22 import daal.algorithms.kmeans
as kmeans
23 import daal.algorithms.kmeans.init
as init
24 from daal.data_management
import FileDataSource, DataSourceIface
25 from daal.services
import Environment
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder
not in sys.path:
29 sys.path.insert(0, utils_folder)
30 from utils
import printNumericTable
32 datasetFileName = os.path.join(
'..',
'data',
'batch',
'kmeans_dense.csv')
41 if __name__ ==
"__main__":
44 dataSource = FileDataSource(
45 datasetFileName, DataSourceIface.doAllocateNumericTable,
46 DataSourceIface.doDictionaryFromContext
50 dataSource.loadDataBlock()
53 initAlg = kmeans.init.Batch(nClusters)
55 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
58 Environment.getInstance().enableThreadPinning(
True)
60 res = initAlg.compute()
63 Environment.getInstance().enableThreadPinning(
False)
65 centroids = res.get(kmeans.init.centroids)
68 algorithm = kmeans.Batch(nClusters, nIterations)
70 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
71 algorithm.input.set(kmeans.inputCentroids, centroids)
74 unused_result = algorithm.compute()
76 printNumericTable(unused_result.get(kmeans.assignments),
"First 10 cluster assignments:", 10);
77 printNumericTable(unused_result.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10);
78 printNumericTable(unused_result.get(kmeans.objectiveFunction),
"Objective function value:");