examples/daal4py/em_gmm.py

# ==============================================================================
# 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
#
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# 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
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# ==============================================================================

# daal4py em_gmm example for shared memory systems

from pathlib import Path

import numpy as np
from readcsv import pd_read_csv

import daal4py as d4p


def main(readcsv=pd_read_csv):
    nComponents = 2
    data_path = Path(__file__).parent / "data" / "batch"
    infile = data_path / "em_gmm.csv"
    # We load the data
    data = readcsv(infile)

    # configure a em_gmm init object
    algo1 = d4p.em_gmm_init(nComponents)
    # and compute initial model
    result1 = algo1.compute(data)

    # configure a em_gmm object
    algo2 = d4p.em_gmm(nComponents)

    # and compute em_gmm using initial weights and means
    result2 = algo2.compute(data, result1.weights, result1.means, result1.covariances)

    # implicit als prediction result objects provide covariances,
    # goalFunction, means, nIterations and weights
    return result2


if __name__ == "__main__":
    res = main()
    print("Weights:\n", res.weights)
    print("Means:\n", res.means)
    for c in res.covariances:
        print("Covariance:\n", c)
    print("All looks good!")