

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       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list      )  ->						float:
  UpperCAmelCase	:							Optional[Any]						=			np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase      )]      )
  UpperCAmelCase	:							Optional[int]						=			np.array(UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()    ,			UpperCAmelCase      )      )    ,			x.transpose()      )    ,			UpperCAmelCase      )
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )

def       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list      )  ->						float:
  UpperCAmelCase	:							Dict						=			(1, 2, 1)
  UpperCAmelCase	:							List[Any]						=			(1, 1, 0, 7)
  UpperCAmelCase	:							str						=			SARIMAX(
      UpperCAmelCase    ,			exog=UpperCAmelCase    ,			order=UpperCAmelCase    ,			seasonal_order=UpperCAmelCase      )
  UpperCAmelCase	:							List[Any]						=			model.fit(disp=UpperCAmelCase    ,			maxiter=600    ,			method='''nm'''      )
  UpperCAmelCase	:							Optional[int]						=			model_fit.predict(1    ,			len(UpperCAmelCase      )    ,			exog=[test_match]      )
  return result[0]

def       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list      )  ->						float:
  UpperCAmelCase	:							Optional[int]						=			SVR(kernel='''rbf'''    ,			C=1    ,			gamma=0.1    ,			epsilon=0.1      )
  regressor.fit(UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Union[str, Any]						=			regressor.predict(UpperCAmelCase      )
  return y_pred[0]

def       a__					(		UpperCAmelCase							:      list      )  ->						float:
  train_user.sort()
  UpperCAmelCase	:							List[str]						=			np.percentile(UpperCAmelCase    ,			25      )
  UpperCAmelCase	:							int						=			np.percentile(UpperCAmelCase    ,			75      )
  UpperCAmelCase	:							Optional[int]						=			qa - qa
  UpperCAmelCase	:							int						=			qa - (iqr * 0.1)
  return low_lim

def       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      float      )  ->						bool:
  UpperCAmelCase	:							Optional[Any]						=			0
  UpperCAmelCase	:							Optional[int]						=			0
  for i in list_vote:
    if i > actual_result:
      UpperCAmelCase	:							Union[str, Any]						=			not_safe + 1
    else:
      if abs(abs(UpperCAmelCase      ) - abs(UpperCAmelCase      )      ) <= 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    :       int        =							[[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
      _lowerCamelCase    :       Optional[int]        =							pd.DataFrame(
          data_input, columns=["total_user", "total_even", "days"]
      )
      _lowerCamelCase    :       Optional[Any]        =							Normalizer().fit_transform(data_input_df.values)
      # split data
      _lowerCamelCase    :       Optional[Any]        =							normalize_df[:, 2].tolist()
      _lowerCamelCase    :       Tuple        =							normalize_df[:, 0].tolist()
      _lowerCamelCase    :       Tuple        =							normalize_df[:, 1].tolist()

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

      # for linear regression & sarimax
      _lowerCamelCase    :       int        =							total_date[: len(total_date) - 1]
      _lowerCamelCase    :       Dict        =							total_user[: len(total_user) - 1]
      _lowerCamelCase    :       Tuple        =							total_match[: len(total_match) - 1]

      _lowerCamelCase    :       Optional[int]        =							total_date[len(total_date) - 1 :]
      _lowerCamelCase    :       List[Any]        =							total_user[len(total_user) - 1 :]
      _lowerCamelCase    :       Any        =							total_match[len(total_match) - 1 :]

      # voting system with forecasting
      _lowerCamelCase    :       Union[str, Any]        =							[
          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    :       str        =							"" if data_safety_checker(res_vote, tst_user) else "not "
      print("Today's data is {not_str}safe.")








import torch
from torch import nn





class  __UpperCAmelCase					(	nn.Module						):



   def __init__(							self							:					List[Any],		__A							:					List[Any],		__A							:					Optional[Any],		__A							:					int,		__A							:					List[Any],		__A							:					int=1,		__A							:					List[str]=False						):
     super().__init__()

     UpperCAmelCase	:							Union[str, Any]						=			n_token
     UpperCAmelCase	:							List[str]						=			d_embed
     UpperCAmelCase	:							Dict						=			d_proj

     UpperCAmelCase	:							List[Any]						=			cutoffs + [n_token]
     UpperCAmelCase	:							Dict						=			[0] + self.cutoffs
     UpperCAmelCase	:							int						=			div_val

