fann_scale_input

<?php// This example will use the XOR dataset with negative one represented // as zero and one represented as one-hundred and demonstrate how to// scale those values so that FANN can understand them and then how // to de-scale the value FANN returns so that you can understand them.// Scaling allows you to take raw data numbers like -1234.975 or 4502012 // in your dataset and convert them into an input/output range that// your neural network can understand. // De-scaling lets you take the scaled data and convert it back into // the original range.// scale_test.data// Note the values are "raw" or un-scaled./*4 2 10 000 100100100 0100100 1000*/////////////////////// Configure ANN  //////////////////////// New ANN$ann = fann_create_standard_array(3, [2,3,1]);// Set activation functionsfann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);// Read raw (un-scaled) training data from file$train_data = fann_read_train_from_file("scale_test.data");// Scale the data range to -1 to 1fann_set_input_scaling_params($ann , $train_data, -1, 1);fann_set_output_scaling_params($ann , $train_data, -1, 1);///////////// Train /////////////// Presumably you would train here (uncomment to perform training)...// fann_train_on_data($ann, $train_data, 100, 10, 0.01);// But it's not needed to test the scaling because the training file // in this case is just used to compute/derive the scale range. // However, doing the training will improve the answer the ANN gives// in correlation to the training data.//////////// Test ////////////$raw_input = array(0, 100); // test XOR (0,100) input$scaled_input = fann_scale_input ($ann , $raw_input); // scaled XOR (-1,1) input$descaled_input = fann_descale_input ($ann , $scaled_input); // de-scaled XOR (0,100) input$raw_output = fann_run($ann, $scaled_input); // get the answer/output from the ANN$output_descale = fann_descale_output($ann, $raw_output); // de-scale the output ////////////////////// Report Results //////////////////////echo 'The raw_input:' . PHP_EOL;var_dump($raw_input); echo 'The raw_input Scaled then De-Scaled (values are unchanged/correct):' . PHP_EOL;var_dump($descaled_input); echo 'The Scaled input:' . PHP_EOL;var_dump($scaled_input); echo "The raw_output of the ANN (Scaled input):" . PHP_EOL;var_dump($raw_output); echo 'The De-Scaled output:' . PHP_EOL;var_dump($output_descale);   ////////////////////// Example Output ////////////////////// /*The raw_input:array(2) {  [0]=>  float(0)  [1]=>  float(100)}The raw_input Scaled then De-Scaled (values are unchanged/correct):array(2) {  [0]=>  float(0)  [1]=>  float(100)}The Scaled input:array(2) {  [0]=>  float(-1)  [1]=>  float(1)}The raw_output of the ANN (Scaled input):array(1) {  [0]=>  float(1)}The De-Scaled output:array(1) {  [0]=>  float(100)}*/

<?php// This example will use the XOR dataset with negative one represented // as zero and one represented as one-hundred and demonstrate how to// scale those values so that FANN can understand them and then how // to de-scale the value FANN returns so that you can understand them.// Scaling allows you to take raw data numbers like -1234.975 or 4502012 // in your dataset and convert them into an input/output range that// your neural network can understand. // De-scaling lets you take the scaled data and convert it back into // the original range.// scale_test.data// Note the values are "raw" or un-scaled./*4 2 10 000 100100100 0100100 1000*/////////////////////// Configure ANN  //////////////////////// New ANN$ann = fann_create_standard_array(3, [2,3,1]);// Set activation functionsfann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);// Read raw (un-scaled) training data from file$train_data = fann_read_train_from_file("scale_test.data");// Scale the data range to -1 to 1fann_set_input_scaling_params($ann , $train_data, -1, 1);fann_set_output_scaling_params($ann , $train_data, -1, 1);///////////// Train /////////////// Presumably you would train here (uncomment to perform training)...// fann_train_on_data($ann, $train_data, 100, 10, 0.01);// But it's not needed to test the scaling because the training file // in this case is just used to compute/derive the scale range. // However, doing the training will improve the answer the ANN gives// in correlation to the training data.//////////// Test ////////////$raw_input = array(0, 100); // test XOR (0,100) input$scaled_input = fann_scale_input ($ann , $raw_input); // scaled XOR (-1,1) input$descaled_input = fann_descale_input ($ann , $scaled_input); // de-scaled XOR (0,100) input$raw_output = fann_run($ann, $scaled_input); // get the answer/output from the ANN$output_descale = fann_descale_output($ann, $raw_output); // de-scale the output ////////////////////// Report Results //////////////////////echo 'The raw_input:' . PHP_EOL;var_dump($raw_input); echo 'The raw_input Scaled then De-Scaled (values are unchanged/correct):' . PHP_EOL;var_dump($descaled_input); echo 'The Scaled input:' . PHP_EOL;var_dump($scaled_input); echo "The raw_output of the ANN (Scaled input):" . PHP_EOL;var_dump($raw_output); echo 'The De-Scaled output:' . PHP_EOL;var_dump($output_descale);   ////////////////////// Example Output ////////////////////// /*The raw_input:array(2) {  [0]=>  float(0)  [1]=>  float(100)}The raw_input Scaled then De-Scaled (values are unchanged/correct):array(2) {  [0]=>  float(0)  [1]=>  float(100)}The Scaled input:array(2) {  [0]=>  float(-1)  [1]=>  float(1)}The raw_output of the ANN (Scaled input):array(1) {  [0]=>  float(1)}The De-Scaled output:array(1) {  [0]=>  float(100)}*/

fann_scale_input and fann_scale_output return not bool value. This function return scaling vector.Example$r = fann_scale_input($ann, $input);$output = fann_run($ann, $input);$s = fann_scale_output ( $ann, $output);$r and $s is array

Please note -> ALLfann  scaling related functions are not functional.They are implemented wrong so the scaling is calculated within the library but it's not referenced back to the input variables.