/*
	pptdb, generate neural network training data from a playtak.com
	database dump

	Copyright (C) 2021, tslil clingman

	This program is free software: you can redistribute it and/or modify
	it under the terms of the GNU General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.

	This program is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
	GNU General Public License for more details.

	You should have received a copy of the GNU General Public License
	along with this program.  If not, see <https://www.gnu.org/licenses/>.
*/

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

#include <tak.h>
#include <actions.h>

#include <math.h>

int generate;
uint64_t heights[16];
FILE *training_fh = NULL;
float max_flats, outcome_black;

static void
write_input(const int dx, const int dy, const uint8_t swap) {
	// Two numbers for flats remaining
	fprintf(training_fh,"%.8f,%.8f,",
					(float)(white_count & 127)/max_flats,
					(float)(black_count & 127)/max_flats);

	// Write the board layers
	float val;
	int col, row;
	for (uint8_t depth = 0; depth < board_size + 1; depth++) {
		row = (dy>0)?-1:board_size;
		for (int i = 0; i < board_size; i++) {
			row += dy;
			col = (dx>0)?-1:board_size;
			for (int j = 0; j < board_size; j++) {
				col += dx;
				const uint8_t k =
					(swap) ? THE_COORDS(row, col) : THE_COORDS(col, row);
				val = 0;
				if (COUNT_AT(k)>depth) {
					if (depth == 0) {
						// Top layer of stacks is handled differently to indicate
						// stone type
						if (STONE_AT(k) == STONE_STANDING) {
							val = (colours[k] & 1) ? +0.25 : -0.25;
						} else if (STONE_AT(k) == STONE_CAPSTONE) {
							val = (colours[k] & 1) ? +1.00 : -1.00;
						} else {
							val = (colours[k] & 1) ? +0.75 : -0.75;
						}
					} else {
						// Layers underneath
						val = (colours[k] & (1<<depth)) ? +0.75 : -0.75;
					}
				}
				fprintf(training_fh,"%.2f,", val);
			}
		}
	}
	fprintf(training_fh,"%.1f\n", outcome_black);
}

// Warning: performs _no_ checks on input whatsoever
static enum ACT_RESULT
parse_line(const char *pt, const ssize_t read, float *out_avg_actions) {
	ssize_t idx;
	enum ACT_RESULT r;
	int total_plies = 0;

	float avg_actions = 0;
	action_list_t *actions;

	for (idx=0;idx<read;idx++) {
		if (pt[idx]==',') total_plies++;
	}
	for(idx=0;;) {
		actions = action_list_generate();
		avg_actions += (float)(actions->length);
		action_list_free(actions);

		if (pt[idx] == 'P') {
			// P [A-F][1-6] [CF]?,
			idx+=2;
			enum STONE_VARIANT stone;
			const uint8_t col = pt[idx]-'A', row = pt[idx+1]-'1';

			if (idx + 3 < read) {
				switch (pt[idx+3]) {
					case 'W': { stone = STONE_STANDING; break; }
					case 'C': { stone = STONE_CAPSTONE; break; }
					default:  { stone = STONE_FLAT;     break; }
				}
			} else {
				stone = STONE_FLAT;
			}

			r = try_place(THE_COORDS(col,row), current_colour, stone);
			if (r != ACT_OK) return r;
		} else if (pt[idx] == 'M') {
			// M [A-F][1-6] [A-F][1-6]( [1-6])+,
			idx+=2;
			uint8_t drops[board_size];
			const uint8_t s_col =pt[idx]-'A', s_row=pt[idx+1]-'1',
				d_col=pt[idx+3]-'A', d_row=pt[idx+4]-'1';
			idx+=4;

			enum MOVE_DIRECTION dir = M_RIGHT;
			if (s_col < d_col) dir=M_RIGHT;
			else if (s_col > d_col) dir=M_LEFT;
			else if (s_row < d_row) dir=M_UP;
			else if (s_row > d_row) dir=M_DOWN;

			uint8_t steps = 0;
			do {
				idx+=2;
				drops[steps++] = pt[idx] - '0';
			} while (idx+2<read && pt[idx+1] != ',');

			r = try_move(THE_COORDS(s_col, s_row), dir, steps, drops);

			if (r != ACT_OK) return r;

