def buildmshidistance(snb, cog_p, inh_p, output1, input_v1):
show_panel = snb.show_panel
aa = ValidateDoubleLoop.outer_level_loop(cog_p, inh_p, output1, input_v1)
yy = np.array(aa)
y = yy[0]
m = np.array(output1)
n = len(m)
d = ms.mashidistance(y, n, m)
d1 = zi.Orang(d, len(d))
show_panel = snb.show_panel
grid = snb.grid_out
grid.CreateGrid(1, 13)
grid.SetRowLabelValue(0, '马氏距离')
grid.SetRowLabelAlignment(horiz=wx.ALIGN_TOP, vert=wx.ALIGN_TOP)
for i in range(13):
grid.SetColLabelValue(i, '%d' % (i + 1))
if (i < len(d)):
grid.SetCellValue(0, i, str(round(d[i], 3)))
axes = snb.axes
axes2 = snb.axes2
canvas = snb.canvas
canvas2 = snb.canvas2
axes.clear()
# lpred, = axes.plot(y_pred, 'r', label='predict value')
# ltest, = axes.plot(y_test, 'g', label='real value')
print "========================"
x = []
for i in range(len(d)):
x.append(float(str(round(d[i], 8))))
print x
print "========================"
d = x
axes.boxplot(d)
axes.set(ylabel='Markov distance', title='Simulation of Markov distance')
axes2.set(ylabel='Markov distance', title='data analysis')
name_list = ['sum', 'var', '1/4', '3/4', 'median']
axes2.bar(range(len(d1)),
d1,
color='black',
tick_label=name_list,
width=0.6)
# axes.legend(handles=[lpred, ltest], labels=['predict value', 'real value'])
canvas.draw()
canvas2.draw()
show_panel.SetupScrolling()
show_panel.Layout()
def figure_kl(y, m):
plt.figure("4")
KL = KLdistanse(y, m)
if len(KL) == 1:
x = [1]
plt.title("KL")
plt.bar(x, KL, color='black', tick_label=x)
plt.show()
if len(KL) == 2:
x = [1, 2]
plt.title("KL")
plt.bar(x, KL, 0.1, color='black', tick_label=x)
plt.xlabel("X-output")
plt.ylabel("Y-kl")
plt.show()
if len(KL) == 3:
x = [1, 2, 3]
plt.title("KL")
plt.bar(x, KL, 0.1, color='black', tick_label=x)
plt.xlabel("X-output")
plt.ylabel("Y-kl")
plt.xlabel("X-output")
plt.ylabel("Y-kl")
plt.show()
if len(KL) == 4:
x = [1, 2, 3, 4]
plt.title("KL")
plt.bar(x, KL, 0.1, color='black', tick_label=x)
plt.xlabel("X-output")
plt.ylabel("Y-kl")
plt.show()
if len(KL) > 4:
ax9 = plt.subplot(221) #在图表2中创建子图1
s2 = pd.Series(np.array(KL))
data1 = pd.DataFrame({"KL": s2})
plt.title("KL")
num_list5 = zi.Orang(KL, len(KL))
data1.boxplot() # 这里，pandas自己有处理的过程，很方便哦。
ax10 = plt.subplot(222)
name_list = ['sum', 'var', '1/4', '3/4', 'median']
plt.title("data")
plt.bar(range(len(num_list5)),
num_list5,
color='black',
tick_label=name_list)
plt.show()
def figure_mh(y,n,m):
plt.figure("6")
ax3=plt.subplot(221)#在图表2中创建子图1
d2 = manhadundistance(y,n,m) #调用曼哈顿距离公式y 是仿真数据 m样本数据 n 样本数据个数
s3 = pd.Series(np.array(d2))
data2 = pd.DataFrame({"Simulation of Manhattan distance": s3})
plt.title("Manhattan distance")
data2.boxplot()
ax4=plt.subplot(222)
# make a histogram of the data array
num_list2 = zi.Orang(d2,len(d2))
name_list = ['sum','std','1/4','3/4','median']
plt.title("data")
plt.bar(range(len(num_list2)), num_list2,color='black',tick_label=name_list)
plt.show()
def figure_qbxf(y, n, m):
plt.figure("7")
ax5 = plt.subplot(221) #在图表2中创建子图1
d3 = qiebixuefudistance(y, n, m)
s5 = pd.Series(np.array(d3))
data3 = pd.DataFrame({"Simulation of Chebyshev distance": s5})
plt.title("Chebyshev distance")
data3.boxplot()
ax6 = plt.subplot(222)
# make a histogram of the data array
num_list3 = zi.Orang(d3, len(d3))
name_list = ['sum', 'var', '1/4', '3/4', 'median']
plt.title("data")
plt.bar(range(len(num_list3)),
num_list3,
color='black',
tick_label=name_list)
plt.show()
def figure_ms(y, n, m):
plt.figure("8")
d4 = mashidistance(y, n, m)
ax7 = plt.subplot(221) #在图表2中创建子图1
s7 = pd.Series(np.array(d4))
data4 = pd.DataFrame({"Simulation of Markov distance": s7})
plt.title("Markov distance")
data4.boxplot()
ax8 = plt.subplot(222)
# make a histogram of the data array
num_list4 = zi.Orang(d4, len(d4))
name_list = ['sum', 'var', '1/4', '3/4', 'median']
plt.title("data")
plt.bar(range(len(num_list4)),
num_list4,
color='black',
tick_label=name_list)
plt.show()
def figure_ou(y,n,m):
plt.figure("5")
#plt.figure(2)#创建图表2
d1 = oushidistance(y,n,m)
ax1=plt.subplot(221)#在图表2中创建子图1
s1 = pd.Series(np.array(d1))
data1 = pd.DataFrame({"Simulation of Euclidean distance": s1})
#plt.ylabel("ylabel")
#plt.xlabel("xlabel")
plt.title("Euclidean distance")
data1.boxplot() # 这里，pandas自己有处理的过程，很方便哦
ax2=plt.subplot(222)
# make a histogram of the data array
num_list1 = zi.Orang(d1,len(d1))
name_list = ['sum','std','1/4','3/4','median']
plt.title("data")
plt.bar(range(len(num_list1)), num_list1,color='black',tick_label=name_list)
plt.show()
return d1