python - understand how this lambda function works -

i thought understood how lambda functions work, though don't use them myself. lambda below this tutorial totally stumps me:

import matplotlib.pyplot plt import numpy np import sklearn import sklearn.datasets import sklearn.linear_model import matplotlib 

that easy. more:

# generate dataset , plot 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 plot decision boundary. # if don't understand function don't worry, generates contour plot below.  def plot_decision_boundary(pred_func):      # set min , max values , give 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 grid of points distance h between them     xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))      # predict function value whole gid     z = pred_func(np.c_[xx.ravel(), yy.ravel()])     z = z.reshape(xx.shape)      # plot contour , training examples     plt.contourf(xx, yy, z, cmap=plt.cm.spectral)     plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.spectral) 

now line don't understand:

plot_decision_boundary(lambda x: clf.predict(x)) 

i've read many times how lambdas work, don't how x here passing correct values before. how x mapped relevant values?

x concatenated numpy object pass in here:

z = pred_func(np.c_[xx.ravel(), yy.ravel()]) 

pred_func argument plot_decision_boundary(); calling call function object defined lambda. above line translates to:

clf.predict(np.c_[xx.ravel(), yy.ravel()])