X = fetch_great_wall()
#------------------------------------------------------------
# Create the grid on which to evaluate the results
Nx = 50
Ny = 125
xmin, xmax = (-375, -175)
ymin, ymax = (-300, 200)
#------------------------------------------------------------
# Evaluate for several models
Xgrid = np.vstack(map(np.ravel, np.meshgrid(np.linspace(xmin, xmax, Nx),
np.linspace(ymin, ymax, Ny)))).T
kde = KDE(metric='gaussian', h=5)
dens_KDE = kde.fit(X).eval(Xgrid).reshape((Ny, Nx))
knn5 = KNeighborsDensity('bayesian', 5)
dens_k5 = knn5.fit(X).eval(Xgrid).reshape((Ny, Nx))
knn40 = KNeighborsDensity('bayesian', 40)
dens_k40 = knn40.fit(X).eval(Xgrid).reshape((Ny, Nx))
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(9, 4.0))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.14, top=0.9,
hspace=0.01, wspace=0.01)
# First plot: scatter the points
ax1 = plt.subplot(221, aspect='equal')
If indices are used to specify the first three coordinates, the main factor in deciding density SHOULD be the snr (since indices are all evenly spaced)
"""
print qMatrix.shape
wIdx = num.arange(qMatrix.shape[0])
dIdx = num.arange(qMatrix.shape[1])
pBinIdx = num.arange(qMatrix.shape[2])
P, D, W = meshgrid_3d(pBinIdx, dIdx, wIdx)
w = W.ravel()
d = D.ravel()
p = P.ravel()
# qMatrix[w, d, p] is equivalent to qMatrix.ravel()
# take the transpose of this array (need each ROW to be a set of coordinates, not each column)
kde_qMatrix = num.vstack( (w, d, p, qMatrix[w, d, p]) ).T
# fit the density estimator to the qats matrix
densEstimator.fit(kde_qMatrix)
#import pdb; pdb.set_trace()
# evaluate the KDE on the qats matrix
# apparently evaluation is extremely expensive...
# so we cut down the array to a manageable size
cheapSlice = 4096
print kde_qMatrix[::cheapSlice].shape
if False:
print 'evaluating...'
out = densEstimator.eval(kde_qMatrix[::cheapSlice])
print 'evaluation complete'
num.savetxt('densityEstimationOut.txt', out)
else:
out = num.loadtxt('densityEstimationOut.txt')
#------------------------------------------------------------
# Create the grid on which to evaluate the results
Nx = 50
Ny = 125
xmin, xmax = (-375, -175)
ymin, ymax = (-300, 200)
#------------------------------------------------------------
# Evaluate for several models
Xgrid = np.vstack(
map(np.ravel,
np.meshgrid(np.linspace(xmin, xmax, Nx), np.linspace(ymin, ymax,
Ny)))).T
kde = KDE(metric='gaussian', h=5)
dens_KDE = kde.fit(X).eval(Xgrid).reshape((Ny, Nx))
knn5 = KNeighborsDensity('bayesian', 5)
dens_k5 = knn5.fit(X).eval(Xgrid).reshape((Ny, Nx))
knn40 = KNeighborsDensity('bayesian', 40)
dens_k40 = knn40.fit(X).eval(Xgrid).reshape((Ny, Nx))
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 2.2))
fig.subplots_adjust(left=0.12,
right=0.95,
bottom=0.2,
top=0.9,
hspace=0.01,
X = fetch_great_wall()
#------------------------------------------------------------
# Create the grid on which to evaluate the results
Nx = 50
Ny = 125
xmin, xmax = (-375, -175)
ymin, ymax = (-300, 200)
#------------------------------------------------------------
# Evaluate for several models
Xgrid = np.vstack(map(np.ravel, np.meshgrid(np.linspace(xmin, xmax, Nx),
np.linspace(ymin, ymax, Ny)))).T
kde1 = KDE(metric='gaussian', h=5)
dens1 = kde1.fit(X).eval(Xgrid).reshape((Ny, Nx))
kde2 = KDE(metric='tophat', h=5)
dens2 = kde2.fit(X).eval(Xgrid).reshape((Ny, Nx))
kde3 = KDE(metric='exponential', h=5)
dens3 = kde3.fit(X).eval(Xgrid).reshape((Ny, Nx))
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 2.2))
fig.subplots_adjust(left=0.12, right=0.95, bottom=0.2, top=0.9,
hspace=0.01, wspace=0.01)
# First plot: scatter the points
ax1 = plt.subplot(221, aspect='equal')
