understand how this lambda function works
<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>