The SML naive fuzzy logic engine. More...

Data Structures
struct	sml_fuzzy_term

Macros
#define	SML_TERM_NAME_MAX_LEN (127)

Enumerations
enum	sml_fuzzy_defuzzifier { SML_FUZZY_DEFUZZIFIER_BISECTOR, SML_FUZZY_DEFUZZIFIER_CENTROID, SML_FUZZY_DEFUZZIFIER_LARGEST_OF_MAXIMUM, SML_FUZZY_DEFUZZIFIER_MEAN_OF_MAXIMUM, SML_FUZZY_DEFUZZIFIER_SMALLEST_OF_MAXIMUM, SML_FUZZY_DEFUZZIFIER_WEIGHTED_AVERAGE, SML_FUZZY_DEFUZZIFIER_WEIGHTED_SUM }
	The types of defuzzifier. More...

enum	sml_fuzzy_snorm { SML_FUZZY_SNORM_ALGEBRAIC_SUM, SML_FUZZY_SNORM_BOUNDED_SUM, SML_FUZZY_SNORM_DRASTIC_SUM, SML_FUZZY_SNORM_EINSTEIN_SUM, SML_FUZZY_SNORM_HAMACHER_SUM, SML_FUZZY_SNORM_MAXIMUM, SML_FUZZY_SNORM_NILPOTENT_MAXIMUM, SML_FUZZY_SNORM_NORMALIZED_SUM }
	SNorm rules are also known as accumulation. More...

enum	sml_fuzzy_tnorm { SML_FUZZY_TNORM_ALGEBRAIC_PRODUCT, SML_FUZZY_TNORM_BOUNDED_DIFFERENCE, SML_FUZZY_TNORM_DRASTIC_PRODUCT, SML_FUZZY_TNORM_EINSTEIN_PRODUCT, SML_FUZZY_TNORM_HAMACHER_PRODUCT, SML_FUZZY_TNORM_MINIMUM, SML_FUZZY_TNORM_NILPOTENT_MINIMUM }
	TNorm rules are also known as conjunction. More...

Functions
bool	sml_fuzzy_conjunction_set (struct sml_object *sml, enum sml_fuzzy_tnorm norm)
	Set the conjunction fuzzy rule. More...

struct sml_object *	sml_fuzzy_new (void)
	Creates a SML fuzzy engine. More...

bool	sml_fuzzy_output_set_accumulation (struct sml_object sml, struct sml_variable sml_variable, enum sml_fuzzy_snorm accumulation)
	Set the output accumulation. More...

bool	sml_fuzzy_output_set_defuzzifier (struct sml_object sml, struct sml_variable sml_variable, enum sml_fuzzy_defuzzifier defuzzifier, int defuzzifier_resolution)
	Set the defuzzifier for an output variable. More...

bool	sml_fuzzy_set_simplification_disabled (struct sml_object *sml, bool disabled)
	Disable/Enable simplification. More...

bool	sml_fuzzy_set_variable_terms_auto_balance (struct sml_object *sml, bool variable_terms_auto_balance)
	Set terms autobalance. More...

bool	sml_fuzzy_supported (void)
	Check if SML was built with fuzzy support. More...

struct sml_fuzzy_term *	sml_fuzzy_variable_add_term_cosine (struct sml_object sml, struct sml_variable variable, const char *name, float center, float width)
	Add a cosine term for a variable. More...

struct sml_fuzzy_term *	sml_fuzzy_variable_add_term_gaussian (struct sml_object sml, struct sml_variable variable, const char *name, float mean, float standard_deviation)
	Add a gaussian term for a variable. More...

struct sml_fuzzy_term *	sml_fuzzy_variable_add_term_ramp (struct sml_object sml, struct sml_variable variable, const char *name, float start, float end)
	Add a ramp term for a variable. More...

