22 import daal.algorithms.kmeans.init
23 from daal.algorithms
import kmeans
24 from daal.data_management
import FileDataSource, DataSourceIface
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder
not in sys.path:
28 sys.path.insert(0, utils_folder)
29 from utils
import printNumericTable
31 DAAL_PREFIX = os.path.join(
'..',
'data')
34 datasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'kmeans_dense.csv')
40 if __name__ ==
"__main__":
43 dataSource = FileDataSource(
45 DataSourceIface.doAllocateNumericTable,
46 DataSourceIface.doDictionaryFromContext
50 dataSource.loadDataBlock()
53 initAlg = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)
55 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
57 res = initAlg.compute()
58 centroidsResult = res.get(kmeans.init.centroids)
61 algorithm = kmeans.Batch(nClusters, nIterations, method=kmeans.lloydDense)
63 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
64 algorithm.input.set(kmeans.inputCentroids, centroidsResult)
66 res = algorithm.compute()
69 printNumericTable(res.get(kmeans.assignments),
"First 10 cluster assignments:", 10)
70 printNumericTable(res.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10)
71 printNumericTable(res.get(kmeans.objectiveFunction),
"Objective function value:")