def plotPDS(self):
pds = PDS(self.Rm, self.Rs)
# 窗口大小选择
pds.model(self.win, self.k) # pds 建模 得到pds.F
Rsun = self.Rss[:self.n, :] # 选取子机上同样n 组未知样本进行PDS 预测
Rsunp = pds.predict(Rsun) # Rsunp 为预测结果
win = self.win
# 舍弃窗口两端
Rmswin = self.Rms[:, win:Rsun.shape[1] - win] #Rmswin 源机上去掉两端的光谱
x = np.arange(1100, 2500, 2)
xwin = x[win:Rsun.shape[1] - win]
p = PlotCanvas()
# 差值
for i in range(Rsunp.shape[0]):
y1 = p.averSpec(Rmswin) # 源机均值光谱
p.axes.plot(xwin, y1, 'r-', label='源机均值光谱')
y2 = Rsunp[i, :] # 从子机预测的光谱
p.axes.plot(xwin, y2, label='样本' + str(i + 1) + '预测值')
# 差值
y = y2 - y1 # 目标机光谱
p.axes.plot(xwin, y, label='样本' + str(i + 1) + '预测偏差')
p.axes.legend()
p.axes.set_title("PDS算法预测结果")
self.hlayout4.replaceWidget(self.wg, p)
self.wg = p
Rmun = Rmswin[:self.n, :]
sep = p.computeSEP(Rmun, Rsunp)
self.lb5.setVisible(True)
self.lb5.setText('%.5f' % sep)
pass
def plot(self):
p = PlotCanvas(dpi=75)
data = self.data
matdata = data[self.cbx.currentText()]
arrdata = np.array(matdata.__getitem__("data")[0][0])
x = np.arange(1100, 2500, 2)
for i in range(arrdata.shape[0]):
y = arrdata[i]
p.axes.plot(x, y, 'r-')
p.axes.set_title(self.cbx.currentText()+ "80组玉米样本光谱")
self.hlayout3.replaceWidget(self.wg1, p)
self.wg1 = p
# 绘制均值光谱
p2 = PlotCanvas(dpi=75)
y = p2.averSpec(arrdata)
p2.axes.plot(x, y, 'r-')
p2.axes.set_title(self.cbx.currentText())
self.hlayout3.replaceWidget(self.wg2, p2)
self.wg2 = p2
def plotShenks(self):
win = self.win
sks = Shenks(self.Rm, self.Rs)
# 窗口大小选择
sks.model(win) # sks 建模
xnew = sks.xnew # 由一元二次模型校正后的波长
ynew = sks.ynew # 由校正后波长插值后的吸光度
Rsun = self.Rss[:self.n, :] # 选取子机上同样n 组未知样本进行Sks预测
Rsunp = sks.predict(Rsun)
x = np.arange(1100, 2500, 2)
# 舍弃窗口两端
xnew = sks.xnew # 由校正模型 校正后的 波长
Rmswin = self.Rms[:, win:Rsun.shape[1] - win]
p = PlotCanvas()
y1 = p.averSpec(Rmswin) # 源机均值光谱
y1 = y1[:xnew.shape[0]] # Rmswin 源机上去掉多余波长的光谱
# 差值
for i in range(Rsunp.shape[0]):
p.axes.plot(xnew, y1, 'r-', label='源机均值光谱')
y2 = Rsunp[i] # 从子机预测的光谱
p.axes.plot(xnew, y2, label='样本' + str(i + 1) + '预测值')
# 差值
y = y2 - y1 # 目标机光谱
p.axes.plot(xnew, y, label='样本' + str(i + 1) + '预测偏差')
p.axes.legend()
p.axes.set_title("Shenk's算法预测结果")
self.hlayout4.replaceWidget(self.wg, p)
self.wg = p
Rmswin = Rmswin[:, :xnew.shape[0]]
Rmun = Rmswin[:self.n, :]
sep = p.computeSEP(Rmun, Rsunp)
self.lb5.setVisible(True)
self.lb5.setText('%.5f' % sep)
pass
def plotDS(self):
p = PlotCanvas()
mlr = MLR(self.Rs, self.Rm) # 由选取的n 组样本进行DS 建模
mlr.modelling()
Rsun = self.Rss[:self.n, :] # 选取子机上同样n 组未知样本进行DS 预测
Rsunp = np.dot(Rsun, mlr.A) # A 为得到的转换矩阵，Rsunp 为预测结果
x = np.arange(1100, 2500, 2)
for i in range(Rsunp.shape[0]):
y1 = p.averSpec(self.Rms) # 源机均值光谱
p.axes.plot(x, y1, 'r-', label='源机均值光谱')
y2 = Rsunp[i, :] # 从子机预测的光谱
p.axes.plot(x, y2, label='样本' + str(i + 1) + '预测值')
# 差值
y = y2 - y1 # 目标机光谱
p.axes.plot(x, y, label='样本' + str(i + 1) + '预测偏差')
p.axes.legend()
p.axes.set_title("DS算法预测结果")
self.hlayout4.replaceWidget(self.wg, p)
self.wg = p
Rmun = self.Rms[:self.n, :]
sep = p.computeSEP(Rmun, Rsunp)
self.lb5.setVisible(True)
self.lb5.setText('%.5f' % sep)
pass