Current File : /home/graphicd/public_html/vebto/vendor/jetbrains/phpstorm-stubs/fann/fann.php
<?php
// Start of Fann v.1
/**
* Class FANNConnection
*/
class FANNConnection
{
public $weight;
public $to_neuron;
public $from_neuron;
/**
* The connection constructor
*
* @param int $from_neuron
* @param int $to_neuron
* @param float $weight
*/
public function __construct($from_neuron, $to_neuron, $weight)
{
}
/**
* Returns the postions of starting neuron.
*
* @return int The postions of starting neuron.
*/
public function getFromNeuron()
{
}
/**
* Returns the postions of terminating neuron
*
* @return int The postions of terminating neuron.
*/
public function getToNeuron()
{
}
/**
* Returns the connection weight
*
* @return void The connection weight.
*/
public function getWeight()
{
}
/**
* Sets the connections weight
*
* @param float $weight
*
* @return bool
*/
public function setWeight($weight)
{
}
}
/**
* Trains on an entire dataset, for a period of time using the Cascade2 training algorithm
*
* @stub
*
* @param resource $ann
* @param resource $data
* @param int $max_neurons
* @param int $neurons_between_reports
* @param float $desired_error
*
* @return bool
*/
function fann_cascadetrain_on_data($ann, $data, $max_neurons, $neurons_between_reports, $desired_error)
{
}
/**
* Trains on an entire dataset read from file, for a period of time using the Cascade2 training algorithm.
*
* @stub
*
* @param resource $ann
* @param string $filename
* @param int $max_neurons
* @param int $neurons_between_reports
* @param float $desired_error
*
* @return bool
*/
function fann_cascadetrain_on_file($ann, $filename, $max_neurons, $neurons_between_reports, $desired_error)
{
}
/**
* Clears scaling parameters
*
* @stub
*
* @param resource $ann
*
* @return bool
*/
function fann_clear_scaling_params($ann)
{
}
/**
* Creates a copy of a fann structure
*
* @stub
*
* @param resource $ann
*
* @return resource|false Returns a copy of neural network resource on success, or false on error
*/
function fann_copy($ann)
{
}
/**
* Constructs a backpropagation neural network from a configuration file
*
* @stub
*
* @param string $configuration_file
*
* @return resource
*/
function fann_create_from_file($configuration_file)
{
}
/**
* Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
*
* @stub
*
* @param int $num_layers
* @param array $layers
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_shortcut_array($num_layers, $layers)
{
}
/**
* Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
*
* @stub-variable-parameters
* @stub
*
* @param int $num_layers
* @param int $num_neurons1
* @param int $num_neurons2
* @param int $_
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_shortcut($num_layers, $num_neurons1, $num_neurons2, $_ = NULL)
{
}
/**
* Creates a standard backpropagation neural network, which is not fully connected using an array of layer sizes
*
* @stub
*
* @param float $connection_rate
* @param int $num_layers
* @param array $layers
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_sparse_array($connection_rate, $num_layers, $layers)
{
}
/**
* Creates a standard backpropagation neural network, which is not fully connected
*
* @stub-variable-parameters
* @stub
*
* @param float $connection_rate
* @param int $num_layers
* @param int $num_neurons1
* @param int $num_neurons2
* @param int $_
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_sparse($connection_rate, $num_layers, $num_neurons1, $num_neurons2, $_ = NULL)
{
}
/**
* Creates a standard fully connected backpropagation neural network using an array of layer sizes
*
* @stub
*
* @param int $num_layers
* @param array $layers
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_standard_array($num_layers, $layers)
{
}
/**
* Creates a standard fully connected backpropagation neural network
*
* @stub-variable-parameters
* @stub
*
* @param int $num_layers
* @param int $num_neurons1
* @param int $num_neurons2
* @param int $_
*
* @return resource|false Returns a neural network resource on success, or false on error.
