old_contents: import matplotlib.pyplot as plt
  import numpy as np
  from sklearn.svm import SVC
  from sklearn.datasets import make_blobs
  from .plot_2d_separator import plot_2d_separator
  
  
  def make_handcrafted_dataset():
      # a carefully hand-designed dataset lol
      X, y = make_blobs(centers=2, random_state=4, n_samples=30)
      y[np.array([7, 27])] = 0
      mask = np.ones(len(X), dtype=np.bool)
      mask[np.array([0, 1, 5, 26])] = 0
      X, y = X[mask], y[mask]
      return X, y
  
  
  def plot_rbf_svm_parameters():
      X, y = make_handcrafted_dataset()
  
      fig, axes = plt.subplots(1, 3, figsize=(12, 4))
      for ax, C in zip(axes, [1e0, 5, 10, 100]):
          ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
  
          svm = SVC(kernel='rbf', C=C).fit(X, y)
          plot_2d_separator(svm, X, ax=ax, eps=.5)
          ax.set_title("C = %f" % C)
  
      fig, axes = plt.subplots(1, 4, figsize=(15, 3))
      for ax, gamma in zip(axes, [0.1, .5, 1, 10]):
          ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
          svm = SVC(gamma=gamma, kernel='rbf', C=1).fit(X, y)
          plot_2d_separator(svm, X, ax=ax, eps=.5)
          ax.set_title("gamma = %f" % gamma)
  
  
  def plot_svm(log_C, log_gamma):
      X, y = make_blobs(centers=2, random_state=4, n_samples=30)
      # a carefully hand-designed dataset lol
      y[7] = 0
      y[27] = 0
      C = 10. ** log_C
      gamma = 10. ** log_gamma
      svm = SVC(kernel='rbf', C=C, gamma=gamma).fit(X, y)
      ax = plt.gca()
      plot_2d_separator(svm, X, ax=ax, eps=.5)
      # plot data
      ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
      # plot support vectors
      sv = svm.support_vectors_
      ax.scatter(sv[:, 0], sv[:, 1], s=230, facecolors='none', zorder=10, linewidth=3)
      ax.set_title("C = %.4f gamma = %.4f" % (C, gamma))
  
  
  def plot_svm_interactive():
      from IPython.html.widgets import interactive, FloatSlider
      C_slider = FloatSlider(min=-3, max=3, step=.1, value=0, readout=False)
      gamma_slider = FloatSlider(min=-2, max=2, step=.1, value=0, readout=False)
      return interactive(plot_svm, log_C=C_slider, log_gamma=gamma_slider)
  
new_contents: import matplotlib.pyplot as plt
  import numpy as np
  from sklearn.svm import SVC
  from sklearn.datasets import make_blobs
  from .plot_2d_separator import plot_2d_separator
  
  
  def make_handcrafted_dataset():
      # a carefully hand-designed dataset lol
      X, y = make_blobs(centers=2, random_state=4, n_samples=30)
      y[np.array([7, 27])] = 0
      mask = np.ones(len(X), dtype=np.bool)
      mask[np.array([0, 1, 5, 26])] = 0
      X, y = X[mask], y[mask]
      return X, y
  
  
  def plot_rbf_svm_parameters():
      X, y = make_handcrafted_dataset()
  
      fig, axes = plt.subplots(1, 3, figsize=(12, 4))
      for ax, C in zip(axes, [1e0, 5, 10, 100]):
          ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
  
          svm = SVC(kernel='rbf', C=C).fit(X, y)
          plot_2d_separator(svm, X, ax=ax, eps=.5)
          ax.set_title("C = %f" % C)
  
      fig, axes = plt.subplots(1, 4, figsize=(15, 3))
      for ax, gamma in zip(axes, [0.1, .5, 1, 10]):
          ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
          svm = SVC(gamma=gamma, kernel='rbf', C=1).fit(X, y)
          plot_2d_separator(svm, X, ax=ax, eps=.5)
          ax.set_title("gamma = %f" % gamma)
  
  
  def plot_svm(log_C, log_gamma):
  	X, y = make_handcrafted_dataset()
      C = 10. ** log_C
      gamma = 10. ** log_gamma
      svm = SVC(kernel='rbf', C=C, gamma=gamma).fit(X, y)
      ax = plt.gca()
      plot_2d_separator(svm, X, ax=ax, eps=.5)
      # plot data
      ax.scatter(X[:, 0], X[:, 1], s=150, c=np.array(['red', 'blue'])[y])
      # plot support vectors
      sv = svm.support_vectors_
      ax.scatter(sv[:, 0], sv[:, 1], s=230, facecolors='none', zorder=10, linewidth=3)
      ax.set_title("C = %.4f gamma = %.4f" % (C, gamma))
  
  
  def plot_svm_interactive():
      from IPython.html.widgets import interactive, FloatSlider
      C_slider = FloatSlider(min=-3, max=3, step=.1, value=0, readout=False)
      gamma_slider = FloatSlider(min=-2, max=2, step=.1, value=0, readout=False)
      return interactive(plot_svm, log_C=C_slider, log_gamma=gamma_slider)
  
subject: use balanced dataset in rbf svm interactive plot