0.4 WIP

CHANGELOG.md

@@ -3,7 +3,7 @@
 ## Unreleased
 
 ### Added
-- Explicit `SafeType` for algebras and buffers.
+- Reification. Explicit `SafeType` for algebras and buffers.
 - Integer division algebras.
 - Float32 geometries.
 - New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
@@ -16,6 +16,7 @@
 - kmath-geometry is split into `euclidean2d` and `euclidean3d`
 - Features replaced with Attributes.
 - Transposed refactored.
+- Kmath-memory is moved on top of core.
 
 ### Deprecated
 

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attribute.kt

@@ -24,14 +24,3 @@ public interface AttributeWithDefault<T> : Attribute<T> {
  */
 public interface SetAttribute<V> : Attribute<Set<V>>
 
-/**
- * An attribute that has a type parameter for value
- * @param type parameter-type
- */
-public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : Attribute<T> {
-    override fun equals(other: Any?): Boolean = other != null &&
-            (this::class == other::class) &&
-            (other as? PolymorphicAttribute<*>)?.type == this.type
-
-    override fun hashCode(): Int = this::class.hashCode() + type.hashCode()
-}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributeContainer.kt

@@ -6,8 +6,14 @@
 package space.kscience.attributes
 
 /**
- * A container for attributes. [attributes] could be made mutable by implementation
+ * A container for [Attributes]
  */
 public interface AttributeContainer {
     public val attributes: Attributes
 }
+
+/**
+ * A scope, where attribute keys could be resolved
+ */
+public interface AttributeScope<O>
+

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt

@@ -7,21 +7,27 @@ package space.kscience.attributes
 
 import kotlin.jvm.JvmInline
 
-@JvmInline
+/**
-public value class Attributes internal constructor(public val content: Map<out Attribute<*>, Any?>) {
+ * A set of attributes. The implementation must guarantee that [content] keys correspond to its value types.
+ */
+public interface Attributes {
+    public val content: Map<out Attribute<*>, Any?>
 
     public val keys: Set<Attribute<*>> get() = content.keys
 
     @Suppress("UNCHECKED_CAST")
     public operator fun <T> get(attribute: Attribute<T>): T? = content[attribute] as? T
 
-    override fun toString(): String = "Attributes(value=${content.entries})"
-
     public companion object {
-        public val EMPTY: Attributes = Attributes(emptyMap())
+        public val EMPTY: Attributes = AttributesImpl(emptyMap())
     }
 }
 
+@JvmInline
+internal value class AttributesImpl(override val content: Map<out Attribute<*>, Any?>) : Attributes {
+    override fun toString(): String = "Attributes(value=${content.entries})"
+}
+
 public fun Attributes.isEmpty(): Boolean = content.isEmpty()
 
 /**
@@ -33,19 +39,19 @@ public fun <T> Attributes.getOrDefault(attribute: AttributeWithDefault<T>): T =
  * Check if there is an attribute that matches given key by type and adheres to [predicate].
  */
 @Suppress("UNCHECKED_CAST")
-public inline fun <T, reified A : Attribute<T>> Attributes.any(predicate: (value: T) -> Boolean): Boolean =
+public inline fun <T, reified A : Attribute<T>> Attributes.hasAny(predicate: (value: T) -> Boolean): Boolean =
     content.any { (mapKey, mapValue) -> mapKey is A && predicate(mapValue as T) }
 
 /**
  * Check if there is an attribute of given type (subtypes included)
  */
-public inline fun <T, reified A : Attribute<T>> Attributes.any(): Boolean =
+public inline fun <reified A : Attribute<*>> Attributes.hasAny(): Boolean =
     content.any { (mapKey, _) -> mapKey is A }
 
 /**
  * Check if [Attributes] contains a flag. Multiple keys that are instances of a flag could be present
  */
-public inline fun <reified A : FlagAttribute> Attributes.has(): Boolean =
+public inline fun <reified A : FlagAttribute> Attributes.hasFlag(): Boolean =
     content.keys.any { it is A }
 
 /**
@@ -54,7 +60,7 @@ public inline fun <reified A : FlagAttribute> Attributes.has(): Boolean =
 public fun <T, A : Attribute<T>> Attributes.withAttribute(
     attribute: A,
     attrValue: T,
-): Attributes = Attributes(content + (attribute to attrValue))
+): Attributes = AttributesImpl(content + (attribute to attrValue))
 
 public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attributes =
     withAttribute(attribute, Unit)
@@ -62,7 +68,7 @@ public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attribu
 /**
  * Create a new [Attributes] by modifying the current one
  */
-public fun Attributes.modify(block: AttributesBuilder.() -> Unit): Attributes = Attributes {
+public fun <T> Attributes.modify(block: AttributesBuilder<T>.() -> Unit): Attributes = Attributes<T> {
     from(this@modify)
     block()
 }
@@ -70,7 +76,7 @@ public fun Attributes.modify(block: AttributesBuilder.() -> Unit): Attributes =
 /**
  * Create new [Attributes] by removing [attribute] key
  */
-public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = Attributes(content.minus(attribute))
+public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = AttributesImpl(content.minus(attribute))
 
 /**
  * Add an element to a [SetAttribute]
@@ -80,7 +86,7 @@ public fun <T, A : SetAttribute<T>> Attributes.withAttributeElement(
     attrValue: T,
 ): Attributes {
     val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return Attributes(
+    return AttributesImpl(
         content + (attribute to (currentSet + attrValue))
     )
 }
@@ -93,9 +99,7 @@ public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
     attrValue: T,
 ): Attributes {
     val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return Attributes(
+    return AttributesImpl(content + (attribute to (currentSet - attrValue)))
-        content + (attribute to (currentSet - attrValue))
-    )
 }
 
 /**
@@ -104,13 +108,13 @@ public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
 public fun <T, A : Attribute<T>> Attributes(
     attribute: A,
     attrValue: T,
-): Attributes = Attributes(mapOf(attribute to attrValue))
+): Attributes = AttributesImpl(mapOf(attribute to attrValue))
 
 /**
  * Create Attributes with a single [Unit] valued attribute
  */
 public fun <A : Attribute<Unit>> Attributes(
-    attribute: A
+    attribute: A,
-): Attributes = Attributes(mapOf(attribute to Unit))
+): Attributes = AttributesImpl(mapOf(attribute to Unit))
 
-public operator fun Attributes.plus(other: Attributes): Attributes = Attributes(content + other.content)
+public operator fun Attributes.plus(other: Attributes): Attributes = AttributesImpl(content + other.content)

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributesBuilder.kt

@@ -10,19 +10,24 @@ package space.kscience.attributes
  *
  * @param O type marker of an owner object, for which these attributes are made
  */
-public class TypedAttributesBuilder<in O> internal constructor(private val map: MutableMap<Attribute<*>, Any?>) {
+public class AttributesBuilder<out O> internal constructor(
+    private val map: MutableMap<Attribute<*>, Any?>,
+) : Attributes {
 
     public constructor() : this(mutableMapOf())
 
-    @Suppress("UNCHECKED_CAST")
+    override val content: Map<out Attribute<*>, Any?> get() = map
-    public operator fun <T> get(attribute: Attribute<T>): T? = map[attribute] as? T
+
+    public operator fun <T> set(attribute: Attribute<T>, value: T?) {
+        if (value == null) {
+            map.remove(attribute)
+        } else {
+            map[attribute] = value
+        }
+    }
 
     public operator fun <V> Attribute<V>.invoke(value: V?) {
-        if (value == null) {
+        set(this, value)
-            map.remove(this)
-        } else {
-            map[this] = value
-        }
     }
 
     public fun from(attributes: Attributes) {
@@ -46,14 +51,8 @@ public class TypedAttributesBuilder<in O> internal constructor(private val map:
         map[this] = currentSet - attrValue
     }
 
-    public fun build(): Attributes = Attributes(map)
+    public fun build(): Attributes = AttributesImpl(map)
 }
 
-public typealias AttributesBuilder = TypedAttributesBuilder<Any?>
+public inline fun <O> Attributes(builder: AttributesBuilder<O>.() -> Unit): Attributes =
-
+    AttributesBuilder<O>().apply(builder).build()
-public fun AttributesBuilder(
-    attributes: Attributes,
-): AttributesBuilder = AttributesBuilder(attributes.content.toMutableMap())
-
-public inline fun Attributes(builder: AttributesBuilder.() -> Unit): Attributes =
-    AttributesBuilder().apply(builder).build()

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/PolymorphicAttribute.kt

@@ -0,0 +1,31 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.attributes
+
+/**
+ * An attribute that has a type parameter for value
+ * @param type parameter-type
+ */
+public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : Attribute<T> {
+    override fun equals(other: Any?): Boolean = other != null &&
+            (this::class == other::class) &&
+            (other as? PolymorphicAttribute<*>)?.type == this.type
+
+    override fun hashCode(): Int = this::class.hashCode() + type.hashCode()
+}
+
+
+/**
+ * Get a polymorphic attribute using attribute factory
+ */
+public operator fun <T> Attributes.get(attributeKeyBuilder: () -> PolymorphicAttribute<T>): T? = get(attributeKeyBuilder())
+
+/**
+ * Set a polymorphic attribute using its factory
+ */
+public operator fun <O, T> AttributesBuilder<O>.set(attributeKeyBuilder: () -> PolymorphicAttribute<T>, value: T) {
+    set(attributeKeyBuilder(), value)
+}

buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt

@@ -19,6 +19,8 @@ public class Ejml${type}Vector<out M : $ejmlMatrixType>(override val origin: M)
         require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
     }
 
+    override val type: SafeType<${type}> get() = safeTypeOf()
+
     override operator fun get(index: Int): $type = origin[0, index]
 }"""
     appendLine(text)
@@ -30,6 +32,8 @@ private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
  * [EjmlMatrix] specialization for [$type].
  */
 public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
+    override val type: SafeType<${type}> get() = safeTypeOf()
+
     override operator fun get(i: Int, j: Int): $type = origin[i, j]
 }"""
     appendLine(text)
@@ -46,7 +50,9 @@ private fun Appendable.appendEjmlLinearSpace(
     denseOps: String,
     isDense: Boolean,
 ) {
-    @Language("kotlin") val text = """/**
+    @Language("kotlin") val text = """
+
+/**
  * [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
  * [${ejmlMatrixType}] matrices.
  */
@@ -56,7 +62,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
      */
     override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
 
-    override val elementType: KType get() = typeOf<$type>()
+    override val type: SafeType<${type}> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
     override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
@@ -385,6 +391,8 @@ import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
 import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.safeTypeOf
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.UnstableKMathAPI

examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt

@@ -15,7 +15,7 @@ import space.kscience.kmath.operations.asIterable
 import space.kscience.kmath.operations.toList
 import space.kscience.kmath.optimization.FunctionOptimizationTarget
 import space.kscience.kmath.optimization.optimizeWith
-import space.kscience.kmath.optimization.resultPoint
+import space.kscience.kmath.optimization.result
 import space.kscience.kmath.optimization.resultValue
 import space.kscience.kmath.random.RandomGenerator
 import space.kscience.kmath.real.DoubleVector
@@ -98,7 +98,7 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
+                y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
                 name = "fit"
             }
         }

examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt

@@ -94,13 +94,13 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.model(result.startPoint + result.resultPoint + (Symbol.x to it)) })
+                y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
                 name = "fit"
             }
         }
         br()
         h3 {
-            +"Fit result: ${result.resultPoint}"
+            +"Fit result: ${result.result}"
         }
         h3 {
             +"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt

