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Preparing scripts for TP-Link plug.
[pingpong.git]
/
python_ml
/
silhouette.py
diff --git
a/python_ml/silhouette.py
b/python_ml/silhouette.py
index bf8c1eb3b037e6c8dc5bb6de1a96d03c81eb0148..3ddca71e76537b0cee7bff300216c76ac9d96aa5 100644
(file)
--- a/
python_ml/silhouette.py
+++ b/
python_ml/silhouette.py
@@
-27,21
+27,21
@@
range_n_clusters = [2, 3, 4, 5, 6]
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
-
#
fig, (ax1, ax2) = plt.subplots(1, 2)
-
#
fig.set_size_inches(18, 7)
+ fig, (ax1, ax2) = plt.subplots(1, 2)
+ fig.set_size_inches(18, 7)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
-
#
ax1.set_xlim([-0.1, 1])
+ ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
-
#
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
+ ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
-
# clusterer = KMeans(n_clusters=n_clusters, random_state=2
0)
-
#
cluster_labels = clusterer.fit_predict(X)
+
clusterer = KMeans(n_clusters=n_clusters, random_state=1
0)
+ cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
@@
-53,7
+53,7
@@
for n_clusters in range_n_clusters:
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
-
'''
y_lower = 10
+ y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them