Build tools update. Cleanup
This commit is contained in:
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@ -15,7 +15,7 @@ allprojects {
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}
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group = "space.kscience"
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version = "0.3.0-dev-8"
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version = "0.3.0-dev-9"
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}
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subprojects {
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@ -11,36 +11,32 @@ import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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// Dataset normalization
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fun main() {
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fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
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// take dataset of 5-element vectors from normal distribution
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val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
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// work in context with broadcast methods
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BroadcastDoubleTensorAlgebra {
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// take dataset of 5-element vectors from normal distribution
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val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
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dataset += fromArray(
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intArrayOf(5),
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doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
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)
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dataset += fromArray(
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intArrayOf(5),
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doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
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)
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// find out mean and standard deviation of each column
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val mean = dataset.mean(0, false)
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val std = dataset.std(0, false)
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// find out mean and standard deviation of each column
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val mean = dataset.mean(0, false)
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val std = dataset.std(0, false)
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println("Mean:\n$mean")
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println("Standard deviation:\n$std")
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println("Mean:\n$mean")
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println("Standard deviation:\n$std")
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// also we can calculate other statistic as minimum and maximum of rows
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println("Minimum:\n${dataset.min(0, false)}")
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println("Maximum:\n${dataset.max(0, false)}")
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// also we can calculate other statistic as minimum and maximum of rows
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println("Minimum:\n${dataset.min(0, false)}")
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println("Maximum:\n${dataset.max(0, false)}")
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// now we can scale dataset with mean normalization
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val datasetScaled = (dataset - mean) / std
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// now we can scale dataset with mean normalization
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val datasetScaled = (dataset - mean) / std
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// find out mean and std of scaled dataset
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// find out mean and std of scaled dataset
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println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
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println("Mean of scaled:\n${datasetScaled.std(0, false)}")
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}
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println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
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println("Mean of scaled:\n${datasetScaled.std(0, false)}")
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}
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@ -6,92 +6,88 @@
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package space.kscience.kmath.tensors
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensors.core.DoubleTensor
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensor
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// solving linear system with LUP decomposition
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fun main () {
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fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
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// work in context with linear operations
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BroadcastDoubleTensorAlgebra {
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// set true value of x
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val trueX = fromArray(
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intArrayOf(4),
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doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
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)
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// set true value of x
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val trueX = fromArray(
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intArrayOf(4),
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doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
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// and A matrix
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val a = fromArray(
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intArrayOf(4, 4),
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doubleArrayOf(
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0.5, 10.5, 4.5, 1.0,
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8.5, 0.9, 12.8, 0.1,
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5.56, 9.19, 7.62, 5.45,
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1.0, 2.0, -3.0, -2.5
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)
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)
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// and A matrix
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val a = fromArray(
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intArrayOf(4, 4),
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doubleArrayOf(
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0.5, 10.5, 4.5, 1.0,
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8.5, 0.9, 12.8, 0.1,
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5.56, 9.19, 7.62, 5.45,
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1.0, 2.0, -3.0, -2.5
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)
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)
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// calculate y value
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val b = a dot trueX
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// calculate y value
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val b = a dot trueX
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// check out A and b
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println("A:\n$a")
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println("b:\n$b")
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// check out A and b
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println("A:\n$a")
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println("b:\n$b")
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// solve `Ax = b` system using LUP decomposition
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// solve `Ax = b` system using LUP decomposition
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// get P, L, U such that PA = LU
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val (p, l, u) = a.lu()
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// get P, L, U such that PA = LU
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val (p, l, u) = a.lu()
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// check that P is permutation matrix
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println("P:\n$p")
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// L is lower triangular matrix and U is upper triangular matrix
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println("L:\n$l")
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println("U:\n$u")
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// and PA = LU
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println("PA:\n${p dot a}")
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println("LU:\n${l dot u}")
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// check that P is permutation matrix
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println("P:\n$p")
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// L is lower triangular matrix and U is upper triangular matrix
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println("L:\n$l")
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println("U:\n$u")
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// and PA = LU
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println("PA:\n${p dot a}")
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println("LU:\n${l dot u}")
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/* Ax = b;
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PAx = Pb;
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LUx = Pb;
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let y = Ux, then
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Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
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Ux = y can be solved the same way, since the matrix L is upper triangular
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*/
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/* Ax = b;
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PAx = Pb;
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LUx = Pb;
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let y = Ux, then
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Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
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Ux = y can be solved the same way, since the matrix L is upper triangular
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*/
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// this function returns solution x of a system lx = b, l should be lower triangular
