code: 
  
  
  
  """simple docstring"""
  import colorsys
  import io
  
  import cva
  import matplotlib as mpl
  import matplotlib.colors as mplc
  import matplotlib.figure as mplfigure
  import numpy as np
  import torch
  from matplotlib.backends.backend_agg import FigureCanvasAgg
  
  from utils import img_tensorize
  
  
  _lowerCAmelCase					:Optional[Any]  =      1_000
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A=1.2 ,			A="g" ,			A=0.5 ,			A="-" ,			A="test_out.jpg" ,			A=True ,			A=False ,			A=None ,			A=None ,			A=0.7 ,			)							->      str:
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Any       							=					img.numpy().astype('''np.uint8'''	)
           if isinstance(A ,			A	):
               _UpperCAmelCase							:						Tuple       							=					img_tensorize(A	)
           assert isinstance(A ,			np.ndarray	)
  
           _UpperCAmelCase							:						Tuple       							=					img.shape[1], img.shape[0]
           _UpperCAmelCase							:						int       							=					mplfigure.Figure(frameon=A	)
           _UpperCAmelCase							:						Tuple       							=					fig.get_dpi()
           _UpperCAmelCase							:						Any       							=					(width * scale + 1E-2) / dpi
           _UpperCAmelCase							:						Tuple       							=					(height * scale + 1E-2) / dpi
           fig.set_size_inches(A ,			A	)
           _UpperCAmelCase							:						List[Any]       							=					fig.add_axes([0.0, 0.0, 1.0, 1.0]	)
           ax.axis('''off'''	)
           ax.set_xlim(0.0 ,			A	)
           ax.set_ylim(A	)
  
           _UpperCAmelCase							:						int       							=					saveas
           _UpperCAmelCase							:						Optional[int]       							=					rgb
           _UpperCAmelCase							:						Optional[int]       							=					pynb
           _UpperCAmelCase							:						int       							=					img
           _UpperCAmelCase							:						Optional[Any]       							=					edgecolor
           _UpperCAmelCase							:						Tuple       							=					0.5
           _UpperCAmelCase							:						Optional[Any]       							=					linestyle
           _UpperCAmelCase							:						str       							=					int(np.sqrt(min(A ,			A	)	) * scale // 3	)
           _UpperCAmelCase							:						Optional[Any]       							=					width
           _UpperCAmelCase							:						Dict       							=					height
           _UpperCAmelCase							:						Any       							=					scale
           _UpperCAmelCase							:						Dict       							=					fig
           _UpperCAmelCase							:						str       							=					ax
           _UpperCAmelCase							:						Tuple       							=					pad
           _UpperCAmelCase							:						Optional[int]       							=					idaobj
           _UpperCAmelCase							:						str       							=					idaattr
           _UpperCAmelCase							:						Tuple       							=					FigureCanvasAgg(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      Optional[int]:
           if color is None:
               _UpperCAmelCase							:						str       							=					self.edgecolor
           (_UpperCAmelCase)							:						List[Any]       							=					box
           _UpperCAmelCase							:						List[str]       							=					xa - xa
           _UpperCAmelCase							:						Any       							=					ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya) ,			A ,			A ,			fill=A ,			edgecolor=A ,			linewidth=self.font_size // 3 ,			alpha=self.alpha ,			linestyle=self.linestyle ,			)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None ,			A=None ,			A=None ,			A=None	)							->      List[Any]:
           if len(boxes.shape	) > 2:
               _UpperCAmelCase							:						Any       							=					boxes[0]
           if len(obj_ids.shape	) > 1:
               _UpperCAmelCase							:						int       							=					obj_ids[0]
           if len(obj_scores.shape	) > 1:
               _UpperCAmelCase							:						int       							=					obj_scores[0]
           if len(attr_ids.shape	) > 1:
               _UpperCAmelCase							:						Optional[int]       							=					attr_ids[0]
           if len(attr_scores.shape	) > 1:
               _UpperCAmelCase							:						Any       							=					attr_scores[0]
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Union[str, Any]       							=					boxes.numpy()
           if isinstance(A ,			A	):
               _UpperCAmelCase							:						Union[str, Any]       							=					np.array(A	)
           assert isinstance(A ,			np.ndarray	)
           _UpperCAmelCase							:						Union[str, Any]       							=					np.prod(boxes[:, 2:] - boxes[:, :2] ,			axis=1	)
           _UpperCAmelCase							:						Any       							=					np.argsort(-areas	).tolist()
           _UpperCAmelCase							:						Tuple       							=					boxes[sorted_idxs] if boxes is not None else None
           _UpperCAmelCase							:						Optional[Any]       							=					obj_ids[sorted_idxs] if obj_ids is not None else None
           _UpperCAmelCase							:						int       							=					obj_scores[sorted_idxs] if obj_scores is not None else None
           _UpperCAmelCase							:						List[Any]       							=					attr_ids[sorted_idxs] if attr_ids is not None else None
           _UpperCAmelCase							:						str       							=					attr_scores[sorted_idxs] if attr_scores is not None else None
  
