_lambda = _lambda_tmp / eye_norm
lst_factors_ = op_factors.get_list_of_factors()
op_centroids = SparseFactors([lst_factors_[1] * _lambda] +
lst_factors_[2:])
return op_centroids
if __name__ == "__main__":
logger.info("Command line: " + " ".join(sys.argv))
log_memory_usage("Memory at startup")
arguments = docopt.docopt(__doc__)
paraman = ParameterManager(arguments)
initialized_results = dict((v, None) for v in lst_results_header)
resprinter = ResultPrinter(output_file=paraman["--output-file_resprinter"])
resprinter.add(initialized_results)
resprinter.add(paraman)
objprinter = ObjectiveFunctionPrinter(
output_file=paraman["--output-file_objprinter"])
has_failed = False
if paraman["--verbose"]:
daiquiri.setup(level=logging.DEBUG)
else:
daiquiri.setup(level=logging.INFO)
try:
dataset = paraman.get_dataset()
dataset["x_train"] = dataset["x_train"].astype(np.float)