|
37 | 37 | heterogeneity, |
38 | 38 | k |
39 | 39 | ) |
40 | | - 5. Transfers Dataframe into excel format it must have feature called |
| 40 | + 5. Plot the labeled 3D data points with centroids. |
| 41 | + plot_kmeans( |
| 42 | + X, |
| 43 | + centroids, |
| 44 | + cluster_assignment |
| 45 | + ) |
| 46 | + 6. Transfers Dataframe into excel format it must have feature called |
41 | 47 | 'Clust' with k means clustering numbers in it. |
42 | 48 | """ |
43 | 49 |
|
@@ -126,6 +132,19 @@ def plot_heterogeneity(heterogeneity, k): |
126 | 132 | plt.show() |
127 | 133 |
|
128 | 134 |
|
| 135 | +defplot_kmeans(data,centroids,cluster_assignment): |
| 136 | +ax=plt.axes(projection="3d") |
| 137 | +ax.scatter(data[:,0],data[:,1],data[:,2],c=cluster_assignment,cmap="viridis") |
| 138 | +ax.scatter( |
| 139 | +centroids[:,0],centroids[:,1],centroids[:,2],c="red",s=100,marker="x" |
| 140 | + ) |
| 141 | +ax.set_xlabel("X") |
| 142 | +ax.set_ylabel("Y") |
| 143 | +ax.set_zlabel("Z") |
| 144 | +ax.set_title("3D K-Means Clustering Visualization") |
| 145 | +plt.show() |
| 146 | + |
| 147 | + |
129 | 148 | defkmeans( |
130 | 149 | data,k,initial_centroids,maxiter=500,record_heterogeneity=None,verbose=False |
131 | 150 | ): |
@@ -193,6 +212,7 @@ def kmeans( |
193 | 212 | verbose=True, |
194 | 213 | ) |
195 | 214 | plot_heterogeneity(heterogeneity,k) |
| 215 | +plot_kmeans(dataset["data"],centroids,cluster_assignment) |
196 | 216 |
|
197 | 217 |
|
198 | 218 | defreport_generator( |
|