Remove Typos

Signed-off-by:nirmalsinghania2008 <[email protected]>

sml/include/sml.h

@@ -66,7 +66,7 @@ extern "C" {
  * the person leaves. For that, this person has a presence sensor.
  *
  * Choosing engines and creating variables is straight-forward after
- * the solution is modeled. Let's say fuzzy was the choosen one.
+ * the solution is modeled. Let's say fuzzy was the chosen one.
  * Registering callbacks is simple as well:
  *
  * @dontinclude example_doc.c
@@ -105,7 +105,7 @@ extern "C" {
  * depending in your product. It may be synchronous or asynchronous.
  *
  * To keep it simple, yet illustrative, we're going to simulate the values
- * in a function read_state_cb() Variable sensor_state represents presense
+ * in a function read_state_cb() Variable sensor_state represents presence
  * sensor reading, switch_state represents light state.
  *
  * This variable will be global since its going to be used by the callback
@@ -154,7 +154,7 @@ extern "C" {
  *  - If you want to control a light and an air-conditioning and they
  *    are independent of each other. Create two SML objects,
  *    one will control the light and another one the air-conditioning.
- *  - Try to avoid adding unnecessary inputs/outpus to SML (or forget
+ *  - Try to avoid adding unnecessary inputs/outputs to SML (or forget
  *    to add relevant inputs/outputs), this may lead the poor
  *    predictions.
  *  - Test both engines and check which one has the best results
@@ -169,8 +169,8 @@ extern "C" {
 /**
  * @struct sml_object
  *
- * Instance of the choosen machine learning engine, it may be created with
- * sml_fuzzy_new() or sml_ann_new() and shold be deleted after usage with
+ * Instance of the chosen machine learning engine, it may be created with
+ * sml_fuzzy_new() or sml_ann_new() and should be deleted after usage with
  * sml_free().
  */
 struct sml_object;
@@ -236,7 +236,7 @@ bool sml_load_fll_file(struct sml_object *sml, const char *filename);
 void sml_free(struct sml_object *sml);
 
 /**
- * @brief Register a read calblack.
+ * @brief Register a read callblack.
  *
  * It should be used to set a callback function to read variables
  * values. This callback must return true if it was able to
@@ -303,7 +303,7 @@ int sml_process(struct sml_object *sml);
 /**
  * @brief Make a prediction based on the most recent observations.
  *
- * This is usefull for making predictions without the normal SML flow,
+ * This is useful for making predictions without the normal SML flow,
  * without a mainloop and a registered ::sml_change_cb. Take a look
  * in the following example:
  *
@@ -475,7 +475,7 @@ struct sml_variables_list *sml_get_output_list(struct sml_object *sml);
  * New input variables start with NAN set as value.
  *
  * @param sml The ::sml_object object.
- * @param name The variable name. If lenght is greater
+ * @param name The variable name. If length is greater
  * than ::SML_VARIABLE_NAME_MAX_LEN variable creation will fail.
  * @return ::sml_variable on success.
  * @return @c NULL on failure.
@@ -489,7 +489,7 @@ struct sml_variable *sml_new_input(struct sml_object *sml, const char *name);
  * the value set on NAN will be used.
  *
  * @param sml The ::sml_object object.
- * @param name The variable name. If lenght is greater
+ * @param name The variable name. If length is greater
  * than ::SML_VARIABLE_NAME_MAX_LEN variable creation will fail.
  * @return ::sml_variable on success.
  * @return @c NULL on failure.
@@ -522,7 +522,7 @@ struct sml_variable *sml_get_output(struct sml_object *sml, const char *name);
  * @param sml The ::sml_object object.
  * @param sml_variable The ::sml_variable
  * @param value The desired value.
- * @return @c true on succcess.
+ * @return @c true on success.
  * @return @c false on failure.
  */
 bool sml_variable_set_value(struct sml_object *sml, struct sml_variable *sml_variable, float value);
@@ -532,7 +532,7 @@ bool sml_variable_set_value(struct sml_object *sml, struct sml_variable *sml_var
  *
  * @param sml The ::sml_object object.
  * @param sml_variable The ::sml_variable
- * @return @c true on succcess.
+ * @return @c true on success.
  * @return @c false on failure.
  */
 float sml_variable_get_value(struct sml_object *sml, struct sml_variable *sml_variable);
@@ -569,7 +569,7 @@ int sml_variable_set_enabled(struct sml_object *sml, struct sml_variable *variab
  *
  * @param sml The ::sml_object object.
  * @param variable The ::sml_variable
- * @return @c true on succcess.
+ * @return @c true on success.
  * @return @c false on failure.
  */
 bool sml_variable_is_enabled(struct sml_object *sml, struct sml_variable *variable);
@@ -583,7 +583,7 @@ bool sml_variable_is_enabled(struct sml_object *sml, struct sml_variable *variab
  *
  * @param sml The ::sml_object object.
  * @param variable The ::sml_variable to be removed
- * @return @c true on succcess.
+ * @return @c true on success.
  * @return @c false on failure.
  */
 bool sml_remove_variable(struct sml_object *sml, struct sml_variable *variable);
@@ -604,7 +604,7 @@ uint16_t sml_variables_list_get_length(struct sml_object *sml, struct sml_variab
  * @param list The ::sml_variables_list.
  * @param index The list index.
  * @return ::sml_variable on success.
- * @return @c NULL on faiulre..
+ * @return @c NULL on failure..
  */
 struct sml_variable *sml_variables_list_index(struct sml_object *sml, struct sml_variables_list *list, uint16_t index);
 