     UpperCAmelCase	:							Union[str, Any]						=			self.cutoffs[0]
     UpperCAmelCase	:							str						=			len(self.cutoffs						) - 1
     UpperCAmelCase	:							Optional[int]						=			self.shortlist_size + self.n_clusters

     if self.n_clusters > 0:
       UpperCAmelCase	:							str						=			nn.Parameter(torch.zeros(self.n_clusters,		self.d_embed						)						)
       UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.zeros(self.n_clusters						)						)

     UpperCAmelCase	:							Dict						=			nn.ModuleList()
     UpperCAmelCase	:							Optional[int]						=			nn.ParameterList()

     if div_val == 1:
       for i in range(len(self.cutoffs						)						):
         if d_proj != d_embed:
           self.out_projs.append(nn.Parameter(torch.FloatTensor(__A,		__A						)						)						)
         else:
           self.out_projs.append(__A						)

       self.out_layers.append(nn.Linear(__A,		__A						)						)
     else:
       for i in range(len(self.cutoffs						)						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
         UpperCAmelCase	:							str						=			d_embed // (div_val**i)

         self.out_projs.append(nn.Parameter(torch.FloatTensor(__A,		__A						)						)						)

         self.out_layers.append(nn.Linear(__A,		r_idx - l_idx						)						)

     UpperCAmelCase	:							Optional[int]						=			keep_order



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[str],		__A							:					Any,		__A							:					Dict,		__A							:					Optional[Any]						):
     if proj is None:
       UpperCAmelCase	:							List[Any]						=			nn.functional.linear(__A,		__A,		bias=__A						)
     else:
       # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
       UpperCAmelCase	:							Union[str, Any]						=			nn.functional.linear(__A,		proj.t().contiguous()						)
       UpperCAmelCase	:							Optional[int]						=			nn.functional.linear(__A,		__A,		bias=__A						)
       # else:
       #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
       #     if bias is not None:
       #         logit = logit + bias

     return logit



   def 	__magic_name__   (							self							:					int,		__A							:					int,		__A							:					List[Any]=None,		__A							:					Dict=False						):

     if labels is not None:
       # Shift so that tokens < n predict n
       UpperCAmelCase	:							List[Any]						=			hidden[..., :-1, :].contiguous()
       UpperCAmelCase	:							Any						=			labels[..., 1:].contiguous()
       UpperCAmelCase	:							Optional[Any]						=			hidden.view(-1,		hidden.size(-1						)						)
       UpperCAmelCase	:							Union[str, Any]						=			labels.view(-1						)
       if hidden.size(0						) != labels.size(0						):
         raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''						)
     else:
       UpperCAmelCase	:							str						=			hidden.view(-1,		hidden.size(-1						)						)

     if self.n_clusters == 0:
       UpperCAmelCase	:							List[str]						=			self._compute_logit(__A,		self.out_layers[0].weight,		self.out_layers[0].bias,		self.out_projs[0]						)
       if labels is not None:
         UpperCAmelCase	:							Optional[int]						=			labels != -1_0_0
         UpperCAmelCase	:							Dict						=			torch.zeros_like(__A,		dtype=hidden.dtype,		device=hidden.device						)
         UpperCAmelCase	:							Any						=			(
             -nn.functional.log_softmax(__A,		dim=-1						)[mask].gather(1,		labels[mask].unsqueeze(1						)						).squeeze(1						)
         )
       else:
         UpperCAmelCase	:							Any						=			nn.functional.log_softmax(__A,		dim=-1						)
     else:
       # construct weights and biases
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			[], []
       for i in range(len(self.cutoffs						)						):
         if self.div_val == 1:
           UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCAmelCase	:							List[str]						=			self.out_layers[0].weight[l_idx:r_idx]
           UpperCAmelCase	:							Optional[int]						=			self.out_layers[0].bias[l_idx:r_idx]
         else:
           UpperCAmelCase	:							List[Any]						=			self.out_layers[i].weight
           UpperCAmelCase	:							Dict						=			self.out_layers[i].bias

         if i == 0:
           UpperCAmelCase	:							List[str]						=			torch.cat([weight_i, self.cluster_weight],		dim=0						)
           UpperCAmelCase	:							List[Any]						=			torch.cat([bias_i, self.cluster_bias],		dim=0						)

         weights.append(__A						)
         biases.append(__A						)

       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			weights[0], biases[0], self.out_projs[0]