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fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
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val n = l.shape[0]
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val x = zeros(intArrayOf(n))
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for (i in 0 until n){
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x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
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}
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return x
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}
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val y = solveLT(l, p dot b)
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// solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
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// create it by placing ones on side diagonal
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val revMat = u.zeroesLike()
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val n = revMat.shape[0]
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// this function returns solution x of a system lx = b, l should be lower triangular
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fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
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val n = l.shape[0]
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val x = zeros(intArrayOf(n))
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for (i in 0 until n) {
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revMat[intArrayOf(i, n - 1 - i)] = 1.0
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x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
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}
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// solution of system ux = b, u should be upper triangular
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fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
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revMat dot u dot revMat, revMat dot b
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)
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val x = solveUT(u, y)
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println("True x:\n$trueX")
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println("x founded with LU method:\n$x")
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return x
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}
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val y = solveLT(l, p dot b)
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// solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
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// create it by placing ones on side diagonal
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val revMat = u.zeroesLike()
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val n = revMat.shape[0]
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for (i in 0 until n) {
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revMat[intArrayOf(i, n - 1 - i)] = 1.0
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}
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// solution of system ux = b, u should be upper triangular
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fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
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revMat dot u dot revMat, revMat dot b
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)
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val x = solveUT(u, y)
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println("True x:\n$trueX")
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println("x founded with LU method:\n$x")
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}
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@ -25,7 +25,7 @@ interface Layer {
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// activation layer
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open class Activation(
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val activation: (DoubleTensor) -> DoubleTensor,
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val activationDer: (DoubleTensor) -> DoubleTensor
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val activationDer: (DoubleTensor) -> DoubleTensor,
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) : Layer {
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override fun forward(input: DoubleTensor): DoubleTensor {
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return activation(input)
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@ -62,7 +62,7 @@ class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
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class Dense(
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private val inputUnits: Int,
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private val outputUnits: Int,
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private val learningRate: Double = 0.1
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private val learningRate: Double = 0.1,
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) : Layer {
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private val weights: DoubleTensor = DoubleTensorAlgebra {
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@ -74,8 +74,8 @@ class Dense(
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private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
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override fun forward(input: DoubleTensor): DoubleTensor {
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return BroadcastDoubleTensorAlgebra { (input dot weights) + bias }
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override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
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(input dot weights) + bias
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}
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override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
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@ -116,7 +116,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
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onesForAnswers[intArrayOf(index, label)] = 1.0
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}
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val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
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val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
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(-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
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}
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@ -175,67 +175,65 @@ class NeuralNetwork(private val layers: List<Layer>) {
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@OptIn(ExperimentalStdlibApi::class)
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fun main() {
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BroadcastDoubleTensorAlgebra {
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val features = 5
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val sampleSize = 250
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val trainSize = 180
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//val testSize = sampleSize - trainSize
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fun main() = BroadcastDoubleTensorAlgebra {
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val features = 5
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val sampleSize = 250
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val trainSize = 180
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//val testSize = sampleSize - trainSize
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// take sample of features from normal distribution
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val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
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// take sample of features from normal distribution
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val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
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x += fromArray(
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intArrayOf(5),
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doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
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)
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x += fromArray(
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intArrayOf(5),
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doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
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)
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// define class like '1' if the sum of features > 0 and '0' otherwise
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val y = fromArray(
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intArrayOf(sampleSize, 1),
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DoubleArray(sampleSize) { i ->
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if (x[i].sum() > 0.0) {
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1.0
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} else {
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0.0
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}
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// define class like '1' if the sum of features > 0 and '0' otherwise
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val y = fromArray(
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intArrayOf(sampleSize, 1),
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DoubleArray(sampleSize) { i ->
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if (x[i].sum() > 0.0) {
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1.0
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} else {
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0.0
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}
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)
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// split train ans test
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val trainIndices = (0 until trainSize).toList().toIntArray()
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val testIndices = (trainSize until sampleSize).toList().toIntArray()
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val xTrain = x.rowsByIndices(trainIndices)
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val yTrain = y.rowsByIndices(trainIndices)
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val xTest = x.rowsByIndices(testIndices)
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val yTest = y.rowsByIndices(testIndices)
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// build model
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val layers = buildList {
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add(Dense(features, 64))
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add(ReLU())
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add(Dense(64, 16))
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add(ReLU())
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add(Dense(16, 2))