@@ -5,6 +5,8 @@
 
 package space.kscience.kmath.ast
 
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.WithType
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.operations.Algebra
@@ -15,7 +17,7 @@ import space.kscience.kmath.operations.NumericAlgebra
  *
  * @param T the type.
  */
-public sealed interface TypedMst<T> {
+public sealed interface TypedMst<T> : WithType<T> {
     /**
      * A node containing a unary operation.
      *
@@ -24,8 +26,13 @@ public sealed interface TypedMst<T> {
      * @property function The function implementing this operation.
      * @property value The argument of this operation.
      */
-    public class Unary<T>(public val operation: String, public val function: (T) -> T, public val value: TypedMst<T>) :
+    public class Unary<T>(
-        TypedMst<T> {
+        public val operation: String,
+        public val function: (T) -> T,
+        public val value: TypedMst<T>,
+    ) : TypedMst<T> {
+        override val type: SafeType<T> get() = value.type
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -59,6 +66,13 @@ public sealed interface TypedMst<T> {
         public val left: TypedMst<T>,
         public val right: TypedMst<T>,
     ) : TypedMst<T> {
+
+        init {
+            require(left.type==right.type){"Left and right expressions must be of the same type"}
+        }
+
+        override val type: SafeType<T> get() = left.type
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -89,7 +103,12 @@ public sealed interface TypedMst<T> {
      * @property value The held value.
      * @property number The number this value corresponds.
      */
-    public class Constant<T>(public val value: T, public val number: Number?) : TypedMst<T> {
+    public class Constant<T>(
+        override val type: SafeType<T>,
+        public val value: T,
+        public val number: Number?,
+    ) : TypedMst<T> {
+
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -114,7 +133,7 @@ public sealed interface TypedMst<T> {
      * @param T the type.
      * @property symbol The symbol of the variable.
      */
-    public class Variable<T>(public val symbol: Symbol) : TypedMst<T> {
+    public class Variable<T>(override val type: SafeType<T>, public val symbol: Symbol) : TypedMst<T> {
         override fun equals(other: Any?): Boolean {
             if (this === other) return true
             if (other == null || this::class != other::class) return false
@@ -167,6 +186,7 @@ public fun <T> TypedMst<T>.interpret(algebra: Algebra<T>, vararg arguments: Pair
 /**
  * Interpret this [TypedMst] node as expression.
  */
-public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> = Expression { arguments ->
+public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> =
+    Expression(algebra.type) { arguments ->
         interpret(algebra, arguments)
-}
+    }

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt

@@ -16,6 +16,7 @@ import space.kscience.kmath.operations.bindSymbolOrNull
  */
 public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (this) {
     is MST.Numeric -> TypedMst.Constant(
+        algebra.type,
         (algebra as? NumericAlgebra<T>)?.number(value) ?: error("Numeric nodes are not supported by $algebra"),
         value,
     )
@@ -27,7 +28,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
                 arg.value,
             )
 
-            TypedMst.Constant(value, if (value is Number) value else null)
+            TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
         }
 
         else -> TypedMst.Unary(operation, algebra.unaryOperationFunction(operation), arg)
@@ -59,7 +60,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
                     )
                 }
 
-                TypedMst.Constant(value, if (value is Number) value else null)
+                TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
             }
 
             algebra is NumericAlgebra && left is TypedMst.Constant && left.number != null -> TypedMst.Binary(
@@ -84,8 +85,8 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
         val boundSymbol = algebra.bindSymbolOrNull(this)
 
         if (boundSymbol != null)
-            TypedMst.Constant(boundSymbol, if (boundSymbol is Number) boundSymbol else null)
+            TypedMst.Constant(algebra.type, boundSymbol, if (boundSymbol is Number) boundSymbol else null)
         else
-            TypedMst.Variable(this)
+            TypedMst.Variable(algebra.type, this)
     }
 }

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt

@@ -22,7 +22,7 @@ import space.kscience.kmath.operations.Algebra
 @OptIn(UnstableKMathAPI::class)
 public fun <T : Any> MST.compileToExpression(algebra: Algebra<T>): Expression<T> {
     val typed = evaluateConstants(algebra)
-    if (typed is TypedMst.Constant<T>) return Expression { typed.value }
+    if (typed is TypedMst.Constant<T>) return Expression(algebra.type) { typed.value }
 
     fun ESTreeBuilder<T>.visit(node: TypedMst<T>): BaseExpression = when (node) {
         is TypedMst.Constant -> constant(node.value)
@@ -36,7 +36,7 @@ public fun <T : Any> MST.compileToExpression(algebra: Algebra<T>): Expression<T>
         )
     }
 
-    return ESTreeBuilder { visit(typed) }.instance
+    return ESTreeBuilder(algebra.type) { visit(typed) }.instance
 }
 
 /**

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/ESTreeBuilder.kt

@@ -5,13 +5,22 @@
 
 package space.kscience.kmath.estree.internal
 
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.WithType
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.internal.astring.generate
 import space.kscience.kmath.internal.estree.*
 
-internal class ESTreeBuilder<T>(val bodyCallback: ESTreeBuilder<T>.() -> BaseExpression) {
+internal class ESTreeBuilder<T>(
-    private class GeneratedExpression<T>(val executable: dynamic, val constants: Array<dynamic>) : Expression<T> {
+    override val type: SafeType<T>,
+    val bodyCallback: ESTreeBuilder<T>.() -> BaseExpression,
+) : WithType<T> {
+    private class GeneratedExpression<T>(
+        override val type: SafeType<T>,
+        val executable: dynamic,
+        val constants: Array<dynamic>,
+    ) : Expression<T> {
         @Suppress("UNUSED_VARIABLE")
         override fun invoke(arguments: Map<Symbol, T>): T {
             val e = executable
@@ -30,7 +39,7 @@ internal class ESTreeBuilder<T>(val bodyCallback: ESTreeBuilder<T>.() -> BaseExp
         )
 
         val code = generate(node)
-        GeneratedExpression(js("new Function('constants', 'arguments_0', code)"), constants.toTypedArray())
+        GeneratedExpression(type, js("new Function('constants', 'arguments_0', code)"), constants.toTypedArray())
     }
 
     private val constants = mutableListOf<Any>()

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt

@@ -29,7 +29,7 @@ import space.kscience.kmath.operations.Int64Ring
 @PublishedApi
 internal fun <T : Any> MST.compileWith(type: Class<T>, algebra: Algebra<T>): Expression<T> {
     val typed = evaluateConstants(algebra)
-    if (typed is TypedMst.Constant<T>) return Expression { typed.value }
+    if (typed is TypedMst.Constant<T>) return Expression(algebra.type) { typed.value }
 
     fun GenericAsmBuilder<T>.variablesVisitor(node: TypedMst<T>): Unit = when (node) {
         is TypedMst.Unary -> variablesVisitor(node.value)

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/expressions/CmDsExpression.kt

@@ -8,10 +8,13 @@
 package space.kscience.kmath.commons.expressions
 
 import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.expressions.*
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.ExtendedField
 import space.kscience.kmath.operations.NumbersAddOps
+import space.kscience.kmath.structures.MutableBufferFactory
 
 /**
  * A field over commons-math [DerivativeStructure].
@@ -26,6 +29,9 @@ public class CmDsField(
     bindings: Map<Symbol, Double>,
 ) : ExtendedField<DerivativeStructure>, ExpressionAlgebra<Double, DerivativeStructure>,
     NumbersAddOps<DerivativeStructure> {
+
+    override val bufferFactory: MutableBufferFactory<DerivativeStructure> = MutableBufferFactory()
+
     public val numberOfVariables: Int = bindings.size
 
     override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
@@ -77,7 +83,9 @@ public class CmDsField(
 
     override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value)
 
-    override fun multiply(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure = left.multiply(right)
+    override fun multiply(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure =
+        left.multiply(right)
+
     override fun divide(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure = left.divide(right)
     override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin()
     override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos()
@@ -125,13 +133,16 @@ public object CmDsProcessor : AutoDiffProcessor<Double, DerivativeStructure, CmD
 public class CmDsExpression(
     public val function: CmDsField.() -> DerivativeStructure,
 ) : DifferentiableExpression<Double> {
+
+    override val type: SafeType<Double> get() = DoubleField.type
+
     override operator fun invoke(arguments: Map<Symbol, Double>): Double =
         CmDsField(0, arguments).function().value
 
     /**
      * Get the derivative expression with given orders
      */
-    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
+    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression(type) { arguments ->
         with(CmDsField(symbols.size, arguments)) { function().derivative(symbols) }
     }
 }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt

@@ -16,7 +16,7 @@ public class CMGaussRuleIntegrator(
     private var type: GaussRule = GaussRule.LEGENDRE,
 ) : UnivariateIntegrator<Double> {
 
-    override fun process(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
         val range = integrand[IntegrationRange]
             ?: error("Integration range is not provided")
         val integrator: GaussIntegrator = getIntegrator(range)
@@ -79,7 +79,7 @@ public class CMGaussRuleIntegrator(
             numPoints: Int = 100,
             type: GaussRule = GaussRule.LEGENDRE,
             function: (Double) -> Double,
-        ): Double = CMGaussRuleIntegrator(numPoints, type).process(
+        ): Double = CMGaussRuleIntegrator(numPoints, type).integrate(
             UnivariateIntegrand({IntegrationRange(range)},function)
         ).value
     }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt

@@ -7,6 +7,7 @@ package space.kscience.kmath.commons.integration
 
 import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator
 import org.apache.commons.math3.analysis.integration.SimpsonIntegrator
+import space.kscience.attributes.Attributes
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.integration.*
 import org.apache.commons.math3.analysis.integration.UnivariateIntegrator as CMUnivariateIntegrator
@@ -19,7 +20,7 @@ public class CMIntegrator(
     public val integratorBuilder: (Integrand<Double>) -> CMUnivariateIntegrator,
 ) : UnivariateIntegrator<Double> {
 
-    override fun process(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
+    override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
         val integrator = integratorBuilder(integrand)
         val maxCalls = integrand[IntegrandMaxCalls] ?: defaultMaxCalls
         val remainingCalls = maxCalls - integrand.calls
@@ -73,15 +74,9 @@ public class CMIntegrator(
 }
 
 @UnstableKMathAPI
-public var MutableList<IntegrandFeature>.targetAbsoluteAccuracy: Double?
+public val Attributes.targetAbsoluteAccuracy: Double?
-    get() = filterIsInstance<IntegrandAbsoluteAccuracy>().lastOrNull()?.accuracy
+    get() = get(IntegrandAbsoluteAccuracy)
-    set(value) {
-        value?.let { add(IntegrandAbsoluteAccuracy(value)) }
-    }
 
 @UnstableKMathAPI
-public var MutableList<IntegrandFeature>.targetRelativeAccuracy: Double?
+public val Attributes.targetRelativeAccuracy: Double?
-    get() = filterIsInstance<IntegrandRelativeAccuracy>().lastOrNull()?.accuracy
+    get() = get(IntegrandRelativeAccuracy)
-    set(value) {
-        value?.let { add(IntegrandRelativeAccuracy(value)) }
-    }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt

@@ -6,18 +6,20 @@
 package space.kscience.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
+import org.apache.commons.math3.linear.LUDecomposition
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.linear.*
+import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.nd.StructureAttribute
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.Float64Buffer
-import kotlin.reflect.KClass
-import kotlin.reflect.KType
 import kotlin.reflect.cast
-import kotlin.reflect.typeOf
 
 public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
+    override val type: SafeType<Double> get() = DoubleField.type
     override val rowNum: Int get() = origin.rowDimension
     override val colNum: Int get() = origin.columnDimension
 
@@ -26,6 +28,7 @@ public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
 
 @JvmInline
 public value class CMVector(public val origin: RealVector) : Point<Double> {
+    override val type: SafeType<Double> get() = DoubleField.type
     override val size: Int get() = origin.dimension
 
     override operator fun get(index: Int): Double = origin.getEntry(index)
@@ -40,7 +43,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
 public object CMLinearSpace : LinearSpace<Double, Float64Field> {
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val elementType: KType = typeOf<Double>()
+    override val type: SafeType<Double> get() = DoubleField.type
 
     override fun buildMatrix(
         rows: Int,
@@ -102,19 +105,14 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
     override fun Double.times(v: Point<Double>): CMVector =
         v * this
 
