minus_cycles"] { name="minus_cycles"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="minus_cycles"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["sse"] { name="sse"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="sse"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["ext_rew"] { name="ext_rew"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="ext_rew"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["sv1"] { name="sv1"; computed=1; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="ext_rew"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["sv2"] { name="sv2"; computed=1; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="ext_rew"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["sv3"] { name="sv3"; computed=1; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="ext_rew"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["prob1"] { name="prob1"; computed=0; object_type=ProgVar; object=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob1"]$$; variable="real_val"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["prob2"] { name="prob2"; computed=0; object_type=ProgVar; object=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob2"]$$; variable="real_val"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; }; network=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; data=$.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$; rmv_orphan_cols=1; }; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=Network; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to record data from"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["trial_mon_data"] { name="trial_mon_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="data table to record trial-level data to (this program writes new data to this table!)"; init_from=NULL; }; ProgVar @["trial_netmon"] { name="trial_netmon"; var_type=T_Object; object_type=NetMonitor; object_val=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].objs["trial_netmon"]$$; objs_ptr=1; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="pointer to the NetMonitor object in objs secton of this program that contains configuration for what to record and where to get it"; init_from=NULL; }; ProgVar @["sv1"] { name="sv1"; var_type=T_Real; real_val=0.3399930000305176; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["sv2"] { name="sv2"; var_type=T_Real; real_val=0.3227014243602753; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["sv3"] { name="sv3"; var_type=T_Real; real_val=0.3774972856044769; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="set the network and datatable for the NetMonitor"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_netmon"]$$; method=NetMonitor::SetDataNetwork; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataTable_ptr; type="DataTable*"; name="dt"; required=1; def_val=; expr { expr="trial_mon_data"; }; }; ProgArg @[1] { arg_type=Network_ptr; type="Network*"; name="net"; required=1; def_val=; expr { expr="network"; }; }; }; }; MethodCall @[1] { desc="check the configuration of the network monitor -- will emit warnings and errors for misconfigurations"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_netmon"]$; method=taBase::CheckConfig; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr="false"; }; }; }; }; MethodCall @[2] { desc="update the monitor items and data schema based on current settings of the NetMonitor object"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_netmon"]$; method=NetMonitor::UpdateMonitors; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="reset_first"; required=0; def_val="false"; expr { expr="true"; }; }; }; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="add a new blank row to the data"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_mon_data"]$$; method=DataTable::AddBlankRow; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; MethodCall @[1] { desc="get the new monitor data from the network and other sources -- this does the main work"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_netmon"]$; method=NetMonitor::GetMonVals; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; AssignExpr @[2] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv1"]$$; expr { expr="network.layers.SV1.units[0].act_m"; }; }; AssignExpr @[3] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv2"]$$; expr { expr="network.layers.SV2.units[0].act_m"; }; }; AssignExpr @[4] { desc=; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv3"]$$; expr { expr="network.layers.SV3.units[0].act_m"; }; }; DataVarProg @[5] { desc=; flags=0; orig_prog_code=; data_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_mon_data"]$; set_data=1; row_spec=CUR_ROW; row_var=NULL; quiet=0; var_1=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv1"]$; var_2=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv2"]$; var_3=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["sv3"]$; var_4=NULL; }; MethodCall @[6] { desc="update views and other things after writing new data to monitor data table"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].vars["trial_mon_data"]$; method=DataBlock::WriteClose; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; MethodCall @[7] { desc="only functional for dmem projects: synchronizes trial data across processes so that all distributed memory processors have the same trial-level data, despite having run only a subset of them each"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"].args["network"]$$; method=Network::DMem_ShareTrialData; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataTable_ptr; type="DataTable*"; name="dt"; required=1; def_val=; expr { expr="trial_mon_data"; }; }; ProgArg @[1] { arg_type=int; type="int"; name="n_rows"; required=0; def_val="1"; expr { expr="1"; }; }; }; }; }; step_prog=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraTrialMonitor"]$; step_n=1; }; Program @["LeabraEpochMonitor"] { name="LeabraEpochMonitor"; short_nm="EpcMon"; tags="Leabra, Std, Monitor"; desc="monitor epoch-level data from the network to a datatable (EpochOutputData typically) for use in graphing and viewing and further analysis"; flags=NO_STOP_STEP; objs { name=; el_typ=NetMonitor; el_def=0; NetMonitor @["epoch_netmon"] { name="epoch_netmon"; items { name=; el_typ=NetMonItem; el_def=0; NetMonItem @["batch"] { name="batch"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="batch"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["epoch"] { name="epoch"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="epoch"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_sse"] { name="avg_sse"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="avg_sse"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["cnt_err"] { name="cnt_err"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="cnt_err"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_ext_rew"] { name="avg_ext_rew"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="avg_ext_rew"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_cycles"] { name="avg_cycles"; computed=0; object_type=LeabraNetwork; object=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; variable="avg_cycles"; var_label=; name_style=AUTO_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["epoch_time_tot"] { name="epoch_time_tot"; computed=1; object_type=NULL; object=NULL; variable="act"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["epoch_time_usr"] { name="epoch_time_usr"; computed=1; object_type=NULL; object=NULL; variable="act"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_sv1"] { name="avg_sv1"; computed=1; object_type=NULL; object=NULL; variable="act"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=1; data_src=$.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$; agg_col { col_name="sv1"; }; agg {name="AggregateSpec_21": op=MEAN: rel={name="Relation_59": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 2 0=0; var=NULL; enable_var=NULL; }; pre_proc_1 {name="SimpleMathSpec_63": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="SimpleMathSpec_64": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="SimpleMathSpec_65": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_sv2"] { name="avg_sv2"; computed=1; object_type=NULL; object=NULL; variable="act"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=1; data_src=$.