def render(self, output_port):
self.render_title(output_port, "Simple Random Sampling: %s" % self.project_name)
self.render_summary(output_port)
self.render_overall(output_port
,("untyped", config.is_untyped)
,("typed", config.is_typed))
self.render_absolute(output_port
,*[(str(n), self.in_random_sample(num_typed=n))
for n in range(self.get_num_modules())])
self.render_sample_results(output_port)
self.strategy = constants.CACHE
# self.render_all_paths(output_port, [1,2,3,4])
print(latex.end(), file=output_port)
def render(self, output_port):
title = "Ground Truth Results: %s" % self.project_name
self.render_title(output_port, title)
self.render_summary(output_port)
best_cfgs = self.best_rows(
config.is_gradual, lambda x, y: self.stats_by_config[x]["mean"] > self.stats_by_config[y]["mean"]
)
worst_cfgs = self.best_rows(
config.is_gradual, lambda x, y: self.stats_by_config[x]["mean"] < self.stats_by_config[y]["mean"]
)
self.render_overall(
output_port,
("untyped", config.is_untyped),
("gradual", config.is_gradual),
("fastest(%s)" % best_cfgs[0], lambda x: x == best_cfgs[0]),
("slowest(%s)" % worst_cfgs[0], lambda x: x == worst_cfgs[0]),
("typed", config.is_typed),
)
print(
"Num. within 2x: %s"
% len(
self.stats_of_predicate(
lambda x: self.stats_by_config[x]["mean"]
< 2 * self.stats_by_config["0" * self.get_num_modules()]["mean"]
)
),
file=output_port,
)
print(latex.subsection("Aggregate Figures"), file=output_port)
self.render_normalized(
output_port,
("untyped", config.is_untyped),
("gradual", config.is_gradual),
("top %s" % len(best_cfgs), lambda x: x in best_cfgs),
("bottom %s" % len(worst_cfgs), lambda x: x in worst_cfgs),
("typed", config.is_typed),
)
self.render_absolute(
output_port, *[(str(n), config.has_typed_modules(n)) for n in range(self.get_num_modules())]
)
baseline = self.stats_of_config("0" * self.get_num_modules())["mean"]
self.render_graphs(
output_port, worst_cfgs, baseline, title="Top %s slowest gradually-typed configurations" % len(worst_cfgs)
)
self.render_graphs(
output_port, best_cfgs, baseline, title="Top %s fastest gradually-typed configurations" % len(best_cfgs)
)
# self.render_all_paths(output_port, [1,2,3,4])
# self.render_cutoff_paths(output_port)
print(latex.end(), file=output_port)
# Adding identity
size = len(mat)
for i in range(len(mat)):
add = [0] * len(mat)
add[i] = 1
mat[i] += add
latex_line(mat)
# Eliminating
for i in range(len(mat)):
if is_zero(mat[i][i]):
print("Can't invert the matrix.")
latex.output("\\end{array}")
latex.enq()
latex.output("\\\\Can't invert the matrix.\n")
latex.end("Inverting the matrix")
exit()
matrix.mult(mat, i, 1/mat[i][i])
for j in range(len(mat)):
if j == i:
continue
matrix.add(mat, j, -mat[j][i], i)
latex_line(mat)
# Output
invert = []
for i in range(len(mat)):
invert += [[0] * size]
for j in range(size):
invert[i][j] = mat[i][j + size]
matrix.output(invert)
print("Usage: ./summary FILE.rktd")
### Entry point, accepts a list of files.
if __name__ == "__main__":
if len(sys.argv) < 2:
print_help()
elif sys.argv[1] in ["-a", "--all", "--aggregate"]:
main_aggregate(sys.argv[2::])
else:
init(constants.OUTPUT_DIR)
results = "%s/results.tex" % constants.OUTPUT_DIR
output_port = open(results, "w")
print(latex.PREAMBLE, file=output_port)
print("\\begin{figure}", file=output_port)
print(latex.L_HEADER, file=output_port)
summs = []
for fname in sys.argv[1::]:
print("\\hbox{", file=output_port)
summs.append(main(fname, default_out=constants.OUTPUT_DIR, default_port=output_port))
print("}", file=output_port)
print("\\caption{\\emph{L-step, M/N-usable} results for selected benchmarks.}", file=output_port)
print("\\end{figure}", file=output_port)
print(latex.end(), file=output_port)
output_port.close()
## BEGIN HACKS
os.system("cd output-summary; xelatex results.tex; cd ..;")
os.system("cp output-summary/results.pdf /home/ben/Downloads/results.pdf")
## END HACKS
######### Main loop ##########
if __name__ == "__main__":
coeffs, b = matrix.read(sys.argv[1])
if not b:
print("Invalid file :", sys.argv[1])
exit()
p = len(coeffs[0]) - 1
print("Solving :")
matrix.output(coeffs)
latex.begin("latex.matrix")
latex.beq()
latex.matrix(coeffs)
latex.output("\\\\")
print("Triangularized :")
comp = triangularize(coeffs, p)
bs = base(p, comp)
matrix.output(coeffs)
latex.enq()
if not solve(coeffs, bs, p):
print("No solution.")
latex.string("No solution.\n")
else:
print("Solution : ")
output_results(bs)
latex.base(bs)
latex.end("Solving a linear system")