JarCastingokAlgo
Idiomatic Kotlin extensions for ojAlgo, with some inspirations from PuLP.
Linear Algebra DSL
Below is an example of how to use the linear algebra DSL. In this particular example, we create a Markov chain to calculate the probability of 5 consecutive heads in 10 coin flips.
import org.ojalgo.okalgo.populate
import org.ojalgo.okalgo.primitivematrix
import org.ojalgo.okalgo.times
fun main() {
val transitionMatrix = primitivematrix(rows = 6, cols = 6) {
populate {row, col ->
when {
col == 0L -> .50
row + 1L == col -> .50
row == 5L && col == 5L -> 1.0
else -> 0.0
}
}
}
println("\r\nTransition Matrix:")
println(transitionMatrix)
val toTenthPower = generateSequence(transitionMatrix) { it * transitionMatrix }.take(10).last()
println("\r\nTransition Matrix Raised to 10th Power")
println(toTenthPower)
println("\r\nMARKOV CHAIN RESULT: ${toTenthPower[0,5]}")
}
// REFERENCE: https://www.quora.com/What-is-the-probability-of-getting-5-consecutive-heads-in-10-tosses-of-a-fair-coin
OUTPUT:
Transition Matrix:
org.ojalgo.matrix.PrimitiveMatrix < 6 x 6 >
{ { 0.5, 0.5, 0.0, 0.0, 0.0, 0.0 },
{ 0.5, 0.0, 0.5, 0.0, 0.0, 0.0 },
{ 0.5, 0.0, 0.0, 0.5, 0.0, 0.0 },
{ 0.5, 0.0, 0.0, 0.0, 0.5, 0.0 },
{ 0.5, 0.0, 0.0, 0.0, 0.0, 0.5 },
{ 0.5, 0.0, 0.0, 0.0, 0.0, 1.0 } }
Transition Matrix Raised to 10th Power
org.ojalgo.matrix.PrimitiveMatrix < 6 x 6 >
{ { 0.5546875, 0.267578125, 0.1298828125, 0.0634765625, 0.03125, 0.109375 },
{ 0.6015625, 0.287109375, 0.1376953125, 0.06640625, 0.0322265625, 0.140625 },
{ 0.7109375, 0.333984375, 0.1572265625, 0.07421875, 0.03515625, 0.2041015625 },
{ 0.9609375, 0.443359375, 0.2041015625, 0.09375, 0.04296875, 0.333984375 },
{ 1.5244140625, 0.693359375, 0.3134765625, 0.140625, 0.0625, 0.6015625 },
{ 2.78125, 1.2568359375, 0.5634765625, 0.25, 0.109375, 1.15625 } }
MARKOV CHAIN RESULT: 0.109375
MIP Solver DSL
EXAMPLE 1
expressionsbasedmodel {
val v1 = variable(lower = 3, upper = 6)
val v2 = variable(lower = 10, upper = 12)
expression(weight = 1) {
set(v1, 1)
set(v2, 1)
}
maximise()
println("v1=${v1.value.toDouble()} v2=${v2.value.toDouble()}")
}
EXAMPLE 2
val model = ExpressionsBasedModel()
val v1 = model.variable(lower = 3, upper = 6)
val v2 = model.variable(lower = 10, upper = 12)
model.expression(weight=1) {
set(v1, 1)
set(v2, 1)
}
model.maximise()
println("v1=${v1.value.toDouble()} v2=${v2.value.toDouble()}")
Expression building with Kotlin extensions is also being explored:
EXAMPLE 3
expressionsbasedmodel {
val v1 = variable(lower = 2, upper = 10, isInteger = true)
val v2 = variable(lower = 2, upper = 10, isInteger = true)
expression(v1 + 2*v2) {
weight(1)
}
expression {
set(v1 + v2 EQ 16)
}
minimise().run(::println)
println("v1=${v1.value.toDouble()} v2=${v2.value.toDouble()}")
}
Artifact Instructions
Until this gets deployed to Maven Central, you can use JitPack to import this project as a dependency.
Maven
<dependency>
<groupId>org.ojalgo</groupId>
<artifactId>okalgo</artifactId>
<version>0.0.2</version>
</dependency>
Gradle
compile 'org.ojalgo:okalgo:0.0.2'
