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Daniel Lemire 2018-12-24 15:46:12 -05:00
parent d3821a4b21
commit d975fc7543
2 changed files with 14 additions and 18 deletions
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26
scripts/modeldata/skylake/analysis.txt
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@ -2,27 +2,23 @@ loading modeltable.txt
chosenpredictors= ['integer_count', 'float_count', 'string_count', 'backslash_count', 'nonasciibyte_count', 'object_count', 'array_count', 'null_count', 'true_count', 'false_count', 'byte_count', 'structural_indexes_count']
target = stage1_cycle_count
0.98 cycles per nonasciibyte_count
0.46 cycles per byte_count
R2 = 0.9987704927515433
0.49 cycles per byte_count
R2 = 0.9927537176836411
target = stage2_cycle_count
2 cycles per structural_indexes_count
0.11 cycles per byte_count
R2 = 0.9940031972098434
R2 = 0.9944225230951305
target = stage3_cycle_count
2.6 cycles per float_count
2.4 cycles per string_count
1.4 cycles per structural_indexes_count
0.094 cycles per byte_count
R2 = 0.9981627163839895
18 cycles per float_count
9 cycles per structural_indexes_count
0.36 cycles per byte_count
R2 = 0.9861501257004178
target = total_cycles
7 cycles per string_count
6.2 cycles per float_count
2.6 cycles per structural_indexes_count
0.77 cycles per nonasciibyte_count
0.63 cycles per byte_count
R2 = 0.999372564385096
18 cycles per float_count
11 cycles per structural_indexes_count
0.95 cycles per byte_count
R2 = 0.9922642870761073

6
scripts/modeldata/skylake/learn.py
View File

@ -36,13 +36,13 @@ print()
chosentargets=["stage1_cycle_count", "stage2_cycle_count", "stage3_cycle_count","total_cycles"]
for t in chosentargets:
print("target = ", t)
howmany = 2 # we want at most two predictors
howmany = 1 # we want at most one predictors
if(t.startswith("stage2")):
howmany = 2 # we allow for less
if(t.startswith("stage3")):
howmany = 4 # we allow for more
howmany = 3 # we allow for more
if(t.startswith("total")):
howmany = 5 # we allow for more
howmany = 3 # we allow for more
A=10000000.0
while(True):
regressor = Lasso(max_iter=100000, alpha=A, positive = True, normalize=False, fit_intercept=False) #LinearRegression(normalize=False, fit_intercept=False)