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$; agg_col { col_name="sv2"; }; agg {name="AggregateSpec_21": op=MEAN: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 2 0=0; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["avg_sv3"] { name="avg_sv3"; computed=1; object_type=NULL; object=NULL; variable="act"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=1; data_src=$.projects["scalar_val_test"].data.gp["OutputData"]["TrialOutputData"]$; agg_col { col_name="sv3"; }; agg {name="AggregateSpec_21": op=MEAN: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 2 0=0; var=NULL; enable_var=NULL; }; pre_proc_1 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["prob1"] { name="prob1"; computed=0; object_type=ProgVar; object=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob1"]$; variable="real_val"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="Relation_61": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="SimpleMathSpec_69": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="SimpleMathSpec_70": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="SimpleMathSpec_71": opr=NONE: arg=0: lw=-1: hi=1: }; }; NetMonItem @["prob2"] { name="prob2"; computed=0; object_type=ProgVar; object=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob2"]$; variable="real_val"; var_label=; name_style=MY_NAME; max_name_len=6; val_type=VT_FLOAT; matrix=0; matrix_geom{ }; data_agg=0; data_src=NULL; agg_col { col_name=; }; agg {name="": op=NONE: rel={name="Relation_62": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: }; select_rows=0; select_spec { col_name=; on=1; rel=EQUAL; use_var=0; cmp 0 1; var=NULL; enable_var=NULL; }; pre_proc_1 {name="SimpleMathSpec_72": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_2 {name="SimpleMathSpec_73": opr=NONE: arg=0: lw=-1: hi=1: }; pre_proc_3 {name="SimpleMathSpec_74": opr=NONE: arg=0: lw=-1: hi=1: }; }; }; network=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"]$$; rmv_orphan_cols=1; }; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=Network; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network to get data from"; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["epoch_mon_data"] { name="epoch_mon_data"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="data table to write the epoch data to"; init_from=NULL; }; ProgVar @["epoch_netmon"] { name="epoch_netmon"; var_type=T_Object; object_type=NetMonitor; object_val=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].objs["epoch_netmon"]$$; objs_ptr=1; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="network monitor object that contains full specs for what to record and where to get it"; init_from=NULL; }; ProgVar @["epoch_timer"] { name="epoch_timer"; var_type=T_Object; object_type=TimeUsed; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"].epoch_time$; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="pointer to the network's epoch-level timer, to record how long it took to process an epoch"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; AssignExpr @[0] { desc="get the epoch timer from current network"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_timer"]$$; expr { expr="network.epoch_time"; }; }; MethodCall @[1] { desc="set data and network on NetMonitor object"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_netmon"]$$; method=NetMonitor::SetDataNetwork; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=DataTable_ptr; type="DataTable*"; name="dt"; required=1; def_val=; expr { expr="epoch_mon_data"; }; }; ProgArg @[1] { arg_type=Network_ptr; type="Network*"; name="net"; required=1; def_val=; expr { expr="network"; }; }; }; }; MethodCall @[2] { desc="check configuration and emit errors/warnings for problems"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_netmon"]$; method=taBase::CheckConfig; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr="false"; }; }; }; }; MethodCall @[3] { desc="update the monitor items and data schema based on current settings of NetMonitor"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_netmon"]$; method=NetMonitor::UpdateMonitors; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=bool; type="bool"; name="reset_first"; required=0; def_val="false"; expr { expr="true"; }; }; }; }; }; prog_code { name=; el_typ=ProgCode; el_def=0; MethodCall @[0] { desc="add a new blank row to the data"; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_mon_data"]$$; method=DataTable::AddBlankRow; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; MethodCall @[1] { desc="get the new monitor data and stor it into the data table -- this does the main job here"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_netmon"]$; method=NetMonitor::GetMonVals; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; AssignExpr @[2] { desc="get the epoch timer from current network"; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_timer"]$; expr { expr="network.epoch_time"; }; }; MethodCall @[3] { desc="set the total time to compute the epoch (epoch_time_tot -- wall clock time) to time used data from network timer"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_mon_data"]$; method=DataTable::SetValColName; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="epoch_timer.used.GetTotSecs()"; }; }; ProgArg @[1] { arg_type=const_taString_ref; type="taString&"; name="col_name"; required=1; def_val="\"\""; expr { expr="\"epoch_time_tot\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="-1"; }; }; ProgArg @[3] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr=; }; }; }; }; MethodCall @[4] { desc="set the user process time (cpu time for this process, epoch_time_usr) to time used data from network timer"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_mon_data"]$; method=DataTable::SetValColName; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="epoch_timer.used.GetUsrSecs()"; }; }; ProgArg @[1] { arg_type=const_taString_ref; type="taString&"; name="col_name"; required=1; def_val="\"\""; expr { expr="\"epoch_time_usr\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="-1"; }; }; ProgArg @[3] { arg_type=bool; type="bool"; name="quiet"; required=0; def_val="false"; expr { expr=; }; }; }; }; MethodCall @[5] { desc="update after writing new data to monitor data table"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"].vars["epoch_mon_data"]$; method=DataBlock::WriteClose; meth_args { name=; el_typ=ProgArg; el_def=0; }; }; Comment @[6] { desc="NOTE: if doing a TestEpoch and wanting to include test results in this train epoch log, then see these instructions: http://grey.colorado.edu/emergent/index.php/Test_Programs quick summary here: * Add computed, data_agg, op = LAST items to epoch_netmon to grab from the EpochTestOutputData table. * duplicate this program, call the new one LeabraEpochMonitor_postest, remove all netmon items for training, leaving only test, turn off rmv_orphan_cols flag, and set arg for UpdateMonitors in init_code to false * in original LeabraEpochMonitor, click OFF flag for WriteClose() line * in LeabraEpoch add call to LeabraEpochTest, then _posttest after the first LeabraEpochMonitor call. This will ensure that training data is monitored first, right after the training epoch, and then the test epoch is run, and the testing data is updated. The first epoch monitor should have all the items, so that it formats the data table correctly. "; flags=0; orig_prog_code=; }; }; step_prog=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["LeabraEpochMonitor"]$; step_n=1; }; Program @["SaveWeights"] { name="SaveWeights"; short_nm="SavWts"; tags="Network, Weights"; desc="save network's current weight values to file using WriteWeights function, with file name based on project name + batch + epoch values"; flags=NO_STOP_STEP; objs { name=; el_typ=taNBase; el_def=0; }; types { name=; el_typ=DynEnumType; el_def=0; }; args { name=; el_typ=ProgVar; el_def=0; ProgVar @["network"] { name="network"; var_type=T_Object; object_type=Network; object_val=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; vars { name=; el_typ=ProgVar; el_def=0; ProgVar @["tag"] { name="tag"; var_type=T_String; string_val="_gui"; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="user-provided tag (startup script will set this!)"; init_from=NULL; }; ProgVar @["wts_subdir"] { name="wts_subdir"; var_type=T_String; string_val=; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="user-provided subdirectory to save weights in"; init_from=NULL; }; ProgVar @["fname"] { name="fname"; var_type=T_String; string_val="/Users/oreilly/emergent/test/leabra_std.00_0010.wts.gz"; objs_ptr=0; flags=CTRL_PANEL|CTRL_READ_ONLY|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="final generated file name -- do not edit!"; init_from=NULL; }; ProgVar @["epoch_str"] { name="epoch_str"; var_type=T_String; string_val="0010"; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="string rep of epoch with leading zeros"; init_from=NULL; }; ProgVar @["batch_str"] { name="batch_str"; var_type=T_String; string_val="00"; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="string rep of batch with leading zeros"; init_from=NULL; }; ProgVar @["final_tag"] { name="final_tag"; var_type=T_String; string_val=".00_0010"; objs_ptr=0; flags=NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc="batch + epoch"; init_from=NULL; }; }; functions { name=; el_typ=Function; el_def=0; }; init_code { name=; el_typ=ProgCode; el_def=0; }; prog_code { name=; el_typ=ProgCode; el_def=0; IfReturn @[0] { desc="do not save if not the first dmem process (only relevant for dmem = distributed memory processing)"; flags=0; orig_prog_code=; cond { expr="taMisc::dmem_proc > 0"; }; }; MiscCall @[1] { desc="get current batch counter for file name, with leading zeros to length 3"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].vars["batch_str"]$$; object_type=taMisc; method=taMisc::LeadingZeros; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=int; type="int"; name="num"; required=1; def_val=; expr { expr="network.batch"; }; }; ProgArg @[1] { arg_type=int; type="int"; name="len"; required=1; def_val=; expr { expr="2"; }; }; }; }; MiscCall @[2] { desc="get current epoch counter with leading zeros to length 4"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].vars["epoch_str"]$$; object_type=taMisc; method=taMisc::LeadingZeros; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=int; type="int"; name="num"; required=1; def_val=; expr { expr="network.epoch"; }; }; ProgArg @[1] { arg_type=int; type="int"; name="len"; required=1; def_val=; expr { expr="4"; }; }; }; }; AssignExpr @[3] { desc="string 'tag' to identify the batch, epoch, and other user id info for the weights"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].vars["final_tag"]$$; expr { expr="tag + \".\" + batch_str + \"_\" + epoch_str"; }; }; MethodCall @[4] { desc="get a file name based on the project's current file name, for saving the weights"; flags=0; orig_prog_code=; result_var=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].vars["fname"]$$; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].args["network"]$$; method=taBase::GetFileNameFmProject; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_taString_ref; type="taString&"; name="ext"; required=1; def_val=; expr { expr="\".wts.gz\""; }; }; ProgArg @[1] { arg_type=const_taString_ref; type="taString&"; name="tag"; required=0; def_val="\"\""; expr { expr="final_tag"; }; }; ProgArg @[2] { arg_type=const_taString_ref; type="taString&"; name="subdir"; required=0; def_val="\"\""; expr { expr="wts_subdir"; }; }; ProgArg @[3] { arg_type=bool; type="bool"; name="dmem_proc_no"; required=0; def_val="false"; expr { expr="false"; }; }; }; }; MethodCall @[5] { desc="save the weights to that file name"; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["SaveWeights"].args["network"]$; method=Network::SaveWeights; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_taString_ref; type="taString&"; name="fname"; required=0; def_val="\"\""; expr { expr="fname"; }; }; ProgArg @[1] { arg_type=Network::WtSaveFormat; type="Network::WtSaveFormat"; name="fmt"; required=0; def_val="Network::NET_FMT"; expr { expr="Network::NET_FMT"; }; }; }; }; }; step_prog=NULL; step_n=1; }; Program @["RandomProbabilities"] { name="RandomProbabilities"; short_nm="RndPrb"; tags=; desc=; flags=NO_STOP_STEP; objs { name="ProgObjList_11"; el_typ=taNBase; el_def=0; }; types { name="ProgType_List_0"; el_typ=DynEnumType; el_def=0; }; args { name="ProgVar_List_0"; el_typ=ProgVar; el_def=0; ProgVar @["FrequencyInputs"] { name="FrequencyInputs"; var_type=T_Object; object_type=DataTable; object_val=$.projects["scalar_val_test"].data.gp["InputData"]["FrequencyInputs"]$; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; vars { name="ProgVar_List_0"; el_typ=ProgVar; el_def=0; ProgVar @["prob1"] { name="prob1"; var_type=T_Real; real_val=0.3349942334308851; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; ProgVar @["prob2"] { name="prob2"; var_type=T_Real; real_val=0.6650057665691149; objs_ptr=0; flags=CTRL_PANEL|NULL_CHECK|SAVE_VAL|USED|EDIT_VAL; reference=0; desc=; init_from=NULL; }; }; functions { name="Function_List_11"; el_typ=Function; el_def=0; }; init_code { name="ProgEl_List_99"; el_typ=ProgCode; el_def=0; }; prog_code { name="ProgEl_List_100"; el_typ=ProgCode; el_def=0; RandomCall @[0] { desc=; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob1"]$; object_type=Random; method=Random::ZeroOne; meth_args { name="ProgArg_List_0"; el_typ=ProgArg; el_def=0; }; }; AssignExpr @[1] { desc=; flags=0; orig_prog_code=; result_var=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].vars["prob2"]$; expr { expr="1.0 - prob1"; }; }; MethodCall @[2] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].args["FrequencyInputs"]$$; method=DataTable::SetVal; meth_args { name="ProgArg_List_0"; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="prob1"; }; }; ProgArg @[1] { arg_type=const_Variant_ref; type="Variant&"; name="col"; required=1; def_val=; expr { expr="\"frequency\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="0"; }; }; }; }; MethodCall @[3] { desc=; flags=0; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].args["FrequencyInputs"]$; method=DataTable::SetVal; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="prob2"; }; }; ProgArg @[1] { arg_type=const_Variant_ref; type="Variant&"; name="col"; required=1; def_val=; expr { expr="\"frequency\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="1"; }; }; }; }; MethodCall @[4] { desc=; flags=OFF; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].args["FrequencyInputs"]$; method=DataTable::SetVal; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="prob2"; }; }; ProgArg @[1] { arg_type=const_Variant_ref; type="Variant&"; name="col"; required=1; def_val=; expr { expr="\"frequency\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="2"; }; }; }; }; MethodCall @[5] { desc=; flags=OFF; orig_prog_code=; result_var=NULL; obj=$.projects["scalar_val_test"].programs.gp["LeabraAll_Std"]["RandomProbabilities"].args["FrequencyInputs"]$; method=DataTable::SetVal; meth_args { name=; el_typ=ProgArg; el_def=0; ProgArg @[0] { arg_type=const_Variant_ref; type="Variant&"; name="val"; required=1; def_val=; expr { expr="prob1"; }; }; ProgArg @[1] { arg_type=const_Variant_ref; type="Variant&"; name="col"; required=1; def_val=; expr { expr="\"frequency\""; }; }; ProgArg @[2] { arg_type=int; type="int"; name="row"; required=1; def_val=; expr { expr="3"; }; }; }; }; }; step_prog=NULL; step_n=1; }; }; }; viewers { name="DataViewer_List_0"; el_typ=MainWindowViewer; el_def=0; MainWindowViewer @["Browser3"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_53"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_win_lft"] { name="view_win_lft"; value 6 0=0; val_type_fixed=0; }; UserDataItem @["view_win_top"] { name="view_win_top"; value 6 0=0.05432937294244766; val_type_fixed=0; }; UserDataItem @["view_win_wd"] { name="view_win_wd"; value 6 0=1; val_type_fixed=0; }; UserDataItem @["view_win_ht"] { name="view_win_ht"; value 6 0=0.7996604442596436; val_type_fixed=0; }; UserDataItem @["view_win_iconified"] { name="view_win_iconified"; value 1 0=0; val_type_fixed=0; }; UserDataItem @["view_splitter_state"] { name="view_splitter_state"; value 9 