*/
function fann_create_standard($num_layers, $num_neurons1, $num_neurons2, $_ = NULL)
{
}
/**
* Creates the training data struct from a user supplied function
*
* @stub
*
* @param int $num_data
* @param int $num_input
* @param int $num_output
* @param callable $user_function
*
* @return resource
*/
function fann_create_train_from_callback($num_data, $num_input, $num_output, $user_function)
{
}
/**
* Creates an empty training data struct
*
* @stub
*
* @param int $num_data
* @param int $num_input
* @param int $num_output
*
* @return resource
*/
function fann_create_train($num_data, $num_input, $num_output)
{
}
/**
* Scale data in input vector after get it from ann based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param array $input_vector
*
* @return bool
*/
function fann_descale_input($ann, $input_vector)
{
}
/**
* Scale data in output vector after get it from ann based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param array $output_vector
*
* @return bool
*/
function fann_descale_output($ann, $output_vector)
{
}
/**
* Descale input and output data based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param resource $train_data
*
* @return bool
*/
function fann_descale_train($ann, $train_data)
{
}
/**
* Destroys the entire network and properly freeing all the associated memory
*
* @stub
*
* @param resource $ann
*
* @return bool
*/
function fann_destroy($ann)
{
}
/**
* Destructs the training data
*
* @stub
*
* @param resource $train_data
*
* @return bool
*/
function fann_destroy_train($train_data)
{
}
/**
* Returns an exact copy of a fann train data
*
* @stub
*
* @param resource $data
*
* @return resource
*/
function fann_duplicate_train_data($data)
{
}
/**
* Returns the activation function
*
* @stub
*
* @param resource $ann
* @param int $layer
* @param int $neuron
*
* @return int|false constant or -1 if the neuron is not defined in the neural network, or false on error.
*/
function fann_get_activation_function($ann, $layer, $neuron)
{
}
/**
* Returns the activation steepness for supplied neuron and layer number
*
* @stub
*
* @param resource $ann
* @param int $layer
* @param int $neuron
*
* @return float|false The activation steepness for the neuron or -1 if the neuron is not defined in the neural network, or false on error.
*/
function fann_get_activation_steepness($ann, $layer, $neuron)
{
}
/**
* Get the number of bias in each layer in the network
*
* @stub
*
* @param resource $ann
*
* @return array An array of numbers of bias in each layer
*/
function fann_get_bias_array($ann)
{
}
/**
* Returns the bit fail limit used during training
*
* @stub
*
* @param resource $ann
*
* @return float|false The bit fail limit, or false on error.
*/
function fann_get_bit_fail_limit($ann)
{
}
/**
* The number of fail bits
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of bits fail, or false on error.
*/
function fann_get_bit_fail($ann)
{
}
/**
* Returns the number of cascade activation functions
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of cascade activation functions, or false on error.
*/
function fann_get_cascade_activation_functions_count($ann)
{
}
/**
* Returns the cascade activation functions
*
* @stub
*
* @param resource $ann
*
* @return array|false The cascade activation functions, or false on error.
*/
function fann_get_cascade_activation_functions($ann)
{
}
/**
* The number of activation steepnesses
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of activation steepnesses, or false on error.
*/
function fann_get_cascade_activation_steepnesses_count($ann)
{
}
/**
* Returns the cascade activation steepnesses
*
* @stub
*
* @param resource $ann
*
* @return array|false The cascade activation steepnesses, or false on error.
*/
function fann_get_cascade_activation_steepnesses($ann)
{
}
/**
* Returns the cascade candidate change fraction
*
* @stub
*
* @param resource $ann
*
* @return float|false The cascade candidate change fraction, or false on error.
*/
function fann_get_cascade_candidate_change_fraction($ann)
{
}
/**
* Return the candidate limit
*
* @stub
*
* @param resource $ann
*
* @return float|false The candidate limit, or false on error.
*/
function fann_get_cascade_candidate_limit($ann)
{
}
/**
* Returns the number of cascade candidate stagnation epochs
*
* @stub
*
* @param resource $ann
*
* @return float|false The number of cascade candidate stagnation epochs, or false on error.
*/
function fann_get_cascade_candidate_stagnation_epochs($ann)
{
}
/**
* Returns the maximum candidate epochs
*
* @stub
*
* @param resource $ann
*
* @return int|false The maximum candidate epochs, or false on error.
*/
function fann_get_cascade_max_cand_epochs($ann)
{
}
/**
* Returns the maximum out epochs
*
* @stub
*
* @param resource $ann
*
* @return int|false The maximum out epochs, or false on error.
*/
function fann_get_cascade_max_out_epochs($ann)
{
}
/**
* Returns the minimum candidate epochs
*
* @stub
*
* @param resource $ann
*
* @return int|false The minimum candidate epochs, or false on error.
*/
function fann_get_cascade_min_cand_epochs($ann)
{
}
/**
* Returns the minimum out epochs
*
* @stub
*
* @param resource $ann
*
* @return int|false The minimum out epochs, or false on error.
