import numpy as np from matplotlib import pyplot as plt from sklearn.neighbors import KNeighborsRegressor from astroML.datasets import fetch_sdss_galaxy_colors from astroML.plotting import scatter_contour n_neighbors = 1 data = fetch_sdss_galaxy_colors() N = len(data) # shuffle data np.random.seed(0) np.random.shuffle(data) # put colors in a matrix X = np.zeros((N, 4)) X[:, 0] = data['u'] - data['g'] X[:, 1] = data['g'] - data['r'] X[:, 2] = data['r'] - data['i'] X[:, 3] = data['i'] - data['z'] z = data['redshift'] # divide into training and testing data Ntrain = N // 2 Xtrain = X[:Ntrain] ztrain = z[:Ntrain]
scatter-plot the results """ # Author: Jake VanderPlas <*****@*****.**> # License: BSD # The figure is an example from astroML: see http://astroML.github.com import numpy as np from matplotlib import pyplot as plt from sklearn.neighbors import KNeighborsRegressor from astroML.datasets import fetch_sdss_galaxy_colors from astroML.plotting import scatter_contour #------------------------------------------------------------ # Download data data = fetch_sdss_galaxy_colors() data = data[::10] # truncate for plotting # Extract colors and spectral class ug = data['u'] - data['g'] gr = data['g'] - data['r'] spec_class = data['specClass'] stars = (spec_class == 2) qsos = (spec_class == 3) #------------------------------------------------------------ # Prepare plot fig = plt.figure() ax = fig.add_subplot(111)
