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Silviu Marian Udrescu 2020-03-23 03:35:19 -04:00 • committed by GitHub
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commit f0cc7dfcaa
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19 changed files with 1210 additions and 182 deletions
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Code/S_symmetry.py
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@ -14,6 +14,7 @@ from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAnnealingLR
from matplotlib import pyplot as plt
from S_remove_input_neuron import remove_input_neuron
import time
is_cuda = torch.cuda.is_available()
@ -156,6 +157,7 @@ def do_translational_symmetry_minus(pathdir, filename, i,j):
with torch.no_grad():
file_name = filename + "-translated_minus"
ct_median = torch.median(torch.from_numpy(variables[:,j]))
data_translated = variables
data_translated[:,i] = variables[:,i]-variables[:,j]
data_translated = np.delete(data_translated, j, axis=1)
@ -165,8 +167,9 @@ def do_translational_symmetry_minus(pathdir, filename, i,j):
except:
pass
np.savetxt("results/translated_data_minus/"+file_name , data_translated)
remove_input_neuron(model,n_variables,j,ct_median,"results/NN_trained_models/models/"+filename + "-translated_minus_pretrained.h5")
return ("results/translated_data_minus/",file_name)
except Exception as e:
print(e)
return (-1,-1)
@ -286,6 +289,7 @@ def do_translational_symmetry_divide(pathdir, filename, i,j):
with torch.no_grad():
file_name = filename + "-translated_divide"
data_translated = variables
ct_median =torch.median(torch.from_numpy(variables[:,j]))
data_translated[:,i] = variables[:,i]/variables[:,j]
data_translated = np.delete(data_translated, j, axis=1)
data_translated = np.column_stack((data_translated,f_dependent))
@ -294,8 +298,9 @@ def do_translational_symmetry_divide(pathdir, filename, i,j):
except:
pass
np.savetxt("results/translated_data_divide/"+file_name , data_translated)
remove_input_neuron(model,n_variables,j,ct_median,"results/NN_trained_models/models/"+filename + "-translated_divide_pretrained.h5")
return ("results/translated_data_divide/",file_name)
except Exception as e:
print(e)
return (-1,1)
@ -413,6 +418,7 @@ def do_translational_symmetry_multiply(pathdir, filename, i,j):
with torch.no_grad():
file_name = filename + "-translated_multiply"
data_translated = variables
ct_median =torch.median(torch.from_numpy(variables[:,j]))
data_translated[:,i] = variables[:,i]*variables[:,j]
data_translated = np.delete(data_translated, j, axis=1)
data_translated = np.column_stack((data_translated,f_dependent))
@ -421,8 +427,9 @@ def do_translational_symmetry_multiply(pathdir, filename, i,j):
except:
pass
np.savetxt("results/translated_data_multiply/"+file_name , data_translated)
remove_input_neuron(model,n_variables,j,ct_median,"results/NN_trained_models/models/"+filename + "-translated_multiply_pretrained.h5")
return ("results/translated_data_multiply/",file_name)
except Exception as e:
print(e)
return (-1,1)
@ -539,6 +546,7 @@ def do_translational_symmetry_plus(pathdir, filename, i,j):
with torch.no_grad():
file_name = filename + "-translated_plus"
data_translated = variables
ct_median =torch.median(torch.from_numpy(variables[:,j]))
data_translated[:,i] = variables[:,i]+variables[:,j]
data_translated = np.delete(data_translated, j, axis=1)
data_translated = np.column_stack((data_translated,f_dependent))
@ -547,6 +555,7 @@ def do_translational_symmetry_plus(pathdir, filename, i,j):
except:
pass
np.savetxt("results/translated_data_plus/"+file_name , data_translated)
remove_input_neuron(model,n_variables,j,ct_median,"results/NN_trained_models/models/"+filename + "-translated_plus_pretrained.h5")
return ("results/translated_data_plus/", file_name)
except Exception as e: