K-均值聚类映射

Question

我正在使用sklearn的K-Means聚类，并希望使用经过训练的K-Means模型将计算的K-Means聚类标签替换为质心值。

我使用的代码如下：

# Initialize K-Means clustering model-
kmeans_conv1 = KMeans(n_clusters = 5)

# Train model on training data (compute k-means clustering)-
kmeans_conv1.fit(conv1_nonzero.reshape(-1, 1))

# number of clusters used-
kmeans_conv1.n_clusters
# 5

# Get centroids-
kmeans_conv1.cluster_centers_
'''
array([[-0.05669265],
       [ 0.06742188],
       [-0.08835593],
       [ 0.03749201],
       [ 0.0896403 ]], dtype=float32)
'''


# Clustered labels of each data point-
kmeans_conv1.labels_

set(kmeans_conv1.labels_)                                             
Out[142]: {0, 1, 2, 3, 4}

# Get clustered label for each data point-
clustered_labels = kmeans_conv1.labels_

目前，我使用if-else条件将标签映射到质心值，如下所示：

new_clusters = []


for clabel in clustered_labels:
    if clabel == 0:
        new_clusters.append(kmeans_conv1.cluster_centers_[0][0])
    elif clabel == 1:
        new_clusters.append(kmeans_conv1.cluster_centers_[1][0])
    elif clabel == 2:
        new_clusters.append(kmeans_conv1.cluster_centers_[2][0])
    elif clabel == 3:
        new_clusters.append(kmeans_conv1.cluster_centers_[3][0])
    elif clabel == 4:
        new_clusters.append(kmeans_conv1.cluster_centers_[4][0])

最后，我希望'new_clusters‘列表或np.array变量包含质心值，而不是集群标签。

然而，有没有更好的方法来实现这一点，而不使用if-else条件呢？

Answer 1

这就足够了：

for clabel in clustered_labels:
    new_clusters.append(
        kmeans_conv1.cluster_centers_[clabel][0]
    )

Answer 2

找到此方法：

# First conv layer condition-
cond_conv1 = [clustered_labels == 0, clustered_labels == 1, clustered_labels == 2, clustered_labels == 3, clustered_labels == 4]

# values-
val_conv1 = kmeans_conv1.cluster_centers_[:, 0]

# Get new clustered value weights-
new_weights_conv1 = np.select(cond_conv1, val_conv1)