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# daal4py K-Means example for shared memory systems

import daal4py as d4p
import numpy as np

# let's try to use pandas' fast csv reader
try:
    import pandas
    read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main(readcsv=read_csv, method='defaultDense'):
    infile = "./data/batch/kmeans_dense.csv"
    nClusters = 20
    maxIter = 5

    initrain_algo = d4p.kmeans_init(nClusters, method="randomDense")
    # Load the data
    data = readcsv(infile, range(20))
    # compute initial centroids
    initrain_result = initrain_algo.compute(data)
    # The results provides the initial centroids
    assert initrain_result.centroids.shape[0] == nClusters

    # configure kmeans main object: we also request the cluster assignments
    algo = d4p.kmeans(nClusters, maxIter, assignFlag=True)
    # compute the clusters/centroids
    result = algo.compute(data, initrain_result.centroids)
    
    # Note: we could have done this in just one line:
    # d4p.kmeans(nClusters, maxIter, assignFlag=True).compute(data, d4p.kmeans_init(nClusters, method="plusPlusDense").compute(data).centroids)

    # Kmeans result objects provide assignments (if requested), centroids, goalFunction, nIterations and objectiveFunction
    assert result.centroids.shape[0] == nClusters
    assert result.assignments.shape == (data.shape[0], 1)
    assert result.nIterations <= maxIter

    return result


if __name__ == "__main__":
    result = main()
    print("\nFirst 10 cluster assignments:\n", result.assignments[0:10])
    print("\nFirst 10 dimensions of centroids:\n", result.centroids[:,0:10])
    print("\nObjective function value:\n", result.objectiveFunction)
    print('All looks good!')
