tensorflow_probability/examples/logistic_regression.py
def visualize_decision(features, labels, true_w_b, candidate_w_bs, fname):
  """Utility method to visualize decision boundaries in R^2.

  Args:
    features: Input points, as a Numpy `array` of shape `[num_examples, 2]`.
    labels: Numpy `float`-like array of shape `[num_examples, 1]` giving a
      label for each point.
    true_w_b: A `tuple` `(w, b)` where `w` is a Numpy array of
       shape `[2]` and `b` is a scalar `float`, interpreted as a
       decision rule of the form `dot(features, w) + b > 0`.
    candidate_w_bs: Python `iterable` containing tuples of the same form as
       true_w_b.
    fname: The filename to save the plot as a PNG image (Python `str`).
  """
  fig = figure.Figure(figsize=(6, 6))
  canvas = backend_agg.FigureCanvasAgg(fig)
  ax = fig.add_subplot(1, 1, 1)
  ax.scatter(features[:, 0], features[:, 1],
             c=np.float32(labels[:, 0]),
             cmap=cm.get_cmap("binary"),
             edgecolors="k")

  def plot_weights(w, b, **kwargs):
    w1, w2 = w
    x1s = np.linspace(-1, 1, 100)
    x2s = -(w1  * x1s + b) / w2
    ax.plot(x1s, x2s, **kwargs)

  for w, b in candidate_w_bs:
    plot_weights(w, b,
                 alpha=1./np.sqrt(len(candidate_w_bs)),
                 lw=1, color="blue")

  if true_w_b is not None:
    plot_weights(*true_w_b, lw=4,
                 color="green", label="true separator")

  ax.set_xlim([-1.5, 1.5])
  ax.set_ylim([-1.5, 1.5])
  ax.legend()

  canvas.print_figure(fname, format="png")
  print("saved {}".format(fname))
def visualize_decision(features, labels, true_w_b, candidate_w_bs, fname):
  """Utility method to visualize decision boundaries in R^2.

  Args:
    features: Input points, as a Numpy `array` of shape `[num_examples, 2]`.
    labels: Numpy `float`-like array of shape `[num_examples, 1]` giving a
      label for each point.
    true_w_b: A `tuple` `(w, b)` where `w` is a Numpy array of
       shape `[2]` and `b` is a scalar `float`, interpreted as a
       decision rule of the form `dot(features, w) + b > 0`.
    candidate_w_bs: Python `iterable` containing tuples of the same form as
       true_w_b.
    fname: The filename to save the plot as a PNG image (Python `str`).
  """
  fig = figure.Figure(figsize=(6, 6))
  canvas = backend_agg.FigureCanvasAgg(fig)
  ax = fig.add_subplot(1, 1, 1)
  ax.scatter(features[:, 0], features[:, 1],
             c=np.float32(labels[:, 0]),
             cmap=cm.get_cmap("binary"),
             edgecolors="k")

  def plot_weights(w, b, **kwargs):
    w1, w2 = w
    x1s = np.linspace(-1, 1, 100)
    x2s = -(w1  * x1s + b) / w2
    ax.plot(x1s, x2s, **kwargs)

  for w, b in candidate_w_bs:
    plot_weights(w, b,
                 alpha=1./np.sqrt(len(candidate_w_bs)),
                 lw=1, color="blue")

  if true_w_b is not None:
    plot_weights(*true_w_b, lw=4,
                 color="green", label="true separator")

  ax.set_xlim([-1.5, 1.5])
  ax.set_ylim([-1.5, 1.5])
  ax.legend()

  canvas.print_figure(fname, format="png")
  print("saved {}".format(fname))
def visualize_decision ( features , labels , true_w_b , candidate_w_bs , fname ) : fig = figure . Figure ( figsize = ( 6 , 6 ) ) canvas = backend_agg . FigureCanvasAgg ( fig ) ax = fig . add_subplot ( 1 , 1 , 1 ) ax . scatter ( features [ : , 0 ] , features [ : , 1 ] , c = np . float32 ( labels [ : , 0 ] ) , cmap = cm . get_cmap ( "binary" ) , edgecolors = "k" ) def plot_weights ( w , b , * * kwargs ) : w1 , w2 = w x1s = np . linspace ( - 1 , 1 , 100 ) x2s = - ( w1 * x1s + b ) / w2 ax . plot ( x1s , x2s , * * kwargs ) for w , b in candidate_w_bs : plot_weights ( w , b , alpha = 1. / np . sqrt ( len ( candidate_w_bs ) ) , lw = 1 , color = "blue" ) if true_w_b is not None : plot_weights ( * true_w_b , lw = 4 , color = "green" , label = "true separator" ) ax . set_xlim ( [ - 1.5 , 1.5 ] ) ax . set_ylim ( [ - 1.5 , 1.5 ] ) ax . legend ( ) canvas . print_figure ( fname , format = "png" ) print ( "saved {}" . format ( fname ) )
Utility method to visualize decision boundaries in R^2.

  Args:
    features: Input points, as a Numpy `array` of shape `[num_examples, 2]`.
    labels: Numpy `float`-like array of shape `[num_examples, 1]` giving a
      label for each point.
    true_w_b: A `tuple` `(w, b)` where `w` is a Numpy array of
       shape `[2]` and `b` is a scalar `float`, interpreted as a
       decision rule of the form `dot(features, w) + b > 0`.
    candidate_w_bs: Python `iterable` containing tuples of the same form as
       true_w_b.
    fname: The filename to save the plot as a PNG image (Python `str`).
Utility method to visualize decision boundaries in R^2 .