Use @fastmath for evaluating on array

README.md

@@ -299,10 +299,10 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 - [x] Sympy evaluation
 - [x] Threaded recursion
 - [x] Test suite
-- [ ] Add true multi-node processing, with MPI, or just file sharing. Multiple populations per core.
-    - Ongoing in cluster branch
 - [x] Performance: - Use an enum for functions instead of storing them?
     - Gets ~40% speedup on small test.
+- [ ] Add true multi-node processing, with MPI, or just file sharing. Multiple populations per core.
+    - Ongoing in cluster branch
 - [ ] Consider allowing multi-threading turned off, for faster testing (cache issue on travis). Or could simply fix the caching issue there.
 - [ ] Dump scores alongside MSE to .csv (and return with Pandas).
 - [ ] Consider returning only the equation of interest; rather than all equations.
@@ -320,6 +320,7 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 - [ ] Use NN to generate weights over all probability distribution conditional on error and existing equation, and train on some randomly-generated equations
 - [ ] Add GPU capability?
      - Not sure if possible, as binary trees are the real bottleneck.
+     - Could generate on CPU, evaluate score on GPU?
 - [ ] 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?
 - [ ] For hierarchical idea: after running some number of iterations, do a search for "most common pattern". Then, turn that subtree into its own operator.
 - [ ] Additional degree operators?
@@ -332,4 +333,8 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 - [ ] Call function to read from csv after running
 - [ ] Add function to plot equations
 - [ ] Sort this todo list by priority
+- [ ] Consider printing output sorted by score, not by complexity.
+- [ ] Performance: try inling things?
+- [ ] Multi targets (vector ops)
+
 

julia/sr.jl

@@ -247,7 +247,7 @@ function evalTreeArray(tree::Node)::Array{Float32, 1}
     elseif tree.degree == 1
         return unaops[tree.op].(evalTreeArray(tree.l))
     else
-        return binops[tree.op].(evalTreeArray(tree.l), evalTreeArray(tree.r))
+        return @fastmath binops[tree.op].(evalTreeArray(tree.l), evalTreeArray(tree.r))
     end
 end