def __init__(self, evts, t1, t2, k=10000):
self.evts = evts
#Decide the bandwidth
self.t1 = t1
self.t2 = t2
self.dt = self.t2 - self.t1
#Perform the Kernel Density Estimation
knd = KNeighborsDensity('bayesian', n_neighbors=k)
sys.stderr.write("Fitting...")
knd.fit(evts[:, numpy.newaxis])
sys.stderr.write("done")
#Evaluate the KDE and interpolate it
sys.stderr.write("Evaluating...")
x_grid = numpy.arange(self.t1, self.t2, 10.0)
pdf = knd.eval(x_grid[:, numpy.newaxis]) / evts.shape[0]
sys.stderr.write("done")
sys.stderr.write("Interpolating...")
self.model = interpolate.InterpolatedUnivariateSpline(x_grid, pdf, k=1)
sys.stderr.write("done")
) #dr7objid, petromag_u, petromag_g, petromag_r, pertomag_i, petromag_z, z(redshift),h alpha ew, h beta ew, OII ew, h delta ew, spiral, elliptical, uncertain
umr = data[:, 3] - data[:, 5] #u-r color
data = np.column_stack((data, umr))
coords = data[:, 1:3]
print coords.shape
print "started knd"
knd = KNeighborsDensity(
"bayesian", 10
) #try using something other than 10 for n_neighbors values, to experiment + optimize
knd.fit(coords)
density = knd.eval(coords)
data[:, 1] = density #!!!!! CHECK, is this still in order??
data = np.delete(data, 2, 1) #(col# 2 , 0/1 for row/col)
print "finished knd"
isSpiral = data[:, 12] #with one col removed!!
#print data[:5, :]
isElliptical = data[:, 13] #corresponds to the elliptical bool value
isUncertain = data[:, 14]
ellipticals = data[isElliptical == 1]
spirals = data[isSpiral == 1]
uncertains = data[isUncertain == 1]
ax = fig.add_subplot(subplot)
xN = x[:N]
t = np.linspace(-10, 30, 1000)
# Compute density with KDE
if use_sklearn_KDE:
kde = KernelDensity(0.1, kernel='gaussian')
kde.fit(xN[:, None])
dens_kde = np.exp(kde.score_samples(t[:, None]))
else:
kde = KDE('gaussian', h=0.1).fit(xN[:, None])
dens_kde = kde.eval(t[:, None]) / N
# Compute density with Bayesian nearest neighbors
nbrs = KNeighborsDensity('bayesian', n_neighbors=k).fit(xN[:, None])
dens_nbrs = nbrs.eval(t[:, None]) / N
# plot the results
ax.plot(t,
true_pdf(t),
':',
color='black',
zorder=3,
label="Generating Distribution")
ax.plot(xN, -0.005 * np.ones(len(xN)), '|k')
hist(xN,
bins='blocks',
ax=ax,
normed=True,
zorder=1,
histtype='stepfilled',
N_values = (500, 5000)
subplots = (211, 212)
k_values = (10, 100)
for N, k, subplot in zip(N_values, k_values, subplots):
ax = fig.add_subplot(subplot)
xN = ent2[:N]
t = np.linspace(-10, 30, 1000)
# Compute density with KDE
kde = KDE('gaussian', h=0.1).fit(xN[:, None])
dens_kde = kde.eval(t[:, None]) / N
# Compute density with Bayesian nearest neighbors
nbrs = KNeighborsDensity('bayesian', n_neighbors=k).fit(xN[:, None])
dens_nbrs = nbrs.eval(t[:, None]) / N
# plot the results
#ax.plot(t, true_pdf(t), ':', color='black', zorder=3,
# label="Generating Distribution")
ax.plot(xN, -0.005 * np.ones(len(xN)), '|k', lw=1.5)
hist(xN, bins='blocks', ax=ax, normed=True, zorder=1,
histtype='stepfilled', lw=1.5, color='k', alpha=0.2,
label="Bayesian Blocks")
ax.plot(t, dens_nbrs, '-', lw=2, color='gray', zorder=2,
label="Nearest Neighbors (k=%i)" % k)
ax.plot(t, dens_kde, '-', color='black', zorder=3,
label="Kernel Density (h=0.1)")
# label the plot
ax.text(0.02, 0.95, "%i points" % N, ha='left', va='top',
import time
#Trying u-g, g-r, r-i, i-z AS WELL AS raw u, g, r, i, z, still including all 5 spectral lines
#get just spirals and ellipticals in 1 array, shuffle them, then extract the label column
data = np.loadtxt("crossmatched3_combineddata1_srane.txt", delimiter = ",", skiprows = 1, usecols = (1, 2, 3, 49, 50, 51)) #dr7objid, petromag_u, petromag_g, petromag_r, pertomag_i, petromag_z, z(redshift),h alpha ew, h beta ew, OII ew, h delta ew, spiral, elliptical, uncertain
coords = data[:, 1:3]
print coords.shape
knd = KNeighborsDensity("bayesian", 10)
knd.fit(coords)
density = knd.eval(coords)
data[:, 1] = density
data = np.delete(data, 2, 1) #(col# 2 , 0/1 for row/col)
isSpiral = data[:,2]#with one col removed!!
print data[:5, :]
isElliptical = data[:,3] #corresponds to the elliptical bool value
isUncertain = data[:,4]
ellipticals = data[isElliptical == 1]
spirals = data[isSpiral == 1]
uncertains = data[isUncertain == 1]
#Preparing the grid for evaluation. Nx and Ny can be set separately
N = len(fit)
Ny = N
Nx = N
xmin, xmax = (min(fit[:, 0]), max(fit[:, 0]))
ymin, ymax = (min(fit[:, 1]), max(fit[:, 1]))
x = np.linspace(xmin, xmax, Nx)
y = np.linspace(ymin, ymax, Ny)
b = np.vstack(map(np.ravel, np.meshgrid(x, y))).T
#Density estimation fit and evaluation
k = 40
knn = KNeighborsDensity('bayesian', k)
knn.fit(fit)
c = knn.eval(b).reshape((Ny, Nx))
#Getting optimization data and science data from what's left
others = np.delete(table.data, selection, 0)
N2 = 1000
np.random.seed(1)
optimize = np.random.randint(0, len(others), size=N2)
opt = others[optimize]
others = np.delete(others, optimize, 0)
np.random.seed(2)
science = np.random.randint(0, len(others), size=N2)
sci = others[science]