-    @UnstableKMathAPI
+    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
-    override fun <F : StructureAttribute> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
-        //Return the feature if it is intrinsic to the structure
-        structure.getFeature(type)?.let { return it }
 
         val origin = structure.toCM().origin
 
-        return when (type) {
+        return when (attribute) {
-            IsDiagonal::class -> if (origin is DiagonalMatrix) IsDiagonal else null
+            IsDiagonal -> if (origin is DiagonalMatrix) IsDiagonal else null
-
+            Determinant -> LUDecomposition(origin).determinant
-            Determinant::class, LupDecompositionAttribute::class -> object :
+            LUP -> GenericLupDecomposition {
-                Determinant<Double>,
-                LupDecompositionAttribute<Double> {
                 private val lup by lazy { LUDecomposition(origin) }
                 override val determinant: Double by lazy { lup.determinant }
                 override val l: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.l).withAttribute(LowerTriangular) }
@@ -122,20 +120,24 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
                 override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
             }
 
-            CholeskyDecompositionAttribute::class -> object : CholeskyDecompositionAttribute<Double> {
+            CholeskyDecompositionAttribute -> object : CholeskyDecompositionAttribute<Double> {
                 override val l: Matrix<Double> by lazy<Matrix<Double>> {
                     val cholesky = CholeskyDecomposition(origin)
                     CMMatrix(cholesky.l).withAttribute(LowerTriangular)
                 }
             }
 
-            QRDecompositionAttribute::class -> object : QRDecompositionAttribute<Double> {
+            QRDecompositionAttribute -> object : QRDecompositionAttribute<Double> {
                 private val qr by lazy { QRDecomposition(origin) }
-                override val q: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.q).withAttribute(OrthogonalAttribute) }
+                override val q: Matrix<Double> by lazy<Matrix<Double>> {
+                    CMMatrix(qr.q).withAttribute(
+                        OrthogonalAttribute
+                    )
+                }
                 override val r: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.r).withAttribute(UpperTriangular) }
             }
 
-            SVDAttribute::class -> object : SVDAttribute<Double> {
+            SVDAttribute -> object : SVDAttribute<Double> {
                 private val sv by lazy { SingularValueDecomposition(origin) }
                 override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
                 override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt

@@ -13,6 +13,8 @@ import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
 import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
 import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex
 import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
+import space.kscience.attributes.AttributesBuilder
+import space.kscience.attributes.SetAttribute
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.expressions.SymbolIndexer
@@ -26,34 +28,25 @@ import kotlin.reflect.KClass
 public operator fun PointValuePair.component1(): DoubleArray = point
 public operator fun PointValuePair.component2(): Double = value
 
-public class CMOptimizerEngine(public val optimizerBuilder: () -> MultivariateOptimizer) : OptimizationFeature {
+public object CMOptimizerEngine: OptimizationAttribute<() -> MultivariateOptimizer>
-    override fun toString(): String = "CMOptimizer($optimizerBuilder)"
-}
 
 /**
  * Specify a Commons-maths optimization engine
  */
-public fun FunctionOptimizationBuilder<Double>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
+public fun AttributesBuilder<FunctionOptimization<Double>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
-    addFeature(CMOptimizerEngine(optimizerBuilder))
+    set(CMOptimizerEngine, optimizerBuilder)
 }
 
-public class CMOptimizerData(public val data: List<SymbolIndexer.() -> OptimizationData>) : OptimizationFeature {
+public object CMOptimizerData: SetAttribute<SymbolIndexer.() -> OptimizationData>
-    public constructor(vararg data: (SymbolIndexer.() -> OptimizationData)) : this(data.toList())
-
-    override fun toString(): String = "CMOptimizerData($data)"
-}
 
 /**
  * Specify Commons-maths optimization data.
  */
-public fun FunctionOptimizationBuilder<Double>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
+public fun AttributesBuilder<FunctionOptimization<Double>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
-    updateFeature<CMOptimizerData> {
+    CMOptimizerData.add(data)
-        val newData = (it?.data ?: emptyList()) + data
-        CMOptimizerData(newData)
-    }
 }
 
-public fun FunctionOptimizationBuilder<Double>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
+public fun AttributesBuilder<FunctionOptimization<Double>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
     //TODO use convergence checker from features
     cmEngine { SimplexOptimizer(CMOptimizer.defaultConvergenceChecker) }
     cmOptimizationData { NelderMeadSimplex(mapOf(*steps).toDoubleArray()) }
@@ -78,8 +71,8 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
     ): FunctionOptimization<Double> {
         val startPoint = problem.startPoint
 
-        val parameters = problem.getFeature<OptimizationParameters>()?.symbols
+        val parameters = problem.attributes[OptimizationParameters]
-            ?: problem.getFeature<OptimizationStartPoint<Double>>()?.point?.keys
+            ?: problem.attributes[OptimizationStartPoint<Double>()]?.keys
             ?: startPoint.keys
 
 
@@ -90,7 +83,7 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
                 DEFAULT_MAX_ITER
             )
 
-            val cmOptimizer: MultivariateOptimizer = problem.getFeature<CMOptimizerEngine>()?.optimizerBuilder?.invoke()
+            val cmOptimizer: MultivariateOptimizer = problem.attributes[CMOptimizerEngine]?.invoke()
                 ?: NonLinearConjugateGradientOptimizer(
                     NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
                     convergenceChecker
@@ -123,7 +116,7 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
             }
             addOptimizationData(gradientFunction)
 
-            val logger = problem.getFeature<OptimizationLog>()
+            val logger = problem.attributes[OptimizationLog]
 
             for (feature in problem.attributes) {
                 when (feature) {
@@ -139,7 +132,7 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
             }
 
             val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
-            return problem.withFeatures(OptimizationResult(point.toMap()), OptimizationValue(value))
+            return problem.withAttributes(OptimizationResult(point.toMap()), OptimizationValue(value))
         }
     }
 }

kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -31,7 +31,7 @@ internal class OptimizeTest {
     @Test
     fun testGradientOptimization() = runBlocking {
         val result = normal.optimizeWith(CMOptimizer, x to 1.0, y to 1.0)
-        println(result.resultPoint)
+        println(result.result)
         println(result.resultValue)
     }
 
@@ -42,7 +42,7 @@ internal class OptimizeTest {
             //this sets simplex optimizer
         }
 
-        println(result.resultPoint)
+        println(result.result)
         println(result.resultValue)
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DSAlgebra.kt

@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.expressions
 
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.structures.Buffer
@@ -331,10 +332,13 @@ public class DerivativeStructureRingExpression<T, A>(
     public val elementBufferFactory: MutableBufferFactory<T> = algebra.bufferFactory,
     public val function: DSRing<T, A>.() -> DS<T, A>,
 ) : DifferentiableExpression<T> where A : Ring<T>, A : ScaleOperations<T>, A : NumericAlgebra<T> {
+
+    override val type: SafeType<T> get() = elementBufferFactory.type
+
     override operator fun invoke(arguments: Map<Symbol, T>): T =
         DSRing(algebra, 0, arguments).function().value
 
-    override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression { arguments ->
+    override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression(type) { arguments ->
         with(
             DSRing(
                 algebra,
@@ -443,10 +447,13 @@ public class DSFieldExpression<T, A : ExtendedField<T>>(
     public val algebra: A,
     public val function: DSField<T, A>.() -> DS<T, A>,
 ) : DifferentiableExpression<T> {
+
+    override val type: SafeType<T> get() = algebra.type
+
     override operator fun invoke(arguments: Map<Symbol, T>): T =
         DSField(algebra, 0, arguments).function().value
 
-    override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression { arguments ->
+    override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression(type) { arguments ->
         DSField(
             algebra,
             symbols.size,

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt

@@ -5,8 +5,13 @@
 
 package space.kscience.kmath.expressions
 
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.WithType
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.operations.Algebra
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.IntRing
+import space.kscience.kmath.operations.LongRing
 import kotlin.jvm.JvmName
 import kotlin.properties.ReadOnlyProperty
 
@@ -15,7 +20,7 @@ import kotlin.properties.ReadOnlyProperty
  *
  * @param T the type this expression takes as argument and returns.
  */
-public fun interface Expression<T> {
+public interface Expression<T> : WithType<T> {
     /**
      * Calls this expression from arguments.
      *
@@ -25,11 +30,20 @@ public fun interface Expression<T> {
     public operator fun invoke(arguments: Map<Symbol, T>): T
 }
 
+public fun <T> Expression(type: SafeType<T>, block: (Map<Symbol, T>) -> T): Expression<T> = object : Expression<T> {
+    override fun invoke(arguments: Map<Symbol, T>): T = block(arguments)
+
+    override val type: SafeType<T> = type
+}
+
 /**
  * Specialization of [Expression] for [Double] allowing better performance because of using array.
  */
 @UnstableKMathAPI
 public interface DoubleExpression : Expression<Double> {
+
+    override val type: SafeType<Double> get() = DoubleField.type
+
     /**
      * The indexer of this expression's arguments that should be used to build array for [invoke].
      *
@@ -49,7 +63,7 @@ public interface DoubleExpression : Expression<Double> {
      */
     public operator fun invoke(arguments: DoubleArray): Double
 
-    public companion object{
+    public companion object {
         internal val EMPTY_DOUBLE_ARRAY = DoubleArray(0)
     }
 }
@@ -59,6 +73,9 @@ public interface DoubleExpression : Expression<Double> {
  */
 @UnstableKMathAPI
 public interface IntExpression : Expression<Int> {
+
+    override val type: SafeType<Int> get() = IntRing.type
+
     /**
      * The indexer of this expression's arguments that should be used to build array for [invoke].
      *
@@ -78,7 +95,7 @@ public interface IntExpression : Expression<Int> {
      */
     public operator fun invoke(arguments: IntArray): Int
 
-    public companion object{
+    public companion object {
         internal val EMPTY_INT_ARRAY = IntArray(0)
     }
 }
@@ -88,6 +105,9 @@ public interface IntExpression : Expression<Int> {
  */
 @UnstableKMathAPI
 public interface LongExpression : Expression<Long> {
+
+    override val type: SafeType<Long> get() = LongRing.type
+
     /**
      * The indexer of this expression's arguments that should be used to build array for [invoke].
      *
@@ -107,7 +127,7 @@ public interface LongExpression : Expression<Long> {
      */
     public operator fun invoke(arguments: LongArray): Long
 
-    public companion object{
+    public companion object {
         internal val EMPTY_LONG_ARRAY = LongArray(0)
     }
 }
@@ -158,7 +178,6 @@ public operator fun <T> Expression<T>.invoke(vararg pairs: Pair<String, T>): T =
 )
 
 
-
 /**
  * Calls this expression without providing any arguments.
  *

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/ExpressionWithDefault.kt

@@ -5,10 +5,15 @@
 
 package space.kscience.kmath.expressions
 
+import space.kscience.attributes.SafeType
+
 public class ExpressionWithDefault<T>(
     private val origin: Expression<T>,
     private val defaultArgs: Map<Symbol, T>,
 ) : Expression<T> {
+    override val type: SafeType<T>
+        get() = origin.type
+
     override fun invoke(arguments: Map<Symbol, T>): T = origin.invoke(defaultArgs + arguments)
 }
 
@@ -21,6 +26,9 @@ public class DiffExpressionWithDefault<T>(
     private val defaultArgs: Map<Symbol, T>,
 ) : DifferentiableExpression<T> {
 
+    override val type: SafeType<T>
+        get() = origin.type
+
     override fun invoke(arguments: Map<Symbol, T>): T = origin.invoke(defaultArgs + arguments)
 
     override fun derivativeOrNull(symbols: List<Symbol>): Expression<T>? =

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt

@@ -23,26 +23,27 @@ public abstract class FunctionalExpressionAlgebra<T, out A : Algebra<T>>(
     /**
      * Builds an Expression of constant expression that does not depend on arguments.
      */
-    override fun const(value: T): Expression<T> = Expression { value }
+    override fun const(value: T): Expression<T> = Expression(algebra.type) { value }
 
