def plot(data,ur,index,u_idx_pos,u_idx_neg):
fig = plt.figure(figsize=(18, 6))
#Plot the second smallest eigen vetor u2
plt.subplot(131)
x = u_idx_neg[0]
y = ur[:,index[1]][u_idx_neg[0]]
plt.ylim(-0.01,0.01)
plt.scatter(x, y, color='b')
x = u_idx_pos[0]
y = ur[:,index[1]][u_idx_pos[0]]
plt.ylim(-0.01,0.01)
plt.scatter(x, y, color='r')
#Plot the stubburn outlier
x = np.arange(65,66,1)
y = ur[:,index[1]][65]
plt.scatter(x,y, color='g')
#Plot the horizaontal line y=0, which we use to cluster
plt.axhline(y=.0, xmin=-20, xmax=120, linewidth=1, color = 'k')
#Plot the second smallest eigen vetor u2
plt.subplot(132)
x = u_idx_neg[0]
y = ur[:,index[1]][u_idx_neg[0]]
plt.scatter(x, y, color='b')
x = u_idx_pos[0]
y = ur[:,index[1]][u_idx_pos[0]]
plt.scatter(x, y, color='r')
#Plot the stubburn outlier
x = np.arange(65,66,1)
y = ur[:,index[1]][65]
plt.scatter(x,y, color='g')
#Plot the horizaontal line y=0, which we use to cluster
plt.axhline(y=.0, xmin=-20, xmax=120, linewidth=1, color = 'k')
#Plot the cluster result
axs2 = fig.add_subplot(133)
plt_rex.plot2d(data[:,u_idx_neg[0]], colwise_data=True, hatch='bo',
show=False, axs=axs2, set_aspect_equal=False, plotlabel="Neg")
plt_rex.plot2d(data[:,u_idx_pos[0]], colwise_data=True, hatch='ro',
show=False, axs=axs2, set_aspect_equal=False, plotlabel="Pos")
def PlotBaseAndScatter(x,y,axsList):
X = np.vstack((x, y)) # only the measurements; data is col-wise
xmin, ymin = X.min(axis=1)
xmax, ymax = X.max(axis=1)
xlim = [xmin, xmax] # purely for looks
ylim = [ymin, ymax]
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[0], set_aspect_equal=False,
title="Alternative and Median")
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[1], set_aspect_equal=False,
title="Alternative and MidPoint")
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[2], set_aspect_equal=False,
title="Alternative and SlidingMidPoint")
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[3], set_aspect_equal=False,
title="High Variance and Median")
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[4], set_aspect_equal=False,
title="High Variance and MidPoint")
plt_rex.plot2d(X, colwise_data=True, hatch='ro', x_lim=xlim,
y_lim=ylim, show=False, axs=axsList[5], set_aspect_equal=False,
title="High Variance and SlidingMidPoint")
return