*/
function fann_get_cascade_min_out_epochs($ann)
{
}
/**
* Returns the number of candidate groups
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of candidate groups, or false on error.
*/
function fann_get_cascade_num_candidate_groups($ann)
{
}
/**
* Returns the number of candidates used during training
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of candidates used during training, or false on error.
*/
function fann_get_cascade_num_candidates($ann)
{
}
/**
* Returns the cascade output change fraction
*
* @stub
*
* @param resource $ann
*
* @return float|false The cascade output change fraction, or false on error.
*/
function fann_get_cascade_output_change_fraction($ann)
{
}
/**
* Returns the number of cascade output stagnation epochs
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of cascade output stagnation epochs, or false on error.
*/
function fann_get_cascade_output_stagnation_epochs($ann)
{
}
/**
* Returns the weight multiplier
*
* @stub
*
* @param resource $ann
*
* @return float|false The weight multiplier, or false on error.
*/
function fann_get_cascade_weight_multiplier($ann)
{
}
/**
* Get connections in the network
*
* @stub
*
* @param resource $ann
*
* @return array An array of connections in the network
*/
function fann_get_connection_array($ann)
{
}
/**
* Get the connection rate used when the network was created
*
* @stub
*
* @param resource $ann
*
* @return float|false The connection rate used when the network was created, or false on error.
*/
function fann_get_connection_rate($ann)
{
}
/**
* Returns the last error number
*
* @stub
*
* @param resource $errdat
*
* @return int|false The error number, or false on error.
*/
function fann_get_errno($errdat)
{
}
/**
* Returns the last errstr
*
* @stub
*
* @param resource $errdat
*
* @return string|false The last error string, or false on error.
*/
function fann_get_errstr($errdat)
{
}
/**
* Get the number of neurons in each layer in the network
*
* @stub
*
* @param resource $ann
*
* @return array An array of numbers of neurons in each leayer
*/
function fann_get_layer_array($ann)
{
}
/**
* Returns the learning momentum
*
* @stub
*
* @param resource $ann
*
* @return float|false The learning momentum, or false on error.
*/
function fann_get_learning_momentum($ann)
{
}
/**
* Returns the learning rate
*
* @stub
*
* @param resource $ann
*
* @return float|false The learning rate, or false on error.
*/
function fann_get_learning_rate($ann)
{
}
/**
* Reads the mean square error from the network
*
* @stub
*
* @param resource $ann
*
* @return float|false The mean square error, or false on error.
*/
function fann_get_MSE($ann)
{
}
/**
* Get the type of neural network it was created as
*
* @stub
*
* @param resource $ann
*
* @return int|false constant, or false on error.
*/
function fann_get_network_type($ann)
{
}
/**
* Get the number of input neurons
*
* @stub
*
* @param resource $ann
*
* @return int|false Number of input neurons, or false on error
*/
function fann_get_num_input($ann)
{
}
/**
* Get the number of layers in the neural network
*
* @stub
*
* @param resource $ann
*
* @return int|false The number of leayers in the neural network, or false on error.
*/
function fann_get_num_layers($ann)
{
}
/**
* Get the number of output neurons
*
* @stub
*
* @param resource $ann
*
* @return int|false Number of output neurons, or false on error
*/
function fann_get_num_output($ann)
{
}
/**
* Returns the decay which is a factor that weights should decrease in each iteration during quickprop training
*
* @stub
*
* @param resource $ann
*
* @return float|false The decay, or false on error.
*/
function fann_get_quickprop_decay($ann)
{
}
/**
* Returns the mu factor
*
* @stub
*
* @param resource $ann
*
* @return float|false The mu factor, or false on error.
*/
function fann_get_quickprop_mu($ann)
{
}
/**
* Returns the increase factor used during RPROP training
*
* @stub
*
* @param resource $ann
*
* @return float|false The decrease factor, or false on error.
*/
function fann_get_rprop_decrease_factor($ann)
{
}
/**
* Returns the maximum step-size
*
* @stub
*
* @param resource $ann
*
* @return float|false The maximum step-size, or false on error.
*/
function fann_get_rprop_delta_max($ann)
{
}
/**
* Returns the minimum step-size
*
* @stub
*
* @param resource $ann
*
* @return float|false The minimum step-size, or false on error.
*/
function fann_get_rprop_delta_min($ann)
{
}
/**
* Returns the initial step-size
*
* @stub
*
* @param resource $ann
*
* @return int|false The initial step-size, or false on error.