     /**
      * Builds an Expression to access a variable.
      */
-    override fun bindSymbolOrNull(value: String): Expression<T>? = Expression { arguments ->
+    override fun bindSymbolOrNull(value: String): Expression<T>? = Expression(algebra.type) { arguments ->
         algebra.bindSymbolOrNull(value)
             ?: arguments[StringSymbol(value)]
             ?: error("Symbol '$value' is not supported in $this")
     }
 
-    override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
+    override fun binaryOperationFunction(
-        { left, right ->
+        operation: String,
-            Expression { arguments ->
+    ): (left: Expression<T>, right: Expression<T>) -> Expression<T> = { left, right ->
+        Expression(algebra.type) { arguments ->
             algebra.binaryOperationFunction(operation)(left(arguments), right(arguments))
         }
     }
 
     override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> = { arg ->
-        Expression { arguments -> algebra.unaryOperation(operation, arg(arguments)) }
+        Expression(algebra.type) { arguments -> algebra.unaryOperation(operation, arg(arguments)) }
     }
 }
 
@@ -124,7 +125,7 @@ public open class FunctionalExpressionField<T, out A : Field<T>>(
         super<FunctionalExpressionRing>.binaryOperationFunction(operation)
 
     override fun scale(a: Expression<T>, value: Double): Expression<T> = algebra {
-        Expression { args -> a(args) * value }
+        Expression(algebra.type) { args -> a(args) * value }
     }
 
     override fun bindSymbolOrNull(value: String): Expression<T>? =

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MST.kt

@@ -108,4 +108,4 @@ public fun <T> MST.interpret(algebra: Algebra<T>, vararg arguments: Pair<Symbol,
  * Interpret this [MST] as expression.
  */
 public fun <T : Any> MST.toExpression(algebra: Algebra<T>): Expression<T> =
-    Expression { arguments -> interpret(algebra, arguments) }
+    Expression(algebra.type) { arguments -> interpret(algebra, arguments) }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt

@@ -243,12 +243,15 @@ public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
     public val field: F,
     public val function: SimpleAutoDiffField<T, F>.() -> AutoDiffValue<T>,
 ) : FirstDerivativeExpression<T>() {
+
+    override val type: SafeType<T> get() = this.field.type
+
     override operator fun invoke(arguments: Map<Symbol, T>): T {
         //val bindings = arguments.entries.map { it.key.bind(it.value) }
         return SimpleAutoDiffField(field, arguments).function().value
     }
 
-    override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
+    override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression(type) { arguments ->
         //val bindings = arguments.entries.map { it.key.bind(it.value) }
         val derivationResult = SimpleAutoDiffField(field, arguments).differentiate(function)
         derivationResult.derivative(symbol)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSolver.kt

@@ -11,7 +11,7 @@ package space.kscience.kmath.linear
  *
  * @param T the type of items.
  */
-public interface LinearSolver<T : Any> {
+public interface LinearSolver<T> {
     /**
      * Solve a dot x = b matrix equation and return x
      */

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt

@@ -5,10 +5,7 @@
 
 package space.kscience.kmath.linear
 
-import space.kscience.attributes.Attributes
+import space.kscience.attributes.*
-import space.kscience.attributes.SafeType
-import space.kscience.attributes.WithType
-import space.kscience.attributes.withAttribute
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.operations.BufferRingOps
@@ -31,19 +28,13 @@ public typealias MutableMatrix<T> = MutableStructure2D<T>
  */
 public typealias Point<T> = Buffer<T>
 
-/**
- * A marker interface for algebras that operate on matrices
- * @param T type of matrix element
- */
-public interface MatrixOperations<T> : WithType<T>
-
 /**
  * Basic operations on matrices and vectors.
  *
  * @param T the type of items in the matrices.
  * @param A the type of ring over [T].
  */
-public interface LinearSpace<T, out A : Ring<T>> : MatrixOperations<T> {
+public interface LinearSpace<T, out A : Ring<T>> : MatrixScope<T> {
     public val elementAlgebra: A
 
     override val type: SafeType<T> get() = elementAlgebra.type
@@ -177,10 +168,10 @@ public interface LinearSpace<T, out A : Ring<T>> : MatrixOperations<T> {
     /**
      * Compute an [attribute] value for given [structure]. Return null if the attribute could not be computed.
      */
-    public fun <V, A : StructureAttribute<V>> computeAttribute(structure: StructureND<*>, attribute: A): V? = null
+    public fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<T>, attribute: A): V? = null
 
     @UnstableKMathAPI
-    public fun <V, A : StructureAttribute<V>> StructureND<*>.getOrComputeAttribute(attribute: A): V? {
+    public fun <V, A : StructureAttribute<V>> Structure2D<T>.getOrComputeAttribute(attribute: A): V? {
         return attributes[attribute] ?: computeAttribute(this, attribute)
     }
 
@@ -225,7 +216,7 @@ public inline operator fun <LS : LinearSpace<*, *>, R> LS.invoke(block: LS.() ->
 /**
  * Convert matrix to vector if it is possible.
  */
-public fun <T : Any> Matrix<T>.asVector(): Point<T> =
+public fun <T> Matrix<T>.asVector(): Point<T> =
     if (this.colNum == 1) as1D()
     else error("Can't convert matrix with more than one column to vector")
 
@@ -236,4 +227,4 @@ public fun <T : Any> Matrix<T>.asVector(): Point<T> =
  * @receiver a buffer.
  * @return the new matrix.
  */
-public fun <T : Any> Point<T>.asMatrix(): VirtualMatrix<T> = VirtualMatrix(type, size, 1) { i, _ -> get(i) }
+public fun <T> Point<T>.asMatrix(): VirtualMatrix<T> = VirtualMatrix(type, size, 1) { i, _ -> get(i) }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt

@@ -9,12 +9,20 @@ package space.kscience.kmath.linear
 
 import space.kscience.attributes.Attributes
 import space.kscience.attributes.PolymorphicAttribute
-import space.kscience.attributes.SafeType
 import space.kscience.attributes.safeTypeOf
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.operations.*
 import space.kscience.kmath.structures.*
 
+public interface LupDecomposition<T> {
+    public val linearSpace: LinearSpace<T, Field<T>>
+    public val elementAlgebra: Field<T> get() = linearSpace.elementAlgebra
+
+    public val pivot: IntBuffer
+    public val l: Matrix<T>
+    public val u: Matrix<T>
+}
+
 /**
  * Matrices with this feature support LU factorization with partial pivoting: *[p] &middot; a = [l] &middot; [u]* where
  * *a* is the owning matrix.
@@ -22,15 +30,14 @@ import space.kscience.kmath.structures.*
  * @param T the type of matrices' items.
  * @param lu combined L and U matrix
  */
-public class LupDecomposition<T>(
+public class GenericLupDecomposition<T>(
-    public val linearSpace: LinearSpace<T, Ring<T>>,
+    override val linearSpace: LinearSpace<T, Field<T>>,
     private val lu: Matrix<T>,
-    public val pivot: IntBuffer,
+    override val pivot: IntBuffer,
     private val even: Boolean,
-) {
+) : LupDecomposition<T> {
-    public val elementAlgebra: Ring<T> get() = linearSpace.elementAlgebra
 
-    public val l: Matrix<T>
+    override val l: Matrix<T>
         get() = VirtualMatrix(lu.type, lu.rowNum, lu.colNum, attributes = Attributes(LowerTriangular)) { i, j ->
             when {
                 j < i -> lu[i, j]
@@ -39,7 +46,7 @@ public class LupDecomposition<T>(
             }
         }
 
-    public val u: Matrix<T>
+    override val u: Matrix<T>
         get() = VirtualMatrix(lu.type, lu.rowNum, lu.colNum, attributes = Attributes(UpperTriangular)) { i, j ->
             if (j >= i) lu[i, j] else elementAlgebra.zero
         }
@@ -55,13 +62,12 @@ public class LupDecomposition<T>(
 
 }
 
-
+public class LupDecompositionAttribute<T> :
-public class LupDecompositionAttribute<T>(type: SafeType<LupDecomposition<T>>) :
+    PolymorphicAttribute<LupDecomposition<T>>(safeTypeOf()),
-    PolymorphicAttribute<LupDecomposition<T>>(type),
     MatrixAttribute<LupDecomposition<T>>
 
-public val <T> MatrixOperations<T>.LUP: LupDecompositionAttribute<T>
+public val <T> MatrixScope<T>.LUP: LupDecompositionAttribute<T>
-    get() = LupDecompositionAttribute(safeTypeOf())
+    get() = LupDecompositionAttribute()
 
 @PublishedApi
 internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
@@ -79,7 +85,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
     val pivot = IntArray(matrix.rowNum)
 
     //TODO just waits for multi-receivers
-    with(BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory)){
+    with(BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory)) {
 
         val lu = create(matrix)
 
@@ -142,18 +148,17 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
             for (row in col + 1 until m) lu[row, col] /= luDiag
         }
 
-        return LupDecomposition(this@lup, lu.toStructure2D(), pivot.asBuffer(), even)
+        return GenericLupDecomposition(this@lup, lu.toStructure2D(), pivot.asBuffer(), even)
     }
 }
 
 
-
 public fun LinearSpace<Double, Float64Field>.lup(
     matrix: Matrix<Double>,
     singularityThreshold: Double = 1e-11,
 ): LupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
 
-internal fun <T : Any, A : Field<T>> LinearSpace<T, A>.solve(
+internal fun <T> LinearSpace<T, Field<T>>.solve(
     lup: LupDecomposition<T>,
     matrix: Matrix<T>,
 ): Matrix<T> {
@@ -205,7 +210,7 @@ internal fun <T : Any, A : Field<T>> LinearSpace<T, A>.solve(
  * Produce a generic solver based on LUP decomposition
  */
 @OptIn(UnstableKMathAPI::class)
-public fun <T : Comparable<T>, F : Field<T>> LinearSpace<T, F>.lupSolver(
+public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lupSolver(
     singularityCheck: (T) -> Boolean,
 ): LinearSolver<T> = object : LinearSolver<T> {
     override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt

@@ -35,7 +35,7 @@ public val <T : Any> Matrix<T>.origin: Matrix<T>
 /**
  * Add a single feature to a [Matrix]
  */
-public fun <T : Any, A : Attribute<T>> Matrix<T>.withAttribute(
+public fun <T, A : Attribute<T>> Matrix<T>.withAttribute(
     attribute: A,
     attrValue: T,
 ): MatrixWrapper<T> = if (this is MatrixWrapper) {
@@ -44,7 +44,7 @@ public fun <T : Any, A : Attribute<T>> Matrix<T>.withAttribute(
     MatrixWrapper(this, Attributes(attribute, attrValue))
 }
 
-public fun <T : Any, A : Attribute<Unit>> Matrix<T>.withAttribute(
+public fun <T, A : Attribute<Unit>> Matrix<T>.withAttribute(
     attribute: A,
 ): MatrixWrapper<T> = if (this is MatrixWrapper) {
     MatrixWrapper(origin, attributes.withAttribute(attribute))
@@ -55,7 +55,7 @@ public fun <T : Any, A : Attribute<Unit>> Matrix<T>.withAttribute(
 /**
  * Modify matrix attributes
  */
-public fun <T : Any> Matrix<T>.modifyAttributes(modifier: (Attributes) -> Attributes): MatrixWrapper<T> =
+public fun <T> Matrix<T>.modifyAttributes(modifier: (Attributes) -> Attributes): MatrixWrapper<T> =
     if (this is MatrixWrapper) {
         MatrixWrapper(origin, modifier(attributes))
     } else {
@@ -65,7 +65,7 @@ public fun <T : Any> Matrix<T>.modifyAttributes(modifier: (Attributes) -> Attrib
 /**
  * Diagonal matrix of ones. The matrix is virtual, no actual matrix is created.
  */
-public fun <T : Any> LinearSpace<T, Ring<T>>.one(
+public fun <T> LinearSpace<T, Ring<T>>.one(
     rows: Int,
     columns: Int,
 ): MatrixWrapper<T> = VirtualMatrix(type, rows, columns) { i, j ->
@@ -76,7 +76,7 @@ public fun <T : Any> LinearSpace<T, Ring<T>>.one(
 /**
  * A virtual matrix of zeroes
  */
-public fun <T : Any> LinearSpace<T, Ring<T>>.zero(
+public fun <T> LinearSpace<T, Ring<T>>.zero(
     rows: Int,
     columns: Int,
 ): MatrixWrapper<T> = VirtualMatrix(type, rows, columns) { _, _ ->

