Refactored till errors

julia/constants.jl

@@ -0,0 +1,9 @@
+
+const maxdegree = 2
+const actualMaxsize = maxsize + maxdegree
+const len = size(X)[1]
+
+const nuna = size(unaops)[1]
+const nbin = size(binops)[1]
+const nops = nuna + nbin
+const nvar = size(X)[2];

julia/errors.jl

@@ -0,0 +1,37 @@
+# Sum of square error between two arrays
+function SSE(x::Array{Float32}, y::Array{Float32})::Float32
+    diff = (x - y)
+    return sum(diff .* diff)
+end
+function SSE(x::Nothing, y::Array{Float32})::Float32
+    return 1f9
+end
+
+# Sum of square error between two arrays, with weights
+function SSE(x::Array{Float32}, y::Array{Float32}, w::Array{Float32})::Float32
+    diff = (x - y)
+    return sum(diff .* diff .* w)
+end
+function SSE(x::Nothing, y::Array{Float32}, w::Array{Float32})::Float32
+    return Nothing
+end
+
+# Mean of square error between two arrays
+function MSE(x::Nothing, y::Array{Float32})::Float32
+    return 1f9
+end
+
+# Mean of square error between two arrays
+function MSE(x::Array{Float32}, y::Array{Float32})::Float32
+    return SSE(x, y)/size(x)[1]
+end
+
+# Mean of square error between two arrays
+function MSE(x::Nothing, y::Array{Float32}, w::Array{Float32})::Float32
+    return 1f9
+end
+
+# Mean of square error between two arrays
+function MSE(x::Array{Float32}, y::Array{Float32}, w::Array{Float32})::Float32
+    return SSE(x, y, w)/sum(w)
+end

julia/sr.jl

@@ -2,49 +2,10 @@ import Optim
 import Printf: @printf
 import Random: shuffle!, randperm
 
-const maxdegree = 2
-const actualMaxsize = maxsize + maxdegree
 
+include("constants.jl")
 
-# Sum of square error between two arrays
-function SSE(x::Array{Float32}, y::Array{Float32})::Float32
-    diff = (x - y)
-    return sum(diff .* diff)
-end
-function SSE(x::Nothing, y::Array{Float32})::Float32
-    return 1f9
-end
-
-# Sum of square error between two arrays, with weights
-function SSE(x::Array{Float32}, y::Array{Float32}, w::Array{Float32})::Float32
-    diff = (x - y)
-    return sum(diff .* diff .* w)
-end
-function SSE(x::Nothing, y::Array{Float32}, w::Array{Float32})::Float32
-    return Nothing
-end
-
-# Mean of square error between two arrays
-function MSE(x::Nothing, y::Array{Float32})::Float32
-    return 1f9
-end
-
-# Mean of square error between two arrays
-function MSE(x::Array{Float32}, y::Array{Float32})::Float32
-    return SSE(x, y)/size(x)[1]
-end
-
-# Mean of square error between two arrays
-function MSE(x::Nothing, y::Array{Float32}, w::Array{Float32})::Float32
-    return 1f9
-end
-
-# Mean of square error between two arrays
-function MSE(x::Array{Float32}, y::Array{Float32}, w::Array{Float32})::Float32
-    return SSE(x, y, w)/sum(w)
-end
-
-const len = size(X)[1]
+include("errors.jl")
 
 if weighted
     const avgy = sum(y .* weights)/sum(weights)
@@ -59,10 +20,7 @@ function id(x::Float32)::Float32
     x
 end
 
-const nuna = size(unaops)[1]
-const nbin = size(binops)[1]
-const nops = nuna + nbin
-const nvar = size(X)[2];
+
 
 function debug(verbosity, string...)
     verbosity > 0 ? println(string...) : nothing

main.py

@@ -1,15 +1,19 @@
 import numpy as np
 from pysr import pysr, best, get_hof
+import time
 
 # Dataset
 X = 2*np.random.randn(100, 5)
 y = 2*np.cos(X[:, 3]) + X[:, 0]**2 - 2
 
+
 # Learn equations
+start = time.time()
 equations = pysr(X, y, niterations=5,
                 binary_operators=["plus", "mult"],
                 unary_operators=["cos", "exp", "sin"])
 
 ...  # (you can use ctl-c to exit early)
 
-print(best(equations))
+print(best(equations))
+print(f"Took {time.time()-start} seconds")