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lorenzo30salgado 2024-11-17 10:04:40 +01:00 committed by GitHub
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machine_learning/k_means_clust.py
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@ -37,7 +37,13 @@ Usage:
heterogeneity,
k
)
5. Transfers Dataframe into excel format it must have feature called
5. 3D Plot of the labeled data points with centroids.
plot_kmeans(
X,
centroids,
cluster_assignment
)
6. Transfers Dataframe into excel format it must have feature called
'Clust' with k means clustering numbers in it.
"""
@ -126,6 +132,19 @@ def plot_heterogeneity(heterogeneity, k):
plt.show()
def plot_kmeans(data, centroids, cluster_assignment):
ax = plt.axes(projection="3d")
ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=cluster_assignment, cmap="viridis")
ax.scatter(
centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
ax.set_title("3D K-Means Clustering Visualization")
plt.show()
def kmeans(
data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
@ -193,6 +212,7 @@ if False: # change to true to run this test case.
verbose=True,
)
plot_heterogeneity(heterogeneity, k)
plot_kmeans(dataset["data"], centroids, cluster_assignment)
def report_generator(