#===============================================================================
# Copyright 2014 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================

# 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

    def read_csv(f, c, t=np.float64):
        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
except ImportError:
    # fall back to numpy loadtxt
    def read_csv(f, c, t=np.float64):
        return 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!')