sml/include/sml_ann.h

@@ -33,8 +33,8 @@ extern "C" {
  * A neural network consists in a set of neurons that are inter-connected
  * and distributed in layers, usually three (Input, hidden and output layers).
  * For every connection between neurons there is a weight associated to it,
- * these weights are initialized randomly with values beetween -0.2 and 0.2 and
- * adjusted during the traning phase, so the neural networkt
+ * these weights are initialized randomly with values between -0.2 and 0.2 and
+ * adjusted during the training phase, so the neural network
  * output predict the right value.
  *
  * The neuron is the basic unit of the neural network and it's responsible for
@@ -60,7 +60,7 @@ extern "C" {
  *
  * Consider that Bob is present (input is 1) and Alice (input is 0) is not.
  *
- * The first step is to provide the Bob's and Alice's presence to the input nerons,
+ * The first step is to provide the Bob's and Alice's presence to the input neurons,
  * In a neuron network the input neurons are special, because they do not apply the formula (1) to produce an output.
  * The output value from a input neuron is the input value itself, so in this case the
  * N1 and N2 neurons will output 1 and 0 respectively.
@@ -95,9 +95,9 @@ extern "C" {
  * @remark The values used in the neural network above
  * (inputs, outputs, neuron weights) were chosen randomly.
  *
- * The example above uses two neurons in the hidden layer, however for some problem, two nerons is not good enough.
+ * The example above uses two neurons in the hidden layer, however for some problem, two neurons is not good enough.
  * There is no silver bullet about how many neurons one should use in the hidden layer, this number is obtained by trial and error.
- * SML can handle this automatically, during the traning phase SML will automatically choose the neural network topology
+ * SML can handle this automatically, during the training phase SML will automatically choose the neural network topology
  * (how many neurons the hidden layer must have), it will also decide which is the best activation function.
  *
  * The SML neural network engine has two methods of operation, these methods try
@@ -108,8 +108,8 @@ extern "C" {
  * that has learnt in the past and only accumulate recent memory. This happens due the nature
  * of their training. In order to reduce this problem the following methods were implemented.
  *
- * The first method is called pseudohearsal (the default one), in this method only one neural network is created
- * and everytime it needs be retrained, random inputs are genereted and
+ * The first method is called pseudo-rehearsal (the default one), in this method only one neural network is created
+ * and everytime it needs be retrained, random inputs are generated and
  * feed to the network. The corresponding outputs are stored and used to train the neural network
  * with the new collected data.
  *
@@ -188,7 +188,7 @@ bool sml_is_ann(struct sml_object *sml);
 bool sml_ann_supported(void);
 
