正态分布可视化
可视化函数
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
"""设置matplotlib的轴"""
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
axes.set_xscale(xscale)
axes.set_yscale(yscale)
axes.set_xlim(xlim)
axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
def use_svg_display(): #@save
"""使⽤svg格式在Jupyter中显⽰绘图"""
backend_inline.set_matplotlib_formats('svg')
def set_figsize(figsize=(3.5,2.5)):#@save
use_svg_display()
d2l.plt.rcParams['figure.figsize'] = figsize
def plot(X,Y=None,xlabel=None,ylabel=None,legend=None,xlim=None,ylim=None,xscale='linear',yscale='linear',fmts=('-','m--','g-.','r:'),
figsize=(3.5,2.5),axes=None):
if legend is None:
legend = []
set_figsize(figsize)
axes = axes if axes else d2l.plt.gca()
def has_one_axis(X):
return (hasattr(X,"ndim") and X.ndim == 1 or isinstance(X,list)) and not hasattr(X[0],"__len__")
if has_one_axis(X):
X=[X]
if Y is None:
X,Y=[[]]*len(X),X
elif has_one_axis(Y):
Y = [Y]
if len(X) != len(Y):
X = X*len(Y)
axes.cla()
for x,y,fmt in zip(X,Y,fmts):
if len(x):
axes.plot(x,y,fmt)
else:
axes.plot(y,fmt)
set_axes(axes, xlabel,ylabel,xlim,ylim,xscale,yscale,legend)
正态分布函数
def normal(x,mu,sigma):
p = 1/math.sqrt(2*math.pi*sigma**2)
return p*np.exp(-0.5/sigma**2*(x-mu)**2)
将正太分布函数可视化
x = np.arange(-7,7,0.01)
params = [(0,1),(0,2),(3,1)]
plot(x,[normal(x,mu,sigma) for mu,sigma in params], xlabel='x',ylabel='p(x)',figsize=(4.5,2.5),legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])
