Fix typos in doc comments.

src/include/fann.h

@@ -31,8 +31,8 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
    and executed by <fann_run>.
    
    All of this can be done without much knowledge of the internals of ANNs, although the ANNs created will
-   still be powerfull and effective. If you have more knowledge about ANNs, and desire more control, almost
-   every part of the ANNs can be parametized to create specialized and highly optimal ANNs.
+   still be powerful and effective. If you have more knowledge about ANNs, and desire more control, almost
+   every part of the ANNs can be parametrized to create specialized and highly optimal ANNs.
  */
 /* Group: Creation, Destruction & Execution */
 
@@ -259,7 +259,7 @@ FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(unsigned int num_layers
 FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(unsigned int num_layers,
 															   const unsigned int *layers);
 /* Function: fann_destroy
-   Destroys the entire network and properly freeing all the associated memmory.
+   Destroys the entire network and properly freeing all the associated memory.
 
 	This function appears in FANN >= 1.0.0.
 */ 

src/include/fann_cascade.h

@@ -27,10 +27,10 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
    training have also proved better at solving some problems.
    
    The basic idea of cascade training is that a number of candidate neurons are trained separate from the 
-   real network, then the most promissing of these candidate neurons is inserted into the neural network. 
+   real network, then the most promising of these candidate neurons is inserted into the neural network. 
    Then the output connections are trained and new candidate neurons is prepared. The candidate neurons are 
-   created as shorcut connected neurons in a new hidden layer, which means that the final neural network
-   will consist of a number of hidden layers with one shorcut connected neuron in each.
+   created as shortcut connected neurons in a new hidden layer, which means that the final neural network
+   will consist of a number of hidden layers with one shortcut connected neuron in each.
 */
 
 /* Group: Cascade Training */
@@ -102,7 +102,7 @@ FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(struct fann *ann, const ch
    If the cascade output change fraction is low, the output connections will be trained more and if the
    fraction is high they will be trained less.
    
-   The default cascade output change fraction is 0.01, which is equalent to a 1% change in MSE.
+   The default cascade output change fraction is 0.01, which is equivalent to a 1% change in MSE.
 
    See also:
    		<fann_set_cascade_output_change_fraction>, <fann_get_MSE>, <fann_get_cascade_output_stagnation_epochs>
@@ -169,7 +169,7 @@ FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs(struct fan
    If the cascade candidate change fraction is low, the candidate neurons will be trained more and if the
    fraction is high they will be trained less.
    
-   The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE.
+   The default cascade candidate change fraction is 0.01, which is equivalent to a 1% change in MSE.
 
    See also:
    		<fann_set_cascade_candidate_change_fraction>, <fann_get_MSE>, <fann_get_cascade_candidate_stagnation_epochs>

src/include/fann_cpp.h

@@ -100,8 +100,8 @@ namespace FANN
     	
 	    ERRORFUNC_LINEAR - Standard linear error function.
 	    ERRORFUNC_TANH - Tanh error function, usually better 
-		    but can require a lower learning rate. This error function agressively targets outputs that
-		    differ much from the desired, while not targetting outputs that only differ a little that much.
+		    but can require a lower learning rate. This error function aggressively targets outputs that
+		    differ much from the desired, while not targeting outputs that only differ a little that much.
 		    This activation function is not recommended for cascade training and incremental training.
 
 	    See also:
@@ -327,7 +327,7 @@ namespace FANN
         >    unsigned int max_epochs, unsigned int epochs_between_reports,
         >    float desired_error, unsigned int epochs, void *user_data);
     	
-	    The callback can be set by using <neural_net::set_callback> and is very usefull for doing custom 
+	    The callback can be set by using <neural_net::set_callback> and is very useful for doing custom 
 	    things during training. It is recommended to use this function when implementing custom 
 	    training procedures, or when visualizing the training in a GUI etc. The parameters which the
 	    callback function takes is the parameters given to the <neural_net::train_on_data>, plus an epochs
@@ -474,7 +474,7 @@ namespace FANN
            
            Saves the training structure to a fixed point data file.
          
-           This function is very usefull for testing the quality of a fixed point network.
+           This function is very useful for testing the quality of a fixed point network.
            
            Return:
            The function returns true on success and false on failure.
@@ -1246,7 +1246,7 @@ namespace FANN
            But it is saved in fixed point format no matter which
            format it is currently in.
 
-           This is usefull for training a network in floating points,
+           This is useful for training a network in floating points,
            and then later executing it in fixed point.
 
            The function returns the bit position of the fix point, which
@@ -1743,7 +1743,7 @@ namespace FANN
            
            The steepness of an activation function says something about how fast the activation function 
            goes from the minimum to the maximum. A high value for the activation function will also
-           give a more agressive training.
+           give a more aggressive training.
            
            When training neural networks where the output values should be at the extremes (usually 0 and 1, 
            depending on the activation function), a steep activation function can be used (e.g. 1.0).
@@ -1779,7 +1779,7 @@ namespace FANN
            
            The steepness of an activation function says something about how fast the activation function 
            goes from the minimum to the maximum. A high value for the activation function will also
-           give a more agressive training.
+           give a more aggressive training.
            
