def interp_scatter(x,
y,
z,
ranges,
cmap='afmhot',
plot=True,
interp=True,
**kwargs):
xlim, ylim, (vmin, vmax) = ranges
if interp:
logger.debug('Instantiating KernelReg')
model = nparam.KernelReg(
[z], [x, y],
defaults=nparam.EstimatorSettings(efficient=True),
reg_type='ll',
var_type='cc',
bw='cv_ls')
X, Y = np.mgrid[slice(xlim[0], xlim[1], 101j),
slice(ylim[0], ylim[1], 101j)]
positions = np.vstack([X.ravel(), Y.ravel()]).T
logger.debug('Fitting kernel regression model...')
sm_mean, sm_mfx = model.fit(positions)
Z = np.reshape(sm_mean, X.shape)
if plot:
color_args = dict(cmap=cmap, vmin=vmin, vmax=vmax, alpha=1)
if interp:
logger.debug('Plotting fitted model on mesh grid...')
im = plt.pcolormesh(X, Y, Z, shading='gouraud', **color_args)
kwargs.update(color_args)
logger.debug('Plotting scatter...')
if 's' not in kwargs:
kwargs['s'] = 20
plt.scatter(x, y, c=z, lw=0.5, edgecolor='darkgray', **kwargs)
plt.gca().set(xlim=xlim, ylim=ylim)
else:
return X, Y, Z
np.random.seed(500)
nobs = [250, 1000][0]
sig_fac = 1
x = np.random.uniform(-2, 2, size=nobs)
x.sort()
y_true = np.sin(x * 5) / x + 2 * x
y = y_true + sig_fac * (np.sqrt(
np.abs(3 + x))) * np.random.normal(size=nobs)
model = nparam.KernelReg(endog=[y],
exog=[x],
reg_type='lc',
var_type='c',
bw='cv_ls',
defaults=nparam.EstimatorSettings(efficient=True))
sm_bw = model.bw
sm_mean, sm_mfx = model.fit()
model1 = nparam.KernelReg(endog=[y],
exog=[x],
reg_type='lc',
var_type='c',
bw='cv_ls')
mean1, mfx1 = model1.fit()
model2 = nparam.KernelReg(endog=[y],
exog=[x],
reg_type='ll',