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commit
46979a3b44
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@ -37,7 +37,13 @@ Usage:
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heterogeneity,
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heterogeneity,
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k
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k
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)
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)
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5. Transfers Dataframe into excel format it must have feature called
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5. 3D Plot of the labeled data points with centroids.
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plot_kmeans(
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X,
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centroids,
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cluster_assignment
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)
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6. Transfers Dataframe into excel format it must have feature called
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'Clust' with k means clustering numbers in it.
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'Clust' with k means clustering numbers in it.
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"""
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"""
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@ -126,6 +132,19 @@ def plot_heterogeneity(heterogeneity, k):
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plt.show()
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plt.show()
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def plot_kmeans(data, centroids, cluster_assignment):
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ax = plt.axes(projection="3d")
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ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=cluster_assignment, cmap="viridis")
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ax.scatter(
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centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
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)
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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ax.set_zlabel("Z")
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ax.set_title("3D K-Means Clustering Visualization")
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plt.show()
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def kmeans(
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def kmeans(
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data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
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data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
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):
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):
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@ -193,6 +212,7 @@ if False: # change to true to run this test case.
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verbose=True,
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verbose=True,
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)
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)
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plot_heterogeneity(heterogeneity, k)
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plot_heterogeneity(heterogeneity, k)
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plot_kmeans(dataset["data"], centroids, cluster_assignment)
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def report_generator(
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def report_generator(
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