0="AAAA/wAAAAAAAAADAAABAAAAAfwAAAKMAQAAAAcBAAAAAQ=="; val_type_fixed=0; }; }; name="Browser3"; m_data=.projects["scalar_val_test"]$$; visible=1; m_is_root=0; m_is_viewer_xor_browser=0; m_is_proj_viewer=1; m_is_dialog=0; toolbars { name="ToolBar_List_2"; el_typ=ToolBar; el_def=0; ToolBar @["Application"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_54"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_win_visible"] { name="view_win_visible"; value 1 0=1; val_type_fixed=0; }; }; name="Application"; m_data=NULL; visible=0; lft=0; top=0; o=Horizontal; }; }; frames { name="FrameViewer_List_0"; el_typ=BrowseViewerTaBase; el_def=0; BrowseViewerTaBase @["Tree"] { name="Tree"; m_data=NULL; visible=1; root_typ=LeabraProject; root_md=NULL; m_root=$.projects["scalar_val_test"]$; }; PanelViewer @["Panels"] { name="Panels"; m_data=NULL; visible=1; }; T3PanelViewer @["T3Frames"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_55"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_panel_selected"] { name="view_panel_selected"; value 1 0=1; val_type_fixed=0; }; }; name="T3Frames"; m_data=NULL; visible=1; panels { name="T3DataViewFrame_List_0"; el_typ=T3Panel; el_def=0; T3Panel @["ScalarValTestNet"] { name="ScalarValTestNet"; m_data=NULL; visible=1; root_view { name="T3DataViewRoot_0"; m_data=NULL; m_transform=NULL; children { name="T3DataView_List_0"; el_typ=NetView; el_def=0; NetView @["ScalarValTestNet_View"] { name="ScalarValTestNet_View"; m_data=$.projects["scalar_val_test"].networks["ScalarValTestNet"]$; taTransform @*(.m_transform) {scale={x=1: y=1: z=1: }: rotate={x=1: y=0: z=0: rot=0.35: }: translate={x=0: y=0: z=0: }: }; main_xform {scale={x=1: y=1: z=1: }: rotate={x=1: y=0: z=0: rot=0.35: }: translate={x=0: y=0: z=0: }: }; annotations { name=; el_typ=T3Annotation; el_def=0; }; annote_children { name=; el_typ=T3DataView; el_def=0; }; display=1; lay_layout=THREE_D; lay_mv=0; net_text=1; show_iconified=0; net_text_xform {scale={x=0.5: y=0.5000002: z=0.5000002: }: rotate={x=1: y=0: z=0: rot=1.570796: }: translate={x=0.4965247: y=-0.1656941: z=-0.227156: }: }; net_text_rot=-90; cur_unit_vals{ act_m; }; unit_src_path=".layers[3].units[0]"; con_type=ANY_CON; hist_idx=0; hist_save=1; hist_max=100; hist_ff=5; unit_disp_mode=UDM_BLOCK; unit_text_disp=UTD_NAMES; max_size {x=12: y=4: z=1.5: }; max_size2d {x=12: y=13: z=1: }; eff_max_size {x=12: y=4: z=1.5: }; font_sizes { net_name=0.05; net_vals=0.05; layer=0.04; layer_min=0.01; layer_vals=0.03; prjn=0.01; unit=0.02; un_nm_len=3; }; view_params { xy_square=0; unit_spacing=0.05; prjn_disp=L_R_B; prjn_name=0; prjn_width=0.002; prjn_trans=0.5; lay_trans=0.5; unit_trans=0.6; laygp_width=1; show_laygp=1; }; wt_line_disp=0; wt_line_width=4; wt_line_thr=0.8; wt_line_swt=0; wt_prjn_k_un=4; wt_prjn_k_gp=1; wt_prjn_lay=NULL; snap_bord_disp=0; snap_bord_width=4; scale { name="ColorScale_0"; chunks=133; min=-1; max=1; range=1; zero=0; spec=.colorspecs["C_ColdHot"]$$; auto_scale=0; }; scale_ranges { name="ScaleRange_List_0"; el_typ=ScaleRange; el_def=0; ScaleRange @[0] { var_name="act"; auto_scale=0; min=-1; max=1; }; ScaleRange @[1] { var_name="act_m"; auto_scale=0; min=-1; max=1; }; ScaleRange @[2] { var_name="act_p"; auto_scale=0; min=-1; max=1; }; ScaleRange @[3] { var_name="s.wt"; auto_scale=0; min=-1; max=1; }; ScaleRange @[4] { var_name="r.wt"; auto_scale=0; min=-1; max=1; }; ScaleRange @[5] { var_name="net"; auto_scale=0; min=-1; max=1; }; }; lay_disp_modes{ Input=-1;SV1=-1;SV2=-1;SV3=-1; }; }; }; }; bg_color {r=0.8000001: g=0.8000001: b=0.8000001: a=1: }; text_color {r=0: g=0: b=0: a=1: }; headlight_on=1; stereo_view=STEREO_NONE; saved_views { name="T3SavedView_List_2"; el_typ=T3SavedView; el_def=0; T3SavedView @["Vw_0"] { name="Vw_0"; view_saved=1; pos {x=0.436: y=0.7865535: z=1.382823: }; orient {x=-1: y=0: z=0: rot=0.1919999: }; focal_dist=1.61565; }; T3SavedView @["Vw_1"] { name="Vw_1"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_2"] { name="Vw_2"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_3"] { name="Vw_3"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_4"] { name="Vw_4"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_5"] { name="Vw_5"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_6"] { name="Vw_6"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_7"] { name="Vw_7"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_8"] { name="Vw_8"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_9"] { name="Vw_9"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; }; }; T3Panel @["EpochOutputData"] { name="EpochOutputData"; m_data=NULL; visible=1; root_view { name="T3DataViewRoot_0"; m_data=NULL; m_transform=NULL; children { name="T3DataView_List_0"; el_typ=GraphTableView; el_def=0; GraphTableView @["EpochOutputData_Graph"] { name="EpochOutputData_Graph"; m_data=$.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"]$; taTransform @*(.m_transform) {scale={x=1: y=1: z=1: }: rotate={x=0: y=0: z=1: rot=0: }: translate={x=1: y=0: z=0: }: }; children { name="T3DataView_List_0"; el_typ=GraphColView; el_def=0; GraphColView @["batch"] { name="batch"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["batch"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["epoch"] { name="epoch"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["epoch"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_sse"] { name="avg_sse"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_sse"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["cnt_err"] { name="cnt_err"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["cnt_err"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_ext_rew"] { name="avg_ext_rew"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_ext_rew"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_cycles"] { name="avg_cycles"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_cycles"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["epoch_time_tot"] { name="epoch_time_tot"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["epoch_time_tot"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["epoch_time_usr"] { name="epoch_time_usr"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["epoch_time_usr"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_sv1"] { name="avg_sv1"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_sv1"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_sv2"] { name="avg_sv2"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_sv2"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["avg_sv3"] { name="avg_sv3"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["avg_sv3"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["prob1"] { name="prob1"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["prob1"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; GraphColView @["prob2"] { name="prob2"; m_data=.projects["scalar_val_test"].data.gp["OutputData"]["EpochOutputData"].data["prob2"]$$; m_transform=NULL; visible=1; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; data_range {min=0: max=0: }; }; }; main_xform {scale={x=1: y=1: z=1: }: rotate={x=0: y=0: z=1: rot=0: }: translate={x=1: y=0: z=0: }: }; annotations { name=; el_typ=T3Annotation; el_def=0; }; annote_children { name=; el_typ=T3DataView; el_def=0; }; view_rows=10000; view_range {min=0: max=-1: }; display_on=1; manip_ctrl_on=1; graph_type=XY; plot_style=LINE; negative_draw=0; negative_draw_z=1; line_width=2; point_size=MEDIUM; point_spacing=1; bar_space=0.2; bar_depth=0.01; label_spacing=-1; width=1; depth=1; axis_font_size=0.05; label_font_size=0.04; x_axis { name="GraphAxisView_0"; m_data=NULL; m_transform=NULL; on=1; axis=X; col_name="epoch"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=1: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; row_num=0; }; z_axis { name="GraphAxisView_1"; m_data=NULL; m_transform=NULL; on=0; axis=Z; col_name="batch"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; row_num=0; }; plot_1 { name="GraphPlotView_0"; m_data=NULL; m_transform=NULL; on=1; axis=Y; col_name="prob1"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=1: min=0: fix_max=1: max=1: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; plot_2 { name="GraphPlotView_1"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name="prob2"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0.990987: }; color {name="red": r=1: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=SQUARE; alt_y=0; }; plot_3 { name="GraphPlotView_2"; m_data=NULL; m_transform=NULL; on=1; axis=Y; col_name="avg_sv1"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=1: }; color {name="blue": r=0: g=0: b=1: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=DIAMOND; alt_y=0; }; plot_4 { name="GraphPlotView_3"; m_data=NULL; m_transform=NULL; on=1; axis=Y; col_name="avg_sv2"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=1: }; color {name="green3": r=0: g=0.8039216: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=TRIANGLE; alt_y=0; }; plot_5 { name="GraphPlotView_4"; m_data=NULL; m_transform=NULL; on=1; axis=Y; col_name="avg_sv3"; is_string=0; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=1: }; color {name="purple": r=0.627451: g=0.1254902: b=0.9411765: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=PLUS; alt_y=0; }; plot_6 { name="GraphPlotView_5"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="orange": r=1: g=0.6470588: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CROSS; alt_y=0; }; plot_7 { name="GraphPlotView_6"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="brown": r=0.6470588: g=0.1647059: b=0.1647059: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=STAR; alt_y=0; }; plot_8 { name="GraphPlotView_7"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="chartreuse": r=0.4980392: g=1: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=MINUS; alt_y=0; }; err_1 { name="GraphPlotView_8"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_2 { name="GraphPlotView_9"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_3 { name="GraphPlotView_10"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_4 { name="GraphPlotView_11"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_5 { name="GraphPlotView_12"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_6 { name="GraphPlotView_13"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_7 { name="GraphPlotView_14"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_8 { name="GraphPlotView_15"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; line_style=SOLID; point_style=CIRCLE; alt_y=0; }; err_spacing=1; err_bar_width=0.02; color_mode=FIXED_COLOR; color_axis { name="GraphAxisView_2"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; row_num=0; }; colorscale { name="ColorScale_0"; chunks=133; min=-1; max=1; range=0; zero=0; spec=$.colorspecs["C_ColdHot"]$; auto_scale=0; }; raster_axis { name="GraphAxisView_3"; m_data=NULL; m_transform=NULL; on=0; axis=Y; col_name=; is_string=1; is_matrix=0; n_cells=1; matrix_cell=0; fixed_range {fix_min=0: min=0: fix_max=0: max=0: }; color {name="black": r=0: g=0: b=0: a=1: desc="": }; n_ticks=10; axis_length=1; row_num=0; }; thresh=0.5; thr_line_len=0.48; matrix_mode=SEP_GRAPHS; mat_layout=BOT_ZERO; mat_odd_vert=1; two_d_font=0; two_d_font_scale=350; }; }; }; bg_color {r=0.8: g=0.8: b=0.8: a=1: }; text_color {r=0: g=0: b=0: a=1: }; headlight_on=1; stereo_view=STEREO_NONE; saved_views { name="T3SavedView_List_4"; el_typ=T3SavedView; el_def=0; T3SavedView @["Vw_0"] { name="Vw_0"; view_saved=1; pos {x=1.461625: y=0.5309999: z=1.554803: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=2.064803; }; T3SavedView @["Vw_1"] { name="Vw_1"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_2"] { name="Vw_2"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_3"] { name="Vw_3"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_4"] { name="Vw_4"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_5"] { name="Vw_5"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_6"] { name="Vw_6"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_7"] { name="Vw_7"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_8"] { name="Vw_8"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; T3SavedView @["Vw_9"] { name="Vw_9"; view_saved=0; pos {x=0: y=0: z=0: }; orient {x=0: y=0: z=1: rot=0: }; focal_dist=0; }; }; }; }; }; }; docks { name="DockViewer_List_0"; el_typ=ToolBoxDockViewer; el_def=0; ToolBoxDockViewer @["Tools"] { UserDataItem_List @*(.user_data_) { name="UserDataItem_List_56"; el_typ=UserDataItem; el_def=0; UserDataItem @["view_win_lft"] { name="view_win_lft"; value 6 0=0; val_type_fixed=0; }; UserDataItem @["view_win_top"] { name="view_win_top"; value 6 0=0.007640067953616381; val_type_fixed=0; }; UserDataItem @["view_win_wd"] { name="view_win_wd"; value 6 0=0.0405549630522728; val_type_fixed=0; }; UserDataItem @["view_win_ht"] { name="view_win_ht"; value 6 0=0.7376909852027893; val_type_fixed=0; }; UserDataItem @["view_win_iconified"] { name="view_win_iconified"; value 1 0=0; val_type_fixed=0; }; UserDataItem @["view_visible"] { name="view_visible"; value 1 0=1; val_type_fixed=0; }; }; name="Tools"; m_data=NULL; visible=1; dock_flags=DV_MOVABLE|DV_FLOATABLE; dock_area=1; }; }; }; }; save_view=1; save_as_only=0; auto_name=1; last_change_desc=; networks { name="Network_Group_1"; el_typ=LeabraNetwork; el_def=0; LeabraNetwork @["ScalarValTestNet"] { UserDataItem_List @*(.user_data_) { name=; el_typ=UserDataItem; el_def=0; UserDataItem @["cos_diff"] { name="cos_diff"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["cos_err"] { name="cos_err"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["norm_err"] { name="norm_err"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["ext_rew"] { name="ext_rew"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["maxda"] { name="maxda"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["minus_output_name"] { name="minus_output_name"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["minus_cycles"] { name="minus_cycles"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["ct_cycle"] { name="ct_cycle"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["phase_no"] { name="phase_no"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["phase"] { name="phase"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["sse"] { name="sse"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["output_name"] { name="output_name"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["trial_name"] { name="trial_name"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["group_name"] { name="group_name"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["time"] { name="time"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["cycle"] { name="cycle"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["tick"] { name="tick"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["trial"] { name="trial"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["group"] { name="group"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["epoch"] { name="epoch"; value 1 0=1; val_type_fixed=1; }; UserDataItem @["batch"] { name="batch"; value 1 0=1; val_type_fixed=1; }; }; name="ScalarValTestNet"; desc=; specs { name=; el_typ=LeabraUnitSpec; el_def=0; LeabraUnitSpec @["LeabraUnitSpec_0"] { name="LeabraUnitSpec_0"; desc=; unique{ hyst;acc; }; children { name=; el_typ=LeabraUnitSpec; el_def=0; LeabraUnitSpec @["SV1"] { name="SV1"; desc=; unique{ }; children { name=; el_typ=LeabraUnitSpec; el_def=0; }; act_range {min=0: max=1: range=1: scale=1: }; bias_con_type=LeabraCon; bias_spec {type=LeabraBiasSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["LeabraBiasSpec_0"]$: }; sse_tol=0.5; act_fun=NOISY_XX1; act {gelin=1: thr=0.5: gain=100: nvar=0.005: avg_dt=0.005: avg_init=0.15: i_thr=STD: }; act_lrn_on=0; act_lrn {gelin=1: thr=0.5: gain=100: nvar=0.005: }; spike {rise=0: decay=0.05: g_gain=9: window=3: eq_gain=10: eq_dt=0.02: }; spike_misc { exp_slope=0.02; spk_thr=1.2; clamp_max_p=0.12; clamp_type=REGULAR; vm_r=0.3; t_r=6; vm_dend=0.3; vm_dend_dt=0.16; vm_dend_time=6.25; }; opt_thresh {send=0.1: delta=0.005: phase_dif=0: xcal_lrn=0.01: }; maxda {val=INET_DA: inet_scale=1: lay_avg_thr=0.01: }; clamp_range {min=0: max=0.95: range=0.95: scale=1.052632: }; vm_range {min=0: max=2: range=2: scale=0.5: }; v_m_init {name="": type=UNIFORM: mean=0.300000011920929: var=0: par=1: }; dt {integ=1: vm=0.3: net=0.7: midpoint=0: d_vm_max=100: vm_eq_cyc=0: vm_eq_dt=1: integ_time=1: vm_time=3.333333: net_time=1.428571: }; act_avg {l_up_dt=0.6: l_dn_dt=0.05: m_dt=0.1: s_dt=0.2: ss_dt=1: use_nd=0: l_time=20: m_time=10: s_time=5: ss_time=1: }; g_bar {e=0.9999999: l=0.1: i=0.9999999: h=0.015: a=0.0145: }; e_rev {e=1: l=0.3: i=0.25: h=1: a=0: }; hyst {on=0: b_inc_dt=0.05: b_dec_dt=0.05: a_thr=0.8: d_thr=0.7: g_dt=0.1: init=0: trl=0: }; acc {on=0: b_inc_dt=0.01: b_dec_dt=0.01: a_thr=0.5: d_thr=0.1: g_dt=0.1: init=0: trl=0: }; adapt { on=0; dt=0.006944444; vm_gain=0.04; spike_gain=0.008049999; interval=10; dt_time=144; }; depress {on=0: rec=0.2: asymp_act=0.5: depl=0.2105263: interval=1: max_amp=2: }; syn_delay { on=0; delay=4; }; da_mod {on=0: mod=PLUS_CONT: gain=0.1: }; noise_type=NO_NOISE; noise {name="": type=GAUSSIAN: mean=0: var=0.001: par=1: }; noise_adapt {trial_fixed=1: k_pos_noise=0: mode=FIXED_NOISE: min_pct=0.5: min_pct_c=0.5: }; noise_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=1; }; }; LeabraUnitSpec @["SV2"] { name="SV2"; desc=; unique{ }; children { name=; el_typ=LeabraUnitSpec; el_def=0; }; act_range {min=0: max=1: range=1: scale=1: }; bias_con_type=LeabraCon; bias_spec {type=LeabraBiasSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["LeabraBiasSpec_0"]$: }; sse_tol=0.5; act_fun=NOISY_XX1; act {gelin=1: thr=0.5: gain=100: nvar=0.005: avg_dt=0.005: avg_init=0.15: i_thr=STD: }; act_lrn_on=0; act_lrn {gelin=1: thr=0.5: gain=100: nvar=0.005: }; spike {rise=0: decay=0.05: g_gain=9: window=3: eq_gain=10: eq_dt=0.02: }; spike_misc { exp_slope=0.02; spk_thr=1.2; clamp_max_p=0.12; clamp_type=REGULAR; vm_r=0.3; t_r=6; vm_dend=0.3; vm_dend_dt=0.16; vm_dend_time=6.25; }; opt_thresh {send=0.1: delta=0.005: phase_dif=0: xcal_lrn=0.01: }; maxda {val=INET_DA: inet_scale=1: lay_avg_thr=0.01: }; clamp_range {min=0: max=0.95: range=0.95: scale=1.052632: }; vm_range {min=0: max=2: range=2: scale=0.5: }; v_m_init {name="": type=UNIFORM: mean=0.300000011920929: var=0: par=1: }; dt {integ=1: vm=0.3: net=0.7: midpoint=0: d_vm_max=100: vm_eq_cyc=0: vm_eq_dt=1: integ_time=1: vm_time=3.333333: net_time=1.428571: }; act_avg {l_up_dt=0.6: l_dn_dt=0.05: m_dt=0.1: s_dt=0.2: ss_dt=1: use_nd=0: l_time=20: m_time=10: s_time=5: ss_time=1: }; g_bar {e=0.9999999: l=0.1: i=0.9999999: h=0.015: a=0.0145: }; e_rev {e=1: l=0.3: i=0.25: h=1: a=0: }; hyst {on=0: b_inc_dt=0.05: b_dec_dt=0.05: a_thr=0.8: d_thr=0.7: g_dt=0.1: init=0: trl=0: }; acc {on=0: b_inc_dt=0.01: b_dec_dt=0.01: a_thr=0.5: d_thr=0.1: g_dt=0.1: init=0: trl=0: }; adapt { on=0; dt=0.006944444; vm_gain=0.04; spike_gain=0.008049999; interval=10; dt_time=144; }; depress {on=0: rec=0.2: asymp_act=0.5: depl=0.2105263: interval=1: max_amp=2: }; syn_delay { on=0; delay=4; }; da_mod {on=0: mod=PLUS_CONT: gain=0.1: }; noise_type=NO_NOISE; noise {name="": type=GAUSSIAN: mean=0: var=0.001: par=1: }; noise_adapt {trial_fixed=1: k_pos_noise=0: mode=FIXED_NOISE: min_pct=0.5: min_pct_c=0.5: }; noise_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=1; }; }; LeabraUnitSpec @["SV3"] { name="SV3"; desc=; unique{ dt; }; children { name=; el_typ=LeabraUnitSpec; el_def=0; }; act_range {min=0: max=1: range=1: scale=1: }; bias_con_type=LeabraCon; bias_spec {type=LeabraBiasSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["LeabraBiasSpec_0"]$: }; sse_tol=0.5; act_fun=NOISY_XX1; act {gelin=1: thr=0.5: gain=100: nvar=0.005: avg_dt=0.005: avg_init=0.15: i_thr=STD: }; act_lrn_on=0; act_lrn {gelin=1: thr=0.5: gain=100: nvar=0.005: }; spike {rise=0: decay=0.05: g_gain=9: window=3: eq_gain=10: eq_dt=0.02: }; spike_misc { exp_slope=0.02; spk_thr=1.2; clamp_max_p=0.12; clamp_type=REGULAR; vm_r=0.3; t_r=6; vm_dend=0.3; vm_dend_dt=0.16; vm_dend_time=6.25; }; opt_thresh {send=0.1: delta=0.005: phase_dif=0: xcal_lrn=0.01: }; maxda {val=INET_DA: inet_scale=1: lay_avg_thr=0.01: }; clamp_range {min=0: max=0.95: range=0.95: scale=1.052632: }; vm_range {min=0: max=2: range=2: scale=0.5: }; v_m_init {name="": type=UNIFORM: mean=0.300000011920929: var=0: par=1: }; dt {integ=1: vm=0.2: net=0.7: midpoint=0: d_vm_max=100: vm_eq_cyc=0: vm_eq_dt=1: integ_time=1: vm_time=5: net_time=1.428571: }; act_avg {l_up_dt=0.6: l_dn_dt=0.05: m_dt=0.1: s_dt=0.2: ss_dt=1: use_nd=0: l_time=20: m_time=10: s_time=5: ss_time=1: }; g_bar {e=0.9999999: l=0.1: i=0.9999999: h=0.015: a=0.0145: }; e_rev {e=1: l=0.3: i=0.25: h=1: a=0: }; hyst {on=0: b_inc_dt=0.05: b_dec_dt=0.05: a_thr=0.8: d_thr=0.7: g_dt=0.1: init=0: trl=0: }; acc {on=0: b_inc_dt=0.01: b_dec_dt=0.01: a_thr=0.5: d_thr=0.1: g_dt=0.1: init=0: trl=0: }; adapt { on=0; dt=0.006944444; vm_gain=0.04; spike_gain=0.008049999; interval=10; dt_time=144; }; depress {on=0: rec=0.2: asymp_act=0.5: depl=0.2105263: interval=1: max_amp=2: }; syn_delay { on=0; delay=4; }; da_mod {on=0: mod=PLUS_CONT: gain=0.1: }; noise_type=NO_NOISE; noise {name="": type=GAUSSIAN: mean=0: var=0.001: par=1: }; noise_adapt {trial_fixed=1: k_pos_noise=0: mode=FIXED_NOISE: min_pct=0.5: min_pct_c=0.5: }; noise_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=1; }; }; }; act_range {min=0: max=1: range=1: scale=1: }; bias_con_type=LeabraCon; bias_spec {type=LeabraBiasSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["LeabraBiasSpec_0"]$: }; sse_tol=0.5; act_fun=NOISY_XX1; act {gelin=1: thr=0.5: gain=100: nvar=0.005: avg_dt=0.005: avg_init=0.15: i_thr=STD: }; act_lrn_on=0; act_lrn {gelin=1: thr=0.5: gain=100: nvar=0.005: }; spike {rise=0: decay=0.05: g_gain=9: window=3: eq_gain=10: eq_dt=0.02: }; spike_misc { exp_slope=0.02; spk_thr=1.2; clamp_max_p=0.12; clamp_type=REGULAR; vm_r=0.3; t_r=6; vm_dend=0.3; vm_dend_dt=0.16; vm_dend_time=6.25; }; opt_thresh {send=0.1: delta=0.005: phase_dif=0: xcal_lrn=0.01: }; maxda {val=INET_DA: inet_scale=1: lay_avg_thr=0.01: }; clamp_range {min=0: max=0.95: range=0.95: scale=1.052632: }; vm_range {min=0: max=2: range=2: scale=0.5: }; v_m_init {name="": type=UNIFORM: mean=0.300000011920929: var=0: par=1: }; dt {integ=1: vm=0.3: net=0.7: midpoint=0: d_vm_max=100: vm_eq_cyc=0: vm_eq_dt=1: integ_time=1: vm_time=3.333333: net_time=1.428571: }; act_avg {l_up_dt=0.6: l_dn_dt=0.05: m_dt=0.1: s_dt=0.2: ss_dt=1: use_nd=0: l_time=20: m_time=10: s_time=5: ss_time=1: }; g_bar {e=0.9999999: l=0.1: i=0.9999999: h=0.015: a=0.0145: }; e_rev {e=1: l=0.3: i=0.25: h=1: a=0: }; hyst {on=0: b_inc_dt=0.05: b_dec_dt=0.05: a_thr=0.8: d_thr=0.7: g_dt=0.1: init=0: trl=0: }; acc {on=0: b_inc_dt=0.01: b_dec_dt=0.01: a_thr=0.5: d_thr=0.1: g_dt=0.1: init=0: trl=0: }; adapt { on=0; dt=0.006944444; vm_gain=0.04; spike_gain=0.008049999; interval=10; dt_time=144; }; depress {on=0: rec=0.2: asymp_act=0.5: depl=0.2105263: interval=1: max_amp=2: }; syn_delay { on=0; delay=4; }; da_mod {on=0: mod=PLUS_CONT: gain=0.1: }; noise_type=NO_NOISE; noise {name="": type=GAUSSIAN: mean=0: var=0.001: par=1: }; noise_adapt {trial_fixed=1: k_pos_noise=0: mode=FIXED_NOISE: min_pct=0.5: min_pct_c=0.5: }; noise_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=1; }; }; LeabraLayerSpec @["LeabraLayerSpec_0"] { name="LeabraLayerSpec_0"; desc=; unique{ }; children { name=; el_typ=ScalarValLayerSpec; el_def=0; ScalarValLayerSpec @["SV1"] { name="SV1"; desc=; unique{ compute_i;i_kwta_pt;scalar;unit_range;bias_val; }; children { name=; el_typ=ScalarValLayerSpec; el_def=0; }; inhib_group=ENTIRE_LAYER; inhib { type=FF_FB_INHIB; kwta_pt=0.25; gi=2.2; ff=1; fb=0.5; fbx=0; infl=0.3; dt=0.7; ff0=0.1; min_i=0; }; kwta {k_from=USE_PCT: k=1: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; gp_kwta {k_from=USE_PCT: k=1: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; lay_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; unit_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; clamp {hard=1: gain=0.5: max_plus=0: plus=0.01: min_clamp=0.5: minus_targ_gain=0: }; decay {event=1: phase=0: phase2=0: }; cos_diff_lrate {on=0: lo_diff=0: lo_lrate=0.01: hi_diff=1: hi_lrate=0.01: avg_dt=0.002: }; ct_inhib_mod { manual_sravg=0; sravg_delay=0; use_sin=0; burst_i=0.02; trough_i=0.02; use_fin=0; inhib_i=0; }; tie_brk {on=0: k_thr=1: diff_thr=0.2: thr_gain=0.005: loser_gain=1: }; adapt_i {type=NONE: tol=0.05: p_dt=0: mx_d=0.2: l=0.2: a_dt=0.005: }; abs_net_adapt { on=0; trg_net=0.5; tol=0.1; abs_lrate=0.2; }; scalar {rep=LOCALIST: un_width=0.3: norm_width=0: