




"""simple docstring"""



# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Any:
 return 1 / (1 + np.exp(-z						))







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Any:
 return (-y * np.log(lowercase_						) - (1 - y) * np.log(1 - h						)).mean()







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Dict:
 A__	=    np.dot(lowercase_      ,			lowercase_						)
 return np.sum(y * scores - np.log(1 + np.exp(lowercase_						)						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_=7_00_00						)						->		Optional[Any]:
 A__	=    np.zeros(x.shape[1]						)

 for iterations in range(lowercase_						):
  A__	=    np.dot(lowercase_      ,			lowercase_						)
  A__	=    sigmoid_function(lowercase_						)
  A__	=    np.dot(x.T      ,			h - y						) / y.size
  A__	=    theta - alpha * gradient  # updating the weights
  A__	=    np.dot(lowercase_      ,			lowercase_						)
  A__	=    sigmoid_function(lowercase_						)
  A__	=    cost_function(lowercase_      ,			lowercase_						)
  if iterations % 1_00 == 0:
   print(f"""loss: {j} \t"""						)  # printing the loss after every 100 iterations
 return theta


# In[68]:

if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	datasets.load_iris()
      SCREAMING_SNAKE_CASE    =	iris.data[:, :2]
      SCREAMING_SNAKE_CASE    =	(iris.target != 0) * 1

      SCREAMING_SNAKE_CASE    =	0.1
      SCREAMING_SNAKE_CASE    =	logistic_reg(alpha, x, y, max_iterations=7_0000)
      print("theta: ", theta)  # printing the theta i.e our weights vector







      def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		str:
       return sigmoid_function(
           np.dot(lowercase_      ,			lowercase_						)						)  # predicting the value of probability from the logistic regression algorithm

      plt.figure(figsize=(10, 6))
      plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
      plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
      ((SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE))    =	(x[:, 0].min(), x[:, 0].max())
      ((SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE))    =	(x[:, 1].min(), x[:, 1].max())
      ((SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE))    =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
      SCREAMING_SNAKE_CASE    =	np.c_[xxa.ravel(), xxa.ravel()]
      SCREAMING_SNAKE_CASE    =	predict_prob(grid).reshape(xxa.shape)
      plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

      plt.legend()






      plt.show()











"""simple docstring"""



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

 parser.add_argument(
     "--config_file"      ,			default=lowercase_      ,			help="The config file to use for the default values in the launching script."						)

 if subparsers is not None:
  parser.set_defaults(func=lowercase_						)
 return parser







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`Accelerate` version": version,
     "Platform": platform.platform(),
     "Python version": platform.python_version(),
     "Numpy version": np.__version__,
     "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
     "PyTorch XPU available": str(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    torch.cuda.get_device_name()

 print("\nCopy-and-paste the text below in your GitHub issue\n"						)
 print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

 print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


if __name__ == "__main__":
      raise SystemExit(main())





