def uniform(min: Double, max: Double): Double = {

return rng.nextDouble() * (max - min) + min

}

def binomial(n: Int, p: Double): Int = {

if(p < 0 || p > 1) return 0

var c: Int = 0

var r: Double = 0.0

var i: Int = 0

for(i <- 0 until n) {

r = rng.nextDouble()

if(r < p) c += 1

}

return c

}

def sigmoid(x: Double): Double = {

return 1.0 / (1.0 + math.pow(math.E, -x))

}

if(rng == null) rng = new Random(1234)

var a: Double = 0.0

var W: Array[Array[Double]] = Array.ofDim[Double](n_out, n_in)

var b: Array[Double] = new Array[Double](n_out)

var i: Int = 0

if(_W == null) {

a = 1.0 / n_in

for(i <- 0 until n_out) {

for(j <- 0 until n_in) {

W(i)(j) = uniform(-a, a)

}

}

} else {

W = _W

}

if(_b != null) b = _b

def output(input: Array[Int], w: Array[Double], b: Double): Double = {

var linear_output: Double = 0.0

var j: Int = 0

for(j <- 0 until n_in) {

linear_output += w(j) * input(j)

}

linear_output += b

return sigmoid(linear_output)

}

def sample_h_given_v(input: Array[Int], sample: Array[Int]) {

var i: Int = 0

for(i <- 0 until n_out) {

sample(i) = binomial(1, output(input, W(i), b(i)))

}

}