clean up dependencies

README.md

@@ -213,7 +213,7 @@ A regex to NFA compiler is provided. To run the demo, run `./gradlew RegexDemo`.
 
 ### Graph learning
 
-* [Graph Representation Learning](https://www.cs.mcgill.ca/~wlh/grl_book/), Hamilton (2020)
+* [Graph Representation Learning](https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf), Hamilton (2020)
 * [Spectral Graph Theory with Applications to ML](http://www.cs.cmu.edu/afs/cs/academic/class/15859n-s20/recordings.html), Miller (2020)
 * [Neural Execution of Graph Algorithms](https://arxiv.org/abs/1910.10593), Veličković et al. (2020)
 
@@ -277,7 +277,7 @@ A regex to NFA compiler is provided. To run the demo, run `./gradlew RegexDemo`.
 
 - [Unifying Graph Convolutional Neural Networks and Label Propagation](https://arxiv.org/pdf/2002.06755.pdf), Wang and Leskovec (2020)
 - [Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation](https://doi.org/10.3389/fncom.2017.00024) Scellier and Bengio (2017)
-- [Expecation Propagation for Aproximate Bayesian Inference](https://arxiv.org/pdf/1301.2294.pdf), Minka (2001)
+- [Expectation Propagation for Approximate Bayesian Inference](https://arxiv.org/pdf/1301.2294.pdf), Minka (2001)
 - [Propagation Networks: A Flexible and Expressive Substrate for Computation](https://dspace.mit.edu/bitstream/handle/1721.1/54635/603543210-MIT.pdf), Radul (2009)
 - [The Art of the Propagator](http://dspace.mit.edu/bitstream/handle/1721.1/44215/MIT-CSAIL-TR-2009-002.pdf), Radul and Sussman (2009)
 - [Fusion, propagation, and structuring in belief networks](https://bi.snu.ac.kr/~scai/Info/AI/Fusion,%20Propagation,%20and%20Structuring%20in%20Belief%20Networks.pdf), Pearl (1986)

build.gradle.kts

@@ -27,27 +27,27 @@ dependencies {
   api("org.ejml:ejml-all:$ejmlVersion")
   api("guru.nidi:graphviz-kotlin:0.18.0")
   api("io.github.vovak.astminer:astminer:0.6")
-  implementation("org.jetbrains.kotlin:kotlin-scripting-jsr223")
 
   val commonsRngVersion = "1.3"
   implementation("org.apache.commons:commons-rng-sampling:$commonsRngVersion")
   implementation("org.apache.commons:commons-rng-simple:$commonsRngVersion")
-  implementation("com.github.kwebio:kweb-core:0.7.33")
   implementation("org.slf4j:slf4j-simple:1.7.30")
-  implementation("com.github.breandan:tensor:master-SNAPSHOT")
 
+  testImplementation("com.github.breandan:tensor:master-SNAPSHOT")
+  testImplementation("org.jetbrains.kotlin:kotlin-scripting-jsr223")
+  testImplementation("com.github.kwebio:kweb-core:0.7.33")
   // Remove pending: https://github.com/sosy-lab/java-smt/issues/88
-  implementation("io.github.tudo-aqua:z3-turnkey:4.8.7.1")
-  implementation("org.sosy-lab:java-smt:3.7.0")
+  testImplementation("io.github.tudo-aqua:z3-turnkey:4.8.7.1")
+  testImplementation("org.sosy-lab:java-smt:3.7.0")
 
   // http://www.ti.inf.uni-due.de/fileadmin/public/tools/grez/grez-manual.pdf
   // implementation(files("$projectDir/libs/grez.jar"))
 
   // http://www.informatik.uni-bremen.de/agbkb/lehre/rbs/seminar/AGG-ShortManual.pdf
-  implementation(files("$projectDir/libs/aggEngine_V21_classes.jar"))
+  testImplementation(files("$projectDir/libs/aggEngine_V21_classes.jar"))
 
   // https://github.com/jgralab/jgralab/wiki
-  implementation("de.uni-koblenz.ist:jgralab:8.1.0")
+  testImplementation("de.uni-koblenz.ist:jgralab:8.1.0")
 
 //  val kmathVersion by extra { "0.2.0-dev-2" }
 //  implementation("scientifik:kmath-core:$kmathVersion")

src/main/kotlin/edu/mcgill/kaliningraph/Graph.kt

@@ -3,7 +3,6 @@ package edu.mcgill.kaliningraph
 import edu.mcgill.kaliningraph.typefamily.*
 import guru.nidi.graphviz.attribute.Label
 import guru.nidi.graphviz.model.*
-import kweb.shoebox.toArrayList
 import org.apache.commons.rng.sampling.DiscreteProbabilityCollectionSampler
 import org.apache.commons.rng.simple.RandomSource
 import org.apache.commons.rng.simple.RandomSource.JDK
@@ -32,7 +31,7 @@ abstract class Graph<G, E, V>(override val vertices: Set<V> = setOf()):
 
   protected class VIndex<G: Graph<G, E, V>, E : Edge<G, E, V>, V : Vertex<G, E, V>>(val set: Set<V>) {
     operator fun plus(vertexIdx: VIndex<G, E, V>) = VIndex(set + vertexIdx.set)
-    val array: ArrayList<V> = set.toList().toArrayList()
+    val array: List<V> = set.toList()
     val map: Map<V, Int> = array.mapIndexed { index, a -> a to index }.toMap()
     operator fun get(it: Vertex<G, E, V>): Int? = map[it]
     operator fun get(it: Int): V = array[it]
@@ -260,4 +259,4 @@ class RandomWalk<G, E, V>(
   override fun iterator() = generateSequence(this) { it.tail }.iterator()
 }
 
-interface Encodable { fun encode(): DoubleArray }
+interface Encodable { fun encode(): DoubleArray }