def plot_Radvis(objectives, ax, name):
class_dummy = np.zeros(len(objectives))
visualizer = RadViz(classes=[name], ax=ax, alpha=.75)
visualizer.fit(objectives, class_dummy)
visualizer.show()
plt.show()
#
# # Instantiate the visualizer
set_palette('yellowbrick')
plt.figure()
classes = np.array([0, 1.])
plt.xticks(fontsize=9)
visualizerRadViz = RadViz(classes=classes,
features=features,
title=' ')
visualizerRadViz.fit(X, y) # Fit the data to the visualizer
visualizerRadViz.transform(X) # Transform the data
locationFileNameRVZ = os.path.join('/home/ak/Documents/Research/Papers/figures',str(symbols[symbolIdx]) \
+'_idx_'+str(idx)+'_label_'+str(labelsIdx)+'_date_'+str(dateIdx)+'_radviz.png')
visualizerRadViz.show(outpath=locationFileNameRVZ)
plt.show()
## MDS
# Instantiate the clustering model and visualizer
model = KMeans(6)
plt.figure()
plt.xlabel('features', fontsize=12)
plt.ylabel('features', fontsize=12)
plt.xticks(fontsize=14)
plt.yticks(fontsize=12)
visualizerID = InterclusterDistance(model)
visualizerID.fit(X) # Fit the data to the visualizer