forked from kscience/kmath
Update features
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@ -24,12 +24,10 @@ class EjmlMatrix(val origin: SimpleMatrix, features: Set<MatrixFeature>? = null)
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get() = intArrayOf(origin.numRows(), origin.numCols())
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get() = intArrayOf(origin.numRows(), origin.numCols())
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override val features: Set<MatrixFeature> = hashSetOf(
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override val features: Set<MatrixFeature> = hashSetOf(
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object : DeterminantFeature<Double> {
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object : LUPDecompositionFeature<Double>, DeterminantFeature<Double> {
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override val determinant: Double
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override val determinant: Double
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get() = origin.determinant()
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get() = origin.determinant()
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},
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object : LUPDecompositionFeature<Double> {
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private val lup by lazy {
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private val lup by lazy {
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val ludecompositionF64 = DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
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val ludecompositionF64 = DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
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.also { it.decompose(origin.ddrm.copy()) }
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.also { it.decompose(origin.ddrm.copy()) }
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@ -50,7 +48,7 @@ class EjmlMatrix(val origin: SimpleMatrix, features: Set<MatrixFeature>? = null)
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override val p: FeaturedMatrix<Double>
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override val p: FeaturedMatrix<Double>
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get() = lup.first
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get() = lup.first
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}
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}
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).addAll(features)
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).addAll(features.orEmpty())
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override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<Double> =
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override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<Double> =
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EjmlMatrix(origin, this.features + features)
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EjmlMatrix(origin, this.features + features)
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