Print average speed of cycles

README.md

@@ -227,9 +227,7 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 
 # TODO
 
-- [ ] Print out speed of equation evaluation over time. Measure time it takes per 
-- [ ] Refresh screen rather than dumping to stdout
-- [ ] Add ability to pass an operator as an anonymous function string. E.g., `binary_operators=["g(x, y) = x+y"]`.
+- [ ] Refresh screen rather than dumping to stdout?
 - [ ] Test suite
 - [ ] Add ability to save state from python
 - [ ] Add true multi-node processing, with MPI, or just file sharing. Multiple populations per core.
@@ -250,6 +248,8 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
     - Current most expensive operations:
         - [ ] Calculating the loss function - there is duplicate calculations happening.
         - [x] Declaration of the weights array every iteration
+- [x] Print out speed of equation evaluation over time. Measure time it takes per cycle
+- [x] Add ability to pass an operator as an anonymous function string. E.g., `binary_operators=["g(x, y) = x+y"]`.
 - [x] Add error bar capability (thanks Johannes Buchner for suggestion)
 - [x] Why don't the constants continually change? It should optimize them every time the equation appears.
     - Restart the optimizer to help with this.

julia/sr.jl

@@ -755,6 +755,7 @@ function fullRun(niterations::Integer;
 
     for k=1:niterations
         # Spawn threads to run indepdent evolutions, then gather them
+        start_time = time()
         @inbounds Threads.@threads for i=1:nthreads
             allPops[i] = run(allPops[i], ncyclesperiteration, verbosity=verbosity)
             for j=1:allPops[i].n
@@ -768,6 +769,10 @@ function fullRun(niterations::Integer;
             end
             bestSubPops[i] = bestSubPop(allPops[i], topn=topn)
         end
+        total_time = time() - start_time
+
+        number_equations_created = ncyclesperiteration * nthreads * npop / ns
+        equations_created_per_second = number_equations_created / total_time
 
         # Get best 10 models from each evolution. Copy because we re-assign later.
         # bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
@@ -787,6 +792,8 @@ function fullRun(niterations::Integer;
         # Dominating pareto curve - must be better than all simpler equations
         dominating = PopMember[]
         open(hofFile, "w") do io
+            debug(verbosity, "\n")
+            debug(verbosity, "Total cycles per second: $(round(Int, equations_created_per_second, sigdigits=3))\n")
             debug(verbosity, "Hall of Fame:")
             debug(verbosity, "-----------------------------------------")
             debug(verbosity, "Complexity \t MSE \t Equation")