Visualize KDE

import numpy as np from scipy import stats import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d from matplotlib import cm data = np.array([[1, 4, 3], [2, .6, 1.2], [2, 1, 1.2], [2, 0.5, 1.4], [5, .5, 0], [0, 0, 0], [1, 4, 3], [5, .5, 0], [2, .5, 1.2]]) data = data.T ############################################################## ## in case of sklearn we can use ## # using grid search cross-validation to optimize the bandwidth params = {'bandwidth': np.logspace(-1, 1, 20)} grid = GridSearchCV(KernelDensity(), params) grid.fit(data) print("best bandwidth: {0}".format(grid.best_estimator_.bandwidth)) ## how to use this with scipy KDE() ?######## ############################################################## kde = stats.gaussian_kde(data) # Here is the basic way to evaluate the estimated pdf on a regular n-dimensional mesh # Create a regular N-dimensional grid with (arbitrary) 20 points in each dimension minima = data.T.min(axis=0) maxima = data.T.max(axis=0) space = [np.linspace(mini,maxi,20) for mini, maxi in zip(minima,maxima)] grid = np.meshgrid(*space) #Turn the grid into N-dimensional coordinates for each point #Note - coords will get very large as N increases... coords = np.vstack(map(np.ravel, grid)) #Evaluate the KD estimated pdf at each coordinate density = kde(coords) x, y, z = data fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) ax.contour(x, y, z, zdir='x', offset=-40, cmap=cm.coolwarm) ## not wroking ### plt.show()