



"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX






def        snake_case_  (      A_					:		list,				A_					:		list,				A_					:		list,				A_					:		list,				A_					:		list						):


       '''simple docstring'''





       _lowerCamelCase						:				Any    			= np.array([[1, item, train_mtch[i]] for i, item in enumerate(A_						)]						)
       _lowerCamelCase						:				Optional[int]    			= np.array(A_						)
       _lowerCamelCase						:				List[str]    			= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(),				A_						)						),				x.transpose()						),				A_						)
       return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]						)






def        snake_case_  (      A_					:		list,				A_					:		list,				A_					:		list						):


       '''simple docstring'''





       _lowerCamelCase						:				List[str]    			= (1, 2, 1)
       _lowerCamelCase						:				Any    			= (1, 1, 0, 7)
       _lowerCamelCase						:				int    			= SARIMAX(
           A_,				exog=A_,				order=A_,				seasonal_order=A_						)
       _lowerCamelCase						:				Optional[int]    			= model.fit(disp=A_,				maxiter=6_00,				method='''nm'''						)
       _lowerCamelCase						:				Any    			= model_fit.predict(1,				len(A_						),				exog=[test_match]						)
       return result[0]






def        snake_case_  (      A_					:		list,				A_					:		list,				A_					:		list						):


       '''simple docstring'''





       _lowerCamelCase						:				Any    			= SVR(kernel='''rbf''',				C=1,				gamma=0.1,				epsilon=0.1						)
       regressor.fit(A_,				A_						)
       _lowerCamelCase						:				Optional[Any]    			= regressor.predict(A_						)
       return y_pred[0]






def        snake_case_  (      A_					:		list						):


       '''simple docstring'''





       train_user.sort()
       _lowerCamelCase						:				Dict    			= np.percentile(A_,				25						)
       _lowerCamelCase						:				Optional[int]    			= np.percentile(A_,				75						)
       _lowerCamelCase						:				Dict    			= qa - qa
       _lowerCamelCase						:				Tuple    			= qa - (iqr * 0.1)
       return low_lim






def        snake_case_  (      A_					:		list,				A_					:		float						):


       '''simple docstring'''





       _lowerCamelCase						:				Any    			= 0
       _lowerCamelCase						:				Dict    			= 0
       for i in list_vote:
              if i > actual_result:
                     _lowerCamelCase						:				Optional[Any]    			= not_safe + 1
              else:
                     if abs(abs(A_						) - abs(A_						)						) <= 0.1:
                            safe += 1
                     else:
                            not_safe += 1
       return safe > not_safe


if __name__ == "__main__":
   # data_input_df = pd.read_csv("ex_data.csv", header=None)
   lowerCAmelCase__        =				[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
   lowerCAmelCase__        =				pd.DataFrame(
       data_input, columns=['''total_user''', '''total_even''', '''days''']
   )
   lowerCAmelCase__        =				Normalizer().fit_transform(data_input_df.values)
   # split data
   lowerCAmelCase__        =				normalize_df[:, 2].tolist()
   lowerCAmelCase__        =				normalize_df[:, 0].tolist()
   lowerCAmelCase__        =				normalize_df[:, 1].tolist()

   # for svr (input variable = total date and total match)
   lowerCAmelCase__        =				normalize_df[:, [1, 2]].tolist()
   lowerCAmelCase__        =				x[: len(x) - 1]
   lowerCAmelCase__        =				x[len(x) - 1 :]

   # for linear regression & sarimax
   lowerCAmelCase__        =				total_date[: len(total_date) - 1]
   lowerCAmelCase__        =				total_user[: len(total_user) - 1]
   lowerCAmelCase__        =				total_match[: len(total_match) - 1]

   lowerCAmelCase__        =				total_date[len(total_date) - 1 :]
   lowerCAmelCase__        =				total_user[len(total_user) - 1 :]
   lowerCAmelCase__        =				total_match[len(total_match) - 1 :]

   # voting system with forecasting
   lowerCAmelCase__        =				[
       linear_regression_prediction(
           trn_date, trn_user, trn_match, tst_date, tst_match
       ),
       sarimax_predictor(trn_user, trn_match, tst_match),
       support_vector_regressor(x_train, x_test, trn_user),
   ]

   # check the safety of today's data
   lowerCAmelCase__        =				'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
   print('''Today\'s data is {not_str}safe.''')




from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
