Module Ann_results

module Ann_results: sig .. end

Compute and print a few indicators of the neural network's performance.

val regression_results : Ann_func.nn_func ->
       Ann_topology.nn_topology ->
       (float array * float array) array ->
       float array ->
       float * float * float * float * float * int * float * float * float * float

regression_results fn nn patterns w returns a tuple

(sum_squares,mss,normalized_error,likelihood,mll,nb_conn,aic,mean_aic,
   bic,mean_bic)

where sum_squares is the usual sum of squares error, mss is the mean sum of squares, averaged on the number of patterns, normalized_error is the sum of squares normalized by the variance of the target vectors (see section 7.5, p. 263 of [Bishop96]), likelihood is the log-likelihood, mll is the mean log-likelihood, nb_conn is the number of unadjusted parameters (number of weights), aic is the Akaike information criterion (AIC), mean_aic is the average value of the AIC over all patterns, bic is Schwartz's Bayes information criterion (BIC), and mean_bic is the mean value of the BIC.

val print_regression_results : float * float * float * float * float * int * float * float * float * float ->
       unit

print_regression_results res prints different kinds of results (see regression_results) on the standard output.

val classification_results : Ann_func.nn_func ->
       Ann_topology.nn_topology ->
       (float array * float array) array ->
       float array ->
       float * float * float * float * float * int array array * int * float *
       float * float * float

classification_results fn nn patterns w returns a tuple

(sum_squares,mss,normalized_error,entropy,mce,class_mat,
   nb_conn,aic,mean_aic,bic,mean_bic)

where sum_squares, mss, and normalized_error are respectively the raw, mean, and normalized sum of squares error, entropy is the cross entropy (see [Bishop96], section 6.9), mce is the mean cross entropy, class_mat is the matrix of correct/incorrect classifications, nb_conn is the number of unadjusted parameters (number of weights), aic is the Akaike information criterion (AIC), mean_aic is the average value of the AIC over all patterns, bic is Schwartz's Bayes information criterion (BIC), and mean_bic is the mean value of the BIC.

val print_classification_results : float * float * float * float * float * int array array * int * float *
       float * float * float -> unit

print_classification_results res prints different kinds of results (see classification_results) on the standard output.

val print_results : Ann_func.nn_func ->
       Ann_topology.nn_topology ->
       (float array * float array) array -> float array -> bool -> unit

print_results fn nn patterns w prc_flag prints various indicators of the performance of a neural network with functions fn, topology nn, and weights w, when applied to a set of patterns patterns. When prc_flag is true, the function prints results corresponding to a classification (see classification_results). In the other case, regression results are printed (see regression_results).