clap integration for feature flagging

Author: krukah

src/clustering/layer.rs

@@ -36,6 +36,11 @@ impl Layer {
     #[cfg(feature = "native")]
     pub fn learn() {
         use crate::save::disk::Disk;
+        Street::all()
+            .into_iter()
+            .rev()
+            .filter(|&&s| Self::done(s))
+            .for_each(|_| log::info!("{:<32}{:<32}", "using kmeans layer", Self::name()));
         Street::all()
             .into_iter()
             .rev()
@@ -238,7 +243,12 @@ impl Layer {
 #[cfg(feature = "native")]
 impl crate::save::disk::Disk for Layer {
     fn name() -> String {
-        unimplemented!()
+        format!(
+            "{:<16}{:<16}{:<16}",
+            Lookup::name(),
+            Decomp::name(),
+            Metric::name()
+        )
     }
     fn done(street: Street) -> bool {
         Lookup::done(street) && Decomp::done(street) && Metric::done(street)

src/lib.rs

@@ -18,7 +18,6 @@ pub use gameplay::*;
 pub use mccfr::*;
 pub use transport::*;
 pub use wasm::*;
-
 /// dimensional analysis types
 type Chips = i16;
 type Equity = f32;
@@ -65,6 +64,25 @@ const SAMPLING_EXPLORATION: Probability = 0.01;
 const POLICY_MIN: Probability = Probability::MIN_POSITIVE;
 const REGRET_MIN: Utility = -3e5;
 
+#[derive(clap::Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+#[cfg(feature = "native")]
+/// Simple program to run robopoker routines
+pub struct Args {
+    /// Run the clustering algorithm
+    #[arg(long)]
+    pub cluster: bool,
+    /// Run the MCCFR training
+    #[arg(long)]
+    pub solve: bool,
+    /// Publish results to the database
+    #[arg(long)]
+    pub publish: bool,
+    /// Run the analysis server
+    #[arg(long)]
+    pub analyze: bool,
+}
+
 /// trait for random generation, mainly (strictly?) for testing
 pub trait Arbitrary {
     fn random() -> Self;

src/main.rs

@@ -1,17 +1,21 @@
+use clap::Parser;
 use robopoker::*;
 
 #[tokio::main]
 async fn main() {
-    // Behold!
     crate::init();
-    // The k-means earth mover's distance hand-clustering algorithm.
-    crate::clustering::Layer::learn();
-    // Monte Carlo counter-factual regret minimization. External sampling, alternating regret updates, linear weighting schedules.
-    crate::mccfr::NLHE::train();
-    // Let's upload the data to the database.
-    crate::save::Writer::publish().await.unwrap();
-    // Let's support our api.
-    crate::analysis::Server::run().await.unwrap();
+    if crate::Args::parse().cluster {
+        crate::clustering::Layer::learn();
+    }
+    if crate::Args::parse().solve {
+        crate::mccfr::NLHE::train();
+    }
+    if crate::Args::parse().publish {
+        crate::save::Writer::publish().await.unwrap();
+    }
+    if crate::Args::parse().analyze {
+        crate::analysis::Server::run().await.unwrap();
+    }
 }
 
 /*
@@ -44,12 +48,12 @@ async fn main() {
   2015. Simultaneous Abstraction and Equilibrium Finding in Games. (http://www.cs.cmu.edu/~sandholm/simultaneous.ijcai15.pdf) In IJCAI.
   2015. Limited Lookahead in Imperfect-Information Games. (http://www.cs.cmu.edu/~sandholm/limited-look-ahead.ijcai15.pdf) IJCAI.
   2015. Faster First-Order Methods for Extensive-Form Game Solving. (http://www.cs.cmu.edu/~sandholm/faster.ec15.pdf) In EC.
-  2015. Hierarchical Abstraction, Distributed Equilibrium Computation, and Post-Processing, with Application to a Champion No-Limit Texas Hold’em Agent. (http://www.cs.cmu.edu/~sandholm/hierarchical.aamas15.pdf) In AAMAS.
+  2015. Hierarchical Abstraction, Distributed Equilibrium Computation, and Post-Processing, with Application to a Champion No-Limit Texas Hold'em Agent. (http://www.cs.cmu.edu/~sandholm/hierarchical.aamas15.pdf) In AAMAS.
   2015. Discretization of Continuous Action Spaces in Extensive-Form Games. (http://www.cs.cmu.edu/~sandholm/discretization.aamas15.fromACM.pdf) In AAMAS.
   2015. Endgame Solving in Large Imperfect-Information Games. (http://www.cs.cmu.edu/~sandholm/endgame.aamas15.fromACM.pdf) In AAMAS.
   2014. Extensive-Form Game Abstraction With Bounds. (http://www.cs.cmu.edu/~sandholm/extensiveGameAbstraction.ec14.pdf) In EC.
   2014. Regret Transfer and Parameter Optimization. (http://www.cs.cmu.edu/~sandholm/regret_transfer.aaai14.pdf) In AAAI.
-+ 2014. Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games. (http://www.cs.cmu.edu/~sandholm/potential-aware_imperfect-recall.aaai14.pdf) In AAAI.
++ 2014. Potential-Aware Imperfect-Recall Abstraction with Earth Mover's Distance in Imperfect-Information Games. (http://www.cs.cmu.edu/~sandholm/potential-aware_imperfect-recall.aaai14.pdf) In AAAI.
 + 2013. Action Translation in Extensive-Form Games with Large Action Spaces: Axioms, Paradoxes, and the Pseudo-Harmonic Mapping. (http://www.cs.cmu.edu/~sandholm/reverse%20mapping.ijcai13.pdf) In IJCAI.
 + 2013. A Fast and Optimal Hand Isomorphism Algorithm. (https://www.cs.cmu.edu/~waugh/publications/isomorphism13.pdf). In AAAI.
   2012. Lossy Stochastic Game Abstraction with Bounds. (http://www.cs.cmu.edu/~sandholm/lossyStochasticGameAbstractionWBounds.ec12.pdf) In EC.

src/mccfr/traits/profile.rs

@@ -234,8 +234,8 @@ pub trait Profile {
             .inspect(|r| assert!(*r >= 0.))
             .map(|r| r.max(crate::POLICY_MIN))
             .sum::<crate::Probability>();
-        let numer = self.activation() + self.threshold() * numer;
         let denom = self.activation() + denom;
+        let numer = self.activation() + numer * self.threshold();
         (numer / denom).max(self.exploration())
     }
 
@@ -260,7 +260,7 @@ pub trait Profile {
         leaf: &Node<Self::T, Self::E, Self::G, Self::I>,
     ) -> crate::Probability {
         leaf.into_iter()
-            .take_while(|(parent, _)| parent != root)
+            .take_while(|(parent, _)| parent != root) // parent.index() > root.index()
             .map(|(parent, incoming)| self.outgoing_reach(&parent, &incoming))
             .product::<crate::Probability>()
     }