 /**
- * @brief Set the neural network trainning algorithm.
+ * @brief Set the neural network training algorithm.
  *
  * The training algorithm is responsible for adjusting the neural network weights.
  *
@@ -207,7 +207,7 @@ bool sml_ann_set_training_algorithm(struct sml_object *sml, enum sml_ann_trainin
  * Activation functions resides inside the neurons and they are responsible for producing the
  * neuron output value. As choosing the correct activation functions may required a
  * lot of trial and error tests, the SML uses an algorithm that tries to suit the best
- * activiation functions for a given problem.
+ * activation functions for a given problem.
  *
  * @remark By default all ::sml_ann_activation_function are used as candidates.
  *
@@ -244,7 +244,7 @@ bool sml_ann_set_max_neurons(struct sml_object *sml, unsigned int max_neurons);
  * To do so, it will create (M * 4) neuron candidates (where M is
  * the number of activation function candidates) and then N (where N is the number of candidate groups)
  * candidate groups will be created, ending up with M*4*N candidate neurons.
- * The only different beetwen these candidate groups is the initial weight values.
+ * The only different between these candidate groups is the initial weight values.
  *
  * @remark The default number of candidates is 6
  *
@@ -259,7 +259,7 @@ bool sml_ann_set_max_neurons(struct sml_object *sml, unsigned int max_neurons);
 bool sml_ann_set_candidate_groups(struct sml_object *sml, unsigned int candidate_groups);
 
 /**
- * @brief Set the neral network train epochs
+ * @brief Set the neural network train epochs
  *
  * The training epochs is used to know when to stop the training.
  * If the desired error is never reached, the training phase will stop
@@ -329,7 +329,7 @@ bool sml_ann_set_cache_max_size(struct sml_object *sml, unsigned int max_size);
 bool sml_ann_set_initial_required_observations(struct sml_object *sml, unsigned int required_observations);
 
 /**
- * @brief Set the pseudoreharsal strategy
+ * @brief Set the pseudorehearsal strategy
  *
  * For more information about the pseudorehearsal strategy look at the
  * \ref Neural_Network_Engine_Introduction

sml/include/sml_fuzzy.h

@@ -57,7 +57,7 @@ enum sml_fuzzy_snorm {
 
 /**
  * @enum sml_fuzzy_tnorm
- * @brief TNorm rules are also known as conjuntion.
+ * @brief TNorm rules are also known as conjunction.
  *
  * @see ::sml_fuzzy_conjunction_set
  */
@@ -81,11 +81,11 @@ enum sml_fuzzy_tnorm {
 enum sml_fuzzy_defuzzifier {
     SML_FUZZY_DEFUZZIFIER_BISECTOR,     /**< The point that divide the curve into two equal sub-regions */
     SML_FUZZY_DEFUZZIFIER_CENTROID,     /**< The center of the area under the curve */
-    SML_FUZZY_DEFUZZIFIER_LARGEST_OF_MAXIMUM,     /**< The largest value of the maximun degrees of membership */
+    SML_FUZZY_DEFUZZIFIER_LARGEST_OF_MAXIMUM,     /**< The largest value of the maximum degrees of membership */
     SML_FUZZY_DEFUZZIFIER_MEAN_OF_MAXIMUM,     /**< The mean of the maximum degrees of membership */
     SML_FUZZY_DEFUZZIFIER_SMALLEST_OF_MAXIMUM,     /**< The smallest value of the maximun degrees of membership */
-    SML_FUZZY_DEFUZZIFIER_WEIGHTED_AVERAGE,     /**< The avarage of the activation degrees multipled by a weight*/
-    SML_FUZZY_DEFUZZIFIER_WEIGHTED_SUM,     /**< The sum of the activation degrees multipled by a weight*/
+    SML_FUZZY_DEFUZZIFIER_WEIGHTED_AVERAGE,     /**< The average of the activation degrees multiplied by a weight*/
+    SML_FUZZY_DEFUZZIFIER_WEIGHTED_SUM,     /**< The sum of the activation degrees multiplied by a weight*/
 };     /**< A fuzzy Defuzzifier type. */
 
