def selectPath(pathVar, top):
path_ = askdirectory()
pathVar.set(path_)
path = pathVar.get()
if (path == ""):
ms.showwarning("警告", "请选择正确的路径")
else:
tools.PathSeting(path=path)
ms.showinfo("提示", "路径选择成功")
tools.output()
top.destroy()
def main():
note = \
"""
important note, you might be suprised that in some of the problems,
going to a higher dimensional feature space results in a greater
in sample error, something thats not suppose to happen seeing that
higher dimensional feature spaces are suppose to be a superset of
the original feature space. This paradoxical result can be
explained by the learning algorithim optimizing for linear
result instead of classification
"""
print(note)
output(simulations)
def main():
note = \
"""
important note, you might be suprised that in some of the problems,
going to a higher dimensional feature space results in a greater
in sample error, something thats not suppose to happen seeing that
higher dimensional feature spaces are suppose to be a superset of
the original feature space. This paradoxical result can be
explained by the learning algorithim optimizing for linear
result instead of classification
"""
print(note)
output(simulations)
def main():
print("The following simulations are computationally intensive")
output(simulations)
def main():
output(simulations)
def main():
print("The following simulations are computationally intensive")
output(simulations)
def main():
tests()
print("the following simulations are computationally intensive,\n this one took 2min 30s on my computer")
output(simulations)
def main():
output(simulations)
