if np.any(all_compare[i]<0):
kmeans_best_1se.append( np.min(np.arange(1,10)[all_compare[i]<0]) )
kmeans_best_gap.append( np.arange(1,11)[np.max(all_gaps[i])==all_gaps[i]][0])
else:
kmeans_best_1se.append( 1 )
kmeans_best_gap.append( np.arange(1,11)[np.max(all_gaps[i])==all_gaps[i]][0])
sns.set_style("white")
fig = plt.figure()
# gaussian
ax1 = fig.add_subplot(321,projection='3d')
kmean = sklearn.cluster.KMeans(kmeans_best_1se[0])
_ = three_d_cluster(X_used,predictions=kmean.fit_predict(X_used),fig_to_continue = ax1)
ax1.set_title(null_names[0]+" 1se best comparison "+str(kmeans_best_1se[0])+" clusters")
ax2 = fig.add_subplot(322,projection='3d')
kmean = sklearn.cluster.KMeans(kmeans_best_gap[0])
_ = three_d_cluster(X_used,predictions=kmean.fit_predict(X_used),fig_to_continue = ax2)
ax2.set_title(null_names[0]+" best gap "+str(kmeans_best_gap[0])+" clusters")
# uniform
ax3 = fig.add_subplot(323,projection='3d')
kmean = sklearn.cluster.KMeans(kmeans_best_1se[1])
_ = three_d_cluster(X_used,predictions=kmean.fit_predict(X_used),fig_to_continue = ax3)
ax3.set_title(null_names[1]+" 1se best comparison "+str(kmeans_best_1se[1])+" clusters")
ax4 = fig.add_subplot(324,projection='3d')
kmean = sklearn.cluster.KMeans(kmeans_best_gap[1])
from visuals_functions import three_d_cluster,pair_plot_funct
rot_elev=[(76,30),(103,24),(113,24),(46,24),(145,12)]
sns.set_style("white")
for i in np.arange(2,7):
kmean = sklearn.cluster.KMeans(i)
pred = kmean.fit_predict(X_used)
fig1 = plt.figure()
ax1 = fig1.add_subplot(111,projection='3d')
three_d_cluster(X_used[:,-3:],rotation_angle=rot_elev[i-2][0],
elevation=rot_elev[i-2][1],predictions=pred,type_marker="numeric",
fig_to_continue =ax1)
plt.savefig(images+"/presentation/"+data_info+"_"+str(i)+"_clusters3d.png")
plt.close()
# pairs plot:
pairplotsX = pd.concat([pd.DataFrame(X_used),pd.DataFrame(pred)],axis=1)
pairplotsX.columns = ["x"+str(i) for i in range(X_used.shape[-1])]+["prediction"]
pair_plot_funct(pairplotsX,save=False)
plt.savefig(images+"/presentation/"+data_info+"_"+str(i)+"_clusters_pairs.png")
plt.close()