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)
	Add a rectangle term for a variable. More...

struct sml_fuzzy_term *	sml_fuzzy_variable_add_term_triangle (struct sml_object sml, struct sml_variable variable, const char *name, float vertex_a, float vertex_b, float vertex_c)
	Add a triangle term for a variable. More...

float	sml_fuzzy_variable_get_default_term_width (struct sml_object sml, struct sml_variable sml_variable)
	Get the default term width used by fuzzy to create terms. More...

bool	sml_fuzzy_variable_get_is_id (struct sml_object sml, struct sml_variable sml_variable)
	Check if this variable is used as an id field. More...

bool	sml_fuzzy_variable_remove_term (struct sml_object sml, struct sml_variable variable, struct sml_fuzzy_term *term)
	Remove a fuzzy term from the engine. More...

bool	sml_fuzzy_variable_set_default_term_width (struct sml_object sml, struct sml_variable sml_variable, float width)
	Set the default term width used by fuzzy to create terms. More...

bool	sml_fuzzy_variable_set_is_id (struct sml_object sml, struct sml_variable sml_variable, bool is_id)
	Check if this variable is used as an id field. More...

bool	sml_is_fuzzy (struct sml_object *sml)
	Check if the SML object is a fuzzy engine. More...

bool	sml_set_rule_weight_threshold (struct sml_object *sml, float weight_threshold)
	Rules below a given value will be ignored. More...

Detailed Description

The SML naive fuzzy logic engine.

The SML may use a modified fuzzy engine to predict values. The main difference from a normal fuzzy engine, is that the SML engine will create the fuzzy rules by itself. A good start to know more about fuzzy logic is visiting https://github.com/solettaproject/soletta/wiki/Soletta-Machine-Learning#fuzzy-logic

Macro Definition Documentation

#define SML_TERM_NAME_MAX_LEN (127)

Maximum size of terms name

Enumeration Type Documentation

enum sml_fuzzy_defuzzifier

The types of defuzzifier.

See Also: sml_fuzzy_output_set_defuzzifier

Enumerator
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 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 average of the activation degrees multiplied by a weight
SML_FUZZY_DEFUZZIFIER_WEIGHTED_SUM	The sum of the activation degrees multiplied by a weight

enum sml_fuzzy_snorm

SNorm rules are also known as accumulation.

See Also: sml_fuzzy_output_set_accumulation

Enumerator
SML_FUZZY_SNORM_ALGEBRAIC_SUM	A or B -> A+B - (A*B)
SML_FUZZY_SNORM_BOUNDED_SUM	A or B -> min(A+B,1)
SML_FUZZY_SNORM_DRASTIC_SUM	A or B -> if A=0 then B else if B=0 then A else 0
SML_FUZZY_SNORM_EINSTEIN_SUM	A or B -> A+B/1+A*B
SML_FUZZY_SNORM_HAMACHER_SUM	A or B -> if A=B=0 then 0 else AB/A+B-AB
SML_FUZZY_SNORM_MAXIMUM	A or B -> max(A,B)
SML_FUZZY_SNORM_NILPOTENT_MAXIMUM	A or B -> if A+B < 1 then max(A,B) else 1
SML_FUZZY_SNORM_NORMALIZED_SUM	A or B -> A+B/max(1, max(A,B))

enum sml_fuzzy_tnorm

TNorm rules are also known as conjunction.

See Also: sml_fuzzy_conjunction_set

Enumerator
SML_FUZZY_TNORM_ALGEBRAIC_PRODUCT	A and B -> A*B
SML_FUZZY_TNORM_BOUNDED_DIFFERENCE	A and B -> max(A+B-1, 0)
SML_FUZZY_TNORM_DRASTIC_PRODUCT	A and B -> if A=1 then B else if B=1 then A else 0
SML_FUZZY_TNORM_EINSTEIN_PRODUCT	A and B -> AB/2-(A+B-AB)
SML_FUZZY_TNORM_HAMACHER_PRODUCT	A and B -> AB/A+B-AB
SML_FUZZY_TNORM_MINIMUM	A and B -> min(A,B)
SML_FUZZY_TNORM_NILPOTENT_MINIMUM	A and B -> if A+B > 1 then min(A,B) else 0

Function Documentation

bool sml_fuzzy_conjunction_set	(	struct sml_object *	sml,
		enum sml_fuzzy_tnorm	norm
	)

Set the conjunction fuzzy rule.

The conjunction rule is equivalent to the boolean and operation. Example: 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

Remarks: The default value is SML_FUZZY_TNORM_MINIMUM

Parameters

sml	The sml_object object.
norm	The desired sml_fuzzy_tnorm.

Returns: true on success.; false on failure.