*/
function fann_get_rprop_delta_zero($ann)
{
}
/**
* Returns the increase factor used during RPROP training
*
* @stub
*
* @param resource $ann
*
* @return float|false The increase factor, or false on error.
*/
function fann_get_rprop_increase_factor($ann)
{
}
/**
* Returns the sarprop step error shift
*
* @stub
*
* @param resource $ann
*
* @return float|false The sarprop step error shift , or false on error.
*/
function fann_get_sarprop_step_error_shift($ann)
{
}
/**
* Returns the sarprop step error threshold factor
*
* @stub
*
* @param resource $ann
*
* @return float|false The sarprop step error threshold factor, or false on error.
*/
function fann_get_sarprop_step_error_threshold_factor($ann)
{
}
/**
* Returns the sarprop temperature
*
* @stub
*
* @param resource $ann
*
* @return float|false The sarprop temperature, or false on error.
*/
function fann_get_sarprop_temperature($ann)
{
}
/**
* Returns the sarprop weight decay shift
*
* @stub
*
* @param resource $ann
*
* @return float|false The sarprop weight decay shift, or false on error.
*/
function fann_get_sarprop_weight_decay_shift($ann)
{
}
/**
* Get the total number of connections in the entire network
*
* @stub
*
* @param resource $ann
*
* @return int|false Total number of connections in the entire network, or false on error
*/
function fann_get_total_connections($ann)
{
}
/**
* Get the total number of neurons in the entire network
*
* @stub
*
* @param resource $ann
*
* @return int|false Total number of neurons in the entire network, or false on error.
*/
function fann_get_total_neurons($ann)
{
}
/**
* Returns the error function used during training
*
* @stub
*
* @param resource $ann
*
* @return int|false The constant, or false on error.
*/
function fann_get_train_error_function($ann)
{
}
/**
* Returns the training algorithm
*
* @stub
*
* @param resource $ann
*
* @return int|false constant, or false on error.
*/
function fann_get_training_algorithm($ann)
{
}
/**
* Returns the stop function used during training
*
* @stub
*
* @param resource $ann
*
* @return int|false The constant, or false on error.
*/
function fann_get_train_stop_function($ann)
{
}
/**
* Initialize the weights using Widrow + Nguyen’s algorithm
*
* @stub
*
* @param resource $ann
* @param resource $train_data
*
* @return bool
*/
function fann_init_weights($ann, $train_data)
{
}
/**
* Returns the number of training patterns in the train data
*
* @stub
*
* @param resource $data
*
* @return int|false Number of elements in the train data ``resource``, or false on error.
*/
function fann_length_train_data($data)
{
}
/**
* Merges the train data
*
* @stub
*
* @param resource $data1
* @param resource $data2
*
* @return resource|false New merged train data ``resource``, or false on error.
*/
function fann_merge_train_data($data1, $data2)
{
}
/**
* Returns the number of inputs in each of the training patterns in the train data
*
* @stub
*
* @param resource $data
*
* @return int|false The number of inputs, or false on error.
*/
function fann_num_input_train_data($data)
{
}
/**
* Returns the number of outputs in each of the training patterns in the train data
*
* @stub
*
* @param resource $data
*
* @return int|false The number of outputs, or false on error.
