text: #Libraries to include; you can add more libraries to extend beyond the
  # functionality in the tutorial
  import numpy as np
  import geneMLLib as ml #our custom library
  from sklearn import metrics
  from sklearn.cluster import KMeans
  
  def main():
      #load data, X is gene values, genes is names of genes, y are the labels
      dir = "../data/sample-yeast/"
      X = ml.loadGeneExpression(dir+'expression.csv')
      geneNames = ml.loadGeneNames(dir+'names.txt')
  
      k = 5
  
      model = KMeans(n_clusters=k)
      model.fit(X)
      clusterIndex = model.predict(X)
      print(metrics.silhouette_score(X,clusterIndex))
      for i in range(k):
          print("Cluster %d" % i)
          for name in geneNames[clusterIndex == i]:
              print("\t"+name)
          print()
  
  main()
  