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add rank models for retrain #20

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3 changes: 2 additions & 1 deletion .gitignore
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output
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__pycache__/
176 changes: 176 additions & 0 deletions retraining/_converts.py
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# convert rank model encoding to model genotype
rank_0 = [[1, 1],
[1, 1, 1, 1],
[1, 1, 0, 1],
[1, 0, 1, 1],
[1, 1, 0, 1]]

rank_10 = [[1, 1],
[1, 0, 3, 1],
[1, 3, 0, 1],
[1, 1, 1, 0],
[3, 0, 1, 1]]
rank_20 = [[1, 0],
[1, 2, 3, 1],
[1, 3, 1, 1],
[1, 2, 2, 1],
[3, 1, 3, 1]]
rank_30 = [[1, 0],
[3, 1, 1, 0],
[1, 2, 2, 0],
[1, 0, 0, 0],
[3, 0, 2, 1]]

rank_40 = [[0, 1],
[3, 1, 0, 0],
[3, 2, 0, 1],
[3, 0, 1, 1],
[0, 3, 0, 1]]
rank_50 = [[0, 0],
[3, 0, 2, 1],
[3, 1, 2, 0],
[3, 3, 1, 1],
[0, 0, 3, 1]]
rank_60 = [[0, 0],
[3, 2, 3, 1],
[3, 3, 3, 1],
[0, 0, 1, 0],
[0, 0, 0, 1]]
rank_70 = [[3, 1],
[2, 1, 1, 1],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]]

rank_80 = [[2, 1],
[0, 3, 3, 1],
[1, 0, 3, 3],
[3, 2, 0, 1],
[0, 2, 2, 1]]
rank_90 = [[2, 1],
[2, 1, 3, 1],
[1, 2, 2, 3],
[2, 0, 3, 1],
[1, 2, 0, 0]]
rank_100 = [[3, 0],
[0, 0, 0, 0],
[0, 1, 0, 0],
[2, 3, 2, 1],
[3, 0, 2, 0]]
rank_110 = [[3, 0],
[3, 2, 2, 0],
[3, 0, 1, 3],
[2, 0, 2, 1],
[0, 0, 3, 0]]
rank_120 =[[2, 0],
[0, 3, 2, 0],
[0, 0, 2, 0],
[1, 1, 1, 2],
[0, 0, 0, 0]]

rank_130 = [[1, 3],
[2, 3, 2, 1],
[0, 2, 0, 1],
[1, 1, 2, 3],
[1, 1, 0, 3]]
rank_140 = [[1, 3],
[3, 1, 0, 3],
[0, 2, 0, 0],
[1, 2, 3, 3],
[1, 3, 3, 3]]
rank_150 =[[1, 2],
[0, 2, 2, 0],
[0, 3, 3, 0],
[1, 0, 0, 2],
[1, 3, 1, 2]]

rank_160 = [[0, 3],
[2, 0, 2, 0],
[2, 1, 0, 1],
[1, 0, 2, 2],
[3, 2, 3, 3]]
rank_170 = [[0, 3],
[0, 0, 1, 3],
[2, 0, 3, 1],
[0, 3, 1, 2],
[0, 1, 0, 3]]
rank_180 = [[0, 2],
[3, 2, 0, 3],
[2, 3, 1, 1],
[3, 1, 3, 3],
[0, 2, 1, 3]]
rank_190 = [[0, 2],
[3, 0, 3, 2],
[2, 3, 2, 0],
[3, 2, 1, 3],
[0, 3, 0, 3]]
rank_200 = [[3, 3],
[0, 0, 3, 3],
[0, 1, 2, 3],
[3, 3, 3, 3],
[1, 0, 0, 2]]
rank_220 = [[3, 2],
[2, 1, 3, 3],
[2, 1, 0, 2],
[0, 2, 2, 3],
[0, 0, 2, 3]]
rank_250 = [[2, 2],
[2, 2, 0, 2],
[2, 3, 2, 3],
[2, 0, 0, 2],
[0, 0, 2, 2]]

epoch_0 = [[3, 2],
[2, 1, 0, 2],
[0, 0, 0, 2],
[3, 2, 1, 1],
[1, 1, 0, 0]]
epoch_2 = [[1, 1],
[1, 1, 0, 1],
[1, 1, 0, 0],
[1, 1, 0, 1],
[3, 2, 1, 1]]
epoch_4 = [[1, 1],
[1, 1, 1, 1],
[1, 1, 0, 0],
[1, 1, 3, 1],
[3, 0, 1, 1]]
epoch_6 = [[1, 1],
[1, 1, 1, 1],
[1, 0, 0, 0],
[1, 3, 1, 1],
[1, 3, 1, 1]]
epoch_8 = [[1, 1],
[1, 1, 1, 1],
[1, 1, 0, 0],
[1, 1, 1, 1],
[1, 1, 0, 1]]

def converter(encoding):
# seting table and model template
table = {
'0': (3, 6), # k=3,e=6
'1': (5, 6),
'2': (3, 3),
'3': (5, 3)
}
model_geno = [['ir_r1_k{}_s2_e{}_c24_se0.25', 'ir_r1_k{}_s1_e{}_c24_se0.25'],
['ir_r1_k{}_s2_e{}_c40_se0.25', 'ir_r1_k{}_s1_e{}_c40_se0.25', 'ir_r1_k{}_s1_e{}_c40_se0.25',
'ir_r1_k{}_s1_e{}_c40_se0.25'],
['ir_r1_k{}_s2_e{}_c80_se0.25', 'ir_r1_k{}_s1_e{}_c80_se0.25', 'ir_r1_k{}_s1_e{}_c80_se0.25',
'ir_r1_k{}_s1_e{}_c80_se0.25'],
['ir_r1_k{}_s1_e{}_c112_se0.25', 'ir_r1_k{}_s1_e{}_c112_se0.25', 'ir_r1_k{}_s1_e{}_c112_se0.25',
'ir_r1_k{}_s1_e{}_c112_se0.25'],
['ir_r1_k{}_s2_e{}_c192_se0.25', 'ir_r1_k{}_s1_e{}_c192_se0.25', 'ir_r1_k{}_s1_e{}_c192_se0.25',
'ir_r1_k{}_s1_e{}_c192_se0.25']]

model = [['ds_r1_k3_s1_c16_se0.25']]
for b_encoding, b_geno in zip(encoding,model_geno):
block = []
for num, geno in zip(b_encoding,b_geno):
get_ke = table[str(num)]
block.append(geno.format(get_ke[0],get_ke[1]))
model.append(block)
model.append(['ir_r1_k3_s1_e6_c320_se0.25'])
return model

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