# Copyright (c) Microsoft Corporation
# All rights reserved.
#
# MIT License
#
# Permission is hereby granted, free of charge,
# to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and
# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.

import os
import sys
from operator import itemgetter

import sklearn.mixture as mm

sys.path.insert(1, os.path.join(sys.path[0], '..'))


def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    '''
    Create the Gaussian Mixture Model
    '''
    samples = [samples_x[i] + [samples_y_aggregation[i]]
               for i in range(0, len(samples_x))]

    # Sorts so that we can get the top samples
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]

    samples_x_goodbatch = [sample_goodbatch[0:-1]
                           for sample_goodbatch in samples_goodbatch]
    #samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1]
                          for sample_badbatch in samples_badbatch]

    # === Trains GMM clustering models === #
    #sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
    bgmm_goodbatch = mm.BayesianGaussianMixture(
        n_components=max(1, samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)

    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model

dd3229862c4331edb1001c91af743114e0d4b4d2
<mask token>

<mask token>


def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    """
    Create the Gaussian Mixture Model
    """
    samples = [(samples_x[i] + [samples_y_aggregation[i]]) for i in range(0,
        len(samples_x))]
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]
    samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in
        samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in
        samples_badbatch]
    bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, 
        samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)
    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model

<mask token>
sys.path.insert(1, os.path.join(sys.path[0], '..'))


def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    """
    Create the Gaussian Mixture Model
    """
    samples = [(samples_x[i] + [samples_y_aggregation[i]]) for i in range(0,
        len(samples_x))]
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]
    samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in
        samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in
        samples_badbatch]
    bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, 
        samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)
    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model

import os
import sys
from operator import itemgetter
import sklearn.mixture as mm
sys.path.insert(1, os.path.join(sys.path[0], '..'))


def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    """
    Create the Gaussian Mixture Model
    """
    samples = [(samples_x[i] + [samples_y_aggregation[i]]) for i in range(0,
        len(samples_x))]
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]
    samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in
        samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in
        samples_badbatch]
    bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, 
        samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)
    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model

# Copyright (c) Microsoft Corporation
# All rights reserved.
#
# MIT License
#
# Permission is hereby granted, free of charge,
# to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and
# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.

import os
import sys
from operator import itemgetter

import sklearn.mixture as mm

sys.path.insert(1, os.path.join(sys.path[0], '..'))


def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    '''
    Create the Gaussian Mixture Model
    '''
    samples = [samples_x[i] + [samples_y_aggregation[i]]
               for i in range(0, len(samples_x))]

    # Sorts so that we can get the top samples
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]

    samples_x_goodbatch = [sample_goodbatch[0:-1]
                           for sample_goodbatch in samples_goodbatch]
    #samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1]
                          for sample_badbatch in samples_badbatch]

    # === Trains GMM clustering models === #
    #sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
    bgmm_goodbatch = mm.BayesianGaussianMixture(
        n_components=max(1, samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)

    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model