       UpperCAmelCase	:							Dict						=			self._compute_logit(__A,		__A,		__A,		__A						)
       UpperCAmelCase	:							int						=			nn.functional.log_softmax(__A,		dim=1						)

       if labels is None:
         UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       else:
         UpperCAmelCase	:							Union[str, Any]						=			torch.zeros_like(__A,		dtype=hidden.dtype,		device=hidden.device						)

       UpperCAmelCase	:							Union[str, Any]						=			0
       UpperCAmelCase	:							Any						=			[0] + self.cutoffs
       for i in range(len(__A						) - 1						):
         UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			cutoff_values[i], cutoff_values[i + 1]

         if labels is not None:
           UpperCAmelCase	:							List[str]						=			(labels >= l_idx) & (labels < r_idx)
           UpperCAmelCase	:							Tuple						=			mask_i.nonzero().squeeze()

           if indices_i.numel() == 0:
             continue

           UpperCAmelCase	:							Any						=			labels.index_select(0,		__A						) - l_idx
           UpperCAmelCase	:							Dict						=			head_logprob.index_select(0,		__A						)
           UpperCAmelCase	:							List[str]						=			hidden.index_select(0,		__A						)
         else:
           UpperCAmelCase	:							Tuple						=			hidden

         if i == 0:
           if labels is not None:
             UpperCAmelCase	:							Union[str, Any]						=			head_logprob_i.gather(1,		target_i[:, None]						).squeeze(1						)
           else:
             UpperCAmelCase	:							Optional[int]						=			head_logprob[:, : self.cutoffs[0]]
         else:
           UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			weights[i], biases[i], self.out_projs[i]

           UpperCAmelCase	:							List[str]						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)
           UpperCAmelCase	:							int						=			self.cutoffs[0] + i - 1  # No probability for the head cluster
           if labels is not None:
             UpperCAmelCase	:							Union[str, Any]						=			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                 1,		target_i[:, None]						).squeeze(1						)
           else:
             UpperCAmelCase	:							int						=			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
             UpperCAmelCase	:							Optional[Any]						=			logprob_i

         if labels is not None:
           if (hasattr(self,		'''keep_order'''						) and self.keep_order) or keep_order:
             out.index_copy_(0,		__A,		-logprob_i						)
           else:
             out[offset : offset + logprob_i.size(0						)].copy_(-logprob_i						)
           offset += logprob_i.size(0						)

     return out





   def 	__magic_name__   (							self							:					Tuple,		__A							:					List[str]						):
     if self.n_clusters == 0:
       UpperCAmelCase	:							int						=			self._compute_logit(__A,		self.out_layers[0].weight,		self.out_layers[0].bias,		self.out_projs[0]						)
       return nn.functional.log_softmax(__A,		dim=-1						)
     else:
       # construct weights and biases
       UpperCAmelCase			,						UpperCAmelCase	:							Any						=			[], []
       for i in range(len(self.cutoffs						)						):
         if self.div_val == 1:
           UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCAmelCase	:							List[Any]						=			self.out_layers[0].weight[l_idx:r_idx]
           UpperCAmelCase	:							List[str]						=			self.out_layers[0].bias[l_idx:r_idx]
         else:
           UpperCAmelCase	:							List[str]						=			self.out_layers[i].weight
           UpperCAmelCase	:							Dict						=			self.out_layers[i].bias

         if i == 0:
           UpperCAmelCase	:							Dict						=			torch.cat([weight_i, self.cluster_weight],		dim=0						)
           UpperCAmelCase	:							str						=			torch.cat([bias_i, self.cluster_bias],		dim=0						)

         weights.append(__A						)
         biases.append(__A						)

       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			weights[0], biases[0], self.out_projs[0]
       UpperCAmelCase	:							int						=			self._compute_logit(__A,		__A,		__A,		__A						)

       UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)

       UpperCAmelCase	:							List[str]						=			[0] + self.cutoffs
       for i in range(len(__A						) - 1						):
         UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			cutoff_values[i], cutoff_values[i + 1]

         if i == 0:
           UpperCAmelCase	:							Any						=			head_logprob[:, : self.cutoffs[0]]
         else:
           UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			weights[i], biases[i], self.out_projs[i]

           UpperCAmelCase	:							Tuple						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							List[Any]						=			nn.functional.log_softmax(__A,		dim=1						)

           UpperCAmelCase	:							Optional[int]						=			head_logprob[:, -i] + tail_logprob_i
           UpperCAmelCase	:							Optional[Any]						=			logprob_i

       return out





