





"""simple docstring"""
import matplotlib.pyplot as plt
import numpy as np



def   __lowerCamelCase     (							a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	float							)    ->   np.ndarray:
      __SCREAMING_SNAKE_CASE     :List[str]	       =						len(a_							)  # Number of training samples
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						np.eye(a_							)  # Initialize weights as identity matrix
      for j in range(a_							):
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						point - x_train[j]
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						np.exp(diff @ diff.T / (-2.0 * tau**2)							)

      return weights



def   __lowerCamelCase     (							a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	float							)    ->   np.ndarray:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						weight_matrix(a_				,     a_				,     a_							)
      __SCREAMING_SNAKE_CASE     :List[str]	       =						np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight



def   __lowerCamelCase     (							a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	float							)    ->   np.ndarray:
      __SCREAMING_SNAKE_CASE     :int	       =						np.zeros(len(a_							)							)  # Initialize array of predictions
      for i, item in enumerate(a_							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						item @ local_weight(a_				,     a_				,     a_				,     a_							)

      return y_pred



def   __lowerCamelCase     (							a_  :	str				,     a_  :	str				,     a_  :	str							)    ->   tuple[np.ndarray, np.ndarray, np.ndarray]:
      import seaborn as sns

      __SCREAMING_SNAKE_CASE     :List[str]	       =						sns.load_dataset(a_							)
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						np.array(data[x_name]							)
      __SCREAMING_SNAKE_CASE     :Dict	       =						np.array(data[y_name]							)

      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						np.ones(len(a_							)							)

      # pairing elements of one and x_data
      __SCREAMING_SNAKE_CASE     :str	       =						np.column_stack((one, x_data)							)

      return x_train, x_data, y_data



def   __lowerCamelCase     (							a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	np.ndarray				,     a_  :	str				,     a_  :	str				,     )    ->   None:
      __SCREAMING_SNAKE_CASE     :Any	       =						np.sort(a_				,     axis=0							)
      plt.scatter(a_				,     a_				,     color='''blue'''							)
      plt.plot(
          x_train_sorted[:, 1]				,     preds[x_train[:, 1].argsort(0							)]				,     color='''yellow'''				,     linewidth=5				,     )
      plt.title('''Local Weighted Regression'''							)
      plt.xlabel(a_							)
      plt.ylabel(a_							)
      plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Demo with a dataset from the seaborn module
  lowerCamelCase_    ,  lowerCamelCase_    ,  lowerCamelCase_          =	load_data("tips", "total_bill", "tip")
  lowerCamelCase_          =	local_weight_regression(training_data_x, tip, 5)
  plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")






"""simple docstring"""
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowerCamelCase_          =	(3_0, 4_8_7)
lowerCamelCase_          =	(3_6, 1_0_2_4)
lowerCamelCase_          =	(1_0_0, 1_0_0)
lowerCamelCase_          =	1_0_0

lowerCamelCase_          =	datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

lowerCamelCase_    ,  lowerCamelCase_          =	os.path.split(__file__)
lowerCamelCase_          =	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   List[str]:
      with ArrowWriter(features=a_				,     path=os.path.join(a_				,     '''beta.arrow'''							)							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						my_features.encode_example(a_							)
                  writer.write(a_							)
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						writer.finalize()



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	List[str]				,     a_  :	List[str]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[int]							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Dict	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	Any				,     a_  :	Optional[Any]							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :List[str]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	Optional[Any]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						dataset[col]



@get_duration
def   __lowerCamelCase     (							a_  :	int				,     a_  :	List[str]							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						dataset[col]



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}
      __SCREAMING_SNAKE_CASE     :int	       =						(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': ArrayaD(a_				,     dtype='''float32'''							)}							)
            __SCREAMING_SNAKE_CASE     :Any	       =						generate_examples(features=a_				,     num_examples=a_							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''							)							)}							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}				,     )
            __SCREAMING_SNAKE_CASE     :Any	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with open(a_				,     '''wb'''							) as f:
            f.write(json.dumps(a_							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_array_xd()