            When training neural networks where the output values should be at the extremes (usually 0 and 1, 
            depending on the activation function), a steep activation function can be used (e.g. 1.0).

src/include/fann_data.h

@@ -264,8 +264,8 @@ static char const *const FANN_ACTIVATIONFUNC_NAMES[] = {
 	
 	FANN_ERRORFUNC_LINEAR - Standard linear error function.
 	FANN_ERRORFUNC_TANH - Tanh error function, usually better 
-		but can require a lower learning rate. This error function agressively targets outputs that
-		differ much from the desired, while not targetting outputs that only differ a little that much.
+		but can require a lower learning rate. This error function aggressively targets outputs that
+		differ much from the desired, while not targeting outputs that only differ a little that much.
 		This activation function is not recommended for cascade training and incremental training.
 
 	See also:
@@ -377,7 +377,7 @@ struct fann_train_data;
 	>                                             unsigned int epochs_between_reports, 
 	>                                             float desired_error, unsigned int epochs);
 	
-	The callback can be set by using <fann_set_callback> and is very usefull for doing custom 
+	The callback can be set by using <fann_set_callback> and is very useful for doing custom 
 	things during training. It is recommended to use this function when implementing custom 
 	training procedures, or when visualizing the training in a GUI etc. The parameters which the
 	callback function takes is the parameters given to the <fann_train_on_data>, plus an epochs
@@ -513,7 +513,7 @@ struct fann
 	struct fann_layer *last_layer;
 
 	/* Total number of neurons.
-	 * very usefull, because the actual neurons are allocated in one long array
+	 * very useful, because the actual neurons are allocated in one long array
 	 */
 	unsigned int total_neurons;
 
@@ -563,7 +563,7 @@ struct fann
 #endif
 
 	/* Total number of connections.
-	 * very usefull, because the actual connections
+	 * very useful, because the actual connections
 	 * are allocated in one long array
 	 */
 	unsigned int total_connections;

src/include/fann_io.h

@@ -69,7 +69,7 @@ FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration
    But it is saved in fixed point format no matter which
    format it is currently in.
 
-   This is usefull for training a network in floating points,
+   This is useful for training a network in floating points,
    and then later executing it in fixed point.
 
    The function returns the bit position of the fix point, which
@@ -80,7 +80,7 @@ FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration
    A negative value indicates very low precision, and a very
    strong possibility for overflow.
    (the actual fix point will be set to 0, since a negative
-   fix point does not make sence).
+   fix point does not make sense).
 
    Generally, a fix point lower than 6 is bad, and should be avoided.
    The best way to avoid this, is to have less connections to each neuron,

src/include/fann_train.h

@@ -25,7 +25,7 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
  	There are many different ways of training neural networks and the FANN library supports
  	a number of different approaches. 
  	
- 	Two fundementally different approaches are the most commonly used:
+ 	Two fundamentally different approaches are the most commonly used:
  	
  		Fixed topology training - The size and topology of the ANN is determined in advance
  			and the training alters the weights in order to minimize the difference between
@@ -234,15 +234,15 @@ FANN_EXTERNAL float FANN_API fann_test_data(struct fann *ann, struct fann_train_
    
    The file must be formatted like:
    >num_train_data num_input num_output
-   >inputdata seperated by space
-   >outputdata seperated by space
+   >inputdata separated by space
+   >outputdata separated by space
    >
    >.
    >.
    >.
    >
-   >inputdata seperated by space
-   >outputdata seperated by space
+   >inputdata separated by space
+   >outputdata separated by space
    
    See also:
    	<fann_train_on_data>, <fann_destroy_train>, <fann_save_train>
@@ -305,7 +305,7 @@ FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train_array(unsigned
      num_data      - The number of training data
      num_input     - The number of inputs per training data
      num_output    - The number of ouputs per training data
-     user_function - The user suplied function
+     user_function - The user supplied function
 
    Parameters for the user function:
      num        - The number of the training data set
@@ -663,7 +663,7 @@ FANN_EXTERNAL int FANN_API fann_save_train(struct fann_train_data *data, const c
    
    Saves the training structure to a fixed point data file.
  
-   This function is very usefull for testing the quality of a fixed point network.
+   This function is very useful for testing the quality of a fixed point network.
    
    Return:
    The function returns 0 on success and -1 on failure.
@@ -874,7 +874,7 @@ FANN_EXTERNAL void FANN_API fann_set_activation_function_output(struct fann *ann
    
    The steepness of an activation function says something about how fast the activation function 
    goes from the minimum to the maximum. A high value for the activation function will also
-   give a more agressive training.
+   give a more aggressive training.
    
    When training neural networks where the output values should be at the extremes (usually 0 and 1, 
    depending on the activation function), a steep activation function can be used (e.g. 1.0).
@@ -904,7 +904,7 @@ FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(struct fann *ann,
    
    The steepness of an activation function says something about how fast the activation function 
    goes from the minimum to the maximum. A high value for the activation function will also
-   give a more agressive training.
+   give a more aggressive training.
    
    When training neural networks where the output values should be at the extremes (usually 0 and 1, 
    depending on the activation function), a steep activation function can be used (e.g. 1.0).