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/matrixAttributes.kt

@@ -10,6 +10,13 @@ package space.kscience.kmath.linear
 import space.kscience.attributes.*
 import space.kscience.kmath.nd.StructureAttribute
 
+
+/**
+ * A marker interface for algebras that operate on matrices
+ * @param T type of matrix element
+ */
+public interface MatrixScope<T> : AttributeScope<Matrix<T>>, WithType<T>
+
 /**
  * A marker interface representing some properties of matrices or additional transformations of them. Features are used
  * to optimize matrix operations performance in some cases or retrieve the APIs.
@@ -38,11 +45,11 @@ public object IsUnit : IsDiagonal
  *
  * @param T the type of matrices' items.
  */
-public class Inverted<T>(type: SafeType<Matrix<T>>) :
+public class Inverted<T>() :
-    PolymorphicAttribute<Matrix<T>>(type),
+    PolymorphicAttribute<Matrix<T>>(safeTypeOf()),
     MatrixAttribute<Matrix<T>>
 
-public val <T> MatrixOperations<T>.Inverted: Inverted<T> get() = Inverted(safeTypeOf())
+public val <T> MatrixScope<T>.Inverted: Inverted<T> get() = Inverted()
 
 /**
  * Matrices with this feature can compute their determinant.
@@ -53,7 +60,7 @@ public class Determinant<T>(type: SafeType<T>) :
     PolymorphicAttribute<T>(type),
     MatrixAttribute<T>
 
-public val <T> MatrixOperations<T>.Determinant: Determinant<T> get() = Determinant(type)
+public val <T> MatrixScope<T>.Determinant: Determinant<T> get() = Determinant(type)
 
 /**
  * Matrices with this feature are lower triangular ones.
@@ -77,11 +84,11 @@ public data class LUDecomposition<T>(val l: Matrix<T>, val u: Matrix<T>)
  *
  * @param T the type of matrices' items.
  */
-public class LuDecompositionAttribute<T>(type: SafeType<LUDecomposition<T>>) :
+public class LuDecompositionAttribute<T> :
-    PolymorphicAttribute<LUDecomposition<T>>(type),
+    PolymorphicAttribute<LUDecomposition<T>>(safeTypeOf()),
     MatrixAttribute<LUDecomposition<T>>
 
-public val <T> MatrixOperations<T>.LU: LuDecompositionAttribute<T> get() = LuDecompositionAttribute(safeTypeOf())
+public val <T> MatrixScope<T>.LU: LuDecompositionAttribute<T> get() = LuDecompositionAttribute()
 
 
 /**
@@ -108,12 +115,12 @@ public interface QRDecomposition<out T> {
  *
  * @param T the type of matrices' items.
  */
-public class QRDecompositionAttribute<T>(type: SafeType<QRDecomposition<T>>) :
+public class QRDecompositionAttribute<T>() :
-    PolymorphicAttribute<QRDecomposition<T>>(type),
+    PolymorphicAttribute<QRDecomposition<T>>(safeTypeOf()),
     MatrixAttribute<QRDecomposition<T>>
 
-public val <T> MatrixOperations<T>.QR: QRDecompositionAttribute<T>
+public val <T> MatrixScope<T>.QR: QRDecompositionAttribute<T>
-    get() = QRDecompositionAttribute(safeTypeOf())
+    get() = QRDecompositionAttribute()
 
 public interface CholeskyDecomposition<T> {
     /**
@@ -128,12 +135,12 @@ public interface CholeskyDecomposition<T> {
  *
  * @param T the type of matrices' items.
  */
-public class CholeskyDecompositionAttribute<T>(type: SafeType<CholeskyDecomposition<T>>) :
+public class CholeskyDecompositionAttribute<T> :
-    PolymorphicAttribute<CholeskyDecomposition<T>>(type),
+    PolymorphicAttribute<CholeskyDecomposition<T>>(safeTypeOf()),
     MatrixAttribute<CholeskyDecomposition<T>>
 
-public val <T> MatrixOperations<T>.Cholesky: CholeskyDecompositionAttribute<T>
+public val <T> MatrixScope<T>.Cholesky: CholeskyDecompositionAttribute<T>
-    get() = CholeskyDecompositionAttribute(safeTypeOf())
+    get() = CholeskyDecompositionAttribute()
 
 public interface SingularValueDecomposition<T> {
     /**
@@ -163,12 +170,11 @@ public interface SingularValueDecomposition<T> {
  *
  * @param T the type of matrices' items.
  */
-public class SVDAttribute<T>(type: SafeType<SingularValueDecomposition<T>>) :
+public class SVDAttribute<T>() :
-    PolymorphicAttribute<SingularValueDecomposition<T>>(type),
+    PolymorphicAttribute<SingularValueDecomposition<T>>(safeTypeOf()),
     MatrixAttribute<SingularValueDecomposition<T>>
 
-public val <T> MatrixOperations<T>.SVD: SVDAttribute<T>
+public val <T> MatrixScope<T>.SVD: SVDAttribute<T> get() = SVDAttribute()
-    get() = SVDAttribute(safeTypeOf())
 
 
 //TODO add sparse matrix feature

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt

@@ -39,5 +39,5 @@ public abstract class EjmlLinearSpace<T : Any, out A : Ring<T>, out M : org.ejml
 
     @UnstableKMathAPI
     public fun EjmlMatrix<T, *>.inverted(): Matrix<Double> =
-        attributeForOrNull(this, Float64Field.linearSpace.Inverted)
+        computeAttribute(this, Float64Field.linearSpace.Inverted)!!
 }

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt

@@ -19,9 +19,13 @@ import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
 import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.safeTypeOf
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.linear.Matrix
+import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.StructureAttribute
 import space.kscience.kmath.nd.StructureFeature
 import space.kscience.kmath.operations.Float32Field
 import space.kscience.kmath.operations.Float64Field
@@ -39,6 +43,8 @@ public class EjmlDoubleVector<out M : DMatrix>(override val origin: M) : EjmlVec
         require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
     }
 
+    override val type: SafeType<Double> get() = safeTypeOf()
+
     override operator fun get(index: Int): Double = origin[0, index]
 }
 
@@ -50,6 +56,8 @@ public class EjmlFloatVector<out M : FMatrix>(override val origin: M) : EjmlVect
         require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
     }
 
+    override val type: SafeType<Float> get() = safeTypeOf()
+
     override operator fun get(index: Int): Float = origin[0, index]
 }
 
@@ -57,6 +65,8 @@ public class EjmlFloatVector<out M : FMatrix>(override val origin: M) : EjmlVect
  * [EjmlMatrix] specialization for [Double].
  */
 public class EjmlDoubleMatrix<out M : DMatrix>(override val origin: M) : EjmlMatrix<Double, M>(origin) {
+    override val type: SafeType<Double> get() = safeTypeOf()
+
     override operator fun get(i: Int, j: Int): Double = origin[i, j]
 }
 
@@ -64,9 +74,12 @@ public class EjmlDoubleMatrix<out M : DMatrix>(override val origin: M) : EjmlMat
  * [EjmlMatrix] specialization for [Float].
  */
 public class EjmlFloatMatrix<out M : FMatrix>(override val origin: M) : EjmlMatrix<Float, M>(origin) {
+    override val type: SafeType<Float> get() = safeTypeOf()
+
     override operator fun get(i: Int, j: Int): Float = origin[i, j]
 }
 
+
 /**
  * [EjmlLinearSpace] implementation based on [CommonOps_DDRM], [DecompositionFactory_DDRM] operations and
  * [DMatrixRMaj] matrices.
@@ -77,7 +90,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
      */
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val elementType: KType get() = typeOf<Double>()
+    override val type: SafeType<Double> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
     override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
@@ -205,6 +218,18 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
 
     override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixRMaj> = v * this
 
+    override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
+        val origin = structure.toEjml().origin
+        return when(attribute){
+            Inverted -> {
+                val res = origin.copy()
+                CommonOps_DDRM.invert(res)
+                res.wrapMatrix()
+            }
+            else->
+        }
+    }
+
     @UnstableKMathAPI
     override fun <F : StructureFeature> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
         structure.getFeature(type)?.let { return it }
@@ -305,6 +330,8 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
     }
 }
 
+import org.checkerframework.checker.guieffect.qual.SafeType
+
 /**
  * [EjmlLinearSpace] implementation based on [CommonOps_FDRM], [DecompositionFactory_FDRM] operations and
  * [FMatrixRMaj] matrices.
@@ -315,7 +342,7 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
      */
     override val elementAlgebra: Float32Field get() = Float32Field
 
-    override val elementType: KType get() = typeOf<Float>()
+    override val type: SafeType<Float> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
     override fun Matrix<Float>.toEjml(): EjmlFloatMatrix<FMatrixRMaj> = when {
@@ -543,6 +570,8 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
     }
 }
 
+import org.checkerframework.checker.guieffect.qual.SafeType
+
 /**
  * [EjmlLinearSpace] implementation based on [CommonOps_DSCC], [DecompositionFactory_DSCC] operations and
  * [DMatrixSparseCSC] matrices.
@@ -553,7 +582,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
      */
     override val elementAlgebra: Float64Field get() = Float64Field
 
-    override val elementType: KType get() = typeOf<Double>()
+    override val type: SafeType<Double> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
     override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
@@ -776,6 +805,8 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
     }
 }
 
+import org.checkerframework.checker.guieffect.qual.SafeType
+
 /**
  * [EjmlLinearSpace] implementation based on [CommonOps_FSCC], [DecompositionFactory_FSCC] operations and
  * [FMatrixSparseCSC] matrices.
@@ -786,7 +817,7 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
      */
     override val elementAlgebra: Float32Field get() = Float32Field
 
-    override val elementType: KType get() = typeOf<Float>()
+    override val type: SafeType<Float> get() = safeTypeOf()
 
     @Suppress("UNCHECKED_CAST")
     override fun Matrix<Float>.toEjml(): EjmlFloatMatrix<FMatrixSparseCSC> = when {

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt

@@ -4,7 +4,7 @@
  */
 package space.kscience.kmath.integration
 
-import space.kscience.attributes.TypedAttributesBuilder
+import space.kscience.attributes.AttributesBuilder
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.operations.Field
 import space.kscience.kmath.structures.Buffer
@@ -92,7 +92,7 @@ public inline fun <reified T : Any> GaussIntegrator<T>.integrate(
     range: ClosedRange<Double>,
     order: Int = 10,
     intervals: Int = 10,
-    attributesBuilder: TypedAttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
+    attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
     noinline function: (Double) -> T,
 ): UnivariateIntegrand<T> {
     require(range.endInclusive > range.start) { "The range upper bound should be higher than lower bound" }

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt

@@ -30,7 +30,7 @@ public sealed class IntegrandValue<T> private constructor(): IntegrandAttribute<
     }
 }
 
-public fun <T> TypedAttributesBuilder<Integrand<T>>.value(value: T) {
+public fun <T> AttributesBuilder<Integrand<T>>.value(value: T) {
     IntegrandValue.forType<T>().invoke(value)
 }
 