           _UpperCAmelCase							:						Optional[int]       							=					[self._random_color(maximum=1	) for _ in range(len(A	)	)]
           _UpperCAmelCase							:						Tuple       							=					[assigned_colors[idx] for idx in sorted_idxs]
           if obj_ids is not None:
               _UpperCAmelCase							:						List[str]       							=					self._create_text_labels_attr(A ,			A ,			A ,			A	)
               for i in range(len(A	)	):
                   _UpperCAmelCase							:						Tuple       							=					assigned_colors[i]
                   self.add_box(boxes[i] ,			A	)
                   self.draw_labels(labels[i] ,			boxes[i] ,			A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A	)							->      Any:
           _UpperCAmelCase							:						List[str]       							=					box
           _UpperCAmelCase							:						Optional[Any]       							=					(xa, ya)
           _UpperCAmelCase							:						List[Any]       							=					(ya - ya) * (xa - xa)
           _UpperCAmelCase							:						List[Any]       							=					_SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 4_0 * self.scale:
               if ya >= self.height - 5:
                   _UpperCAmelCase							:						Tuple       							=					(xa, ya)
               else:
                   _UpperCAmelCase							:						Tuple       							=					(xa, ya)
  
           _UpperCAmelCase							:						Any       							=					(ya - ya) / np.sqrt(self.height * self.width	)
           _UpperCAmelCase							:						List[Any]       							=					self._change_color_brightness(A ,			brightness_factor=0.7	)
           _UpperCAmelCase							:						Any       							=					np.clip((height_ratio - 0.02) / 0.08 + 1 ,			1.2 ,			2	)
           font_size *= 0.75 * self.font_size
  