*/
function fann_num_output_train_data($data)
{
}
/**
* Prints the error string
*
* @stub
*
* @param string $errdat
*
* @return void
*/
function fann_print_error($errdat)
{
}
/**
* Give each connection a random weight between min_weight and max_weight
*
* @stub
*
* @param resource $ann
* @param float $min_weight
* @param float $max_weight
*
* @return bool
*/
function fann_randomize_weights($ann, $min_weight, $max_weight)
{
}
/**
* Reads a file that stores training data
*
* @stub
*
* @param string $filename
*
* @return resource
*/
function fann_read_train_from_file($filename)
{
}
/**
* Resets the last error number
*
* @stub
*
* @param resource $errdat
*
* @return void
*/
function fann_reset_errno($errdat)
{
}
/**
* Resets the last error string
*
* @stub
*
* @param resource $errdat
*
* @return void
*/
function fann_reset_errstr($errdat)
{
}
/**
* Resets the mean square error from the network
*
* @stub
*
* @param string $ann
*
* @return bool
*/
function fann_reset_MSE($ann)
{
}
/**
* Will run input through the neural network
*
* @stub
*
* @param resource $ann
* @param array $input
*
* @return array|false Array of output values, or false on error
*/
function fann_run($ann, $input)
{
}
/**
* Saves the entire network to a configuration file
*
* @stub
*
* @param resource $ann
* @param string $configuration_file
*
* @return bool
*/
function fann_save($ann, $configuration_file)
{
}
/**
* Save the training structure to a file
*
* @stub
*
* @param resource $data
* @param string $file_name
*
* @return bool
*/
function fann_save_train($data, $file_name)
{
}
/**
* Scale data in input vector before feed it to ann based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param array $input_vector
*
* @return bool
*/
function fann_scale_input($ann, $input_vector)
{
}
/**
* Scales the inputs in the training data to the specified range
*
* @stub
*
* @param resource $train_data
* @param float $new_min
* @param float $new_max
*
* @return bool
*/
function fann_scale_input_train_data($train_data, $new_min, $new_max)
{
}
/**
* Scale data in output vector before feed it to ann based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param array $output_vector
*
* @return bool
*/
function fann_scale_output($ann, $output_vector)
{
}
/**
* Scales the outputs in the training data to the specified range
*
* @stub
*
* @param resource $train_data
* @param float $new_min
* @param float $new_max
*
* @return bool
*/
function fann_scale_output_train_data($train_data, $new_min, $new_max)
{
}
/**
* Scales the inputs and outputs in the training data to the specified range
*
* @stub
*
* @param resource $train_data
* @param float $new_min
* @param float $new_max
*
* @return bool
*/
function fann_scale_train_data($train_data, $new_min, $new_max)
{
}
/**
* Scale input and output data based on previously calculated parameters
*
* @stub
*
* @param resource $ann
* @param resource $train_data
*
* @return bool
*/
function fann_scale_train($ann, $train_data)
{
}
/**
* Sets the activation function for all of the hidden layers
*
* @stub
*
* @param resource $ann
* @param int $activation_function
*
* @return bool
*/
function fann_set_activation_function_hidden($ann, $activation_function)
{
}
/**
* Sets the activation function for all the neurons in the supplied layer.
*
* @stub
*
* @param resource $ann
* @param int $activation_function
* @param int $layer
*
* @return bool
*/
function fann_set_activation_function_layer($ann, $activation_function, $layer)
{
}
/**
* Sets the activation function for the output layer
*
* @stub
*
* @param resource $ann
* @param int $activation_function
*
* @return bool
*/
function fann_set_activation_function_output($ann, $activation_function)
{
}
/**
* Sets the activation function for supplied neuron and layer
*
* @stub
*
* @param resource $ann
* @param int $activation_function
* @param int $layer
* @param int $neuron
*
* @return bool
*/
function fann_set_activation_function($ann, $activation_function, $layer, $neuron)
{
}
/**
* Sets the steepness of the activation steepness for all neurons in the all hidden layers
*
* @stub
*
* @param resource $ann
* @param float $activation_steepness
*
* @return bool
*/
function fann_set_activation_steepness_hidden($ann, $activation_steepness)
{
}
/**
* Sets the activation steepness for all of the neurons in the supplied layer number
*
* @stub
*
* @param resource $ann
* @param float $activation_steepness
* @param int $layer
*
* @return bool
*/
function fann_set_activation_steepness_layer($ann, $activation_steepness, $layer)
{
}
/**
* Sets the steepness of the activation steepness in the output layer
*
* @stub
*
