24 import daal.algorithms.kmeans
as kmeans
25 import daal.algorithms.kmeans.init
as init
26 from daal.data_management
import FileDataSource, DataSourceIface
27 from daal.services
import Environment
29 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
30 if utils_folder
not in sys.path:
31 sys.path.insert(0, utils_folder)
32 from utils
import printNumericTable
34 datasetFileName = os.path.join(
'..',
'data',
'batch',
'kmeans_dense.csv')
43 if __name__ ==
"__main__":
46 dataSource = FileDataSource(
47 datasetFileName, DataSourceIface.doAllocateNumericTable,
48 DataSourceIface.doDictionaryFromContext
52 dataSource.loadDataBlock()
55 initAlg = kmeans.init.Batch(nClusters)
57 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
60 Environment.getInstance().enableThreadPinning(
True)
62 res = initAlg.compute()
65 Environment.getInstance().enableThreadPinning(
False)
67 centroids = res.get(kmeans.init.centroids)
70 algorithm = kmeans.Batch(nClusters, nIterations)
72 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
73 algorithm.input.set(kmeans.inputCentroids, centroids)
76 unused_result = algorithm.compute()
78 printNumericTable(unused_result.get(kmeans.assignments),
"First 10 cluster assignments:", 10);
79 printNumericTable(unused_result.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10);
80 printNumericTable(unused_result.get(kmeans.objectiveFunction),
"Objective function value:");