var searchIndex = {};
searchIndex["fann_sys"] = {"doc":"Raw bindings to C functions of the Fast Artificial Neural Network library","items":[[3,"fann_error","fann_sys","Structure used to store error-related information, both\n`fann` and `fann_train_data` can be cast to this type.",null,null],[3,"fann","","The fast artificial neural network (`fann`) structure.",null,null],[3,"fann_connection","","Describes a connection between two neurons and its weight.",null,null],[12,"from_neuron","","Unique number used to identify source neuron",0,null],[12,"to_neuron","","Unique number used to identify destination neuron",0,null],[12,"weight","","The numerical value of the weight",0,null],[3,"fann_train_data","","Structure used to store data, for use with training.",null,null],[4,"fann_errno_enum","","Error events on fann and fann_train_data.",null,null],[13,"FANN_E_NO_ERROR","","No error",1,null],[13,"FANN_E_CANT_OPEN_CONFIG_R","","Unable to open configuration file for reading",1,null],[13,"FANN_E_CANT_OPEN_CONFIG_W","","Unable to open configuration file for writing",1,null],[13,"FANN_E_WRONG_CONFIG_VERSION","","Wrong version of configuration file",1,null],[13,"FANN_E_CANT_READ_CONFIG","","Error reading info from configuration file",1,null],[13,"FANN_E_CANT_READ_NEURON","","Error reading neuron info from configuration file",1,null],[13,"FANN_E_CANT_READ_CONNECTIONS","","Error reading connections from configuration file",1,null],[13,"FANN_E_WRONG_NUM_CONNECTIONS","","Number of connections not equal to the number expected",1,null],[13,"FANN_E_CANT_OPEN_TD_W","","Unable to open train data file for writing",1,null],[13,"FANN_E_CANT_OPEN_TD_R","","Unable to open train data file for reading",1,null],[13,"FANN_E_CANT_READ_TD","","Error reading training data from file",1,null],[13,"FANN_E_CANT_ALLOCATE_MEM","","Unable to allocate memory",1,null],[13,"FANN_E_CANT_TRAIN_ACTIVATION","","Unable to train with the selected activation function",1,null],[13,"FANN_E_CANT_USE_ACTIVATION","","Unable to use the selected activation function",1,null],[13,"FANN_E_TRAIN_DATA_MISMATCH","","Irreconcilable differences between two `fann_train_data` structures",1,null],[13,"FANN_E_CANT_USE_TRAIN_ALG","","Unable to use the selected training algorithm",1,null],[13,"FANN_E_TRAIN_DATA_SUBSET","","Trying to take subset which is not within the training set",1,null],[13,"FANN_E_INDEX_OUT_OF_BOUND","","Index is out of bound",1,null],[13,"FANN_E_SCALE_NOT_PRESENT","","Scaling parameters not present",1,null],[4,"fann_train_enum","","The Training algorithms used when training on `fann_train_data` with functions like\n`fann_train_on_data` or `fann_train_on_file`. The incremental training alters the weights\nafter each time it is presented an input pattern, while batch only alters the weights once after\nit has been presented to all the patterns.",null,null],[13,"FANN_TRAIN_INCREMENTAL","","Standard backpropagation algorithm, where the weights are\nupdated after each training pattern. This means that the weights are updated many\ntimes during a single epoch. For this reason some problems will train very fast with\nthis algorithm, while other more advanced problems will not train very well.",2,null],[13,"FANN_TRAIN_BATCH","","Standard backpropagation algorithm, where the weights are updated after calculating the mean\nsquare error for the whole training set. This means that the weights are only updated once\nduring an epoch. For this reason some problems will train slower with this algorithm. But\nsince the mean square error is calculated more correctly than in incremental training, some\nproblems will reach better solutions with this algorithm.",2,null],[13,"FANN_TRAIN_RPROP","","A more advanced batch training algorithm which achieves good results\nfor many problems. The RPROP training algorithm is adaptive, and does therefore not\nuse the `learning_rate`. Some other parameters can however be set to change the way the\nRPROP algorithm works, but it is only recommended for users with insight in how the RPROP\ntraining algorithm works. The RPROP training algorithm is described by\n[Riedmiller and Braun, 1993], but the actual learning algorithm used here is the\niRPROP- training algorithm which is described by [Igel and Husken, 2000] which\nis an variety of the standard RPROP training algorithm.",2,null],[13,"FANN_TRAIN_QUICKPROP","","A more advanced batch training algorithm which achieves good results\nfor many problems. The quickprop training algorithm uses the `learning_rate` parameter\nalong with other more advanced parameters, but it is only recommended to change these\nadvanced parameters for users with insight in how the quickprop training algorithm works.