Merge pull request libfann#36 from afck/master

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 */
 
@@ -159,7 +159,7 @@ extern "C"
 			
 	Example:
 		> // Creating an ANN with 2 input neurons, 1 output neuron, 
-		> // and two hidden neurons with 8 and 9 neurons
+		> // and two hidden layers with 8 and 9 neurons
 		> struct fann *ann = fann_create_standard(4, 2, 8, 9, 1);
 		
 	See also:
@@ -175,7 +175,7 @@ FANN_EXTERNAL struct fann *FANN_API fann_create_standard(unsigned int num_layers
 
 	Example:
 		> // Creating an ANN with 2 input neurons, 1 output neuron, 
-		> // and two hidden neurons with 8 and 9 neurons
+		> // and two hidden layers with 8 and 9 neurons
 		> unsigned int layers[4] = {2, 8, 9, 1};
 		> struct fann *ann = fann_create_standard_array(4, layers);
 
@@ -233,7 +233,7 @@ FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(float connection_ra
 	also has shortcut connections.
 
  	Shortcut connections are connections that skip layers. A fully connected network with shortcut 
-	connections, is a network where all neurons are connected to all neurons in later layers. 
+	connections is a network where all neurons are connected to all neurons in later layers. 
 	Including direct connections from the input layer to the output layer.
 
 	See <fann_create_standard> for a description of the parameters.
@@ -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, properly freeing all the associated memory.
 
 	This function appears in FANN >= 1.0.0.
 */ 
@@ -331,15 +331,15 @@ FANN_EXTERNAL void FANN_API fann_init_weights(struct fann *ann, struct fann_trai
 	>L   2 / N    6 ...BBA
 	>L   2 / N    7 ......
 		  
-	This network have five real neurons and two bias neurons. This gives a total of seven neurons 
+	This network has five real neurons and two bias neurons. This gives a total of seven neurons 
 	named from 0 to 6. The connections between these neurons can be seen in the matrix. "." is a 
 	place where there is no connection, while a character tells how strong the connection is on a 
-	scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has 
-	connection from the three neurons in the previous layer as is visible in the first two lines. 
-	The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is 
+	scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) have 
+	connections from the three neurons in the previous layer as is visible in the first two lines. 
+	The output neuron (6) has connections from the three neurons in the hidden layer 3 - 5 as is 
 	visible in the fourth line.
 
-	To simplify the matrix output neurons is not visible as neurons that connections can come from, 
+	To simplify the matrix output neurons are not visible as neurons that connections can come from, 
 	and input and bias neurons are not visible as neurons that connections can go to.
 
 	This function appears in FANN >= 1.2.0.

src/include/fann_cascade.h

@@ -22,15 +22,15 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 
 /* Section: FANN Cascade Training
    Cascade training differs from ordinary training in the sense that it starts with an empty neural network
-   and then adds neurons one by one, while it trains the neural network. The main benefit of this approach,
+   and then adds neurons one by one, while it trains the neural network. The main benefit of this approach
    is that you do not have to guess the number of hidden layers and neurons prior to training, but cascade 
-   training have also proved better at solving some problems.
+   training has 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. 
-   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.
+   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 are prepared. The candidate neurons are 
+   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:
@@ -143,7 +143,7 @@ namespace FANN
 		    this algorithm, while other more advanced problems will not train very well.
 	    TRAIN_BATCH -  Standard backpropagation algorithm, where the weights are updated after 
 		    calculating the mean square error for the whole training set. This means that the weights 
-		    are only updated once during a epoch. For this reason some problems, will train slower with 
+		    are only updated once during an epoch. For this reason some problems, will train slower with
 		    this algorithm. But since the mean square error is calculated more correctly than in 
 		    incremental training, some problems will reach a better solutions with this algorithm.
 	    TRAIN_RPROP - A more advanced batch training algorithm which achieves good results 
@@ -153,7 +153,7 @@ namespace FANN
 		    training algorithm works. The RPROP training algorithm is described by 
 		    [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the 
 		    iRPROP- training algorithm which is described by [Igel and Husken, 2000] which 
-    	    is an variety of the standard RPROP training algorithm.
+		    is a variant of the standard RPROP training algorithm.
 	    TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results 
 		    for many problems. The quickprop training algorithm uses the learning_rate parameter 
 		    along with other more advanced parameters, but it is only recommended to change these 
@@ -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
@@ -1649,7 +1649,7 @@ namespace FANN
            
            When choosing an activation function it is important to note that the activation 
            functions have different range. FANN::SIGMOID is e.g. in the 0 - 1 range while 
-           FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbound.
+           FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbounded.
            