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/MultivariateIntegrand.kt

@@ -25,10 +25,10 @@ public fun <T, A : Any> MultivariateIntegrand<T>.withAttribute(
 ): MultivariateIntegrand<T> = withAttributes(attributes.withAttribute(attribute, value))
 
 public fun <T> MultivariateIntegrand<T>.withAttributes(
-    block: TypedAttributesBuilder<MultivariateIntegrand<T>>.() -> Unit,
+    block: AttributesBuilder<MultivariateIntegrand<T>>.() -> Unit,
 ): MultivariateIntegrand<T> = withAttributes(attributes.modify(block))
 
 public inline fun <reified T : Any> MultivariateIntegrand(
-    attributeBuilder: TypedAttributesBuilder<MultivariateIntegrand<T>>.() -> Unit,
+    attributeBuilder: AttributesBuilder<MultivariateIntegrand<T>>.() -> Unit,
     noinline function: (Point<T>) -> T,
 ): MultivariateIntegrand<T> = MultivariateIntegrand(safeTypeOf<T>(), Attributes(attributeBuilder), function)

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt

@@ -26,11 +26,11 @@ public fun <T, A : Any> UnivariateIntegrand<T>.withAttribute(
 ): UnivariateIntegrand<T> = withAttributes(attributes.withAttribute(attribute, value))
 
 public fun <T> UnivariateIntegrand<T>.withAttributes(
-    block: TypedAttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
+    block: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
 ): UnivariateIntegrand<T> = withAttributes(attributes.modify(block))
 
 public inline fun <reified T : Any> UnivariateIntegrand(
-    attributeBuilder: TypedAttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
+    attributeBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
     noinline function: (Double) -> T,
 ): UnivariateIntegrand<T> = UnivariateIntegrand(safeTypeOf(), Attributes(attributeBuilder), function)
 
@@ -58,7 +58,7 @@ public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<
 
 public object UnivariateIntegrationNodes : IntegrandAttribute<Buffer<Double>>
 
-public fun TypedAttributesBuilder<UnivariateIntegrand<*>>.integrationNodes(vararg nodes: Double) {
+public fun AttributesBuilder<UnivariateIntegrand<*>>.integrationNodes(vararg nodes: Double) {
     UnivariateIntegrationNodes(Float64Buffer(nodes))
 }
 
@@ -68,7 +68,7 @@ public fun TypedAttributesBuilder<UnivariateIntegrand<*>>.integrationNodes(varar
  */
 @UnstableKMathAPI
 public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
-    attributesBuilder: TypedAttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
+    attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
     noinline function: (Double) -> T,
 ): UnivariateIntegrand<T> = integrate(UnivariateIntegrand(attributesBuilder, function))
 
@@ -79,7 +79,7 @@ public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
 @UnstableKMathAPI
 public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
     range: ClosedRange<Double>,
-    attributeBuilder: TypedAttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},
+    attributeBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},
     noinline function: (Double) -> T,
 ): UnivariateIntegrand<T> {
 

kmath-jafama/build.gradle.kts

@@ -14,7 +14,7 @@ repositories {
 }
 
 readme {
-    maturity = space.kscience.gradle.Maturity.PROTOTYPE
+    maturity = space.kscience.gradle.Maturity.DEPRECATED
     propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
 
     feature("jafama-double", "src/main/kotlin/space/kscience/kmath/jafama/") {

kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/KMathJafama.kt

@@ -7,16 +7,17 @@ package space.kscience.kmath.jafama
 
 import net.jafama.FastMath
 import net.jafama.StrictFastMath
-import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.operations.*
-import space.kscience.kmath.operations.Norm
+import space.kscience.kmath.structures.MutableBufferFactory
-import space.kscience.kmath.operations.PowerOperations
-import space.kscience.kmath.operations.ScaleOperations
 
 /**
  * A field for [Double] (using FastMath) without boxing. Does not produce appropriate field element.
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 public object JafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
+
+    override val bufferFactory: MutableBufferFactory<Double> get() = DoubleField.bufferFactory
+
     override inline val zero: Double get() = 0.0
     override inline val one: Double get() = 1.0
 
@@ -68,6 +69,9 @@ public object JafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, S
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 public object StrictJafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
+
+    override val bufferFactory: MutableBufferFactory<Double> get() = DoubleField.bufferFactory
+
     override inline val zero: Double get() = 0.0
     override inline val one: Double get() = 1.0
 

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt

@@ -20,7 +20,7 @@ public class MultikDoubleAlgebra(
 ) : MultikDivisionTensorAlgebra<Double, Float64Field>(multikEngine),
     TrigonometricOperations<StructureND<Double>>, ExponentialOperations<StructureND<Double>> {
     override val elementAlgebra: Float64Field get() = Float64Field
-    override val type: DataType get() = DataType.DoubleDataType
+    override val dataType: DataType get() = DataType.DoubleDataType
 
     override fun sin(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.sin(arg.asMultik().array).wrap()
 

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikFloatAlgebra.kt

@@ -15,7 +15,7 @@ public class MultikFloatAlgebra(
     multikEngine: Engine
 ) : MultikDivisionTensorAlgebra<Float, Float32Field>(multikEngine) {
     override val elementAlgebra: Float32Field get() = Float32Field
-    override val type: DataType get() = DataType.FloatDataType
+    override val dataType: DataType get() = DataType.FloatDataType
 
     override fun scalar(value: Float): MultikTensor<Float> = Multik.ndarrayOf(value).wrap()
 }

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikIntAlgebra.kt

@@ -15,7 +15,7 @@ public class MultikIntAlgebra(
     multikEngine: Engine
 ) : MultikTensorAlgebra<Int, Int32Ring>(multikEngine) {
     override val elementAlgebra: Int32Ring get() = Int32Ring
-    override val type: DataType get() = DataType.IntDataType
+    override val dataType: DataType get() = DataType.IntDataType
     override fun scalar(value: Int): MultikTensor<Int>  = Multik.ndarrayOf(value).wrap()
 }
 

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikLongAlgebra.kt

@@ -15,7 +15,7 @@ public class MultikLongAlgebra(
     multikEngine: Engine
 ) : MultikTensorAlgebra<Long, Int64Ring>(multikEngine) {
     override val elementAlgebra: Int64Ring get() = Int64Ring
-    override val type: DataType get() = DataType.LongDataType
+    override val dataType: DataType get() = DataType.LongDataType
 
     override fun scalar(value: Long): MultikTensor<Long>  = Multik.ndarrayOf(value).wrap()
 }

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikShortAlgebra.kt

@@ -15,7 +15,7 @@ public class MultikShortAlgebra(
     multikEngine: Engine
 ) : MultikTensorAlgebra<Short, Int16Ring>(multikEngine) {
     override val elementAlgebra: Int16Ring get() = Int16Ring
-    override val type: DataType get() = DataType.ShortDataType
+    override val dataType: DataType get() = DataType.ShortDataType
     override fun scalar(value: Short): MultikTensor<Short>  = Multik.ndarrayOf(value).wrap()
 }
 

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikTensor.kt

@@ -6,13 +6,33 @@
 package space.kscience.kmath.multik
 
 import org.jetbrains.kotlinx.multik.ndarray.data.*
+import space.kscience.attributes.SafeType
+import space.kscience.attributes.safeTypeOf
 import space.kscience.kmath.PerformancePitfall
+import space.kscience.kmath.complex.ComplexField
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.operations.*
 import space.kscience.kmath.tensors.api.Tensor
 import kotlin.jvm.JvmInline
 
+public val DataType.type: SafeType<*>
+    get() = when (this) {
+        DataType.ByteDataType -> ByteRing.type
+        DataType.ShortDataType -> ShortRing.type
+        DataType.IntDataType -> IntRing.type
+        DataType.LongDataType -> LongRing.type
+        DataType.FloatDataType -> Float32Field.type
+        DataType.DoubleDataType -> Float64Field.type
+        DataType.ComplexFloatDataType -> safeTypeOf<Pair<Float, Float>>()
+        DataType.ComplexDoubleDataType -> ComplexField.type
+    }
+
+
 @JvmInline
 public value class MultikTensor<T>(public val array: MutableMultiArray<T, DN>) : Tensor<T> {
+    @Suppress("UNCHECKED_CAST")
+    override val type: SafeType<T> get() = array.dtype.type as SafeType<T>
+
     override val shape: ShapeND get() = ShapeND(array.shape)
 
     @PerformancePitfall

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt

@@ -26,7 +26,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
     private val multikEngine: Engine,
 ) : TensorAlgebra<T, A> where T : Number, T : Comparable<T> {
 
-    public abstract val type: DataType
+    public abstract val dataType: DataType
 
     protected val multikMath: Math = multikEngine.getMath()
     protected val multikLinAl: LinAlg = multikEngine.getLinAlg()
@@ -35,7 +35,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
     @OptIn(UnsafeKMathAPI::class)
     override fun mutableStructureND(shape: ShapeND, initializer: A.(IntArray) -> T): MultikTensor<T> {
         val strides = ColumnStrides(shape)
-        val memoryView = initMemoryView<T>(strides.linearSize, type)
+        val memoryView = initMemoryView<T>(strides.linearSize, dataType)
         strides.asSequence().forEachIndexed { linearIndex, tensorIndex ->
             memoryView[linearIndex] = elementAlgebra.initializer(tensorIndex)
         }
@@ -44,7 +44,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
 
     @OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
     override fun StructureND<T>.map(transform: A.(T) -> T): MultikTensor<T> = if (this is MultikTensor) {
-        val data = initMemoryView<T>(array.size, type)
+        val data = initMemoryView<T>(array.size, dataType)
         var count = 0
         for (el in array) data[count++] = elementAlgebra.transform(el)
         NDArray(data, shape = shape.asArray(), dim = array.dim).wrap()
@@ -58,7 +58,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
     override fun StructureND<T>.mapIndexed(transform: A.(index: IntArray, T) -> T): MultikTensor<T> =
         if (this is MultikTensor) {
             val array = asMultik().array
-            val data = initMemoryView<T>(array.size, type)
+            val data = initMemoryView<T>(array.size, dataType)
             val indexIter = array.multiIndices.iterator()
             var index = 0
             for (item in array) {
@@ -95,7 +95,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
         require(left.shape.contentEquals(right.shape)) { "ND array shape mismatch" } //TODO replace by ShapeMismatchException
         val leftArray = left.asMultik().array
         val rightArray = right.asMultik().array
-        val data = initMemoryView<T>(leftArray.size, type)
+        val data = initMemoryView<T>(leftArray.size, dataType)
         var counter = 0
         val leftIterator = leftArray.iterator()
         val rightIterator = rightArray.iterator()
@@ -114,7 +114,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
     public fun StructureND<T>.asMultik(): MultikTensor<T> = if (this is MultikTensor) {
         this
     } else {
-        val res = mk.zeros<T, DN>(shape.asArray(), type).asDNArray()
+        val res = mk.zeros<T, DN>(shape.asArray(), dataType).asDNArray()
         for (index in res.multiIndices) {
             res[index] = this[index]
         }
@@ -296,7 +296,7 @@ public abstract class MultikDivisionTensorAlgebra<T, A : Field<T>>(
 
     @OptIn(UnsafeKMathAPI::class)
     override fun T.div(arg: StructureND<T>): MultikTensor<T> =
-        Multik.ones<T, DN>(arg.shape.asArray(), type).apply { divAssign(arg.asMultik().array) }.wrap()
+        Multik.ones<T, DN>(arg.shape.asArray(), dataType).apply { divAssign(arg.asMultik().array) }.wrap()
 
     override fun StructureND<T>.div(arg: T): MultikTensor<T> =
         asMultik().array.div(arg).wrap()

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt

@@ -9,20 +9,21 @@ import space.kscience.attributes.*
 import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Symbol
 
-public class OptimizationValue<T>(public val value: T) : OptimizationFeature {
+public class OptimizationValue<V>(type: SafeType<V>) : PolymorphicAttribute<V>(type)
-    override fun toString(): String = "Value($value)"
-}
 
-public enum class FunctionOptimizationTarget {
+public enum class OptimizationDirection {
     MAXIMIZE,
     MINIMIZE
 }
 
