original_docstring:  Plot all training samples.
  
              Plot all training samples and mark the class association.
          
docstring: Plot all training samples.
  Plot all training samples and mark the class association.
docstring_tokens: Plot all training samples . Plot all training samples and mark the class association .
code: def plot_samples_3D(self):
          """ Plot all training samples.
  
              Plot all training samples and mark the class association.
          """
          ax = plt.gca(projection='3d')#generate 3d plot
  
          # TODO: determine size of plot
          xmin = -2.5  # min(numpy.min(class_neg[:,0]), numpy.min(class_pos[:,0]))
          xmax = 2.5   # max(numpy.max(class_neg[:,0]), numpy.max(class_pos[:,0]))
          ymin = -2.5  # min(numpy.min(class_neg[:,1]), numpy.min(class_pos[:,1]))
          ymax = 2.5   # max(numpy.max(class_neg[:,1]), numpy.max(class_pos[:,1]))
          zmin = -2.5
          zmax = 2.5
  
          ax.set_xlim3d(xmin, xmax)
          ax.set_ylim3d(ymin, ymax)
          ax.set_zlim3d(zmin, zmax)
  
          class_neg = []
          class_pos = []
          for idx in numpy.arange(self.num_samples):
              if self.labels[idx] == 0:
                  class_neg.append(self.samples[idx])
              else:
                  class_pos.append(self.samples[idx])
  
          class_neg = numpy.matrix(class_neg)
          class_pos = numpy.matrix(class_pos)
  
          if self.scatStandard is not None:
              self.scatStandard.remove()
              self.scatStandard = None
          if self.scatTarget is not None:
              self.scatTarget.remove()
              self.scatTarget = None
  
          if numpy.shape(class_neg)[1] > 0:
              self.plot_data_3D(
                  class_neg[:, 0], class_neg[:, 1], class_neg[:, 2], False)
          if numpy.shape(class_pos)[1] > 0:
              self.plot_data_3D(
                  class_pos[:, 0], class_pos[:, 1], class_pos[:, 2], True)
code_tokens: def plot_samples_3D ( self ) : ax = plt . gca ( projection = '3d' ) xmin = - 2.5 xmax = 2.5 ymin = - 2.5 ymax = 2.5 zmin = - 2.5 zmax = 2.5 ax . set_xlim3d ( xmin , xmax ) ax . set_ylim3d ( ymin , ymax ) ax . set_zlim3d ( zmin , zmax ) class_neg = [ ] class_pos = [ ] for idx in numpy . arange ( self . num_samples ) : if self . labels [ idx ] == 0 : class_neg . append ( self . samples [ idx ] ) else : class_pos . append ( self . samples [ idx ] ) class_neg = numpy . matrix ( class_neg ) class_pos = numpy . matrix ( class_pos ) if self . scatStandard is not None : self . scatStandard . remove ( ) self . scatStandard = None if self . scatTarget is not None : self . scatTarget . remove ( ) self . scatTarget = None if numpy . shape ( class_neg ) [ 1 ] > 0 : self . plot_data_3D ( class_neg [ : , 0 ] , class_neg [ : , 1 ] , class_neg [ : , 2 ] , False ) if numpy . shape ( class_pos ) [ 1 ] > 0 : self . plot_data_3D ( class_pos [ : , 0 ] , class_pos [ : , 1 ] , class_pos [ : , 2 ] , True )
short_docstring: Plot all training samples.
short_docstring_tokens: Plot all training samples .
comment: """ Plot all training samples.
  
              Plot all training samples and mark the class association.
          """$SEP$#generate 3d plot$SEP$# TODO: determine size of plot$SEP$# min(numpy.min(class_neg[:,0]), numpy.min(class_pos[:,0]))$SEP$# max(numpy.max(class_neg[:,0]), numpy.max(class_pos[:,0]))$SEP$# min(numpy.min(class_neg[:,1]), numpy.min(class_pos[:,1]))$SEP$# max(numpy.max(class_neg[:,1]), numpy.max(class_pos[:,1]))