clamp_pat=0: min_sum_act=0.2: clip_val=1: send_thr=0: init_nms=1: }; unit_range {min=0: max=1: range=1: scale=1: }; bias_val {un=NO_UN: un_shp=VAL: un_gain=1: wt=NO_WT: wt_gain=1: val=0: }; val_range {min=0: max=1: range=1: scale=1: }; }; ScalarValLayerSpec @["SV2"] { name="SV2"; desc=; unique{ kwta;compute_i;i_kwta_pt;scalar;unit_range; }; children { name=; el_typ=ScalarValLayerSpec; el_def=0; }; inhib_group=ENTIRE_LAYER; inhib { type=FF_FB_INHIB; kwta_pt=0.25; gi=2.2; ff=1; fb=0.5; fbx=0; infl=0.3; dt=0.7; ff0=0.1; min_i=0; }; kwta {k_from=USE_K: k=3: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; gp_kwta {k_from=USE_PCT: k=12: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; lay_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; unit_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; clamp {hard=1: gain=0.5: max_plus=0: plus=0.01: min_clamp=0.5: minus_targ_gain=0: }; decay {event=1: phase=0: phase2=0: }; cos_diff_lrate {on=0: lo_diff=0: lo_lrate=0.01: hi_diff=1: hi_lrate=0.01: avg_dt=0.002: }; ct_inhib_mod { manual_sravg=0; sravg_delay=0; use_sin=0; burst_i=0.02; trough_i=0.02; use_fin=0; inhib_i=0; }; tie_brk {on=0: k_thr=1: diff_thr=0.2: thr_gain=0.005: loser_gain=1: }; adapt_i {type=NONE: tol=0.05: p_dt=0: mx_d=0.2: l=0.2: a_dt=0.005: }; abs_net_adapt { on=0; trg_net=0.5; tol=0.1; abs_lrate=0.2; }; scalar {rep=GAUSSIAN: un_width=0.3: norm_width=0: clamp_pat=0: min_sum_act=0.2: clip_val=1: send_thr=0: init_nms=1: }; unit_range {min=-0.5: max=1.5: range=2: scale=0.5: }; bias_val {un=GC: un_shp=VAL: un_gain=1: wt=NO_WT: wt_gain=1: val=0: }; val_range {min=-0.35: max=1.35: range=1.7: scale=0.5882353: }; }; LeabraLayerSpec @["SV3"] { name="SV3"; desc=; unique{ inhib; }; children { name="BaseSpec_Group_171"; el_typ=LeabraLayerSpec; el_def=0; }; inhib_group=ENTIRE_LAYER; inhib { type=FF_FB_INHIB; kwta_pt=0.25; gi=1; ff=1; fb=0.5; fbx=0; infl=0.3; dt=0.7; ff0=0.1; min_i=0; }; kwta {k_from=USE_PCT: k=12: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; gp_kwta {k_from=USE_PCT: k=12: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; lay_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; unit_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; clamp {hard=1: gain=0.5: max_plus=0: plus=0.01: min_clamp=0.5: minus_targ_gain=0: }; decay {event=1: phase=0: phase2=0: }; cos_diff_lrate {on=0: lo_diff=0: lo_lrate=0.01: hi_diff=1: hi_lrate=0.01: avg_dt=0.002: }; ct_inhib_mod { manual_sravg=0; sravg_delay=0; use_sin=0; burst_i=0.02; trough_i=0.02; use_fin=0; inhib_i=0; }; tie_brk {on=0: k_thr=1: diff_thr=0.2: thr_gain=0.005: loser_gain=1: }; adapt_i {type=NONE: tol=0.05: p_dt=0: mx_d=0.2: l=0.2: a_dt=0.005: }; abs_net_adapt { on=0; trg_net=0.5; tol=0.1; abs_lrate=0.2; }; }; }; inhib_group=ENTIRE_LAYER; inhib { type=FF_FB_INHIB; kwta_pt=0.25; gi=2.2; ff=1; fb=0.5; fbx=0; infl=0.3; dt=0.7; ff0=0.1; min_i=0; }; kwta {k_from=USE_PCT: k=12: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; gp_kwta {k_from=USE_PCT: k=12: pct=0.25: pat_q=0.5: diff_act_pct=0: act_pct=0.1: }; lay_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; unit_gp_inhib {on=0: fffb=0: lay_gi=2: gp_g=0.5: diff_act_pct=0: act_pct_mult=0.3333333: pct_fm_frac=1: act_denom=3: }; clamp {hard=1: gain=0.5: max_plus=0: plus=0.01: min_clamp=0.5: minus_targ_gain=0: }; decay {event=1: phase=0: phase2=0: }; cos_diff_lrate {on=0: lo_diff=0: lo_lrate=0.01: hi_diff=1: hi_lrate=0.01: avg_dt=0.002: }; ct_inhib_mod { manual_sravg=0; sravg_delay=0; use_sin=0; burst_i=0.02; trough_i=0.02; use_fin=0; inhib_i=0; }; tie_brk {on=0: k_thr=1: diff_thr=0.2: thr_gain=0.005: loser_gain=1: }; adapt_i {type=NONE: tol=0.05: p_dt=0: mx_d=0.2: l=0.2: a_dt=0.005: }; abs_net_adapt { on=0; trg_net=0.5; tol=0.1; abs_lrate=0.2; }; }; LeabraConSpec @["LeabraConSpec_0"] { name="LeabraConSpec_0"; desc=; unique{ lmix; }; children { name=; el_typ=LeabraConSpec; el_def=0; LeabraBiasSpec @["LeabraBiasSpec_0"] { name="LeabraBiasSpec_0"; desc=; unique{ rnd;wt_limits;wt_scale;lrate; }; children { name=; el_typ=LeabraBiasSpec; el_def=0; }; rnd {name="": type=UNIFORM: mean=0: var=0: par=1: }; wt_limits {type=NONE: min=-1: max=5: sym=0: }; learn_rule=CTLEABRA_XCAL; inhib=0; wt_scale {abs=1: rel=1: sem_extra=2: }; diff_scale_p=0; wt_scale_p {abs=1: rel=1: sem_extra=2: }; wt_scale_init {init=0: abs=1: rel=1: }; learn=1; lrate=0; cur_lrate=0; lrs_value=NO_LRS; lrate_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=0; }; ignore_unlearnable=1; wt_sig {gain=6: off=1: dwt_norm=0: }; stable_mix {stable_pct=0: learn_pct=1: cos_diff_lrate=0: }; lmix {hebb=0: err=1: err_sb=1: }; xcal {thr_l_mix=0.01: thr_m_mix=0.99: s_mix=0.9: m_mix=0.1: d_rev=0.1: d_thr=0.0001: d_rev_ratio=-9: }; savg_cor {cor=0.4: thresh=0.001: }; rel_net_adapt { on=0; trg_fm_input=0.85; trg_fm_output=0.15; trg_lateral=0; trg_sum=1; tol_lg=0.05; tol_sm=0.2; rel_lrate=0.2; }; dwt_thresh=0.1; }; LeabraConSpec @["SV1Cons"] { name="SV1Cons"; desc=; unique{ lmix; }; children { name=; el_typ=LeabraConSpec; el_def=0; }; rnd {name="": type=UNIFORM: mean=0.25: var=0.01: par=1: }; wt_limits {type=MIN_MAX: min=0: max=1: sym=1: }; learn_rule=CTLEABRA_XCAL; inhib=0; wt_scale {abs=1: rel=1: sem_extra=2: }; diff_scale_p=0; wt_scale_p {abs=1: rel=1: sem_extra=2: }; wt_scale_init {init=0: abs=1: rel=1: }; learn=1; lrate=0.002; cur_lrate=0.002; lrs_value=NO_LRS; lrate_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=0; }; ignore_unlearnable=1; wt_sig {gain=6: off=1: dwt_norm=0: }; stable_mix {stable_pct=0: learn_pct=1: cos_diff_lrate=0: }; lmix {hebb=0: err=1: err_sb=0: }; xcal {thr_l_mix=0.01: thr_m_mix=0.99: s_mix=0.9: m_mix=0.1: d_rev=0.1: d_thr=0.0001: d_rev_ratio=-9: }; savg_cor {cor=0.4: thresh=0.001: }; rel_net_adapt { on=0; trg_fm_input=0.85; trg_fm_output=0.15; trg_lateral=0; trg_sum=1; tol_lg=0.05; tol_sm=0.2; rel_lrate=0.2; }; }; LeabraConSpec @["SV2Cons"] { name="SV2Cons"; desc=; unique{ lmix; }; children { name=; el_typ=LeabraConSpec; el_def=0; }; rnd {name="": type=UNIFORM: mean=0.25: var=0.01: par=1: }; wt_limits {type=MIN_MAX: min=0: max=1: sym=1: }; learn_rule=CTLEABRA_XCAL; inhib=0; wt_scale {abs=1: rel=1: sem_extra=2: }; diff_scale_p=0; wt_scale_p {abs=1: rel=1: sem_extra=2: }; wt_scale_init {init=0: abs=1: rel=1: }; learn=1; lrate=0.002; cur_lrate=0.002; lrs_value=NO_LRS; lrate_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=0; }; ignore_unlearnable=1; wt_sig {gain=6: off=1: dwt_norm=0: }; stable_mix {stable_pct=0: learn_pct=1: cos_diff_lrate=0: }; lmix {hebb=0: err=1: err_sb=1: }; xcal {thr_l_mix=0.01: thr_m_mix=0.99: s_mix=0.9: m_mix=0.1: d_rev=0.1: d_thr=0.0001: d_rev_ratio=-9: }; savg_cor {cor=0.4: thresh=0.001: }; rel_net_adapt { on=0; trg_fm_input=0.85; trg_fm_output=0.15; trg_lateral=0; trg_sum=1; tol_lg=0.05; tol_sm=0.2; rel_lrate=0.2; }; }; LeabraConSpec @["SV3Cons"] { name="SV3Cons"; desc=; unique{ lmix;lrate; }; children { name=; el_typ=LeabraConSpec; el_def=0; }; rnd {name="": type=UNIFORM: mean=0.25: var=0.01: par=1: }; wt_limits {type=MIN_MAX: min=0: max=1: sym=1: }; learn_rule=CTLEABRA_XCAL; inhib=0; wt_scale {abs=1: rel=1: sem_extra=2: }; diff_scale_p=0; wt_scale_p {abs=1: rel=1: sem_extra=2: }; wt_scale_init {init=0: abs=1: rel=1: }; learn=1; lrate=0.02; cur_lrate=0.02; lrs_value=NO_LRS; lrate_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=0; }; ignore_unlearnable=1; wt_sig {gain=6: off=1: dwt_norm=0: }; stable_mix {stable_pct=0: learn_pct=1: cos_diff_lrate=0: }; lmix {hebb=0: err=1: err_sb=1: }; xcal {thr_l_mix=0.01: thr_m_mix=0.99: s_mix=0.9: m_mix=0.1: d_rev=0.1: d_thr=0.0001: d_rev_ratio=-9: }; savg_cor {cor=0.4: thresh=0.001: }; rel_net_adapt { on=0; trg_fm_input=0.85; trg_fm_output=0.15; trg_lateral=0; trg_sum=1; tol_lg=0.05; tol_sm=0.2; rel_lrate=0.2; }; }; }; rnd {name="": type=UNIFORM: mean=0.25: var=0.01: par=1: }; wt_limits {type=MIN_MAX: min=0: max=1: sym=1: }; learn_rule=CTLEABRA_XCAL; inhib=0; wt_scale {abs=1: rel=1: sem_extra=2: }; diff_scale_p=0; wt_scale_p {abs=1: rel=1: sem_extra=2: }; wt_scale_init {init=0: abs=1: rel=1: }; learn=1; lrate=0.002; cur_lrate=0.005; lrs_value=NO_LRS; lrate_sched { name=; el_typ=SchedItem; el_def=0; default_val=1; interpolate=0; }; ignore_unlearnable=1; wt_sig {gain=6: off=1: dwt_norm=0: }; stable_mix {stable_pct=0: learn_pct=1: cos_diff_lrate=0: }; lmix {hebb=0: err=1: err_sb=1: }; xcal {thr_l_mix=0.01: thr_m_mix=0.99: s_mix=0.9: m_mix=0.1: d_rev=0.1: d_thr=0.0001: d_rev_ratio=-9: }; savg_cor {cor=0.4: thresh=0.001: }; rel_net_adapt { on=0; trg_fm_input=0.85; trg_fm_output=0.15; trg_lateral=0; trg_sum=1; tol_lg=0.05; tol_sm=0.2; rel_lrate=0.2; }; }; FullPrjnSpec @["FullPrjnSpec_0"] { name="FullPrjnSpec_0"; desc=; unique{ }; children { name=; el_typ=FullPrjnSpec; el_def=0; }; self_con=0; init_wts=0; add_rnd_wts=0; add_rnd_wts_scale=1; }; }; layers { name=; el_typ=LeabraLayer; el_def=0; pos {x=0: y=0: z=0: }; pos2d {x=0: y=0: }; max_disp_size {x=12: y=4: z=2: }; max_disp_size2d {x=12: y=13: }; LeabraLayer @["Input"] { name="Input"; desc=; flags=0; layer_type=INPUT; pos {x=0: y=0: z=0: }; pos2d {x=0: y=0: }; disp_scale=1; un_geom {x=4: y=4: n_not_xy=0: n=16: }; unit_groups=0; virt_groups=1; gp_geom {x=1: y=1: n_not_xy=0: n=1: }; gp_spc {x=0: y=0: }; flat_geom {x=4: y=4: n_not_xy=0: n=16: }; disp_geom {x=4: y=4: n_not_xy=0: n=16: }; scaled_disp_geom {x=4: y=4: n_not_xy=0: n=1: }; projections { name=; el_typ=LeabraPrjn; el_def=0; }; send_prjns { name=; el_typ=LeabraPrjn; el_def=0; Projection_Group @. = [0] = LeabraPrjn .projects["scalar_val_test"].networks["ScalarValTestNet"].layers["SV1"].projections["Fm_Input"]; Projection_Group @. = [1] = LeabraPrjn .projects["scalar_val_test"].networks["ScalarValTestNet"].layers["SV2"].projections["Fm_Input"]; Projection_Group @. = [2] = LeabraPrjn .projects["scalar_val_test"].networks["ScalarValTestNet"].layers["SV3"].projections["Fm_Input"]; }; units { name=; el_typ=LeabraUnit; el_def=0; pos {x=0: y=0: z=0: }; output_name=; }; unit_spec {type=LeabraUnitSpec: spec=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"]$$: }; dmem_dist=DMEM_DIST_DEFAULT; dist { fm_input=-1; fm_output=-1; }; units_lesioned=0; gp_unit_names_4d=0; brain_area=; voxel_fill_pct=1; spec {type=LeabraLayerSpec: spec=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"]$$: }; }; LeabraLayer @["SV1"] { name="SV1"; desc=; flags=0; layer_type=TARGET; pos {x=0: y=0: z=1: }; pos2d {x=0: y=6: }; disp_scale=1; un_geom {x=4: y=1: n_not_xy=0: n=4: }; unit_groups=0; virt_groups=1; gp_geom {x=1: y=1: n_not_xy=0: n=1: }; gp_spc {x=0: y=0: }; flat_geom {x=4: y=1: n_not_xy=0: n=4: }; disp_geom {x=4: y=1: n_not_xy=0: n=4: }; scaled_disp_geom {x=4: y=1: n_not_xy=0: n=1: }; projections { name=; el_typ=LeabraPrjn; el_def=0; LeabraPrjn @["Fm_Input"] { name="Fm_Input"; off=0; disp=1; from_type=CUSTOM; from=.projects["scalar_val_test"].networks["ScalarValTestNet"].layers["Input"]$$; spec {type=FullPrjnSpec: spec=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["FullPrjnSpec_0"]$$: }; con_type=LeabraCon; recvcons_type=LeabraRecvCons; sendcons_type=LeabraSendCons; con_spec {type=LeabraConSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV1Cons"]$: }; recv_idx=0; send_idx=2; recv_n=1; send_n=1; projected=1; direction=DIR_UNKNOWN; trg_netin_rel=-1; }; }; send_prjns { name=; el_typ=LeabraPrjn; el_def=0; }; units { name=; el_typ=LeabraUnit; el_def=0; pos {x=0: y=0: z=0: }; output_name=; }; unit_spec {type=LeabraUnitSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV1"]$: }; dmem_dist=DMEM_DIST_DEFAULT; dist { fm_input=-1; fm_output=-1; }; units_lesioned=0; gp_unit_names_4d=0; brain_area=; voxel_fill_pct=1; spec {type=ScalarValLayerSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV1"]$: }; }; LeabraLayer @["SV2"] { name="SV2"; desc=; flags=0; layer_type=TARGET; pos {x=0: y=2: z=1: }; pos2d {x=0: y=9: }; disp_scale=1; un_geom {x=12: y=1: n_not_xy=0: n=12: }; unit_groups=0; virt_groups=1; gp_geom {x=1: y=1: n_not_xy=0: n=1: }; gp_spc {x=0: y=0: }; flat_geom {x=12: y=1: n_not_xy=0: n=12: }; disp_geom {x=12: y=1: n_not_xy=0: n=12: }; scaled_disp_geom {x=12: y=1: n_not_xy=0: n=1: }; projections { name=; el_typ=LeabraPrjn; el_def=0; LeabraPrjn @["Fm_Input"] { name="Fm_Input"; off=0; disp=1; from_type=CUSTOM; from=$.projects["scalar_val_test"].networks["ScalarValTestNet"].layers["Input"]$; spec {type=FullPrjnSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["FullPrjnSpec_0"]$: }; con_type=LeabraCon; recvcons_type=LeabraRecvCons; sendcons_type=LeabraSendCons; con_spec {type=LeabraConSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV2Cons"]$: }; recv_idx=0; send_idx=1; recv_n=1; send_n=1; projected=1; direction=DIR_UNKNOWN; trg_netin_rel=-1; }; }; send_prjns { name=; el_typ=LeabraPrjn; el_def=0; }; units { name=; el_typ=LeabraUnit; el_def=0; pos {x=0: y=0: z=0: }; output_name=; }; unit_spec {type=LeabraUnitSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV2"]$: }; dmem_dist=DMEM_DIST_DEFAULT; dist { fm_input=-1; fm_output=-1; }; units_lesioned=0; gp_unit_names_4d=0; brain_area=; voxel_fill_pct=1; spec {type=ScalarValLayerSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV2"]$: }; }; LeabraLayer @["SV3"] { name="SV3"; desc=; flags=0; layer_type=TARGET; pos {x=7: y=0: z=1: }; pos2d {x=0: y=12: }; disp_scale=1; un_geom {x=1: y=1: n_not_xy=0: n=1: }; unit_groups=0; virt_groups=1; gp_geom {x=1: y=1: n_not_xy=0: n=1: }; gp_spc {x=0: y=0: }; flat_geom {x=1: y=1: n_not_xy=0: n=1: }; disp_geom {x=1: y=1: n_not_xy=0: n=1: }; scaled_disp_geom {x=1: y=1: n_not_xy=0: n=1: }; projections { name=; el_typ=LeabraPrjn; el_def=0; LeabraPrjn @["Fm_Input"] { name="Fm_Input"; off=0; disp=1; from_type=CUSTOM; from=$.projects["scalar_val_test"].networks["ScalarValTestNet"].layers["Input"]$; spec {type=FullPrjnSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["FullPrjnSpec_0"]$: }; con_type=LeabraCon; recvcons_type=LeabraRecvCons; sendcons_type=LeabraSendCons; con_spec {type=LeabraConSpec: spec=.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraConSpec_0"].children["SV3Cons"]$$: }; recv_idx=0; send_idx=0; recv_n=1; send_n=1; projected=1; direction=DIR_UNKNOWN; trg_netin_rel=-1; }; }; send_prjns { name=; el_typ=LeabraPrjn; el_def=0; }; units { name=; el_typ=LeabraUnit; el_def=0; pos {x=0: y=0: z=0: }; output_name=; }; unit_spec {type=LeabraUnitSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraUnitSpec_0"].children["SV3"]$: }; dmem_dist=DMEM_DIST_DEFAULT; dist { fm_input=-1; fm_output=-1; }; units_lesioned=0; gp_unit_names_4d=0; brain_area=; voxel_fill_pct=1; spec {type=LeabraLayerSpec: spec=$.projects["scalar_val_test"].networks["ScalarValTestNet"].specs["LeabraLayerSpec_0"].children["SV3"]$: }; }; }; flags=0; auto_build=AUTO_BUILD; brain_atlas_name=; train_mode=TRAIN; wt_update=ON_LINE; small_batch_n=10; sse_unit_avg=0; sse_sqrt=0; cnt_err_tol=0; compute_prerr=0; threads { run_time {name="": s_used=0: n_used=0: }; sync_time {name="": s_used=0: n_used=0: }; total_time {name="": s_used=0: n_used=0: }; n_wake_in_sync=0; run_time_pct=0; sync_time_pct=0; wake_in_sync_pct=0.006799862712560232; interleave=1; ignore_lay_sync=0; }; dmem_sync_level=DMEM_SYNC_NETWORK; dmem_nprocs=1; usr1_save_fmt=FULL_NET; wt_save_fmt=TEXT; n_units=33; n_cons=240; max_prjns=1; max_disp_size {x=12: y=4: z=2: }; max_disp_size2d {x=12: y=13: }; learn_rule=CTLEABRA_XCAL; ti_mode=0; phase_order=MINUS_PLUS; no_plus_test=1; sequence_init=DO_NOTHING; phase_max=2; time_inc=1; cycle_max=50; mid_minus_cycle=-1; min_cycles=0; min_cycles_phase2=0; dwt_norm_enabled=0; ct_time { use=1; minus=50; plus=20; inhib=1; n_avg_only_epcs=0; total_cycles=71; inhib_start=70; }; ct_sravg { manual=0; start=30; end=1; interval=1; plus_s_st=19; plus_s_only=0; force_con=0; }; ct_sin_i { start=30; duration=20; n_pi=2; burst_i=0; trough_i=0; }; ct_fin_i { start=20; end=25; inhib_i=0; }; ct_lrn_trig { plus_lrn_cyc=-1; davg_dt=0.1; davg_s_dt=0.05; davg_m_dt=0.03; davg_l_dt=0.0005; thr_min=0; thr_max=0.5; loc_max_cyc=8; loc_max_dec=0.01; lrn_delay=40; lrn_refract=100; davg_l_init=0; davg_max_init=0.001; davg_time=10; davg_s_time=20; davg_m_time=33.33334; davg_l_time=2000; lrn_delay_inc=0.025; lrn_refract_inc=0.01; }; lrn_trig { davg=0; davg_s=0; davg_m=0; davg_smd=0; davg_l=0; davg_max=0.001; cyc_fm_inc=0; cyc_fm_dec=0; loc_max=0; lrn_max=0; lrn_trig=0; lrn=0; }; net_misc { cyc_syn_dep=0; syn_dep_int=20; }; maxda_stopcrit=-1; trg_max_act_stopcrit=1; norew_val=0.5; off_errs=1; on_errs=1; cos_err_lrn_thr=-1; cos_diff_on=0; cos_diff_auto=0; }; }; };