Replace Distribution.normal with NormalDistribution
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e43aad33fe
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@ -9,6 +9,7 @@ import kscience.kmath.real.RealVector
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import kscience.kmath.real.map
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import kscience.kmath.real.map
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import kscience.kmath.real.step
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import kscience.kmath.real.step
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import kscience.kmath.stat.*
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import kscience.kmath.stat.*
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import kscience.kmath.stat.distributions.NormalDistribution
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import kscience.kmath.structures.asIterable
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import kscience.kmath.structures.asIterable
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import kscience.kmath.structures.toList
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import kscience.kmath.structures.toList
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import kscience.plotly.*
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import kscience.plotly.*
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@ -33,10 +34,9 @@ operator fun TraceValues.invoke(vector: RealVector) {
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/**
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/**
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* Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
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* Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
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*/
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*/
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fun main() {
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suspend fun main() {
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//A generator for a normally distributed values
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//A generator for a normally distributed values
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val generator = Distribution.normal()
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val generator = NormalDistribution(2.0, 7.0)
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//A chain/flow of random values with the given seed
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//A chain/flow of random values with the given seed
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val chain = generator.sample(RandomGenerator.default(112667))
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val chain = generator.sample(RandomGenerator.default(112667))
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@ -49,7 +49,7 @@ fun main() {
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//Perform an operation on each x value (much more effective, than numpy)
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//Perform an operation on each x value (much more effective, than numpy)
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val y = x.map {
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val y = x.map {
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val value = it.pow(2) + it + 1
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val value = it.pow(2) + it + 1
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value + chain.nextDouble() * sqrt(value)
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value + chain.next() * sqrt(value)
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}
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}
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// this will also work, but less effective:
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// this will also work, but less effective:
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// val y = x.pow(2)+ x + 1 + chain.nextDouble()
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// val y = x.pow(2)+ x + 1 + chain.nextDouble()
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@ -99,4 +99,4 @@ fun main() {
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}
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}
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page.makeFile()
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page.makeFile()
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}
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}
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