TsetlinMachine.c

/*

Copyright (c) 2026 Ole-Christoffer Granmo and the University of Agder

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SOFTWARE.

This code implements the Tsetlin Machine from paper arXiv:1804.01508
https://arxiv.org/abs/1804.01508

*/

#include <stdio.h>
#include <stdlib.h>

#include "TsetlinMachine.h"

/**************************************/
/*** The Multiclass Tsetlin Machine ***/
/**************************************/

/*** Initialize Tsetlin Machine ***/
struct TsetlinMachine *CreateTsetlinMachine()
{
	struct TsetlinMachine *tm = (void *)malloc(sizeof(struct TsetlinMachine));

	/* Set up the Tsetlin Machine structure */

	tm_initialize(tm);
	
	return tm;
}


void tm_initialize(struct TsetlinMachine *tm)
{
	for (int i = 0; i < CLAUSES; i++) {
		for (int j = 0; j < ROOT_FACTORS; j++) {
			for (int k = 0; k < INTERIOR_ALTERNATIVES; k++) {	
				for (int l = 0; l < INTERIOR_FACTORS; l++) {
					for (int m = 0; m < LEAF_ALTERNATIVES; m++) {
						for (int n = 0; n < LEAF_FACTORS; n++) {

							if (1.0 * rand()/RAND_MAX <= 0.5) {
								(*tm).ta_state[i][j][k][l][m][n] = NUMBER_OF_STATES;
								(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] = NUMBER_OF_STATES + 1;
							} else {
								(*tm).ta_state[i][j][k][l][m][n] = NUMBER_OF_STATES + 1;
								(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] = NUMBER_OF_STATES;
							}
						}
					}
				}
			}
		}
	}
}

/* Translates automata state to action */
static inline int action(int state)
{
		return state > NUMBER_OF_STATES;
}

/* Calculate the output of each clause using the actions of each Tsetline Automaton. */
/* Output is stored an internal output array. */

static inline void calculate_clause_output(struct TsetlinMachine *tm, int Xi[], int predict)
{
	int action_include;

	for (int i = 0; i < CLAUSES; i++) {
		(*tm).clause_output[i] = 1;

		for (int j = 0; j < ROOT_FACTORS; j++) {
			// Evaluates interior subtrees, adding up votes from each
			(*tm).interior_vote_sums[i][j] = 0;

			for (int k = 0; k < INTERIOR_ALTERNATIVES; k++) {
				// Multiplies vote sums from multiple leaf groups (AND-combination)

				(*tm).interior_vote_products[i][j][k] = 1; // Stores how many class votes you get per interior alternative (product of leaf vote sums)

				for (int l = 0; l < INTERIOR_FACTORS; l++) {
					// Evaluates leaf alternatives (clause components), adding up the votes
					(*tm).leaf_vote_sum[i][j][k][l] = 0; // Stores how many class votes you get per feature group (vote summation over leaf alternatives)

					for (int m = 0; m < LEAF_ALTERNATIVES; m++) {
						// Evaluates clause component on its feature group

						(*tm).clause_component_output[i][j][k][l][m] = 1;
						for (int n = 0; n < LEAF_FACTORS; n++) {
							int feature = j * INTERIOR_FACTORS * LEAF_FACTORS + l * LEAF_FACTORS + n;

							action_include = action((*tm).ta_state[i][j][k][l][m][n]);
							if ((action_include == 1 && Xi[feature] == 0)) {
								(*tm).clause_component_output[i][j][k][l][m] = 0;
								break;
							}

							action_include = action((*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS]);
							if ((action_include == 1 && Xi[feature + FEATURES] == 0)) {
								(*tm).clause_component_output[i][j][k][l][m] = 0;
								break;
							}
						}

						// Adds up clause component votes (OR-alternatives)
						(*tm).leaf_vote_sum[i][j][k][l] += (*tm).clause_component_output[i][j][k][l][m];
					}

					// Multiplies leaf vote sums (AND-grouping). 
					(*tm).interior_vote_products[i][j][k] *= (*tm).leaf_vote_sum[i][j][k][l];
				}

				// Adds up interior leaf vote sum products (OR-alternatives)
				(*tm).interior_vote_sums[i][j] += (*tm).interior_vote_products[i][j][k];
			}

			// Multiplies interior vote sums (AND-grouping). 
			(*tm).clause_output[i] *= (*tm).interior_vote_sums[i][j];
		}
	}
}

/* Sum up the votes for each class (this is the multiclass version of the Tsetlin Machine) */
static inline int sum_up_class_votes(struct TsetlinMachine *tm)
{
	int class_sum = 0;
	for (int i = 0; i < CLAUSES; i++) {
		int sign = 1 - 2 * (i & 1);
		class_sum += (*tm).clause_output[i]*sign;
	}
	
	class_sum = (class_sum > THRESHOLD) ? THRESHOLD : class_sum;
	class_sum = (class_sum < -THRESHOLD) ? -THRESHOLD : class_sum;

	return class_sum;
}

/* Get the state of a specific automaton, indexed by clause, feature, and automaton type (include/include negated). */
int tm_get_state(struct TsetlinMachine *tm, int clause, int root_factor, int interior_alternative, int interior_factor, int leaf_alternative, int leaf_factor)
{
	return (*tm).ta_state[clause][root_factor][interior_alternative][interior_factor][leaf_alternative][leaf_factor];
}

