48 import daal.algorithms.kmeans.init
49 from daal.algorithms
import kmeans
50 from daal.data_management
import FileDataSource, DataSourceIface
52 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
53 if utils_folder
not in sys.path:
54 sys.path.insert(0, utils_folder)
55 from utils
import printNumericTable
57 DAAL_PREFIX = os.path.join(
'..',
'data')
60 datasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'kmeans_dense.csv')
66 if __name__ ==
"__main__":
69 dataSource = FileDataSource(
71 DataSourceIface.doAllocateNumericTable,
72 DataSourceIface.doDictionaryFromContext
76 dataSource.loadDataBlock()
79 initAlg = kmeans.init.Batch(nClusters, method=kmeans.init.randomDense)
81 initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
83 res = initAlg.compute()
84 centroidsResult = res.get(kmeans.init.centroids)
87 algorithm = kmeans.Batch(nClusters, nIterations, method=kmeans.lloydDense)
89 algorithm.input.set(kmeans.data, dataSource.getNumericTable())
90 algorithm.input.set(kmeans.inputCentroids, centroidsResult)
92 res = algorithm.compute()
95 printNumericTable(res.get(kmeans.assignments),
"First 10 cluster assignments:", 10)
96 printNumericTable(res.get(kmeans.centroids),
"First 10 dimensions of centroids:", 20, 10)
97 printNumericTable(res.get(kmeans.objectiveFunction),
"Objective function value:")