* @param resource $ann
* @param float $activation_steepness
*
* @return bool
*/
function fann_set_activation_steepness_output($ann, $activation_steepness)
{
}
/**
* Sets the activation steepness for supplied neuron and layer number
*
* @stub
*
* @param resource $ann
* @param float $activation_steepness
* @param int $layer
* @param int $neuron
*
* @return bool
*/
function fann_set_activation_steepness($ann, $activation_steepness, $layer, $neuron)
{
}
/**
* Set the bit fail limit used during training
*
* @stub
*
* @param resource $ann
* @param float $bit_fail_limit
*
* @return bool
*/
function fann_set_bit_fail_limit($ann, $bit_fail_limit)
{
}
/**
* Sets the callback function for use during training
*
* @stub
*
* @param resource $ann
* @param collable $callback
*
* @return bool
*/
function fann_set_callback($ann, $callback)
{
}
/**
* Sets the array of cascade candidate activation functions
*
* @stub
*
* @param resource $ann
* @param array $cascade_activation_functions
*
* @return bool
*/
function fann_set_cascade_activation_functions($ann, $cascade_activation_functions)
{
}
/**
* Sets the array of cascade candidate activation steepnesses
*
* @stub
*
* @param resource $ann
* @param array $cascade_activation_steepnesses_count
*
* @return bool
*/
function fann_set_cascade_activation_steepnesses($ann, $cascade_activation_steepnesses_count)
{
}
/**
* Sets the cascade candidate change fraction
*
* @stub
*
* @param resource $ann
* @param float $cascade_candidate_change_fraction
*
* @return bool
*/
function fann_set_cascade_candidate_change_fraction($ann, $cascade_candidate_change_fraction)
{
}
/**
* Sets the candidate limit
*
* @stub
*
* @param resource $ann
* @param float $cascade_candidate_limit
*
* @return bool
*/
function fann_set_cascade_candidate_limit($ann, $cascade_candidate_limit)
{
}
/**
* Sets the number of cascade candidate stagnation epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_candidate_stagnation_epochs
*
* @return bool
*/
function fann_set_cascade_candidate_stagnation_epochs($ann, $cascade_candidate_stagnation_epochs)
{
}
/**
* Sets the max candidate epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_max_cand_epochs
*
* @return bool
*/
function fann_set_cascade_max_cand_epochs($ann, $cascade_max_cand_epochs)
{
}
/**
* Sets the maximum out epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_max_out_epochs
*
* @return bool
*/
function fann_set_cascade_max_out_epochs($ann, $cascade_max_out_epochs)
{
}
/**
* Sets the min candidate epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_min_cand_epochs
*
* @return bool
*/
function fann_set_cascade_min_cand_epochs($ann, $cascade_min_cand_epochs)
{
}
/**
* Sets the minimum out epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_min_out_epochs
*
* @return bool
*/
function fann_set_cascade_min_out_epochs($ann, $cascade_min_out_epochs)
{
}
/**
* Sets the number of candidate groups
*
* @stub
*
* @param resource $ann
* @param int $cascade_num_candidate_groups
*
* @return bool
*/
function fann_set_cascade_num_candidate_groups($ann, $cascade_num_candidate_groups)
{
}
/**
* Sets the cascade output change fraction
*
* @stub
*
* @param resource $ann
* @param float $cascade_output_change_fraction
*
* @return bool
*/
function fann_set_cascade_output_change_fraction($ann, $cascade_output_change_fraction)
{
}
/**
* Sets the number of cascade output stagnation epochs
*
* @stub
*
* @param resource $ann
* @param int $cascade_output_stagnation_epochs
*
* @return bool
*/
function fann_set_cascade_output_stagnation_epochs($ann, $cascade_output_stagnation_epochs)
{
}
/**
* Sets the weight multiplier
*
* @stub
*
* @param resource $ann
* @param float $cascade_weight_multiplier
*
* @return bool
*/
function fann_set_cascade_weight_multiplier($ann, $cascade_weight_multiplier)
{
}
/**
* Sets where the errors are logged to
*
* @stub
*
* @param resource $errdat
* @param string $log_file
*
* @return void
*/
function fann_set_error_log($errdat, $log_file)
{
}
/**
* Calculate input scaling parameters for future use based on training data
*
* @stub
*
* @param resource $ann
* @param resource $train_data
* @param float $new_input_min
* @param float $new_input_max
*
* @return bool
*/
function fann_set_input_scaling_params($ann, $train_data, $new_input_min, $new_input_max)
{
}
/**
* Sets the learning momentum
*
* @stub
*
* @param resource $ann
* @param float $learning_momentum
*
* @return bool
*/
function fann_set_learning_momentum($ann, $learning_momentum)
{
}
/**
* Sets the learning rate
*
* @stub
*
* @param resource $ann
* @param float $learning_rate
*
* @return bool
*/