/*************************************************/
/*** Type I Feedback (Combats False Negatives) ***/
/*************************************************/

static inline void type_i_feedback(struct TsetlinMachine *tm, int Xi[], int i, int j, int k, int l, int m, float s)
{
	if ((*tm).clause_output[i] == 0 || (*tm).interior_vote_products[i][j][k] == 0 || (*tm).clause_component_output[i][j][k][l][m] == 0)	{
		for (int n = 0; n < LEAF_FACTORS; n++) {
			(*tm).ta_state[i][j][k][l][m][n] -= ((*tm).ta_state[i][j][k][l][m][n] > 1) && (1.0*rand()/RAND_MAX <= 1.0/s);	

			(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] -= ((*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] > 1) && (1.0*rand()/RAND_MAX <= 1.0/s);				
		}
	} else {
		int feature_index = j * INTERIOR_FACTORS * LEAF_FACTORS + l * LEAF_FACTORS;

		for (int n = 0; n < LEAF_FACTORS; n++) {
			if (Xi[feature_index + n] == 1) {
				(*tm).ta_state[i][j][k][l][m][n] += ((*tm).ta_state[i][j][k][l][m][n] < NUMBER_OF_STATES*2) && (BOOST_TRUE_POSITIVE_FEEDBACK == 1 || 1.0*rand()/RAND_MAX <= (s-1)/s);
			} else {				
				(*tm).ta_state[i][j][k][l][m][n] -= ((*tm).ta_state[i][j][k][l][m][n] > 1) && (1.0*rand()/RAND_MAX <= 1.0/s);
			}

			if (Xi[feature_index + n + FEATURES] == 1) {
				(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] += ((*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] < NUMBER_OF_STATES*2) && (BOOST_TRUE_POSITIVE_FEEDBACK == 1 || 1.0*rand()/RAND_MAX <= (s-1)/s);
			} else {				
				(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] -= ((*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] > 1) && (1.0*rand()/RAND_MAX <= 1.0/s);
			}
		}
	}
}


/**************************************************/
/*** Type II Feedback (Combats False Positives) ***/
/**************************************************/

static inline void type_ii_feedback(struct TsetlinMachine *tm, int Xi[], int i, int j, int k, int l, int m) {
	int action_include;

	if ((*tm).clause_output[i] > 0 && (*tm).interior_vote_products[i][j][k] > 0 && (*tm).clause_component_output[i][j][k][l][m] == 1) {
		int feature_index = j * INTERIOR_FACTORS * LEAF_FACTORS + l * LEAF_FACTORS;

		for (int n = 0; n < LEAF_FACTORS; n++) {
			action_include = action((*tm).ta_state[i][j][k][l][m][n]);
			(*tm).ta_state[i][j][k][l][m][n] += (action_include == 0 && (*tm).ta_state[i][j][k][l][m][n] < NUMBER_OF_STATES*2) && (Xi[feature_index + n] == 0);

			action_include = action((*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS]);
			(*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] += (action_include == 0 && (*tm).ta_state[i][j][k][l][m][n + LEAF_FACTORS] < NUMBER_OF_STATES*2) && (Xi[feature_index + n + FEATURES] == 0);
		}
	}
}

/******************************************/
/*** Online Training of Tsetlin Machine ***/
/******************************************/

// The Tsetlin Machine can be trained incrementally, one training example at a time.
// Use this method directly for online and incremental training.

void tm_update(struct TsetlinMachine *tm, int Xi[], int target, float s) {
	/*******************************/
	/*** Calculate Clause Output ***/
	/*******************************/

	calculate_clause_output(tm, Xi, UPDATE);

	/***************************/
	/*** Sum up Clause Votes ***/
	/***************************/

	int class_sum = sum_up_class_votes(tm);

	/*************************************/
	/*** Calculate Feedback to Clauses ***/
	/*************************************/

	// Calculate feedback to clauses

	for (int i = 0; i < CLAUSES; i++) {
		int sign = 1 - 2 * (i & 1);

		for (int j = 0; j < ROOT_FACTORS; j++) {
			for (int k = 0; k < INTERIOR_ALTERNATIVES; k++) {
				for (int l = 0; l < INTERIOR_FACTORS; l++) {
					for (int m = 0; m < LEAF_ALTERNATIVES; m++) {
						(*tm).feedback_to_components[i][j][k][l][m] = sign*(2*target-1)*(1.0*rand()/RAND_MAX <= (1.0/(THRESHOLD*2))*(THRESHOLD + (1 - 2*target)*class_sum));
					}
				}
			}
		}
	}
	
	/*********************************/
	/*** Train Individual Automata ***/
	/*********************************/

	for (int i = 0; i < CLAUSES; i++) {
		for (int j = 0; j < ROOT_FACTORS; j++) {
			for (int k = 0; k < INTERIOR_ALTERNATIVES; k++) {
				for (int l = 0; l < INTERIOR_FACTORS; l++) {
					for (int m = 0; m < LEAF_ALTERNATIVES; m++) {
						if ((*tm).feedback_to_components[i][j][k][l][m] > 0) {
							type_i_feedback(tm, Xi, i, j, k, l, m, s);
						} else if ((*tm).feedback_to_components[i][j][k][l][m] < 0) {
							type_ii_feedback(tm, Xi, i, j, k, l, m);
						}
					}
				}
			}
		}
	}
}

int tm_score(struct TsetlinMachine *tm, int Xi[]) {
	/*******************************/
	/*** Calculate Clause Output ***/
	/*******************************/

	calculate_clause_output(tm, Xi, PREDICT);

	/***************************/
	/*** Sum up Clause Votes ***/
	/***************************/

	return sum_up_class_votes(tm);
}