           self.draw_text(
               text=A ,			position=A ,			color=A ,			)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A="g" ,			A="left" ,			)							->      Dict:
           _UpperCAmelCase							:						Optional[int]       							=					0
           _UpperCAmelCase							:						str       							=					self.font_size
           _UpperCAmelCase							:						int       							=					np.maximum(list(mplc.to_rgb(A	)	) ,			0.2	)
           _UpperCAmelCase							:						Dict       							=					max(0.8 ,			np.max(A	)	)
           _UpperCAmelCase							:						int       							=					{
               '''facecolor''': '''black''',
               '''alpha''': self.alpha,
               '''pad''': self.pad,
               '''edgecolor''': '''none''',
           }
           _UpperCAmelCase							:						Optional[Any]       							=					position
           self.ax.text(
               A ,			A ,			A ,			size=font_size * self.scale ,			family='''sans-serif''' ,			bbox=A ,			verticalalignment='''top''' ,			horizontalalignment=A ,			color=A ,			zorder=1_0 ,			rotation=A ,			)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A=None	)							->      str:
           if saveas is None:
               _UpperCAmelCase							:						str       							=					self.saveas
           if saveas.lower().endswith('''.jpg'''	) or saveas.lower().endswith('''.png'''	):
               cva.imwrite(
                   A ,			self._get_buffer()[:, :, ::-1] ,			)
           else:
               self.fig.savefig(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A	)							->      int:
           _UpperCAmelCase							:						List[str]       							=					[self.idaobj[i] for i in classes]
           _UpperCAmelCase							:						Union[str, Any]       							=					[self.idaattr[i] for i in attr_classes]
           _UpperCAmelCase							:						List[Any]       							=					[
               f'{label} {score:.2f} {attr} {attr_score:.2f}'
               for label, score, attr, attr_score in zip(A ,			A ,			A ,			A	)
           ]
           return labels
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      List[str]:
           _UpperCAmelCase							:						Tuple       							=					[self.idaobj[i] for i in classes]
           if scores is not None:
               if labels is None:
                   _UpperCAmelCase							:						Tuple       							=					['''{:.0f}%'''.format(s * 1_0_0	) for s in scores]
               else:
                   _UpperCAmelCase							:						Any       							=					['''{} {:.0f}%'''.format(A ,			s * 1_0_0	) for li, s in zip(A ,			A	)]
           return labels
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A=2_5_5	)							->      str:
           _UpperCAmelCase							:						Union[str, Any]       							=					np.random.randint(0 ,			len(_COLORS	)	)
           _UpperCAmelCase							:						Any       							=					_COLORS[idx] * maximum
           if not self.rgb:
               _UpperCAmelCase							:						Dict       							=					ret[::-1]
           return ret
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Optional[Any]:
           if not self.pynb:
               _UpperCAmelCase							:						List[Any]       							=					self.canvas.print_to_buffer()
               if (width, height) != (self.width, self.height):
                   _UpperCAmelCase							:						List[str]       							=					cva.resize(self.img ,			(width, height)	)
               else:
                   _UpperCAmelCase							:						Optional[int]       							=					self.img
           else:
               _UpperCAmelCase							:						List[Any]       							=					io.BytesIO()  # works for cairo backend
               self.canvas.print_rgba(A	)
               _UpperCAmelCase							:						Optional[int]       							=					self.width, self.height
               _UpperCAmelCase							:						Any       							=					buf.getvalue()
               _UpperCAmelCase							:						List[Any]       							=					self.img
  
           _UpperCAmelCase							:						int       							=					np.frombuffer(A ,			dtype='''uint8'''	)
           _UpperCAmelCase							:						List[Any]       							=					buffer.reshape(A ,			A ,			4	)
           _UpperCAmelCase							:						Tuple       							=					np.split(A ,			[3] ,			axis=2	)
  
           try:
               import numexpr as ne  # fuse them with numexpr
  
               _UpperCAmelCase							:						Optional[Any]       							=					ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''	)
           except ImportError:
               _UpperCAmelCase							:						str       							=					alpha.astype('''float32'''	) / 255.0
               _UpperCAmelCase							:						List[str]       							=					img * (1 - alpha) + rgb * alpha
  
           return visualized_image.astype('''uint8'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      List[str]:
           assert brightness_factor >= -1.0 and brightness_factor <= 1.0
           _UpperCAmelCase							:						Tuple       							=					mplc.to_rgb(A	)
           _UpperCAmelCase							:						str       							=					colorsys.rgb_to_hls(*mplc.to_rgb(A	)	)
           _UpperCAmelCase							:						Dict       							=					polygon_color[1] + (brightness_factor * polygon_color[1])
           _UpperCAmelCase							:						List[Any]       							=					0.0 if modified_lightness < 0.0 else modified_lightness
           _UpperCAmelCase							:						List[str]       							=					1.0 if modified_lightness > 1.0 else modified_lightness
           _UpperCAmelCase							:						Tuple       							=					colorsys.hls_to_rgb(polygon_color[0] ,			A ,			polygon_color[2]	)
           return modified_color
  
  
  