\nThe quickprop training algorithm is described by [Fahlman, 1988].",2,null],[4,"fann_activationfunc_enum","","The activation functions used for the neurons during training. The activation functions\ncan either be defined for a group of neurons by `fann_set_activation_function_hidden` and\n`fann_set_activation_function_output`, or it can be defined for a single neuron by\n`fann_set_activation_function`.",null,null],[13,"FANN_LINEAR","","Linear activation function.",3,null],[13,"FANN_THRESHOLD","","Threshold activation function.",3,null],[13,"FANN_THRESHOLD_SYMMETRIC","","Threshold activation function.",3,null],[13,"FANN_SIGMOID","","Sigmoid activation function.",3,null],[13,"FANN_SIGMOID_STEPWISE","","Stepwise linear approximation to sigmoid.",3,null],[13,"FANN_SIGMOID_SYMMETRIC","","Symmetric sigmoid activation function, aka. tanh.",3,null],[13,"FANN_SIGMOID_SYMMETRIC_STEPWISE","","Stepwise linear approximation to symmetric sigmoid.",3,null],[13,"FANN_GAUSSIAN","","Gaussian activation function.",3,null],[13,"FANN_GAUSSIAN_SYMMETRIC","","Symmetric gaussian activation function.",3,null],[13,"FANN_GAUSSIAN_STEPWISE","","Stepwise linear approximation to gaussian.\nFaster than gaussian but a bit less precise.\nNOT implemented yet.",3,null],[13,"FANN_ELLIOTT","","Fast (sigmoid like) activation function defined by David Elliott",3,null],[13,"FANN_ELLIOTT_SYMMETRIC","","Fast (symmetric sigmoid like) activation function defined by David Elliott",3,null],[13,"FANN_LINEAR_PIECE","","Bounded linear activation function.",3,null],[13,"FANN_LINEAR_PIECE_SYMMETRIC","","Bounded linear activation function.",3,null],[13,"FANN_SIN_SYMMETRIC","","Periodical sine activation function.",3,null],[13,"FANN_COS_SYMMETRIC","","Periodical cosine activation function.",3,null],[13,"FANN_SIN","","Periodical sine activation function.",3,null],[13,"FANN_COS","","Periodical cosine activation function.",3,null],[4,"fann_errorfunc_enum","","Error function used during training.",null,null],[13,"FANN_ERRORFUNC_LINEAR","","Standard linear error function.",4,null],[13,"FANN_ERRORFUNC_TANH","","Tanh error function, usually better but can require a lower learning rate. This error\nfunction aggressively targets outputs that differ much from the desired, while not targeting\noutputs that only differ a little that much. This activation function is not recommended for\ncascade training and incremental training.",4,null],[4,"fann_stopfunc_enum","","Stop criteria used during training.",null,null],[13,"FANN_STOPFUNC_MSE","","Stop criterion is Mean Square Error (MSE) value.",5,null],[13,"FANN_STOPFUNC_BIT","","Stop criterion is number of bits that fail. The number of bits means the\nnumber of output neurons which differ more than the bit fail limit\n(see fann_get_bit_fail_limit, fann_set_bit_fail_limit).\nThe bits are counted in all of the training data, so this number can be higher than\nthe number of training data.",5,null],[4,"fann_nettype_enum","","Definition of network types used by `fann_get_network_type`.",null,null],[13,"FANN_NETTYPE_LAYER","","Each layer only has connections to the next layer.",6,null],[13,"FANN_NETTYPE_SHORTCUT","","Each layer has connections to all following layers.",6,null],[7,"fann_default_error_log","","",null,null],[5,"fann_set_error_log","","Change where errors are logged to. Both `fann` and `fann_data` can be\ncast to `fann_error`, so this function can be used to set either of these.",null,null],[5,"fann_get_errno","","Returns the last error number.",null,null],[5,"fann_reset_errno","","Resets the last error number.",null,null],[5,"fann_reset_errstr","","Resets the last error string.",null,null],[5,"fann_get_errstr","","Returns the last error string.",null,null],[5,"fann_print_error","","Prints the last error to `stderr`.",null,null],[5,"fann_train","","Train one iteration with a set of inputs, and a set of desired outputs.\nThis training is always incremental training (see `fann_train_enum`), since\nonly one pattern is presented.",null,null],[5,"fann_test","","Test with a set of inputs, and a set of desired outputs.\nThis operation updates the mean square error, but does not\nchange the network in any way.",null,null],[5,"fann_get_MSE","","Reads the mean square error from the network.",null,null],[5,"fann_get_bit_fail","","The number of fail bits; means the number of output neurons which differ more\nthan the bit fail limit (see `fann_get_bit_fail_limit`, `fann_set_bit_fail_limit`).