function fann_set_learning_rate($ann, $learning_rate)
{
}
/**
* Calculate output scaling parameters for future use based on training data
*
* @stub
*
* @param resource $ann
* @param resource $train_data
* @param float $new_output_min
* @param float $new_output_max
*
* @return bool
*/
function fann_set_output_scaling_params($ann, $train_data, $new_output_min, $new_output_max)
{
}
/**
* Sets the quickprop decay factor
*
* @stub
*
* @param resource $ann
* @param float $quickprop_decay
*
* @return bool
*/
function fann_set_quickprop_decay($ann, $quickprop_decay)
{
}
/**
* Sets the quickprop mu factor
*
* @stub
*
* @param resource $ann
* @param float $quickprop_mu
*
* @return bool
*/
function fann_set_quickprop_mu($ann, $quickprop_mu)
{
}
/**
* Sets the decrease factor used during RPROP training
*
* @stub
*
* @param resource $ann
* @param float $rprop_decrease_factor
*
* @return bool
*/
function fann_set_rprop_decrease_factor($ann, $rprop_decrease_factor)
{
}
/**
* Sets the maximum step-size
*
* @stub
*
* @param resource $ann
* @param float $rprop_delta_max
*
* @return bool
*/
function fann_set_rprop_delta_max($ann, $rprop_delta_max)
{
}
/**
* Sets the minimum step-size
*
* @stub
*
* @param resource $ann
* @param float $rprop_delta_min
*
* @return bool
*/
function fann_set_rprop_delta_min($ann, $rprop_delta_min)
{
}
/**
* Sets the initial step-size
*
* @stub
*
* @param resource $ann
* @param float $rprop_delta_zero
*
* @return bool
*/
function fann_set_rprop_delta_zero($ann, $rprop_delta_zero)
{
}
/**
* Sets the increase factor used during RPROP training
*
* @stub
*
* @param resource $ann
* @param float $rprop_increase_factor
*
* @return bool
*/
function fann_set_rprop_increase_factor($ann, $rprop_increase_factor)
{
}
/**
* Sets the sarprop step error shift
*
* @stub
*
* @param resource $ann
* @param float $sarprop_step_error_shift
*
* @return bool
*/
function fann_set_sarprop_step_error_shift($ann, $sarprop_step_error_shift)
{
}
/**
* Sets the sarprop step error threshold factor
*
* @stub
*
* @param resource $ann
* @param float $sarprop_step_error_threshold_factor
*
* @return bool
*/
function fann_set_sarprop_step_error_threshold_factor($ann, $sarprop_step_error_threshold_factor)
{
}
/**
* Sets the sarprop temperature
*
* @stub
*
* @param resource $ann
* @param float $sarprop_temperature
*
* @return bool
*/
function fann_set_sarprop_temperature($ann, $sarprop_temperature)
{
}
/**
* Sets the sarprop weight decay shift
*
* @stub
*
* @param resource $ann
* @param float $sarprop_weight_decay_shift
*
* @return bool
*/
function fann_set_sarprop_weight_decay_shift($ann, $sarprop_weight_decay_shift)
{
}
/**
* Calculate input and output scaling parameters for future use based on training data
*
* @stub
*
* @param resource $ann
* @param resource $train_data
* @param float $new_input_min
* @param float $new_input_max
* @param float $new_output_min
* @param float $new_output_max
*
* @return bool
*/
function fann_set_scaling_params($ann, $train_data, $new_input_min, $new_input_max, $new_output_min, $new_output_max)
{
}
/**
* Sets the error function used during training
*
* @stub
*
* @param resource $ann
* @param int $error_function
*
* @return bool
*/
function fann_set_train_error_function($ann, $error_function)
{
}
/**
* Sets the training algorithm
*
* @stub
*
* @param resource $ann
* @param int $training_algorithm
*
* @return bool
*/
function fann_set_training_algorithm($ann, $training_algorithm)
{
}
/**
* Sets the stop function used during training
*
* @stub
*
* @param resource $ann
* @param int $stop_function
*
* @return bool
*/
function fann_set_train_stop_function($ann, $stop_function)
{
}
/**
* Set connections in the network
*
* @stub
*
* @param resource $ann
* @param array $connections
*
* @return bool
*/
function fann_set_weight_array($ann, $connections)
{
}
/**
* Set a connection in the network
*
* @stub
*
* @param resource $ann
* @param int $from_neuron
* @param int $to_neuron
* @param float $weight
*
* @return bool
*/
function fann_set_weight($ann, $from_neuron, $to_neuron, $weight)
{
}
/**
* Shuffles training data, randomizing the order
*
* @stub
*
* @param resource $train_data
*
* @return bool
*/
function fann_shuffle_train_data($train_data)
{
}
/**
* Returns an copy of a subset of the train data
*
* @stub
*
* @param resource $data
* @param int $pos
* @param int $length
*
* @return resource
*/
function fann_subset_train_data($data, $pos, $length)
{
}
/**
* Test a set of training data and calculates the MSE for the training data
*
* @stub
*
* @param resource $ann
* @param resource $data
*
* @return float|false The updated MSE, or false on error.