See Also: sml_fuzzy_tnorm

struct sml_object* sml_fuzzy_new ( void )

Creates a SML fuzzy engine.

A fuzzy term object.

Remarks: It must be freed with sml_free after usage is done.

Returns: A sml_object object on success.; NULL on failure.

See Also: sml_free; sml_object

bool sml_fuzzy_output_set_accumulation	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable,
		enum sml_fuzzy_snorm	accumulation
	)

Set the output accumulation.

The output accumulation rule is the strategy that will be used by fuzzy lib to select the final result output value using the output values calculated for each fuzzy rule.

Example: If the output value for variable A is 0.6 for Rule 1, the output value for variable A is 0.4 for Rule 2 and the selected accumulator is SML_FUZZY_SNORM_MAXIMUM, then the final output value will be 0.6

Parameters

sml	The sml_object object.
sml_variable	The sml_variable.
accumulation	The sml_fuzzy_snorm rule.

Returns: true on success.; false on failure.

bool sml_fuzzy_output_set_defuzzifier	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable,
		enum sml_fuzzy_defuzzifier	defuzzifier,
		int	defuzzifier_resolution
	)

Set the defuzzifier for an output variable.

Fuzzy defuzzifiers are used to convert degrees of membership to a crisp value (a real number) For example if from a given rule the fuzzy engine predict that the degrees of membership for a given variable for the term Hot is 10% and for the term Warm is 90%. Using the defuzzifier it will be possible the compute a real number to represent these degrees of membership.

Remarks: Defuzzifier only works for outputs variables, also the defuzzifier_resulution will be ignore if sml_fuzzy_defuzzifier is SML_FUZZY_DEFUZZIFIER_WEIGHTED_SUM or SML_FUZZY_DEFUZZIFIER_WEIGHTED_AVERAGE. The default defuzzifier is SML_FUZZY_DEFUZZIFIER_CENTROID.

Parameters

sml	The sml_object object.
sml_variable	The sml_variable to use the given defuzzifier.
defuzzifier	The sml_fuzzy_defuzzifier object.
defuzzifier_resolution	A constant that will be used by the defuzzifier to obtain the crisp value.

Returns: true on success.; false on failure.

bool sml_fuzzy_set_simplification_disabled	(	struct sml_object *	sml,
		bool	disabled
	)

Disable/Enable simplification.

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 have the following rules:

If Weekday is Sunday and TimeOfTheDay is Night then Light is On.
If Weekday is Monday and TimeOfTheDay is Night then Light is On.
....
If Weekday is Saturday and TimeOfTheDay is Night then Light is On.

It is clear that the Weekday is not important, if TimeOfTheDay is Night then the Light should be On. Knowing that, the fuzzy engine will simplify those rules into one rule:

If TimeOfTheDay is Night then Light is On.

However if after the simplification a new rule is created that denies the simplificated rule, the simplification is undone.

Remarks: Rule simplification is enabled by default.

Parameters

sml	The sml_object object.
disabled	`true` to disable, `false` to enable.

Returns: true on success.; false on failure.

bool sml_fuzzy_set_variable_terms_auto_balance	(	struct sml_object *	sml,
		bool	variable_terms_auto_balance
	)

Set terms autobalance.

Defining terms overlaps is a hard task, sometimes the user defined terms are not well suited for an application. If the term definitions are poor, the fuzzy engine the fuzzy predictions will be poor as well. If auto balance is enabled and if the fuzzy engine detects that the terms are not well set, it will recreate the terms.

Remarks: Terms auto balance is disabled by default

Parameters

sml	The sml_object object.
variable_terms_auto_balance	`true` to enable, `false` to disable.

Returns: true on success.; false on failure.

bool sml_fuzzy_supported ( void )

Check if SML was built with fuzzy support.

Returns: true If it is supported.; false If not supported.

struct sml_fuzzy_term* sml_fuzzy_variable_add_term_cosine	(	struct sml_object *	sml,
		struct sml_variable *	variable,
		const char *	name,
		float	center,
		float	width
	)

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 parameter. The maximum value (1.0) of the cosine function is in X coordinate center.

Remarks: The term name can not contain spaces!

Parameters

sml	The sml_object object.
variable	The sml_variable.
name	The term's name. Its length must be smaller than SML_TERM_NAME_MAX_LEN.
center	The center of the cosine curve.
width	The width of the cosine curve.