\nThe bits are counted in all of the training data, so this number can be higher than\nthe number of training data.",null,null],[5,"fann_reset_MSE","","Resets the mean square error from the network.",null,null],[5,"fann_train_on_data","","Trains on an entire dataset, for a period of time.",null,null],[5,"fann_train_on_file","","Does the same as `fann_train_on_data`, but reads the training data directly from a file.",null,null],[5,"fann_train_epoch","","Train one epoch with a set of training data.",null,null],[5,"fann_test_data","","Test a set of training data and calculates the MSE for the training data.",null,null],[5,"fann_read_train_from_file","","Reads a file that stores training data.",null,null],[5,"fann_create_train_from_callback","","Creates the training data struct from a user supplied function.\n As the training data are numerable (data 1, data 2...), the user must write\n a function that receives the number of the training data set (input,output)\n and returns the set.",null,null],[5,"fann_destroy_train","","Destructs the training data and properly deallocates all of the associated data.\nBe sure to call this function when finished using the training data.",null,null],[5,"fann_shuffle_train_data","","Shuffles training data, randomizing the order.\nThis is recommended for incremental training, while it has no influence during batch\ntraining.",null,null],[5,"fann_scale_train","","Scale input and output data based on previously calculated parameters.",null,null],[5,"fann_descale_train","","Descale input and output data based on previously calculated parameters.",null,null],[5,"fann_set_input_scaling_params","","Calculate input scaling parameters for future use based on training data.",null,null],[5,"fann_set_output_scaling_params","","Calculate output scaling parameters for future use based on training data.",null,null],[5,"fann_set_scaling_params","","Calculate input and output scaling parameters for future use based on training data.",null,null],[5,"fann_clear_scaling_params","","Clears scaling parameters.",null,null],[5,"fann_scale_input","","Scale data in input vector before feeding it to the ANN based on previously calculated\nparameters.",null,null],[5,"fann_scale_output","","Scale data in output vector before feeding it to the ANN based on previously calculated\nparameters.",null,null],[5,"fann_descale_input","","Scale data in input vector after getting it from the ANN based on previously calculated\nparameters.",null,null],[5,"fann_descale_output","","Scale data in output vector after getting it from the ANN based on previously calculated\nparameters.",null,null],[5,"fann_scale_input_train_data","","Scales the inputs in the training data to the specified range.",null,null],[5,"fann_scale_output_train_data","","Scales the outputs in the training data to the specified range.",null,null],[5,"fann_scale_train_data","","Scales the inputs and outputs in the training data to the specified range.",null,null],[5,"fann_merge_train_data","","Merges the data from `data1` and `data2` into a new `fann_train_data`.",null,null],[5,"fann_duplicate_train_data","","Returns an exact copy of a `fann_train_data`.",null,null],[5,"fann_subset_train_data","","Returns an copy of a subset of the `fann_train_data`, starting at position `pos`\nand `length` elements forward.",null,null],[5,"fann_length_train_data","","Returns the number of training patterns in the `fann_train_data`.",null,null],[5,"fann_num_input_train_data","","Returns the number of inputs in each of the training patterns in the `fann_train_data`.",null,null],[5,"fann_num_output_train_data","","Returns the number of outputs in each of the training patterns in the `fann_train_data`.",null,null],[5,"fann_save_train","","Save the training structure to a file, with the format specified in\n`fann_read_train_from_file`",null,null],[5,"fann_save_train_to_fixed","","Saves the training structure to a fixed point data file.",null,null],[5,"fann_get_training_algorithm","","Return the training algorithm as described by `fann_train_enum`. This training algorithm\nis used by `fann_train_on_data` and associated functions.",null,null],[5,"fann_set_training_algorithm","","Set the training algorithm.",null,null],[5,"fann_get_learning_rate","","Return the learning rate.",null,null],[5,"fann_set_learning_rate","","Set the learning rate.",null,null],[5,"fann_get_learning_momentum","","Get the learning momentum.",null,null],[5,"fann_set_learning_momentum","","Set the learning momentum.",null,null],[5,"fann_get_activation_function","","Get