title: understand how this lambda function works
body: <p>I thought I understood how lambda functions work, though I don't use them myself. But the lambda below from <a href="https://github.com/dennybritz/nn-from-scratch/blob/master/nn-from-scratch.ipynb" rel="nofollow">this tutorial</a> totally stumps me:</p>
  
  <pre><code>import matplotlib.pyplot as plt
  import numpy as np
  import sklearn
  import sklearn.datasets
  import sklearn.linear_model
  import matplotlib
  </code></pre>
  
  <p>That was easy. More:</p>
  
  <pre><code># Generate a dataset and plot it
  np.random.seed(0)
  X, y = sklearn.datasets.make_moons(200, noise=0.20)
  plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)
  clf = sklearn.linear_model.LogisticRegressionCV()
  clf.fit(X, y)
  
  # Helper function to plot a decision boundary.
  # If you don't fully understand this function don't worry, it just generates the contour plot below.
  
  def plot_decision_boundary(pred_func):
  
      # Set min and max values and give it some padding
      x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
      y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
      h = 0.01
  
      # Generate a grid of points with distance h between them
      xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
  
      # Predict the function value for the whole gid
      Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
  
      # Plot the contour and training examples
      plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
      plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
  </code></pre>
  
  <p>Now the line I don't understand:</p>
  
  <pre><code>plot_decision_boundary(lambda x: clf.predict(x))
  </code></pre>
  
  <p>I've read many times how lambdas work, but I just don't get how the <code>x</code> here passing the correct values from before. How is the <code>x</code> mapped to the relevant values?</p>