<commit_before>from _learning import *
import numpy
import struct
from opengm import index_type,value_type, label_type
from opengm import configuration as opengmConfig, LUnaryFunction

DatasetWithHammingLoss.lossType = 'hamming'
DatasetWithGeneralizedHammingLoss.lossType = 'generalized-hamming'




def _extendedLearn(self, infCls, parameter = None):
    if parameter is None:
        import opengm
        parameter = opengm.InfParam()
    cppParam  =  infCls.get_cpp_parameter(operator='adder',accumulator='minimizer',parameter=parameter)
    self._learn(cppParam)

GridSearch_HammingLoss.learn  =_extendedLearn
GridSearch_GeneralizedHammingLoss.learn  =_extendedLearn

if opengmConfig.withCplex or opengmConfig.withGurobi :
    StructMaxMargin_Bundle_HammingLoss.learn = _extendedLearn
    StructMaxMargin_Bundle_GeneralizedHammingLoss = _extendedLearn
        
def createDataset(numWeights, loss='hamming', numInstances=0):
    weightVals = numpy.ones(numWeights)
    weights = Weights(weightVals)

    if loss not in ['hamming','h','gh','generalized-hamming']:
        raise RuntimeError("loss must be 'hamming' /'h' or 'generalized-hamming'/'gh' ")    <commit_msg>changes<commit_after>from _learning import *
import numpy
import struct
from opengm import index_type,value_type, label_type
from opengm import configuration as opengmConfig, LUnaryFunction

DatasetWithHammingLoss.lossType = 'hamming'
DatasetWithGeneralizedHammingLoss.lossType = 'generalized-hamming'




def _extendedLearn(self, infCls, parameter = None):
    if parameter is None:
        import opengm
        parameter = opengm.InfParam()
    cppParam  =  infCls.get_cpp_parameter(operator='adder',accumulator='minimizer',parameter=parameter)
    self._learn(cppParam)

GridSearch_HammingLoss.learn  =_extendedLearn
GridSearch_GeneralizedHammingLoss.learn  =_extendedLearn

if opengmConfig.withCplex or opengmConfig.withGurobi :
    StructMaxMargin_Bundle_HammingLoss.learn = _extendedLearn
    StructMaxMargin_Bundle_GeneralizedHammingLoss.learn = _extendedLearn
        
def createDataset(numWeights, loss='hamming', numInstances=0):
    weightVals = numpy.ones(numWeights)
    weights = Weights(weightVals)

    if loss not in ['hamming','h','gh','generalized-hamming']:
        raise RuntimeError("loss must be 'hamming' /'h' or 'generalized-hamming'/'gh' ")    
{'fraction_non_alphanumeric': 0.08497011101622545, 'fraction_numerical': 0.0008539709649871904, 'mean_word_length': 5.472375690607735, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1079494', 'n_tokens_mistral': 779, 'n_tokens_neox': 720, 'n_words': 144}