*/
function fann_test_data($ann, $data)
{
}
/**
* Test with a set of inputs, and a set of desired outputs
*
* @stub
*
* @param resource $ann
* @param array $input
* @param array $desired_output
*
* @return bool
*/
function fann_test($ann, $input, $desired_output)
{
}
/**
* Train one epoch with a set of training data
*
* @stub
*
* @param resource $ann
* @param resource $data
*
* @return float|false The MSE, or false on error.
*/
function fann_train_epoch($ann, $data)
{
}
/**
* Trains on an entire dataset for a period of time
*
* @stub
*
* @param resource $ann
* @param resource $data
* @param int $max_epochs
* @param int $epochs_between_reports
* @param float $desired_error
*
* @return bool
*/
function fann_train_on_data($ann, $data, $max_epochs, $epochs_between_reports, $desired_error)
{
}
/**
* Trains on an entire dataset, which is read from file, for a period of time
*
* @stub
*
* @param resource $ann
* @param string $filename
* @param int $max_epochs
* @param int $epochs_between_reports
* @param float $desired_error
*
* @return bool
*/
function fann_train_on_file($ann, $filename, $max_epochs, $epochs_between_reports, $desired_error)
{
}
/**
* Train one iteration with a set of inputs, and a set of desired outputs
*
* @stub
*
* @param resource $ann
* @param array $input
* @param array $desired_output
*
* @return bool
*/
function fann_train($ann, $input, $desired_output)
{
}
define('FANN_TRAIN_INCREMENTAL', 0);
define('FANN_TRAIN_BATCH', 1);
define('FANN_TRAIN_RPROP', 2);
define('FANN_TRAIN_QUICKPROP', 3);
define('FANN_TRAIN_SARPROP', 4);
define('FANN_LINEAR', 0);
define('FANN_THRESHOLD', 1);
define('FANN_THRESHOLD_SYMMETRIC', 2);
define('FANN_SIGMOID', 3);
define('FANN_SIGMOID_STEPWISE', 4);
define('FANN_SIGMOID_SYMMETRIC', 5);
define('FANN_SIGMOID_SYMMETRIC_STEPWISE', 6);
define('FANN_GAUSSIAN', 7);
define('FANN_GAUSSIAN_SYMMETRIC', 8);
define('FANN_GAUSSIAN_STEPWISE', 9);
define('FANN_ELLIOT', 10);
define('FANN_ELLIOT_SYMMETRIC', 11);
define('FANN_LINEAR_PIECE', 12);
define('FANN_LINEAR_PIECE_SYMMETRIC', 13);
define('FANN_SIN_SYMMETRIC', 14);
define('FANN_COS_SYMMETRIC', 15);
define('FANN_SIN', 16);
define('FANN_COS', 17);
define('FANN_ERRORFUNC_LINEAR', 0);
define('FANN_ERRORFUNC_TANH', 1);
define('FANN_STOPFUNC_MSE', 0);
define('FANN_STOPFUNC_BIT', 1);
define('FANN_NETTYPE_LAYER', 0);
define('FANN_NETTYPE_SHORTCUT', 1);
define('FANN_E_NO_ERROR', 0);
define('FANN_E_CANT_OPEN_CONFIG_R', 1);
define('FANN_E_CANT_OPEN_CONFIG_W', 2);
define('FANN_E_WRONG_CONFIG_VERSION', 3);
define('FANN_E_CANT_READ_CONFIG', 4);
define('FANN_E_CANT_READ_NEURON', 5);
define('FANN_E_CANT_READ_CONNECTIONS', 6);
define('FANN_E_WRONG_NUM_CONNECTIONS', 7);
define('FANN_E_CANT_OPEN_TD_W', 8);
define('FANN_E_CANT_OPEN_TD_R', 9);
define('FANN_E_CANT_READ_TD', 10);
define('FANN_E_CANT_ALLOCATE_MEM', 11);
define('FANN_E_CANT_TRAIN_ACTIVATION', 12);
define('FANN_E_CANT_USE_ACTIVATION', 13);
define('FANN_E_TRAIN_DATA_MISMATCH', 14);
define('FANN_E_CANT_USE_TRAIN_ALG', 15);
define('FANN_E_TRAIN_DATA_SUBSET', 16);
define('FANN_E_INDEX_OUT_OF_BOUND', 17);
define('FANN_E_SCALE_NOT_PRESENT', 18);
define('FANN_E_INPUT_NO_MATCH', 19);
define('FANN_E_OUTPUT_NO_MATCH', 20);
define('FANN_VERSION', '2.2');
// End of Fann v.1.0
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