the activation function for neuron number `neuron` in layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_function","","Set the activation function for neuron number `neuron` in layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_function_layer","","Set the activation function for all the neurons in the layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_function_hidden","","Set the activation function for all of the hidden layers.",null,null],[5,"fann_set_activation_function_output","","Set the activation function for the output layer.",null,null],[5,"fann_get_activation_steepness","","Get the activation steepness for neuron number `neuron` in layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_steepness","","Set the activation steepness for neuron number `neuron` in layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_steepness_layer","","Set the activation steepness for all neurons in layer number `layer`,\ncounting the input layer as layer 0.",null,null],[5,"fann_set_activation_steepness_hidden","","Set the steepness of the activation steepness in all of the hidden layers.",null,null],[5,"fann_set_activation_steepness_output","","Set the steepness of the activation steepness in the output layer.",null,null],[5,"fann_get_train_error_function","","Returns the error function used during training.",null,null],[5,"fann_set_train_error_function","","Set the error function used during training.",null,null],[5,"fann_get_train_stop_function","","Returns the the stop function used during training.",null,null],[5,"fann_set_train_stop_function","","Set the stop function used during training.",null,null],[5,"fann_get_bit_fail_limit","","Returns the bit fail limit used during training.",null,null],[5,"fann_set_bit_fail_limit","","Set the bit fail limit used during training.",null,null],[5,"fann_set_callback","","Sets the callback function for use during training.",null,null],[5,"fann_get_quickprop_decay","","The decay is a small negative valued number which is the factor that the weights\nshould become smaller in each iteration during quickprop training. This is used\nto make sure that the weights do not become too high during training.",null,null],[5,"fann_set_quickprop_decay","","Sets the quickprop decay factor.",null,null],[5,"fann_get_quickprop_mu","","The mu factor is used to increase and decrease the step size during quickprop training.\nThe mu factor should always be above 1, since it would otherwise decrease the step size\nwhen it was supposed to increase it.",null,null],[5,"fann_set_quickprop_mu","","Sets the quickprop mu factor.",null,null],[5,"fann_get_rprop_increase_factor","","The increase factor is a value larger than 1, which is used to\nincrease the step size during RPROP training.",null,null],[5,"fann_set_rprop_increase_factor","","The increase factor used during RPROP training.",null,null],[5,"fann_get_rprop_decrease_factor","","The decrease factor is a value smaller than 1, which is used to decrease the step size\nduring RPROP training.",null,null],[5,"fann_set_rprop_decrease_factor","","The decrease factor is a value smaller than 1, which is used to decrease the step size\nduring RPROP training.",null,null],[5,"fann_get_rprop_delta_min","","The minimum step size is a small positive number determining how small the minimum step size\nmay be.",null,null],[5,"fann_set_rprop_delta_min","","The minimum step size is a small positive number determining how small the minimum step size\nmay be.",null,null],[5,"fann_get_rprop_delta_max","","The maximum step size is a positive number determining how large the maximum step size may\nbe.",null,null],[5,"fann_set_rprop_delta_max","","The maximum step size is a positive number determining how large the maximum step size may\nbe.",null,null],[5,"fann_get_rprop_delta_zero","","The initial step size is a positive number determining the initial step size.",null,null],[5,"fann_set_rprop_delta_zero","","The initial step size is a positive number determining the initial step size.",null,null],[5,"fann_cascadetrain_on_data","","Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.