# HG changeset patch
# User Robert McIntyre <rlm@mit.edu>
# Date 1395818337 14400
# Node ID 432f2c4646cbe980c9e6d6d6babc961454f11b43
# Parent  09b7c8dd436562d5a817fec546b44e861353c3fe
sleepig.

diff -r 09b7c8dd4365 -r 432f2c4646cb thesis/Makefile
--- a/thesis/Makefile	Wed Mar 26 02:42:01 2014 -0400
+++ b/thesis/Makefile	Wed Mar 26 03:18:57 2014 -0400
@@ -1,6 +1,7 @@
 #INVOKE_LATEX = pdflatex -shell-escape thesis.tex;
 THESIS_NAME  = rlm-cortex-meng
 INVOKE_LATEX = texi2dvi --shell-escape --pdf -V --batch $(THESIS_NAME).tex;
+#INVOKE_LATEX = texi2dvi --shell-escape --pdf -V  $(THESIS_NAME).tex;
 
 all:
 	./weave-thesis.sh cortex
diff -r 09b7c8dd4365 -r 432f2c4646cb thesis/cortex.org
--- a/thesis/cortex.org	Wed Mar 26 02:42:01 2014 -0400
+++ b/thesis/cortex.org	Wed Mar 26 03:18:57 2014 -0400
@@ -663,8 +663,41 @@
 
 ** Empathy is the process of tracing though \Phi-space 
 
+   Here is the core of a basic empathy algorithm, starting with an
+   experience vector: First, group the experiences into tiered
+   proprioceptive bins. I use powers of 10 and 3 bins, and the
+   smallest bin has and approximate size of 0.001 radians in all
+   proprioceptive dimensions.
+   
+   Then, given a sequence of proprioceptive input, generate a set of
+   matching experience records for each input. 
 
+   Finally, to infer sensory data, select the longest consective chain
+   of experiences as determined by the indexes into the experience
+   vector. 
 
+   This algorithm has three advantages: 
+
+   1. It's simple
+
+   3. It's very fast -- both tracing through possibilites and
+      retrieving possible interpretations take essentially constant
+      time. 
+
+   2. It protects from wrong interpretations of transient ambiguous
+      proprioceptive data : for example, if the worm is flat for just
+      an instant, this flattness will not be interpreted as implying
+      that the worm has its muscles relaxed, since the flattness is
+      part of a longer chain which includes a distinct pattern of
+      muscle activation. A memoryless statistical model such as a
+      markov model that operates on individual frames may very well
+      make this mistake.
+
+   #+caption: Program to convert an experience vector into a 
+   #+caption: proprioceptively binned lookup function.
+   #+name: bin
+   #+begin_listing clojure
+   #+begin_src clojure
 (defn bin [digits]
   (fn [angles]
     (->> angles
@@ -674,11 +707,10 @@
          (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))
 
 (defn gen-phi-scan 
-"Nearest-neighbors with spatial binning. Only returns a result if
- the propriceptive data is within 10% of a previously recorded
- result in all dimensions."
-
-[phi-space]
+  "Nearest-neighbors with binning. Only returns a result if
+   the propriceptive data is within 10% of a previously recorded
+   result in all dimensions."
+  [phi-space]
   (let [bin-keys (map bin [3 2 1])
         bin-maps
         (map (fn [bin-key]
@@ -686,12 +718,20 @@
                 (comp bin-key :proprioception phi-space)
                 (range (count phi-space)))) bin-keys)
         lookups (map (fn [bin-key bin-map]
-                      (fn [proprio] (bin-map (bin-key proprio))))
-                    bin-keys bin-maps)]
+                       (fn [proprio] (bin-map (bin-key proprio))))
+                     bin-keys bin-maps)]
     (fn lookup [proprio-data]
       (set (some #(% proprio-data) lookups)))))
+   #+end_src
+   #+end_listing
 
 
+   #+caption: Program to calculate empathy by tracing though \Phi-space
+   #+caption: and finding the longest (ie. most coherent) interpretation
+   #+caption: of the data.
+   #+name: longest-thread
+   #+begin_listing clojure
+   #+begin_src clojure
 (defn longest-thread
   "Find the longest thread from phi-index-sets. The index sets should
    be ordered from most recent to least recent."
@@ -718,6 +758,9 @@
                     threads)]
         (recur (concat longest-thread result)
                (drop (count longest-thread) phi-index-sets))))))
+   #+end_src
+   #+end_listing
+
 
 There is one final piece, which is to replace missing sensory data
 with a best-guess estimate. While I could fill in missing data by
@@ -747,12 +790,14 @@
    #+end_src
    #+end_listing
 
-
-
-
   
 ** Efficient action recognition with =EMPATH=
 
+   In my exploration with the worm, I can generally infer actions from
+   proprioceptive data exactly as well as when I have the complete
+   sensory data. To reach this level, I have to train the worm with
+   verious exercices for about 1 minute.
+
 ** Digression: bootstrapping touch using free exploration
 
 * Contributions