Returns: sml_fuzzy_term on success.; NULL on failure.

struct sml_fuzzy_term* sml_fuzzy_variable_add_term_gaussian	(	struct sml_object *	sml,
		struct sml_variable *	variable,
		const char *	name,
		float	mean,
		float	standard_deviation
	)

Add a gaussian term for a variable.

Gaussian term value is obtained by the function value from a gaussian function defined by the parameters mean, standard_deviation. The maximum value in Y axis of the gaussian function is 1.0.

Remarks: The term name can not contain spaces!

Parameters

sml	The sml_object object.
variable	The sml_variable.
name	The term's name. Its length must be smaller than SML_TERM_NAME_MAX_LEN.
mean	The mean of the gaussian curve.
standard_deviation	The standard deviation of the curve.

Returns: sml_fuzzy_term on success.; NULL on failure.

struct sml_fuzzy_term* sml_fuzzy_variable_add_term_ramp	(	struct sml_object *	sml,
		struct sml_variable *	variable,
		const char *	name,
		float	start,
		float	end
	)

Add a ramp term for a variable.

A ramp term uses a mathematical function defined by its start and end points. Start parameters is the coordinate in X axis where the ramp is in its minimum value (zero). End parameter is the coordinate in Y axis where the ramp is in its maximum value (1.0). If start < end we will have a positive ramp. If end < start we will have a negative ramp.
For example:

//this will create a ramp going up, connecting points (0.3; 0.0) and (0.7; 1.0). In X position 0.3, the ramp value will be 0.0. In X position 0.7, the ramp value will be 1.0
  sml_fuzzy_variable_add_term_ramp    (sml, var, "ramp_up", 0.3, 0.7);
//this will create a ramp going down, connecting points (0.3; 1.0) and (0.7; 0.0). In X position 0.3, the ramp value will be 1.0. In X position 0.7, the ramp value will be 0.0
sml_fuzzy_variable_add_term_ramp  (sml, var, "ramp_up", 0.7, 0.3);

Remarks: The term name can not contain spaces!

Parameters

sml	The sml_object object.
variable	The sml_variable.
name	The term's name. Its length must be smaller than SML_TERM_NAME_MAX_LEN.
start	The start of the ramp on the X axis.
end	The end of the ramp on the X axis.

Returns: sml_fuzzy_term on success.; NULL on failure.

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
	)

Add a rectangle term for a variable.

A rectangle term uses a mathematical function defined by its start and end points. For any X value from start to end, the function value will be 1.0. For all other X values, the function value will be zero.

Remarks: The term name can not contain spaces!

Parameters

sml	The sml_object object.
variable	The sml_variable.
name	The term's name. Its length must be smaller than SML_TERM_NAME_MAX_LEN.
start	The point on the X axis that the term will start.
end	The point on the X axis that the term will end.

Returns: sml_fuzzy_term on success.; NULL on failure.

struct sml_fuzzy_term* sml_fuzzy_variable_add_term_triangle	(	struct sml_object *	sml,
		struct sml_variable *	variable,
		const char *	name,
		float	vertex_a,
		float	vertex_b,
		float	vertex_c
	)

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 points (vertex_b; 1.0) to (vertex_c; 0). For all other X values, the function value will be zero.

Remarks: The term name can not contain spaces!

Parameters

sml	The sml_object object.
variable	The sml_variable.
name	The term's name. Its length must be smaller than SML_TERM_NAME_MAX_LEN.
vertex_a	The point on the X axis that the term will start.
vertex_b	The point on the X axis that will be the middle of the triangle.
vertex_c	the point on the X axis that the term will end.

Returns: sml_fuzzy_term on success.; NULL on failure.

float sml_fuzzy_variable_get_default_term_width	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable
	)

Get the default term width used by fuzzy to create terms.

Parameters

sml	The sml_object object.
sml_variable	The sml_variable.

See Also: sml_fuzzy_variable_set_default_term_width

bool sml_fuzzy_variable_get_is_id	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable
	)

Check if this variable is used as an id field.

Parameters

sml	The sml_object object.
sml_variable	The sml_variable.

