Cluster-Labels.py

def infer_cluster_labels(kmeans, actual_labels):
  inferred_labels = {}

for i in range(kmeans.n_clusters):

    # find index of points in cluster
    labels = []
    index = np.where(kmeans.labels_ == i)

    # append actual labels for each point in cluster
    labels.append(actual_labels[index])

    # determine most common label
    if len(labels[0]) == 1:
        counts = np.bincount(labels[0])
    else:
        counts = np.bincount(np.squeeze(labels))

    # assign the cluster to a value in the inferred_labels dictionary
    if np.argmax(counts) in inferred_labels:
        # append the new number to the existing array at this slot
        inferred_labels[np.argmax(counts)].append(i)
    else:
        # create a new array in this slot
        inferred_labels[np.argmax(counts)] = [i]

    #print(labels)
    #print('Cluster: {}, label: {}'.format(i, np.argmax(counts)))
    
return inferred_labels

def infer_data_labels(X_labels, cluster_labels):
  # empty array of len(X)
predicted_labels = np.zeros(len(X_labels)).astype(np.uint8)

for i, cluster in enumerate(X_labels):
    for key, value in cluster_labels.items():
        if cluster in value:
            predicted_labels[i] = key
            
return predicted_labels

# test the infer_cluster_labels() and infer_data_labels() functions

cluster_labels = infer_cluster_labels(kmeans, Y)
X_clusters = kmeans.predict(X)
predicted_labels = infer_data_labels(X_clusters, cluster_labels)
print predicted_labels[:20]
print Y[:20]


  

When I try to run this, it says "actual labels is not defined"

@mxa256 Refer this -> https://github.com/xoraus/K-Means-Clustering-for-Imagery-Analysis/blob/master/KMeans%20Clustering%20for%20Imagery%20Analysis%20(Jupyter%20Notebook).ipynb

Thanks--When I try to run the function "infer_cluster_labels" to test it out on line 45, I get the following error: "KeyError: 'key of type tuple not found and not a MultiIndex'"