Add subtraction operator

TODO.md

@@ -78,6 +78,8 @@
 
 ## Algorithmic performance ideas:
 
+- [ ] When doing equation warmup, only migrate those equations with almost the same complexity. Rather than having to consider simple equations later in the game.
+- [ ] Right now we only update the score based on some. Need to update score based on entire data! Note that optimizer only is used sometimes.
 - [ ] Idea: use gradient of equation with respect to each operator (perhaps simply add to each operator) to tell which part is the most "sensitive" to changes. Then, perhaps insert/delete/mutate on that part of the tree?
 - [ ] Start populations staggered; so that there is more frequent printing (and pops that start a bit later get hall of fame already)?
 - [ ] Consider adding mutation for constant<->variable
@@ -93,6 +95,7 @@
 
 ## Code performance ideas:
 
+- [ ] How hard is it to turn the recursive array evaluation into a for loop?
 - [ ] Try defining a binary tree as an array, rather than a linked list. See https://stackoverflow.com/a/6384714/2689923
 - [ ] Add true multi-node processing, with MPI, or just file sharing. Multiple populations per core.
     - Ongoing in cluster branch

docs/operators.md

@@ -8,7 +8,7 @@ of these and other valid operators are stated below.
 
 **Binary**
 
-`plus`, `mult`, `pow`, `div`, `greater`, `mod`, `beta`, `logical_or`,
+`plus`, `sub`, `mult`, `pow`, `div`, `greater`, `mod`, `beta`, `logical_or`,
 `logical_and`
 
 **Unary**

julia/operators.jl

@@ -2,6 +2,7 @@ import SpecialFunctions: gamma, lgamma, erf, erfc, beta
 
 # Define allowed operators. Any julia operator can also be used.
 plus(x::Float32, y::Float32)::Float32 = x+y #Do not change the name of this operator.
+sub(x::Float32, y::Float32)::Float32 = x-y #Do not change the name of this operator.
 mult(x::Float32, y::Float32)::Float32 = x*y #Do not change the name of this operator.
 pow(x::Float32, y::Float32)::Float32 = sign(x)*abs(x)^y
 div(x::Float32, y::Float32)::Float32 = x/y

pysr/sr.py

@@ -17,6 +17,7 @@ sympy_mappings = {
     'div':  lambda x, y : x/y,
     'mult': lambda x, y : x*y,
     'plus': lambda x, y : x + y,
+    'sub':  lambda x, y : x - y,
     'neg':  lambda x    : -x,
     'pow':  lambda x, y : sympy.sign(x)*abs(x)**y,
     'cos':  lambda x    : sympy.cos(x),