afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-06-28 20:00:16 -04:00 • committed by GitHub
parent cfc4d77e7c
commit 28edde1a36
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1 changed files with 28 additions and 17 deletions
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45
Code/S_polyfit.py
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@ -26,17 +26,16 @@ def polyfit(maxdeg, filename):
n_variables = np.loadtxt(filename, dtype='str').shape[1]-1
variables = np.loadtxt(filename, usecols=(0,))
means = [np.mean(variables)]
for j in range(1,n_variables):
v = np.loadtxt(filename, usecols=(j,))
means = means + [np.mean(v)]
variables = np.column_stack((variables,v))
f_dependent = np.loadtxt(filename, usecols=(n_variables,))
if n_variables>1:
C_1_2 = fractional_matrix_power(np.cov(variables.T),-1/2)
x = []
z = []
for ii in range(len(variables[0])):
@ -44,25 +43,35 @@ def polyfit(maxdeg, filename):
x = x + ["x"+str(ii)]
z = z + ["z"+str(ii)]
variables = np.matmul(C_1_2,variables.T).T
res = getBest(variables,f_dependent,maxdeg)
parameters = res[0]
params_error = res[1]
deg = res[2]
if np.isnan(C_1_2).any()==False:
variables = np.matmul(C_1_2,variables.T).T
res = getBest(variables,f_dependent,maxdeg)
parameters = res[0]
params_error = res[1]
deg = res[2]
x = sympy.Matrix(x)
M = sympy.Matrix(C_1_2)
b = sympy.Matrix(means)
M_x = M*(x-b)
x = sympy.Matrix(x)
M = sympy.Matrix(C_1_2)
b = sympy.Matrix(means)
M_x = M*(x-b)
eq = mk_sympy_function(parameters,n_variables,deg)
symb = sympy.Matrix(z)
eq = mk_sympy_function(parameters,n_variables,deg)
symb = sympy.Matrix(z)
for i in range(len(symb)):
eq = eq.subs(symb[i],M_x[i])
for i in range(len(symb)):
eq = eq.subs(symb[i],M_x[i])
eq = simplify(eq)
eq = simplify(eq)
else:
res = getBest(variables,f_dependent,maxdeg)
parameters = res[0]
params_error = res[1]
deg = res[2]
eq = mk_sympy_function(parameters,n_variables,deg)
eq = simplify(eq)
else:
res = getBest(variables,f_dependent,maxdeg)
parameters = res[0]
@ -76,3 +85,5 @@ def polyfit(maxdeg, filename):
return (eq, params_error)