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add(Sigmoid())
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}
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val model = NeuralNetwork(layers)
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)
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// fit it with train data
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model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
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// split train ans test
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val trainIndices = (0 until trainSize).toList().toIntArray()
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val testIndices = (trainSize until sampleSize).toList().toIntArray()
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// make prediction
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val prediction = model.predict(xTest)
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val xTrain = x.rowsByIndices(trainIndices)
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val yTrain = y.rowsByIndices(trainIndices)
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// process raw prediction via argMax
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val predictionLabels = prediction.argMax(1, true)
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// find out accuracy
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val acc = accuracy(yTest, predictionLabels)
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println("Test accuracy:$acc")
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val xTest = x.rowsByIndices(testIndices)
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val yTest = y.rowsByIndices(testIndices)
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// build model
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val layers = buildList {
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add(Dense(features, 64))
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add(ReLU())
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add(Dense(64, 16))
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add(ReLU())
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add(Dense(16, 2))
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add(Sigmoid())
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}
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val model = NeuralNetwork(layers)
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// fit it with train data
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model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
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// make prediction
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val prediction = model.predict(xTest)
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// process raw prediction via argMax
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val predictionLabels = prediction.argMax(1, true)
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// find out accuracy
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val acc = accuracy(yTest, predictionLabels)
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println("Test accuracy:$acc")
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}
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@ -11,68 +11,64 @@ import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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// simple PCA
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fun main(){
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fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
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val seed = 100500L
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// work in context with broadcast methods
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BroadcastDoubleTensorAlgebra {
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// assume x is range from 0 until 10
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val x = fromArray(
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intArrayOf(10),
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(0 until 10).toList().map { it.toDouble() }.toDoubleArray()
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)
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// assume x is range from 0 until 10
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val x = fromArray(
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intArrayOf(10),
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(0 until 10).toList().map { it.toDouble() }.toDoubleArray()
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)
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// take y dependent on x with noise
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val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
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// take y dependent on x with noise
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val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
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println("x:\n$x")
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println("y:\n$y")
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println("x:\n$x")
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println("y:\n$y")
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// stack them into single dataset
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val dataset = stack(listOf(x, y)).transpose()
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// stack them into single dataset
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val dataset = stack(listOf(x, y)).transpose()
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// normalize both x and y
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val xMean = x.mean()
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val yMean = y.mean()
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// normalize both x and y
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val xMean = x.mean()
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val yMean = y.mean()
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val xStd = x.std()
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val yStd = y.std()
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val xStd = x.std()
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val yStd = y.std()
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val xScaled = (x - xMean) / xStd
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val yScaled = (y - yMean) / yStd
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val xScaled = (x - xMean) / xStd
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val yScaled = (y - yMean) / yStd
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// save means ans standard deviations for further recovery
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val mean = fromArray(
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intArrayOf(2),
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doubleArrayOf(xMean, yMean)
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)
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println("Means:\n$mean")
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// save means ans standard deviations for further recovery
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val mean = fromArray(
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intArrayOf(2),
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doubleArrayOf(xMean, yMean)
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)
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println("Means:\n$mean")
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val std = fromArray(
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intArrayOf(2),
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doubleArrayOf(xStd, yStd)
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)
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println("Standard deviations:\n$std")
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val std = fromArray(
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intArrayOf(2),
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doubleArrayOf(xStd, yStd)
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)
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println("Standard deviations:\n$std")
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// calculate the covariance matrix of scaled x and y
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val covMatrix = cov(listOf(xScaled, yScaled))
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println("Covariance matrix:\n$covMatrix")
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// calculate the covariance matrix of scaled x and y
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val covMatrix = cov(listOf(xScaled, yScaled))
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println("Covariance matrix:\n$covMatrix")
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// and find out eigenvector of it
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val (_, evecs) = covMatrix.symEig()
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val v = evecs[0]
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println("Eigenvector:\n$v")
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// and find out eigenvector of it
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val (_, evecs) = covMatrix.symEig()
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val v = evecs[0]
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println("Eigenvector:\n$v")
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// reduce dimension of dataset
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val datasetReduced = v dot stack(listOf(xScaled, yScaled))
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println("Reduced data:\n$datasetReduced")
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// reduce dimension of dataset
|
||||
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
|
||||
println("Reduced data:\n$datasetReduced")
|
||||
|
||||
// we can restore original data from reduced data.
|
||||
// for example, find 7th element of dataset
|
||||
val n = 7
|
||||
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
|
||||
println("Original value:\n${dataset[n]}")
|
||||
println("Restored value:\n$restored")
|
||||
}
|
||||
// we can restore original data from reduced data.
|
||||
// for example, find 7th element of dataset
|
||||
val n = 7
|
||||
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
|
||||
println("Original value:\n${dataset[n]}")
|
||||
println("Restored value:\n$restored")
|
||||
}
|
||||
|
||||
@ -19,6 +19,9 @@ import space.kscience.kmath.structures.Buffer
|
||||
public interface ColumnarData<out T> {
|
||||
public val size: Int
|
||||
|
||||
/**
|
||||
* Provide a column by symbol or null if column with given symbol is not defined
|
||||
*/
|
||||
public operator fun get(symbol: Symbol): Buffer<T>?
|
||||
}
|
||||
|
||||
|
||||
@ -5,7 +5,7 @@ pluginManagement {
|
||||
maven("https://repo.kotlin.link")
|
||||
}
|
||||
|
||||
val toolsVersion = "0.9.6"
|
||||
val toolsVersion = "0.9.7"
|
||||
val kotlinVersion = "1.5.0"
|
||||
|
||||
plugins {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user