+public object FunctionOptimizationTarget: OptimizationAttribute<OptimizationDirection>
+
 public class FunctionOptimization<T>(
-    override val attributes: Attributes,
     public val expression: DifferentiableExpression<T>,
+    override val attributes: Attributes,
 ) : OptimizationProblem<T> {
 
+    override val type: SafeType<T> get() = expression.type
 
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
@@ -47,36 +48,52 @@ public class FunctionOptimization<T>(
     public companion object
 }
 
+public fun <T> FunctionOptimization(
+    expression: DifferentiableExpression<T>,
+    attributeBuilder: AttributesBuilder<FunctionOptimization<T>>.() -> Unit,
+): FunctionOptimization<T> = FunctionOptimization(expression, Attributes(attributeBuilder))
 
-
+public class OptimizationPrior<T> :
-public class OptimizationPrior<T>(type: SafeType<T>):
     PolymorphicAttribute<DifferentiableExpression<T>>(safeTypeOf()),
     Attribute<DifferentiableExpression<T>>
 
-//public val <T> FunctionOptimization.Companion.Optimization get() =
+public fun <T> FunctionOptimization<T>.withAttributes(
-
+    modifier: AttributesBuilder<FunctionOptimization<T>>.() -> Unit,
-
-public fun <T> FunctionOptimization<T>.withFeatures(
-    vararg newFeature: OptimizationFeature,
 ): FunctionOptimization<T> = FunctionOptimization(
-    attributes.with(*newFeature),
     expression,
+    attributes.modify(modifier),
 )
 
 /**
  * Optimizes differentiable expression using specific [optimizer] form given [startingPoint].
  */
-public suspend fun <T : Any> DifferentiableExpression<T>.optimizeWith(
+public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
     optimizer: Optimizer<T, FunctionOptimization<T>>,
     startingPoint: Map<Symbol, T>,
-    vararg features: OptimizationFeature,
+    modifier: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
 ): FunctionOptimization<T> {
-    val problem = FunctionOptimization<T>(FeatureSet.of(OptimizationStartPoint(startingPoint), *features), this)
+    val problem = FunctionOptimization(this){
+        startAt(startingPoint)
+        modifier()
+    }
     return optimizer.optimize(problem)
 }
 
 public val <T> FunctionOptimization<T>.resultValueOrNull: T?
-    get() = getFeature<OptimizationResult<T>>()?.point?.let { expression(it) }
+    get() = attributes[OptimizationResult<T>()]?.let { expression(it) }
 
 public val <T> FunctionOptimization<T>.resultValue: T
     get() = resultValueOrNull ?: error("Result is not present in $this")
+
+
+public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
+    optimizer: Optimizer<T, FunctionOptimization<T>>,
+    vararg startingPoint: Pair<Symbol, T>,
+    builder: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
+): FunctionOptimization<T> {
+    val problem = FunctionOptimization<T>(this) {
+        startAt(mapOf(*startingPoint))
+        builder()
+    }
+    return optimizer.optimize(problem)
+}

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationBuilder.kt

@@ -1,96 +0,0 @@
-/*
- * Copyright 2018-2022 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.optimization
-
-import space.kscience.kmath.UnstableKMathAPI
-import space.kscience.kmath.data.XYColumnarData
-import space.kscience.kmath.expressions.DifferentiableExpression
-import space.kscience.kmath.expressions.Symbol
-import space.kscience.kmath.misc.FeatureSet
-
-public abstract class OptimizationBuilder<T, R : OptimizationProblem<T>> {
-    public val features: MutableList<OptimizationFeature> = ArrayList()
-
-    public fun addFeature(feature: OptimizationFeature) {
-        features.add(feature)
-    }
-
-    public inline fun <reified T : OptimizationFeature> updateFeature(update: (T?) -> T) {
-        val existing = features.find { it.key == T::class } as? T
-        val new = update(existing)
-        if (existing != null) {
-            features.remove(existing)
-        }
-        addFeature(new)
-    }
-
-    public abstract fun build(): R
-}
-
-public fun <T> OptimizationBuilder<T, *>.startAt(startingPoint: Map<Symbol, T>) {
-    addFeature(OptimizationStartPoint(startingPoint))
-}
-
-public class FunctionOptimizationBuilder<T>(
-    private val expression: DifferentiableExpression<T>,
-) : OptimizationBuilder<T, FunctionOptimization<T>>() {
-    override fun build(): FunctionOptimization<T> = FunctionOptimization(FeatureSet.of(features), expression)
-}
-
-public fun <T> FunctionOptimization(
-    expression: DifferentiableExpression<T>,
-    builder: FunctionOptimizationBuilder<T>.() -> Unit,
-): FunctionOptimization<T> = FunctionOptimizationBuilder(expression).apply(builder).build()
-
-public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
-    optimizer: Optimizer<T, FunctionOptimization<T>>,
-    startingPoint: Map<Symbol, T>,
-    builder: FunctionOptimizationBuilder<T>.() -> Unit = {},
-): FunctionOptimization<T> {
-    val problem = FunctionOptimization<T>(this) {
-        startAt(startingPoint)
-        builder()
-    }
-    return optimizer.optimize(problem)
-}
-
-public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
-    optimizer: Optimizer<T, FunctionOptimization<T>>,
-    vararg startingPoint: Pair<Symbol, T>,
-    builder: FunctionOptimizationBuilder<T>.() -> Unit = {},
-): FunctionOptimization<T> {
-    val problem = FunctionOptimization<T>(this) {
-        startAt(mapOf(*startingPoint))
-        builder()
-    }
-    return optimizer.optimize(problem)
-}
-
-
-@OptIn(UnstableKMathAPI::class)
-public class XYOptimizationBuilder(
-    public val data: XYColumnarData<Double, Double, Double>,
-    public val model: DifferentiableExpression<Double>,
-) : OptimizationBuilder<Double, XYFit>() {
-
-    public var pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY
-    public var pointWeight: PointWeight = PointWeight.byYSigma
-
-    override fun build(): XYFit = XYFit(
-        data,
-        model,
-        FeatureSet.of(features),
-        pointToCurveDistance,
-        pointWeight
-    )
-}
-
-@OptIn(UnstableKMathAPI::class)
-public fun XYOptimization(
-    data: XYColumnarData<Double, Double, Double>,
-    model: DifferentiableExpression<Double>,
-    builder: XYOptimizationBuilder.() -> Unit,
-): XYFit = XYOptimizationBuilder(data, model).apply(builder).build()

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt

@@ -6,64 +6,53 @@
 package space.kscience.kmath.optimization
 
 import space.kscience.attributes.*
-import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.NamedMatrix
 import space.kscience.kmath.expressions.Symbol
-import space.kscience.kmath.misc.*
+import space.kscience.kmath.misc.Loggable
-import kotlin.reflect.KClass
 
-public interface OptimizationAttribute<T>: Attribute<T>
+public interface OptimizationAttribute<T> : Attribute<T>
 
-public interface OptimizationProblem<T> : AttributeContainer
+public interface OptimizationProblem<T> : AttributeContainer, WithType<T>
 
-public inline fun <reified F : OptimizationFeature> OptimizationProblem<*>.getFeature(): F? = getFeature(F::class)
+public class OptimizationStartPoint<T> : OptimizationAttribute<Map<Symbol, T>>,
-
+    PolymorphicAttribute<Map<Symbol, T>>(safeTypeOf())
-public open class OptimizationStartPoint<T>(public val point: Map<Symbol, T>) : OptimizationFeature {
-    override fun toString(): String = "StartPoint($point)"
-}
-
-/**
- * Covariance matrix for
- */
-public class OptimizationCovariance<T>(public val covariance: NamedMatrix<T>) : OptimizationFeature {
-    override fun toString(): String = "Covariance($covariance)"
-}
 
 /**
  * Get the starting point for optimization. Throws error if not defined.
  */
 public val <T> OptimizationProblem<T>.startPoint: Map<Symbol, T>
-    get() = getFeature<OptimizationStartPoint<T>>()?.point
+    get() = attributes[OptimizationStartPoint()] ?: error("Starting point not defined in $this")
-        ?: error("Starting point not defined in $this")
 
-public open class OptimizationResult<T>(public val point: Map<Symbol, T>) : OptimizationFeature {
+public fun <T> AttributesBuilder<OptimizationProblem<T>>.startAt(startingPoint: Map<Symbol, T>) {
-    override fun toString(): String = "Result($point)"
+    set(::OptimizationStartPoint, startingPoint)
 }
 
-public val <T> OptimizationProblem<T>.resultPointOrNull: Map<Symbol, T>?
-    get() = getFeature<OptimizationResult<T>>()?.point
 
-public val <T> OptimizationProblem<T>.resultPoint: Map<Symbol, T>
+/**
-    get() = resultPointOrNull ?: error("Result is not present in $this")
+ * Covariance matrix for optimization
+ */
+public class OptimizationCovariance<T> : OptimizationAttribute<NamedMatrix<T>>,
+    PolymorphicAttribute<NamedMatrix<T>>(safeTypeOf())
 
-public class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature {
+
-    override fun toString(): String = "Log($loggable)"
+public class OptimizationResult<T>() : OptimizationAttribute<Map<Symbol, T>>,
-}
+    PolymorphicAttribute<Map<Symbol, T>>(safeTypeOf())
+
+public val <T> OptimizationProblem<T>.resultOrNull: Map<Symbol, T>? get() = attributes[OptimizationResult()]
+
+public val <T> OptimizationProblem<T>.result: Map<Symbol, T>
+    get() = resultOrNull ?: error("Result is not present in $this")
+
+public object OptimizationLog : OptimizationAttribute<Loggable>
 
 /**
  * Free parameters of the optimization
  */
-public class OptimizationParameters(public val symbols: List<Symbol>) : OptimizationFeature {
+public object OptimizationParameters : OptimizationAttribute<List<Symbol>>
-    public constructor(vararg symbols: Symbol) : this(listOf(*symbols))
-
-    override fun toString(): String = "Parameters($symbols)"
-}
 
 /**
  * Maximum allowed number of iterations
  */
-public class OptimizationIterations(public val maxIterations: Int) : OptimizationFeature {
+public object OptimizationIterations : OptimizationAttribute<Int>
-    override fun toString(): String = "Iterations($maxIterations)"
-}
 
 

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt

@@ -16,13 +16,7 @@ import space.kscience.kmath.structures.Float64Buffer
 import kotlin.math.abs
 
 
-public class QowRuns(public val runs: Int) : OptimizationFeature {
+public object QowRuns: OptimizationAttribute<Int>
-    init {
-        require(runs >= 1) { "Number of runs must be more than zero" }
-    }
-
-    override fun toString(): String = "QowRuns(runs=$runs)"
-}
 
 
 /**
@@ -69,7 +63,7 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
         }
 
         val prior: DifferentiableExpression<Double>?
-            get() = problem.getFeature<OptimizationPrior<Double>>()?.withDefaultArgs(allParameters)
+            get() = problem.attributes[OptimizationPrior<Double>()]?.withDefaultArgs(allParameters)
 
         override fun toString(): String = freeParameters.toString()
     }
@@ -176,7 +170,7 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
         fast: Boolean = false,
     ): QoWeight {
 
-        val logger = problem.getFeature<OptimizationLog>()
+        val logger = problem.attributes[OptimizationLog]
 
         var dis: Double //discrepancy value
 
@@ -231,7 +225,7 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
     }
 
     private fun QoWeight.covariance(): NamedMatrix<Double> {
-        val logger = problem.getFeature<OptimizationLog>()
+        val logger = problem.attributes[OptimizationLog]
 
         logger?.log {
             """
@@ -257,11 +251,11 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
     }
 
     override suspend fun optimize(problem: XYFit): XYFit {
-        val qowRuns = problem.getFeature<QowRuns>()?.runs ?: 2
+        val qowRuns = problem.attributes[QowRuns] ?: 2
-        val iterations = problem.getFeature<OptimizationIterations>()?.maxIterations ?: 50
+        val iterations = problem.attributes[OptimizationIterations] ?: 50
 