            Information about the individual activation functions is available at <FANN::activation_function_enum>.
            
@@ -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

@@ -24,9 +24,9 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 
 /* Section: FANN Datatypes
 
-   The two main datatypes used in the fann library is <struct fann>, 
+   The two main datatypes used in the fann library are <struct fann>, 
    which represents an artificial neural network, and <struct fann_train_data>,
-   which represent training data.
+   which represents training data.
  */
 
 
@@ -42,27 +42,27 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 
 /* Enum: fann_train_enum
 	The Training algorithms used when training on <struct fann_train_data> with functions like
-	<fann_train_on_data> or <fann_train_on_file>. The incremental training looks alters the weights
+	<fann_train_on_data> or <fann_train_on_file>. The incremental training alters the weights
 	after each time it is presented an input pattern, while batch only alters the weights once after
 	it has been presented to all the patterns.
 
 	FANN_TRAIN_INCREMENTAL -  Standard backpropagation algorithm, where the weights are 
 		updated after each training pattern. This means that the weights are updated many 
-		times during a single epoch. For this reason some problems, will train very fast with 
+		times during a single epoch. For this reason some problems will train very fast with 
 		this algorithm, while other more advanced problems will not train very well.
 	FANN_TRAIN_BATCH -  Standard backpropagation algorithm, where the weights are updated after 
 		calculating the mean square error for the whole training set. This means that the weights 
-		are only updated once during a epoch. For this reason some problems, will train slower with 
+		are only updated once during an epoch. For this reason some problems will train slower with 
 		this algorithm. But since the mean square error is calculated more correctly than in 
-		incremental training, some problems will reach a better solutions with this algorithm.
+		incremental training, some problems will reach better solutions with this algorithm.
 	FANN_TRAIN_RPROP - A more advanced batch training algorithm which achieves good results 
 		for many problems. The RPROP training algorithm is adaptive, and does therefore not 
 		use the learning_rate. Some other parameters can however be set to change the way the 
 		RPROP algorithm works, but it is only recommended for users with insight in how the RPROP 
 		training algorithm works. The RPROP training algorithm is described by 
 		[Riedmiller and Braun, 1993], but the actual learning algorithm used here is the 
 		iRPROP- training algorithm which is described by [Igel and Husken, 2000] which 
-    	is an variety of the standard RPROP training algorithm.
+		is a variant of the standard RPROP training algorithm.
 	FANN_TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results 
 		for many problems. The quickprop training algorithm uses the learning_rate parameter 
 		along with other more advanced parameters, but it is only recommended to change these 
@@ -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:
@@ -296,8 +296,8 @@ static char const *const FANN_ERRORFUNC_NAMES[] = {
 /* Enum: fann_stopfunc_enum
 	Stop criteria used during training.
 
-	FANN_STOPFUNC_MSE - Stop criteria is Mean Square Error (MSE) value.
-	FANN_STOPFUNC_BIT - Stop criteria is number of bits that fail. The number of bits; means the
+	FANN_STOPFUNC_MSE - Stop criterion is Mean Square Error (MSE) value.
+	FANN_STOPFUNC_BIT - Stop criterion is number of bits that fail. The number of bits; means the
 		number of output neurons which differ more than the bit fail limit 
 		(see <fann_get_bit_fail_limit>, <fann_set_bit_fail_limit>). 
 		The bits are counted in all of the training data, so this number can be higher than
@@ -377,11 +377,11 @@ 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
-	parameter which tells how many epochs the training have taken so far.
+	callback function takes are the parameters given to <fann_train_on_data>, plus an epochs
+	parameter which tells how many epochs the training has taken so far.
 	
 	The callback function should return an integer, if the callback function returns -1, the training
 	will terminate.
@@ -462,7 +462,7 @@ struct fann_error
 
 
 /* 	Struct: struct fann
-	The fast artificial neural network(fann) structure.
+	The fast artificial neural network (fann) structure.
 
 	Data within this structure should never be accessed directly, but only by using the
 	*fann_get_...* and *fann_set_...* functions.
@@ -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_error.h

@@ -99,7 +99,7 @@ enum fann_errno_enum
    
    If log_file is NULL, no errors will be printed.
    
-   If errdata is NULL, the default log will be set. The default log is the log used when creating 
+   If errdat is NULL, the default log will be set. The default log is the log used when creating 
    <struct fann> and <struct fann_data>. This default log will also be the default for all new structs
    that are created.