step: savepoint & README tweaks

src/compute_constrained_bayes.nim

@@ -1,6 +1,7 @@
 import print
 import strutils
 import sequtils
+import std/math
 import std/sugar
 import std/algorithm
 
@@ -125,8 +126,7 @@ proc jitBayesLoop(
   var num_hypotheses = initial_num_hypotheses
   var hypotheses = seqs[0..<num_hypotheses]
   
-  let l = observations.len
-  for i in n_observations_seen..<l: # to do: make so that this can start at 0.
+  for i in n_observations_seen..<observations.len:
     let predictions = predictContinuation(hypotheses, observations[0..<i])
     echo "### Prediction after seeing ", i, " observations: ", observations[0..<i]
     print predictions
@@ -161,13 +161,12 @@ proc jitBayesLoop(
       echo "Correct continuation was ", correct_continuation
       echo "It was assigned a probability of ", getProbability(predictions[correct_continuation_index])
 
-    echo ""
-
 ## Infrabayesianism
 
 proc miniInfraBayes(
   seqs: seq[seq[string]],
   observations: seq[string],
+  n_observations_seen: int,
   utility_function: string
   ) =
   if utility_function != "logloss":
@@ -175,10 +174,23 @@ proc miniInfraBayes(
     return
   else:
     echo "## Mini-infra-bayesianism over environments, where your utility in an environment is just the log-loss in the predictions you make until you become certain that you are in that environment."
-
-  let l = observations.len
-  for i in 0..<l: # to do: make so that this can start at 0.
+  
+  var losses: seq[float]
+  for i in n_observations_seen..<observations.len:
     let predictions = predictContinuation(seqs, observations[0..<i]) ## See the README for why this ends up being equivalent.
+    echo "### Prediction after seeing ", i, " observations: ", observations[0..<i]
+    print predictions
+    let correct_continuation = observations[i]
+    let considered_continuations = predictions.map(prediction => getHypothesis(prediction))
+    let correct_continuation_index = findIndex(considered_continuations, correct_continuation)
+    let p_correct_continuation = getProbability(predictions[correct_continuation_index])
+    let new_loss = ln(p_correct_continuation)
+    losses.add(new_loss)
+
+    echo "Correct continuation was ", correct_continuation
+    echo "It was assigned a probability of ", p_correct_continuation
+    echo "And hence a loss of ", new_loss
+    echo "Total loss is: ", foldl(losses, a + b, 0.0)
 
 
 ## Infrabayesianism. Part 1: Have hypotheses over just part of the world.
@@ -205,6 +217,8 @@ echo ""
 
 observations = @["1", "2", "3", "23", "11", "18", "77", "46", "84"]
 jitBayesLoop(seqs, observations, 3, 1_000, 30_000)
-
 echo ""
 
+observations = @["1", "2", "3", "23", "11", "18", "77", "46", "84"]
+miniInfraBayes(seqs, observations, 3, "logloss")
+echo ""

src/makefile

@@ -18,6 +18,7 @@ fast:
 
 deps: 
 	nimble install print@1.0.2
+	nimble install https://github.com/CosmicToast/pipe ## backup at github.com/NunoSempere/nim-pipe
 	gzip -d ../data/stripped.gz -c > ../data/stripped
 
 run: compute_constrained_bayes