 /**
@@ -138,9 +138,9 @@ bool sml_fuzzy_supported(void);
 /**
  * @brief Set the conjunction fuzzy rule.
  *
- * The conjuction rule is equivalent to the boolean and operation.
+ * The conjunction rule is equivalent to the boolean and operation.
  * Example:
- * If the fuzzy engie encounters the following expression in a fuzzy rule, "... A and B ...".
+ * If the fuzzy engine encounters the following expression in a fuzzy rule, "... A and B ...".
  * Given that A is 0.6 and B is 0.8 and the conjunction operator is ::SML_FUZZY_TNORM_MINIMUM,
  * the result of the operation will be 0.6
  *
@@ -172,7 +172,7 @@ bool sml_fuzzy_conjunction_set(struct sml_object *sml, enum sml_fuzzy_tnorm norm
  * Values greater than @c 1 or lower than @c 0 are not accepted and rules with
  * weight @c zero are always ignored.
  *
- * @remark The default weight treshold is 0.05
+ * @remark The default weight threshold is 0.05
  *
  * @param sml The ::sml_object object.
  * @param weight_threshold The desired threshold.
@@ -231,7 +231,7 @@ bool sml_fuzzy_output_set_accumulation(struct sml_object *sml, struct sml_variab
  * automatically create terms for this variable. Some properties are important
  * help fuzzy engine to improve the quality of the created terms.
  *
- * Width is the width of each creted term. Other important properties are
+ * Width is the width of each created term. Other important properties are
  * is_id, set by ::sml_fuzzy_variable_set_is_id and range (min and max), set by
  * ::sml_variable_set_range.
  *
@@ -351,13 +351,13 @@ bool sml_fuzzy_variable_get_is_id(struct sml_object *sml, struct sml_variable *s
 struct sml_fuzzy_term *sml_fuzzy_variable_add_term_rectangle(struct sml_object *sml, struct sml_variable *variable, const char *name, float start, float end);
 
 /**
- * @brief Add a tirangle term for a variable.
+ * @brief Add a triangle term for a variable.
  *
  * A triangle term uses is a mathematical function defined by 3 vertex (a, b c).
  * For X coordinates between vertex a and b, function value is
  * obtained by the linear function connecting points (vertex_a; 0) to
  * (vertex_b; 1). In vertex_b, function value will 1.0  and from vertex_b to vertex_c,
- * function value is obtained by the linear function connecting opints (vertex_b; 1.0)
+ * function value is obtained by the linear function connecting points (vertex_b; 1.0)
  * to (vertex_c; 0).
  * For all other X values, the function value will be @c zero.
  *
@@ -378,7 +378,7 @@ struct sml_fuzzy_term *sml_fuzzy_variable_add_term_triangle(struct sml_object *s
  * @brief Add a cosine term for a variable.
  *
  * Cosine term value is obtained by the function value from a cosine function
- * centered in X coordinate center and with width defined by width paramenter.
+ * centered in X coordinate center and with width defined by width parameter.
  * The maximum value (1.0) of the cosine function is in X coordinate center.
  *
  * @remark The term name can not contain spaces!
@@ -397,7 +397,7 @@ struct sml_fuzzy_term *sml_fuzzy_variable_add_term_cosine(struct sml_object *sml
  * @brief Add a gaussian term for a variable.
  *
  * Gaussian term value is obtained by the function value from a gaussian
- * function defined by the parametes mean, standard_deviation. The maximum
+ * function defined by the parameters mean, standard_deviation. The maximum
  * value in Y axis of the gaussian function is 1.0.
  *
  * @remark The term name can not contain spaces!
@@ -475,7 +475,7 @@ bool sml_fuzzy_variable_remove_term(struct sml_object *sml, struct sml_variable
  *
  * The rule simplification uses a heuristic to try to simplify the fuzzy rules.
  * For example, if the fuzzy engine is controlling a a home Light using two variables
- * (Weekday and TimeOfTheDay) we can ha ve the following rules:
+ * (Weekday and TimeOfTheDay) we can have the following rules:
  * <BLOCKQUOTE>
  * If Weekday is Sunday and TimeOfTheDay is Night then Light is On. <BR>
  * If Weekday is Monday and TimeOfTheDay is Night then Light is On. <BR>