-        val freeParameters: Map<Symbol, Double> = problem.getFeature<OptimizationParameters>()?.let { op ->
+        val freeParameters: Map<Symbol, Double> = problem.attributes[OptimizationParameters]?.let { symbols ->
-            problem.startPoint.filterKeys { it in op.symbols }
+            problem.startPoint.filterKeys { it in symbols }
         } ?: problem.startPoint
 
         var qow = QoWeight(problem, freeParameters)
@@ -271,6 +265,9 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
             res = qow.newtonianRun(maxSteps = iterations)
         }
         val covariance = res.covariance()
-        return res.problem.withFeature(OptimizationResult(res.freeParameters), OptimizationCovariance(covariance))
+        return res.problem.withAttributes {
+            set(OptimizationResult(), res.freeParameters)
+            set(OptimizationCovariance(), covariance)
+        }
     }
 }

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt

@@ -2,37 +2,41 @@
  * Copyright 2018-2022 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
-@file:OptIn(UnstableKMathAPI::class)
+@file:OptIn(UnstableKMathAPI::class, UnstableKMathAPI::class)
 
 package space.kscience.kmath.optimization
 
+import space.kscience.attributes.*
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.data.indices
 import space.kscience.kmath.expressions.*
-import space.kscience.kmath.misc.FeatureSet
 import space.kscience.kmath.misc.Loggable
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.bindSymbol
 import kotlin.math.pow
 
 /**
  * Specify the way to compute distance from point to the curve as DifferentiableExpression
  */
-public interface PointToCurveDistance : OptimizationFeature {
+public interface PointToCurveDistance {
     public fun distance(problem: XYFit, index: Int): DifferentiableExpression<Double>
 
-    public companion object {
+    public companion object : OptimizationAttribute<PointToCurveDistance> {
         public val byY: PointToCurveDistance = object : PointToCurveDistance {
             override fun distance(problem: XYFit, index: Int): DifferentiableExpression<Double> {
                 val x = problem.data.x[index]
                 val y = problem.data.y[index]
 
                 return object : DifferentiableExpression<Double> {
+                    override val type: SafeType<Double> get() = DoubleField.type
+
                     override fun derivativeOrNull(
                         symbols: List<Symbol>,
                     ): Expression<Double>? = problem.model.derivativeOrNull(symbols)?.let { derivExpression ->
-                        Expression { arguments ->
+                        Expression(DoubleField.type) { arguments ->
                             derivExpression.invoke(arguments + (Symbol.x to x))
                         }
                     }
@@ -51,18 +55,21 @@ public interface PointToCurveDistance : OptimizationFeature {
  * Compute a wight of the point. The more the weight, the more impact this point will have on the fit.
  * By default, uses Dispersion^-1
  */
-public interface PointWeight : OptimizationFeature {
+public interface PointWeight {
     public fun weight(problem: XYFit, index: Int): DifferentiableExpression<Double>
 
-    public companion object {
+    public companion object : OptimizationAttribute<PointWeight> {
         public fun bySigma(sigmaSymbol: Symbol): PointWeight = object : PointWeight {
             override fun weight(problem: XYFit, index: Int): DifferentiableExpression<Double> =
                 object : DifferentiableExpression<Double> {
+                    override val type: SafeType<Double> get() = DoubleField.type
+
                     override fun invoke(arguments: Map<Symbol, Double>): Double {
                         return problem.data[sigmaSymbol]?.get(index)?.pow(-2) ?: 1.0
                     }
 
-                    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { 0.0 }
+                    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> =
+                        Expression(DoubleField.type) { 0.0 }
                 }
 
             override fun toString(): String = "PointWeightBySigma($sigmaSymbol)"
@@ -79,41 +86,52 @@ public interface PointWeight : OptimizationFeature {
 public class XYFit(
     public val data: XYColumnarData<Double, Double, Double>,
     public val model: DifferentiableExpression<Double>,
-    override val attributes: FeatureSet<OptimizationFeature>,
+    override val attributes: Attributes,
     internal val pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
     internal val pointWeight: PointWeight = PointWeight.byYSigma,
     public val xSymbol: Symbol = Symbol.x,
 ) : OptimizationProblem<Double> {
+
+    override val type: SafeType<Double> get() = Float64Field.type
+
     public fun distance(index: Int): DifferentiableExpression<Double> = pointToCurveDistance.distance(this, index)
 
     public fun weight(index: Int): DifferentiableExpression<Double> = pointWeight.weight(this, index)
 }
 
-public fun XYFit.withFeature(vararg features: OptimizationFeature): XYFit {
+
-    return XYFit(data, model, this.attributes.with(*features), pointToCurveDistance, pointWeight)
+public fun XYOptimization(
-}
+    data: XYColumnarData<Double, Double, Double>,
+    model: DifferentiableExpression<Double>,
+    builder: AttributesBuilder<XYFit>.() -> Unit,
+): XYFit = XYFit(data, model, Attributes(builder))
+
+public fun XYFit.withAttributes(
+    modifier: AttributesBuilder<XYFit>.() -> Unit,
+): XYFit = XYFit(data, model, attributes.modify(modifier), pointToCurveDistance, pointWeight, xSymbol)
 
 public suspend fun XYColumnarData<Double, Double, Double>.fitWith(
     optimizer: Optimizer<Double, XYFit>,
     modelExpression: DifferentiableExpression<Double>,
     startingPoint: Map<Symbol, Double>,
-    vararg features: OptimizationFeature = emptyArray(),
+    attributes: Attributes = Attributes.EMPTY,
     xSymbol: Symbol = Symbol.x,
     pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
     pointWeight: PointWeight = PointWeight.byYSigma,
 ): XYFit {
-    var actualFeatures = FeatureSet.of(*features, OptimizationStartPoint(startingPoint))
 
-    if (actualFeatures.getFeature<OptimizationLog>() == null) {
-        actualFeatures = actualFeatures.with(OptimizationLog(Loggable.console))
-    }
     val problem = XYFit(
         this,
         modelExpression,
-        actualFeatures,
+        attributes.modify<XYFit> {
+            set(::OptimizationStartPoint, startingPoint)
+            if (!hasAny<OptimizationLog>()) {
+                set(OptimizationLog, Loggable.console)
+            }
+        },
         pointToCurveDistance,
         pointWeight,
-        xSymbol
+        xSymbol,
     )
     return optimizer.optimize(problem)
 }
@@ -125,7 +143,7 @@ public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
     optimizer: Optimizer<Double, XYFit>,
     processor: AutoDiffProcessor<Double, I, A>,
     startingPoint: Map<Symbol, Double>,
-    vararg features: OptimizationFeature = emptyArray(),
+    attributes: Attributes = Attributes.EMPTY,
     xSymbol: Symbol = Symbol.x,
     pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
     pointWeight: PointWeight = PointWeight.byYSigma,
@@ -140,7 +158,7 @@ public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
         optimizer = optimizer,
         modelExpression = modelExpression,
         startingPoint = startingPoint,
-        features = features,
+        attributes = attributes,
         xSymbol = xSymbol,
         pointToCurveDistance = pointToCurveDistance,
         pointWeight = pointWeight
@@ -152,7 +170,7 @@ public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
  */
 public val XYFit.chiSquaredOrNull: Double?
     get() {
-        val result = startPoint + (resultPointOrNull ?: return null)
+        val result = startPoint + (resultOrNull ?: return null)
 
         return data.indices.sumOf { index ->
 
@@ -167,4 +185,4 @@ public val XYFit.chiSquaredOrNull: Double?
     }
 
 public val XYFit.dof: Int
-    get() = data.size - (getFeature<OptimizationParameters>()?.symbols?.size ?: startPoint.size)
+    get() = data.size - (attributes[OptimizationParameters]?.size ?: startPoint.size)

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/logLikelihood.kt

@@ -5,6 +5,8 @@
 
 package space.kscience.kmath.optimization
 
+import space.kscience.attributes.AttributesBuilder
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.data.XYColumnarData
 import space.kscience.kmath.data.indices
@@ -12,6 +14,7 @@ import space.kscience.kmath.expressions.DifferentiableExpression
 import space.kscience.kmath.expressions.Expression
 import space.kscience.kmath.expressions.Symbol
 import space.kscience.kmath.expressions.derivative
+import space.kscience.kmath.operations.Float64Field
 import kotlin.math.PI
 import kotlin.math.ln
 import kotlin.math.pow
@@ -22,7 +25,9 @@ private val oneOver2Pi = 1.0 / sqrt(2 * PI)
 
 @UnstableKMathAPI
 internal fun XYFit.logLikelihood(): DifferentiableExpression<Double> = object : DifferentiableExpression<Double> {
-    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
+    override val type: SafeType<Double> get() = Float64Field.type
+
+    override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression(type) { arguments ->
         data.indices.sumOf { index ->
             val d = distance(index)(arguments)
             val weight = weight(index)(arguments)
@@ -53,14 +58,18 @@ internal fun XYFit.logLikelihood(): DifferentiableExpression<Double> = object :
  */
 @UnstableKMathAPI
 public suspend fun Optimizer<Double, FunctionOptimization<Double>>.maximumLogLikelihood(problem: XYFit): XYFit {
-    val functionOptimization = FunctionOptimization(problem.attributes, problem.logLikelihood())
+    val functionOptimization = FunctionOptimization(problem.logLikelihood(), problem.attributes)
-    val result = optimize(functionOptimization.withFeatures(FunctionOptimizationTarget.MAXIMIZE))
+    val result = optimize(
-    return XYFit(problem.data, problem.model, result.attributes)
+        functionOptimization.withAttributes {
+            FunctionOptimizationTarget(OptimizationDirection.MAXIMIZE)
+        }
+    )
+    return XYFit(problem.data,problem.model, result.attributes)
 }
 
 @UnstableKMathAPI
 public suspend fun Optimizer<Double, FunctionOptimization<Double>>.maximumLogLikelihood(
     data: XYColumnarData<Double, Double, Double>,
     model: DifferentiableExpression<Double>,
-    builder: XYOptimizationBuilder.() -> Unit,
+    builder: AttributesBuilder<XYFit>.() -> Unit,
 ): XYFit = maximumLogLikelihood(XYOptimization(data, model, builder))

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt

@@ -5,10 +5,12 @@
 
 package space.kscience.kmath.tensors.core
 
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.PerformancePitfall
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.nd.MutableStructureNDOfDouble
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.core.internal.toPrettyString
 
@@ -88,6 +90,8 @@ public open class DoubleTensor(
     final override val source: OffsetDoubleBuffer,
 ) : BufferedTensor<Double>(shape), MutableStructureNDOfDouble {
 
+    override val type: SafeType<Double> get() = DoubleField.type
+
     init {
         require(linearSize == source.size) { "Source buffer size must be equal tensor size" }
     }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt

@@ -5,8 +5,10 @@
 
 package space.kscience.kmath.tensors.core
 
+import space.kscience.attributes.SafeType
 import space.kscience.kmath.PerformancePitfall
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.operations.IntRing
 import space.kscience.kmath.structures.*
 
 /**
@@ -24,6 +26,8 @@ public class OffsetIntBuffer(
         require(offset + size <= source.size) { "Maximum index must be inside source dimension" }
     }
 
+    override val type: SafeType<Int> get() = IntRing.type
+
     override fun set(index: Int, value: Int) {
         require(index in 0 until size) { "Index must be in [0, size)" }
         source[index + offset] = value
@@ -83,6 +87,8 @@ public class IntTensor(
         require(linearSize == source.size) { "Source buffer size must be equal tensor size" }
     }
 
+    override val type: SafeType<Int> get() = IntRing.type
+
     public constructor(shape: ShapeND, buffer: Int32Buffer) : this(shape, OffsetIntBuffer(buffer, 0, buffer.size))
 
     @OptIn(PerformancePitfall::class)