  _lowerCAmelCase					:int  =      (
      np.array(
          [
              0.000,
              0.447,
              0.741,
              0.850,
              0.325,
              0.098,
              0.929,
              0.694,
              0.125,
              0.494,
              0.184,
              0.556,
              0.466,
              0.674,
              0.188,
              0.301,
              0.745,
              0.933,
              0.635,
              0.078,
              0.184,
              0.300,
              0.300,
              0.300,
              0.600,
              0.600,
              0.600,
              1.000,
              0.000,
              0.000,
              1.000,
              0.500,
              0.000,
              0.749,
              0.749,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              1.000,
              0.667,
              0.000,
              1.000,
              0.333,
              0.333,
              0.000,
              0.333,
              0.667,
              0.000,
              0.333,
              1.000,
              0.000,
              0.667,
              0.333,
              0.000,
              0.667,
              0.667,
              0.000,
              0.667,
              1.000,
              0.000,
              1.000,
              0.333,
              0.000,
              1.000,
              0.667,
              0.000,
              1.000,
              1.000,
              0.000,
              0.000,
              0.333,
              0.500,
              0.000,
              0.667,
              0.500,
              0.000,
              1.000,
              0.500,
              0.333,
              0.000,
              0.500,
              0.333,
              0.333,
              0.500,
              0.333,
              0.667,
              0.500,
              0.333,
              1.000,
              0.500,
              0.667,
              0.000,
              0.500,
              0.667,
              0.333,
              0.500,
              0.667,
              0.667,
              0.500,
              0.667,
              1.000,
              0.500,
              1.000,
              0.000,
              0.500,
              1.000,
              0.333,
              0.500,
              1.000,
              0.667,
              0.500,
              1.000,
              1.000,
              0.500,
              0.000,
              0.333,
              1.000,
              0.000,
              0.667,
              1.000,
              0.000,
              1.000,
              1.000,
              0.333,
              0.000,
              1.000,
              0.333,
              0.333,
              1.000,
              0.333,
              0.667,
              1.000,
              0.333,
              1.000,
              1.000,
              0.667,
              0.000,
              1.000,
              0.667,
              0.333,
              1.000,
              0.667,
              0.667,
              1.000,
              0.667,
              1.000,
              1.000,
              1.000,
              0.000,
              1.000,
              1.000,
              0.333,
              1.000,
              1.000,
              0.667,
              1.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.167,
              0.000,
              0.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.167,
              0.000,
              0.000,
              0.333,
              0.000,
              0.000,
              0.500,
              0.000,
              0.000,
              0.667,
              0.000,
              0.000,
              0.833,
              0.000,
              0.000,
              1.000,
              0.000,
              0.000,
              0.000,
              0.143,
              0.143,
              0.143,
              0.857,
              0.857,
              0.857,
              1.000,
              1.000,
              1.000,
          ]
      )
      .astype(np.floataa)
      .reshape(-1, 3)
  )
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						Tuple       							=					target
           if len(self.target	) == 0:
               raise ValueError('''Target data can not be empty'''	)
           if len(self.sample	) != len(self.target	):
               raise ValueError('''Sample data and Target data do not have matching lengths'''	)
           _UpperCAmelCase							:						List[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      None:
           for sample in self.sample:
               sample.insert(0 ,			self.bias	)
  
           for _ in range(self.col_sample	):
               self.weight.append(random.random()	)
  
           self.weight.insert(0 ,			self.bias	)
  
           _UpperCAmelCase							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						int       							=					epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                   print(('''\nEpoch:\n''', epoch_count)	)
                   print('''------------------------\n'''	)
                   # if epoch_count > self.epoch_number or not error:
                   break
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      None:
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           sample.insert(0 ,			self.bias	)
           _UpperCAmelCase							:						str       							=					0
           for i in range(self.col_sample + 1	):
               _UpperCAmelCase							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					:Optional[int]  =      [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _lowerCAmelCase					:Dict  =      [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
    _lowerCAmelCase					:int  =      Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
    )
    network.training()
    print('Finished training perceptron')
    print('Enter values to predict or q to exit')
    while True:
      _lowerCAmelCase					:list  =      []
      for i in range(len(samples[0])):
        _lowerCAmelCase					:List[Any]  =      input('value: ').strip()
        if user_input == "q":
          break
        _lowerCAmelCase					:Any  =      float(user_input)
        sample.insert(i, observation)
      network.sort(sample)
  
  
  