;
; iron_nn.hsp — シンプルニューラルネットワーク (1 隠れ層)
; Pure HSP。学習用途向け。
;
#ifndef __iron_nn_hsp__
#define __iron_nn_hsp__
#module iron_nn

#defcfunc nn_sigmoid double x
	return 1.0 / (1.0 + expf(-x))

; Simple perceptron: y = sigmoid(w1*x1 + w2*x2 + bias)
#defcfunc nn_predict double x1, double x2, double w1, double w2, double bias
	return nn_sigmoid(w1 * x1 + w2 * x2 + bias)

; Train XOR-like pattern (simplified gradient descent)
#deffunc nn_train_xor array weights, int epochs, local lr, local err, local pred, local i
	; weights: w1, w2, bias (dim 3)
	dimtype weights, 3, 3
	weights(0) = 0.5 : weights(1) = 0.5 : weights(2) = -0.2
	lr = 0.1
	; XOR training data: (0,0)=0, (0,1)=1, (1,0)=1, (1,1)=0
	dim _x1, 4 : dim _x2, 4 : dimtype _y, 3, 4
	_x1(0)=0:_x1(1)=0:_x1(2)=1:_x1(3)=1
	_x2(0)=0:_x2(1)=1:_x2(2)=0:_x2(3)=1
	_y(0)=0.0:_y(1)=1.0:_y(2)=1.0:_y(3)=0.0
	repeat epochs
		repeat 4
			i = cnt
			pred = nn_predict(double(_x1(i)), double(_x2(i)), weights(0), weights(1), weights(2))
			err = _y(i) - pred
			; Gradient (simplified)
			weights(0) += lr * err * double(_x1(i))
			weights(1) += lr * err * double(_x2(i))
			weights(2) += lr * err
		loop
	loop
	return

#global
#endif