Returns: true if sml_variable is an id field; false if sml_variable isn't an id field

See Also: sml_fuzzy_variable_set_is_id

bool sml_fuzzy_variable_remove_term	(	struct sml_object *	sml,
		struct sml_variable *	variable,
		struct sml_fuzzy_term *	term
	)

Remove a fuzzy term from the engine.

Parameters

sml	The sml_object object.
variable	The sml_variable to remove the term.
term	The The sml_fuzzy_term to be removed.

Returns: true on success.; false on failure.

bool sml_fuzzy_variable_set_default_term_width	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable,
		float	width
	)

Set the default term width used by fuzzy to create terms.

If sml_process is called and a variable has no terms fuzzy engine will 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 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.

For example, for the variable created by the following code:

struct sml_variable *var = sml_new_input(sml, "my_var");
sml_variable_set_range(sml, var, 0, 5);
sml_fuzzy_variable_set_default_term_width(sml, var, 1);

Fuzzy engine will create 5 terms:

ramp from 0 to 1
triangle from 1 to 2, with peak at 0.5
triangle from 2 to 3, with peak at 1.5
triangle from 3 to 4, with peak at 3.5
ramp from 4 to 5.

After that, a small overlap will be added between each term. The default overlap value is 10% of width, in this case, 0.1. So at the end we would have:

ramp from 0 to 1.1
triangle from 0.9 to 2.1, with peak at 0.5
triangle from 1.9 to 3.1, with peak at 1.5
triangle from 2.9 to 4.1, with peak at 3.5
ramp from 3.9 to 5.

In case the variable is an id, the first and last element will have a size width/2. This is necessary so we can have a constant distance from the peak of each term. This distance is always equal width.

For example, for the variable created by the following code:

struct sml_variable *var = sml_new_input(sml, "my_var");
sml_variable_set_range(sml, var, 0, 5);
sml_fuzzy_variable_set_default_term_width(sml, var, 1);
sml_fuzzy_variable_set_is_id(sml, var, true);

We would have 5 terms:

ramp from 0 to 0.6
triangle from 0.4 to 1.6, with peak at 1
triangle from 1.4 to 2.6 with peak at 2
triangle from 2.4 to 3.6, with peak at 3
triange from 3.4 to 4.6, with peak at 4
ramp from 4.4 to 5

Terms autocreated are also rebalanced every time sml_variable_set_range is called. So new terms are created to make sure all variable range is covered. And terms that are no longer necessary are removed.

Parameters

sml	The sml_object object.
sml_variable	The sml_variable.
width	Default term width

Remarks: The default value is NAN

See Also: sml_fuzzy_variable_get_default_term_width

bool sml_fuzzy_variable_set_is_id	(	struct sml_object *	sml,
		struct sml_variable *	sml_variable,
		bool	is_id
	)

Check if this variable is used as an id field.

The is_id property is used by fuzzy engine when it is creating terms automatically. Terms created by fuzzy engine will be optimized to be used by variables that keeps ids.

For more information take a look at sml_fuzzy_variable_set_default_term_width

Parameters

sml	The sml_object object.
sml_variable	The sml_variable.
is_id	`true` if this variable should be considered an id. `false` otherwise

Remarks: The default value is false

See Also: sml_fuzzy_variable_get_is_id

bool sml_is_fuzzy ( struct sml_object * sml )

Check if the SML object is a fuzzy engine.

Parameters

sml	The sml_object object.

Returns: true If it is fuzzy.; false If it is not fuzzy.

bool sml_set_rule_weight_threshold	(	struct sml_object *	sml,
		float	weight_threshold
	)

Rules below a given value will be ignored.

Fuzzy rules created by fuzzy engine have weights associated with it, bigger weights means bigger relevance. The range of weight value is from zero (no relevance) to 1 (very relevant).

Weight threshold property is used to select if rules with low relevance are going to be used in output predictions. If weight_threshold is a value close to zero, even low relevance rules are going to be used. If weight_threshold is close to 1, only very relevant rules are going to be used.

Values greater than 1 or lower than 0 are not accepted and rules with weight zero are always ignored.

Remarks: The default weight threshold is 0.05

Parameters

sml	The sml_object object.
weight_threshold	The desired threshold.

Returns: true on success.; false on failure.

Data Structures

Macros

Enumerations

Functions

Detailed Description

Macro Definition Documentation

Enumeration Type Documentation

Function Documentation