48 import daal.algorithms.kmeans
as kmeans
49 import daal.algorithms.kmeans.init
as init
50 from daal.data_management
import FileDataSource, DataSourceIface
51 from daal.services
import Environment
53 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
54 if utils_folder
not in sys.path:
55 sys.path.insert(0, utils_folder)
56 from utils
import printNumericTable
58 datasetFileName = os.path.join(
'..',
'data',
'batch',
'kmeans_dense.csv')
67 if __name__ ==
"__main__":
70 dataSource = FileDataSource(
71 datasetFileName, DataSourceIface.doAllocateNumericTable,
72 DataSourceIface.doDictionaryFromContext
76 dataSource.loadDataBlock()
79 initAlg = kmeans.init.Batch(nClusters)
81 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
84 Environment.getInstance().enableThreadPinning(
True)
86 res = initAlg.compute()
89 Environment.getInstance().enableThreadPinning(
False)
91 centroids = res.get(kmeans.init.centroids)
94 algorithm = kmeans.Batch(nClusters, nIterations)
96 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
97 algorithm.input.set(kmeans.inputCentroids, centroids)
100 unused_result = algorithm.compute()
102 printNumericTable(unused_result.get(kmeans.assignments),
"First 10 cluster assignments:", 10);
103 printNumericTable(unused_result.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10);
104 printNumericTable(unused_result.get(kmeans.objectiveFunction),
"Objective function value:");