24 import daal.algorithms.kmeans.init
25 from daal.algorithms
import kmeans
26 from daal.data_management
import FileDataSource, DataSourceIface
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder
not in sys.path:
30 sys.path.insert(0, utils_folder)
31 from utils
import printNumericTable
33 DAAL_PREFIX = os.path.join(
'..',
'data')
36 datasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'kmeans_dense.csv')
42 if __name__ ==
"__main__":
45 dataSource = FileDataSource(
47 DataSourceIface.doAllocateNumericTable,
48 DataSourceIface.doDictionaryFromContext
52 dataSource.loadDataBlock()
55 initAlg = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)
57 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
59 res = initAlg.compute()
60 centroidsResult = res.get(kmeans.init.centroids)
63 algorithm = kmeans.Batch(nClusters, nIterations, method=kmeans.lloydDense)
65 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
66 algorithm.input.set(kmeans.inputCentroids, centroidsResult)
68 res = algorithm.compute()
71 printNumericTable(res.get(kmeans.assignments),
"First 10 cluster assignments:", 10)
72 printNumericTable(res.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10)
73 printNumericTable(res.get(kmeans.objectiveFunction),
"Objective function value:")