Mentions légales du service

Skip to content
Snippets Groups Projects
Commit 34194266 authored by GUILLEMOT Alexandre's avatar GUILLEMOT Alexandre
Browse files

update table

parent 155795bc
No related branches found
No related tags found
No related merge requests found
......@@ -34,7 +34,7 @@ syn = {
"homotopycontinuation": "HomotopyContinuation.jl",
"macaulay2": "Macaulay2",
"sirocco": "SIROCCO",
"pathjump": "jump",
"pathjump": "jump.",
"maxwmeanprec": "w. mean",
"maxprec": "max.",
}
......
......@@ -158,15 +158,18 @@ def degrees(data):
return [A(f).subs(t=0).degree() for f in d["system"]]
# takes a list of degrees
def tot_degree(degrees):
deg_dict = {}
for d in degrees:
if d not in deg_dict:
deg_dict[d] = 0
deg_dict[d] += 1
return "$" + " \\cdot ".join([f"{d}" + f"^{{{deg_dict[d]}}}"*(deg_dict[d] > 1) for d in deg_dict if d > 1]) + "$"
def instructions(data, derivative=False):
function = "f" if not derivative else "df"
for pkg in ["algpath", "adaptive"]:
......@@ -184,9 +187,18 @@ def name(data):
else:
name = data_names[0]
name = name.split("-")[0]
type = name.split("-")
if type[0] == "wilkinson":
name = f"$W_{{{type[2]}}}" + " + 1"*(type[1] == "eps") + "$"
elif type[0] == "clustered":
name = type[0] + f" ({type[1]}, {type[2]}, {type[3]})"
elif type[0] == "sumpowlin":
name = "structured"
else:
name = type[0]
name += " *"*("random" in data) + \
" \\textsuperscript{N}"*("newton" in data) + " \\textsuperscript{F}"*("factorized" in data)
" \\textsuperscript{N}"*("newton" in data)
return name
......@@ -205,12 +217,14 @@ data_stats = {
"htype": lambda data: "N" if data.split("_")[0] == "newton" else ("T" if data.split("_")[0] == "linear" else "?")
}
def median(L):
if L:
return numpy.median(L)
else:
return None
result_stats = {
"failures": lambda res: "\\textbf{" + str(res["failures"]) + "}" if res["failures"] != 0 else "",
"tottime": lambda res: time_format_2(res["overheadtime"]) if "overheadtime" in res else time_format_2(res["time"]),
......@@ -222,8 +236,8 @@ result_stats = {
"meansteps": lambda res: steps_format(round(numpy.mean([int(p) for p in res["steplist"] if p is not None]), 1)),
"stdsteps": lambda res: steps_format(round(numpy.std([int(p) for p in res["steplist"] if p is not None]), 1)),
"medsteps": lambda res: steps_format(median([int(p) for p in res["steplist"] if p is not None])),
"minsteps": lambda res: steps_format(min([int(p) for p in res["steplist"] if p is not None], default = None)),
"maxsteps": lambda res: steps_format(max([int(p) for p in res["steplist"] if p is not None], default = None)),
"minsteps": lambda res: steps_format(min([int(p) for p in res["steplist"] if p is not None], default=None)),
"maxsteps": lambda res: steps_format(max([int(p) for p in res["steplist"] if p is not None], default=None)),
"q1steps": lambda res: steps_format(round(numpy.quantile([int(p) for p in res["steplist"] if p is not None], 0.25), 1)),
"q3steps": lambda res: steps_format(round(numpy.quantile([int(p) for p in res["steplist"] if p is not None], 0.75), 1)),
"maxprec": lambda res: str(max([int(p) for p in res["maxpreclist"]])),
......
......@@ -17,7 +17,7 @@
"clustered-10-5-10/1"
],
"packages": [
"homotopycontinuation"
"homotopycontinuation"
],
"header": {
"val": "root",
......@@ -50,17 +50,12 @@
"children": []
},
{
"val": "steps",
"children": [
{
"val": "medsteps",
"children": []
},
{
"val": "maxsteps",
"children": []
}
]
"val": "medsteps",
"children": []
},
{
"val": "maxsteps",
"children": []
}
]
},
......@@ -72,33 +67,19 @@
"children": []
},
{
"val": "prec",
"children": [
{
"val": "maxprec",
"children": []
},
{
"val": "maxwmeanprec",
"children": []
}
]
"val": "maxprec",
"children": []
},
{
"val": "steps",
"children": [
{
"val": "medsteps",
"children": []
},
{
"val": "maxsteps",
"children": []
}
]
"val": "medsteps",
"children": []
},
{
"val": "maxsteps",
"children": []
}
]
}
]
}
}
}
\ No newline at end of file
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment