diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-archive.py | 14 | ||||
| -rwxr-xr-x | bin/analyze-kconfig.py | 18 | ||||
| -rwxr-xr-x | bin/analyze-timing.py | 8 |
3 files changed, 23 insertions, 17 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index b29091d..0a2845c 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -800,9 +800,9 @@ if __name__ == "__main__": ) if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) elif xv_method == "kfold": - static_quality = xv.kfold(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) else: static_quality = model.assess(static_model) @@ -811,9 +811,11 @@ if __name__ == "__main__": lut_model = model.get_param_lut() if xv_method == "montecarlo": - lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count) + lut_quality, _ = xv.montecarlo( + lambda m: m.get_param_lut(fallback=True), xv_count + ) elif xv_method == "kfold": - lut_quality = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count) + lut_quality, _ = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count) else: lut_quality = model.assess(lut_model) @@ -933,9 +935,9 @@ if __name__ == "__main__": ) if xv_method == "montecarlo": - analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) + analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) elif xv_method == "kfold": - analytic_quality = xv.kfold(lambda m: m.get_fitted()[0], xv_count) + analytic_quality, _ = xv.kfold(lambda m: m.get_fitted()[0], xv_count) else: analytic_quality = model.assess(param_model) diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py index bd9cccb..048c8c9 100755 --- a/bin/analyze-kconfig.py +++ b/bin/analyze-kconfig.py @@ -237,19 +237,21 @@ def main(): fit_duration = time.time() - fit_start_time if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) - analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) + analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) if lut_model: - lut_quality = xv.montecarlo( + lut_quality, _ = xv.montecarlo( lambda m: m.get_param_lut(fallback=True), xv_count ) else: lut_quality = None elif xv_method == "kfold": - static_quality = xv.kfold(lambda m: m.get_static(), xv_count) - analytic_quality = xv.kfold(lambda m: m.get_fitted()[0], xv_count) + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) + analytic_quality, _ = xv.kfold(lambda m: m.get_fitted()[0], xv_count) if lut_model: - lut_quality = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count) + lut_quality, _ = xv.kfold( + lambda m: m.get_param_lut(fallback=True), xv_count + ) else: lut_quality = None else: @@ -315,9 +317,9 @@ def main(): json.dump(json_model, f, sort_keys=True, cls=dfatool.utils.NpEncoder) if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) elif xv_method == "kfold": - static_quality = xv.kfold(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count) else: static_quality = model.assess(static_model) diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index d67c553..c37ea65 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -305,7 +305,7 @@ if __name__ == "__main__": ) if xv_method == "montecarlo": - static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count) + static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count) else: static_quality = model.assess(static_model) @@ -314,7 +314,9 @@ if __name__ == "__main__": lut_model = model.get_param_lut() if xv_method == "montecarlo": - lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count) + lut_quality, _ = xv.montecarlo( + lambda m: m.get_param_lut(fallback=True), xv_count + ) else: lut_quality = model.assess(lut_model) @@ -412,7 +414,7 @@ if __name__ == "__main__": print("{:10s} {:10s} {}".format("", "", info.model_args)) if xv_method == "montecarlo": - analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) + analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) else: analytic_quality = model.assess(param_model) |