## PCA
pca = PCA(n_components=nband)
pca.fit(Y)
Ypca = pca.transform(Y)
Cpca = pca.transform(center)
Ypca[:, 0] = -Ypca[:, 0]
Cpca[:, 0] = -Cpca[:, 0]
# Ypca[:,1]=-Ypca[:,1]
# Cpca[:,1]=-Cpca[:,1]
## rename clusters
ythe = np.array([0])
label, Cpca, center = SSRS.Cluster_rename(label, ythe, Cpca, center)
## plot PCA and cluster after resign name
SSRS.Cluster_plot(Y, label, center)
#plt.savefig(figdir+'Cluster')
plt.figure()
p1 = plt.scatter(Ypca[:, 0], Ypca[:, 1], c=label)
p2 = plt.plot(Cpca[:, 0], Cpca[:, 1], 'rx', markersize=20, markeredgewidth=5)
plt.colorbar()
#plt.savefig(figdir+'PCA')
## compute distance
dist = np.zeros([nind, nc])
distx = np.zeros([nind, nc])
disty = np.zeros([nind, nc])
for j in range(0, nind):
for i in range(0, nc):
dist[j, i] = SSRS.DistCal(Y[j, :], center[i, :])
string, nodeind, leaf, label = SSRS.traverseTree(regTree, feature_names, Xin)
for i in range(0, regTree.node_count):
plt.figure()
plt.boxplot(Yin[nodeind[i], :])
plt.title(string[i], fontsize=8)
#plt.tight_layout()
plt.savefig(savedir + "Test_node%i" % i)
plt.close()
## PCA of tree leaves
## PCA
Xin = Xn2
Yin = Y
string, nodeind, leaf, label = SSRS.traverseTree(regTree, feature_names, Xin)
nclass = np.unique(leaf).shape[0]
nband = 30
pca = PCA(n_components=30)
pca.fit(Yin)
Ypca = pca.transform(Yin)
center = np.zeros([nclass, nband])
for i in range(0, nclass):
center[i, :] = Yin[nodeind[i], :].mean(axis=0)
Cpca = pca.transform(center)
p1 = plt.scatter(Ypca[:, 0], Ypca[:, 1], c=label)
p2 = plt.plot(Cpca[:, 0], Cpca[:, 1], 'rx', markersize=20, markeredgewidth=5)
plt.colorbar()
SSRS.Cluster_plot(Y, label.astype(int))