			if (generate == 0) {
				// Measure height of stacks exceeding 1
				for (int k = 0; k < board_size * board_size; k++) {
					if (COUNT_AT(k)>1) heights[COUNT_AT(k)]+=1;
				}
			}
		}
		// Generate training data, not too early in the game and not at
		// the end, under all eight symmetries of the board
		if (generate && ply < total_plies && ply + 2 >= total_plies) {
			write_input(+1, +1, 1); write_input(+1, +1, 0);
			write_input(+1, -1, 1); write_input(+1, -1, 0);
			write_input(-1, +1, 1); write_input(-1, +1, 0);
			write_input(-1, -1, 1); write_input(-1, -1, 0);
		}
		// Parse next action
		while (idx<read && pt[idx++]!=',');
		if (idx>=read) goto count_this;
		next_ply();
	}
 count_this:
	avg_actions /= (float) total_plies;
	*out_avg_actions = avg_actions;
	return ACT_OK;
}

const char* license = "pptdb, generate neural network training data from a playtak.com database dump\n\
\n\
Copyright (C) 2021, tslil clingman\n\
\n\
This program comes with ABSOLUTELY NO WARRANTY; and is made available under the terms of the GNU GPL v3 license. This is free software, and you are welcome to redistribute it under certain conditions; see COPYING for details.\n";

int main(int argc, char **argv) {
	(void)(argc);

	enum ACT_RESULT r;
	enum WIN_TYPE win;
	uint32_t games = 0, overflow=0, illegal = 0;
	uint32_t road_wins=0, flat_wins=0, road_turns=0, flat_turns=0,
		white_wins = 0, black_wins = 0;

	for (int k = 0; k < 16; k++) heights[k] = 0;

	size_t len = 0;
	ssize_t read = 0;
	FILE *playtak_fh = NULL;
	char *line = NULL, td_fn[65];

	const uint8_t size = argv[1][0]-'0';

#define LAZY_MAX_GAMES 1000000
	float avg_actions[LAZY_MAX_GAMES];
	for (uint32_t k=0; k<LAZY_MAX_GAMES; k++) avg_actions[k]=0;
	action_list_init(size);

	playtak_fh = fopen(argv[2], "r");
	if (playtak_fh == NULL) exit(EXIT_FAILURE);

	if (argc > 3 && (!strncmp("generate", argv[3], 8))) {
		generate=1;
		max_flats = (size == 5) ? 21.0 : 30.0;
		snprintf(td_fn, 64, "data/training-%d.csv",size);
		training_fh = fopen(td_fn, "w");
		if (training_fh == NULL) exit(EXIT_FAILURE);
	} else generate=0;

	while ((read = getline(&line, &len, playtak_fh)) != -1) {
		// Reset everything
		reset_state(size);
		// Store the outcome of this game. Black win = 1
		if (line[read-4] == '0') outcome_black = 0.9;
		else outcome_black = -0.9;
		// Parse the line
		r = parse_line(line,read-4, avg_actions+games);
		// Adjust counts if we're not generating training data
		if (generate == 0) {
			if (r == ACT_ILLEGAL) {
				illegal++;
				printf("Illegal:\n%s",line);
			} else if (r == ACT_OVERFLOW) {
				printf("Overflow:\n%s",line);
				overflow++;
			} else {
				win = check_win();
				if (win == WIN_FLAT_BLACK
						|| win == WIN_FLAT_WHITE
						|| win == WIN_DRAW) {
					flat_wins++;
					flat_turns += ply/2+1;
				} else {
					road_wins++;
					road_turns += ply/2+1;
				}
				if (win == WIN_FLAT_BLACK || win == WIN_ROAD_BLACK)
					black_wins++;
				else if (win == WIN_FLAT_WHITE || win == WIN_ROAD_WHITE)
					white_wins++;
			}
		}
		games++;
	}

	fclose(playtak_fh);
	if (generate) fclose(training_fh);
	if (line) free(line);

	if (illegal || overflow) putchar('\n');
	printf("Read %d games\n",games);

	if (generate==0) {
		float sum = 0;
		for (uint32_t k=0; k<games; k++) sum+=avg_actions[k];
		const float mean = sum/(float)games;
		float variance = 0;
		for (uint32_t k=0; k<games; k++) variance+=(avg_actions[k]-mean)*(avg_actions[k]-mean);
		variance /= (float) games;

		printf("Illegals:  %d\nOverflows: %d\n\
Black wins: %.3f%%\n\
Road wins: %d\nFlat wins: %d\n\
Average turns to road win: %.3f\n\
Average turns to flat win: %.3f\n\
Mean avaiable actions per ply per game: %.3f ± %.3f σ\n",
					 illegal, overflow,
					 (double)black_wins / (double)(black_wins+white_wins) * 100,
					 road_wins, flat_wins,
					 (double)(road_turns)/(double)(road_wins),
					 (double)(flat_turns)/(double)(flat_wins),
					 mean, sqrt(variance));
		for (int k = 2; k < 16; k++) {
			printf("Height %2d: %7ld\n",k,heights[k]);
		}
	}

	exit(EXIT_SUCCESS);
}