


'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase							:       Dict  						=     load_iris()
iris.keys()


print(f"""Target names: \n {iris.target_names} """)
print(f"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase							:       str  						=     train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

lowerCamelCase							:       str  						=     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase							:       Dict  						=     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase							:       List[Any]  						=     knn.predict(X_new)

print(
    f"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    f""" {iris['target_names'][prediction]}"""
)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:       str  						=     {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Tuple  						=     [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase							:       Optional[Any]  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




