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Gradient descent: Difference between revisions
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<pre>'#(0.10760797905122492 -1.2232993981966753)</pre>
=={{header|Scala}}==
{{trans|Go}}
<lang scala>object GradientDescent {
/** Steepest descent method modifying input values*/
def steepestDescent(x : Array[Double], learningRate : Double, tolerance : Double) = {
val n = x.size
var h = tolerance
var alpha = learningRate
var g0 = g(x) // Initial estimate of result.
// Calculate initial gradient.
var fi = gradG(x,h)
// Calculate initial norm.
var delG = 0.0
for (i <- 0 until n by 1) delG += fi(i) * fi(i)
delG = math.sqrt(delG)
var b = alpha / delG
// Iterate until value is <= tolerance.
while(delG > tolerance){
// Calculate next value.
for (i <- 0 until n by 1) x(i) -= b * fi(i)
h /= 2
// Calculate next gradient.
fi = gradG(x,h)
// Calculate next norm.
delG = 0.0
for (i <- 0 until n by 1) delG += fi(i) * fi(i)
delG = math.sqrt(delG)
b = alpha / delG
// Calculate next value.
var g1 = g(x)
// Adjust parameter.
if(g1 > g0) alpha = alpha / 2
else g0 = g1
}
}
/** Gradient of the input function given in the task*/
def gradG(x : Array[Double], h : Double) : Array[Double] = {
val n = x.size
val z : Array[Double] = Array.fill(n){0}
val y = x
val g0 = g(x)
for(i <- 0 until n by 1){
y(i) += h
z(i) = (g(y) - g0) / h
}
z
}
/** Bivariate function given in the task*/
def g( x : Array[Double]) : Double = {
( (x(0)-1) * (x(0)-1) * math.exp( -x(1)*x(1) ) + x(1) * (x(1)+2) * math.exp( -2*x(0)*x(0) ) )
}
def main(args: Array[String]): Unit = {
val tolerance = 0.0000006
val learningRate = 0.1
val x = Array(0.1, -1) // Initial guess of location of minimum.
steepestDescent(x, learningRate, tolerance)
println("Testing steepest descent method")
println("The minimum is at x : " + x(0) + ", y : " + x(1))
}
}
</lang>
{{out}}
<pre>
Testing steepest descent method
The minimum is at x : 0.10756393294495799, y : -1.2234116852966237
</pre>
=={{header|TypeScript}}==
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