sml/include/sml_log.h

@@ -34,7 +34,7 @@ extern "C" {
  * @brief Log level types
  */
 enum sml_log_level {
-    SML_LOG_LEVEL_DEBUG = 1 << 0,     /**< Show debug messages. The debug messages will not be loged with SML is compiled in Release mode. */
+    SML_LOG_LEVEL_DEBUG = 1 << 0,     /**< Show debug messages. The debug messages will not be logged with SML is compiled in Release mode. */
     SML_LOG_LEVEL_INFO = 1 << 1,     /**< Show info messages. */
     SML_LOG_LEVEL_WARNING = 1 << 2, /**< Show warning messages. */
     SML_LOG_LEVEL_ERROR = 1 << 3, /**< Show error messages. */
@@ -59,7 +59,7 @@ typedef void (*sml_log_handler_cb)(enum sml_log_level level, const char *msg, vo
 /**
  * @brief Set a log handler.
  *
- * This function is usefull if one wants to log SML events in files
+ * This function is useful if one wants to log SML events in files
  * or do not want to log ::SML_LOG_LEVEL_WARNING messages, for example.
  * SML provides a default ::sml_log_handler_cb that is automatically set at
  * startup, the log level is set to ::SML_LOG_LEVEL_ALL and all messages will be
@@ -83,7 +83,7 @@ void sml_log_set_log_handler(enum sml_log_level levels, sml_log_handler_cb cb, v
 void sml_log_print(enum sml_log_level level, const char *format, ...);
 
 /**
- * @brief Syntatic sugar to ::sml_log_print(SML_LOG_LEVEL_DEBUG, "debug message")
+ * @brief Syntactic sugar to ::sml_log_print(SML_LOG_LEVEL_DEBUG, "debug message")
  *
  * @param ... A formatted message
  *
@@ -92,7 +92,7 @@ void sml_log_print(enum sml_log_level level, const char *format, ...);
 #define sml_debug(...) sml_log_print(SML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 
 /**
- * @brief Syntatic sugar to ::sml_log_print(SML_LOG_LEVEL_INFO, "info message")
+ * @brief Syntactic sugar to ::sml_log_print(SML_LOG_LEVEL_INFO, "info message")
  *
  * @param ... A formatted message
  *
@@ -101,7 +101,7 @@ void sml_log_print(enum sml_log_level level, const char *format, ...);
 #define sml_info(...) sml_log_print(SML_LOG_LEVEL_INFO, __VA_ARGS__)
 
 /**
- * @brief Syntatic sugar to ::sml_log_print(SML_LOG_LEVEL_WARNING, "warning message")
+ * @brief Syntactic sugar to ::sml_log_print(SML_LOG_LEVEL_WARNING, "warning message")
  *
  * @param ... A formatted message
  *
@@ -110,7 +110,7 @@ void sml_log_print(enum sml_log_level level, const char *format, ...);
 #define sml_warning(...) sml_log_print(SML_LOG_LEVEL_WARNING, __VA_ARGS__)
 
 /**
- * @brief Syntatic sugar to ::sml_log_print(SML_LOG_LEVEL_ERROR, "error message")
+ * @brief Syntactic sugar to ::sml_log_print(SML_LOG_LEVEL_ERROR, "error message")
  *
  * @param ... A formatted message
  *
@@ -119,7 +119,7 @@ void sml_log_print(enum sml_log_level level, const char *format, ...);
 #define sml_error(...) sml_log_print(SML_LOG_LEVEL_ERROR, __VA_ARGS__)
 
 /**
- * @brief Syntatic sugar to ::sml_log_print(SML_LOG_LEVEL_CRITICAL, "critical message")
+ * @brief Syntactic sugar to ::sml_log_print(SML_LOG_LEVEL_CRITICAL, "critical message")
  *
  * @param ... A formatted message
  *