\nThis algorithm adds neurons to the neural network while training, which means that it\nneeds to start with an ANN without any hidden layers. The neural network should also use\nshortcut connections, so `fann_create_shortcut` should be used to create the ANN like this:",null,null],[5,"fann_cascadetrain_on_file","","Does the same as `fann_cascadetrain_on_data`, but reads the training data directly from a\nfile.",null,null],[5,"fann_get_cascade_output_change_fraction","","The cascade output change fraction is a number between 0 and 1 determining how large a\nfraction the `fann_get_MSE` value should change within\n`fann_get_cascade_output_stagnation_epochs` during training of the output connections, in\norder for the training not to stagnate. If the training stagnates, the training of the\noutput connections will be ended and new candidates will be prepared.",null,null],[5,"fann_set_cascade_output_change_fraction","","Sets the cascade output change fraction.",null,null],[5,"fann_get_cascade_output_stagnation_epochs","","The number of cascade output stagnation epochs determines the number of epochs training is\nallowed to continue without changing the MSE by a fraction of\n`fann_get_cascade_output_change_fraction`.",null,null],[5,"fann_set_cascade_output_stagnation_epochs","","Sets the number of cascade output stagnation epochs.",null,null],[5,"fann_get_cascade_candidate_change_fraction","","The cascade candidate change fraction is a number between 0 and 1 determining how large a\nfraction the `fann_get_MSE` value should change within\n`fann_get_cascade_candidate_stagnation_epochs` during training of the candidate neurons, in\norder for the training not to stagnate. If the training stagnates, the training of the\ncandidate neurons will be ended and the best candidate will be selected.",null,null],[5,"fann_set_cascade_candidate_change_fraction","","Sets the cascade candidate change fraction.",null,null],[5,"fann_get_cascade_candidate_stagnation_epochs","","The number of cascade candidate stagnation epochs determines the number of epochs training\nis allowed to continue without changing the MSE by a fraction of\n`fann_get_cascade_candidate_change_fraction`.",null,null],[5,"fann_set_cascade_candidate_stagnation_epochs","","Sets the number of cascade candidate stagnation epochs.",null,null],[5,"fann_get_cascade_weight_multiplier","","The weight multiplier is a parameter which is used to multiply the weights from the\ncandidate neuron before adding the neuron to the neural network. This parameter is usually\nbetween 0 and 1, and is used to make the training a bit less aggressive.",null,null],[5,"fann_set_cascade_weight_multiplier","","Sets the weight multiplier.",null,null],[5,"fann_get_cascade_candidate_limit","","The candidate limit is a limit for how much the candidate neuron may be trained.\nThe limit is a limit on the proportion between the MSE and candidate score.",null,null],[5,"fann_set_cascade_candidate_limit","","Sets the candidate limit.",null,null],[5,"fann_get_cascade_max_out_epochs","","The maximum out epochs determines the maximum number of epochs the output connections\nmay be trained after adding a new candidate neuron.",null,null],[5,"fann_set_cascade_max_out_epochs","","Sets the maximum out epochs.",null,null],[5,"fann_get_cascade_max_cand_epochs","","The maximum candidate epochs determines the maximum number of epochs the input\nconnections to the candidates may be trained before adding a new candidate neuron.",null,null],[5,"fann_set_cascade_max_cand_epochs","","Sets the max candidate epochs.",null,null],[5,"fann_get_cascade_num_candidates","","The number of candidates used during training (calculated by multiplying\n`fann_get_cascade_activation_functions_count`,\n`fann_get_cascade_activation_steepnesses_count` and\n`fann_get_cascade_num_candidate_groups`).",null,null],[5,"fann_get_cascade_activation_functions_count","","The number of activation functions in the `fann_get_cascade_activation_functions` array.",null,null],[5,"fann_get_cascade_activation_functions","","The cascade activation functions array is an array of the different activation functions\nused by the candidates.",null,null],[5,"fann_set_cascade_activation_functions","","Sets the array of cascade candidate activation functions. The array must be just as long\nas defined by the count.",null,null],[5,"fann_get_cascade_activation_steepnesses_count","","The number of activation steepnesses in the `fann_get_cascade_activation_functions` array.",null,null],[5,"fann_get_cascade_activation_steepnesses","","The cascade activation steepnesses array is an array of the different activation functions\nused by the candidates.",null,null],[5,"fann_set_cascade_activation_steepnesses","","Sets the array of cascade candidate activation steepnesses. The array must be just as long\nas defined by the count.",null,null],[5,"fann_get_cascade_num_candidate_groups","","The number of candidate groups is the number of groups of identical candidates which will be\nused during training.",null,null],[5,"fann_set_cascade_num_candidate_groups","","Sets the number of candidate groups.",null,null],[5,"fann_create_from_file","","Constructs a backpropagation neural network from a configuration file, which has been saved\nby `fann_save`.",null,null],[5,"fann_save","","Save the entire network to a configuration file.",null,null],[5,"fann_save_to_fixed","","Saves the entire network to a configuration file.