0.3.0-dev-8. Cleanup
This commit is contained in:
parent
e4e661a3bf
commit
7ce0829597
@ -35,6 +35,7 @@
|
||||
- `contentEquals` from Buffer. It moved to the companion.
|
||||
- MSTExpression
|
||||
- Expression algebra builders
|
||||
- Comples and Quaternion no longer are elements.
|
||||
|
||||
### Fixed
|
||||
- Ring inherits RingOperations, not GroupOperations
|
||||
|
@ -15,7 +15,7 @@ allprojects {
|
||||
}
|
||||
|
||||
group = "space.kscience"
|
||||
version = "0.3.0-dev-7"
|
||||
version = "0.3.0-dev-8"
|
||||
}
|
||||
|
||||
subprojects {
|
||||
|
@ -6,8 +6,8 @@
|
||||
package space.kscience.kmath.tensors
|
||||
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.tensors.core.DoubleTensor
|
||||
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
|
||||
import space.kscience.kmath.tensors.core.DoubleTensor
|
||||
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
|
||||
import space.kscience.kmath.tensors.core.toDoubleArray
|
||||
import kotlin.math.sqrt
|
||||
@ -106,6 +106,7 @@ fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
|
||||
}
|
||||
|
||||
// neural network class
|
||||
@OptIn(ExperimentalStdlibApi::class)
|
||||
class NeuralNetwork(private val layers: List<Layer>) {
|
||||
private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
|
||||
|
||||
@ -120,7 +121,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
|
||||
(-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
|
||||
}
|
||||
|
||||
@OptIn(ExperimentalStdlibApi::class)
|
||||
|
||||
private fun forward(x: DoubleTensor): List<DoubleTensor> {
|
||||
var input = x
|
||||
|
||||
@ -133,7 +134,6 @@ class NeuralNetwork(private val layers: List<Layer>) {
|
||||
}
|
||||
}
|
||||
|
||||
@OptIn(ExperimentalStdlibApi::class)
|
||||
private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) {
|
||||
val layerInputs = buildList {
|
||||
add(xTrain)
|
||||
@ -180,7 +180,7 @@ fun main() {
|
||||
val features = 5
|
||||
val sampleSize = 250
|
||||
val trainSize = 180
|
||||
val testSize = sampleSize - trainSize
|
||||
//val testSize = sampleSize - trainSize
|
||||
|
||||
// take sample of features from normal distribution
|
||||
val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
|
||||
|
@ -48,7 +48,7 @@ fun main() {
|
||||
|
||||
|
||||
// inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
|
||||
val sigma = diagonalEmbedding(singValues.map{ x -> if (abs(x) < 1e-3) 0.0 else 1.0/x })
|
||||
val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it })
|
||||
|
||||
val alphaOLS = v dot sigma dot u.transpose() dot y
|
||||
println("Estimated alpha:\n" +
|
||||
|
@ -9,7 +9,10 @@ import space.kscience.kmath.memory.MemoryReader
|
||||
import space.kscience.kmath.memory.MemorySpec
|
||||
import space.kscience.kmath.memory.MemoryWriter
|
||||
import space.kscience.kmath.misc.UnstableKMathAPI
|
||||
import space.kscience.kmath.operations.*
|
||||
import space.kscience.kmath.operations.ExtendedField
|
||||
import space.kscience.kmath.operations.Norm
|
||||
import space.kscience.kmath.operations.NumbersAddOperations
|
||||
import space.kscience.kmath.operations.ScaleOperations
|
||||
import space.kscience.kmath.structures.Buffer
|
||||
import space.kscience.kmath.structures.MemoryBuffer
|
||||
import space.kscience.kmath.structures.MutableBuffer
|
||||
@ -180,12 +183,10 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
|
||||
* @property im The imaginary part.
|
||||
*/
|
||||
@OptIn(UnstableKMathAPI::class)
|
||||
public data class Complex(val re: Double, val im: Double) : FieldElement<Complex, ComplexField> {
|
||||
public data class Complex(val re: Double, val im: Double) {
|
||||
public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble())
|
||||
public constructor(re: Number) : this(re.toDouble(), 0.0)
|
||||
|
||||
public override val context: ComplexField get() = ComplexField
|
||||
|
||||
public override fun toString(): String = "($re + i*$im)"
|
||||
|
||||
public companion object : MemorySpec<Complex> {
|
||||
|
@ -27,8 +27,10 @@ public val Quaternion.conjugate: Quaternion
|
||||
*/
|
||||
public val Quaternion.reciprocal: Quaternion
|
||||
get() {
|
||||
val n = QuaternionField { norm(this@reciprocal) }
|
||||
return conjugate / (n * n)
|
||||
QuaternionField {
|
||||
val n = norm(this@reciprocal)
|
||||
return conjugate / (n * n)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
@ -198,7 +200,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
|
||||
@OptIn(UnstableKMathAPI::class)
|
||||
public data class Quaternion(
|
||||
val w: Double, val x: Double, val y: Double, val z: Double,
|
||||
) : FieldElement<Quaternion, QuaternionField> {
|
||||
) {
|
||||
public constructor(w: Number, x: Number, y: Number, z: Number) : this(
|
||||
w.toDouble(),
|
||||
x.toDouble(),
|
||||
@ -219,8 +221,6 @@ public data class Quaternion(
|
||||
require(!z.isNaN()) { "x-component of quaternion is not-a-number" }
|
||||
}
|
||||
|
||||
public override val context: QuaternionField get() = QuaternionField
|
||||
|
||||
/**
|
||||
* Returns a string representation of this quaternion.
|
||||
*/
|
||||
|
@ -5,15 +5,14 @@
|
||||
|
||||
package space.kscience.kmath.stat
|
||||
|
||||
import kotlinx.coroutines.GlobalScope
|
||||
import kotlinx.coroutines.launch
|
||||
import kotlinx.coroutines.runBlocking
|
||||
import org.junit.jupiter.api.Assertions
|
||||
import org.junit.jupiter.api.Test
|
||||
import space.kscience.kmath.samplers.GaussianSampler
|
||||
|
||||
internal class CommonsDistributionsTest {
|
||||
@Test
|
||||
fun testNormalDistributionSuspend() = GlobalScope.launch {
|
||||
fun testNormalDistributionSuspend() = runBlocking {
|
||||
val distribution = GaussianSampler(7.0, 2.0)
|
||||
val generator = RandomGenerator.default(1)
|
||||
val sample = distribution.sample(generator).nextBuffer(1000)
|
||||
|
Loading…
Reference in New Issue
Block a user