\nBut it is saved in fixed point format no matter which\nformat it is currently in.",null,null],[5,"fann_create_standard","","Creates a standard fully connected backpropagation neural network.",null,null],[5,"fann_create_standard_array","","Just like `fann_create_standard`, but with an array of layer sizes\ninstead of individual parameters.",null,null],[5,"fann_create_sparse","","Creates a standard backpropagation neural network, which is not fully connected.",null,null],[5,"fann_create_sparse_array","","Just like `fann_create_sparse`, but with an array of layer sizes\ninstead of individual parameters.",null,null],[5,"fann_create_shortcut","","Creates a standard backpropagation neural network, which is not fully connected and which\nalso has shortcut connections.",null,null],[5,"fann_create_shortcut_array","","Just like `fann_create_shortcut`, but with an array of layer sizes\ninstead of individual parameters.",null,null],[5,"fann_destroy","","Destroys the entire network, properly freeing all the associated memory.",null,null],[5,"fann_run","","Runs input through the neural network, returning an array of outputs, the number of\nwhich being equal to the number of neurons in the output layer.",null,null],[5,"fann_randomize_weights","","Give each connection a random weight between `min_weight` and `max_weight`.",null,null],[5,"fann_init_weights","","Initialize the weights using Widrow + Nguyen's algorithm.",null,null],[5,"fann_print_connections","","Prints the connections of the ann in a compact matrix, for easy viewing of the internals\nof the ANN.",null,null],[5,"fann_print_parameters","","Prints all of the parameters and options of the ANN.",null,null],[5,"fann_get_num_input","","Get the number of input neurons.",null,null],[5,"fann_get_num_output","","Get the number of output neurons.",null,null],[5,"fann_get_total_neurons","","Get the total number of neurons in the entire network. This number does also include the\nbias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons.",null,null],[5,"fann_get_total_connections","","Get the total number of connections in the entire network.",null,null],[5,"fann_get_network_type","","Get the type of neural network it was created as.",null,null],[5,"fann_get_connection_rate","","Get the connection rate used when the network was created.",null,null],[5,"fann_get_num_layers","","Get the number of layers in the network.",null,null],[5,"fann_get_layer_array","","Get the number of neurons in each layer in the network.",null,null],[5,"fann_get_bias_array","","Get the number of bias in each layer in the network.",null,null],[5,"fann_get_connection_array","","Get the connections in the network.",null,null],[5,"fann_set_weight_array","","Set connections in the network.",null,null],[5,"fann_set_weight","","Set a connection in the network.",null,null],[5,"fann_set_user_data","","Store a pointer to user defined data. The pointer can be retrieved with `fann_get_user_data`\nfor example in a callback. It is the user's responsibility to allocate and deallocate any\ndata that the pointer might point to.",null,null],[5,"fann_get_user_data","","Get a pointer to user defined data that was previously set with `fann_set_user_data`. It is\nthe user's responsibility to allocate and deallocate any data that the pointer might point\nto.",null,null],[6,"fann_type","","`fann_type` is the type used for the weights, inputs and outputs of the neural network. In\nthe Rust bindings, it is currently always defined as `c_float`.",null,null],[6,"fann_callback_type","","This callback function can be called during training when using `fann_train_on_data`,\n`fann_train_on_file` or `fann_cascadetrain_on_data`.",null,null],[11,"clone","","",1,null],[11,"clone","","",2,null],[11,"clone","","",3,null],[11,"clone","","",4,null],[11,"clone","","",5,null],[11,"clone","","",6,null]],"paths":[[3,"fann_connection"],[4,"fann_errno_enum"],[4,"fann_train_enum"],[4,"fann_activationfunc_enum"],[4,"fann_errorfunc_enum"],[4,"fann_stopfunc_enum"],[4,"fann_nettype_enum"]]};
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