def preprocessor_apply_to_train(preproc, X):
"""
Apply Preprocessor to training data and memorize parameters
preproc is initially a struct with the following fields [default]
standardize_X - if True, makes columns of X zero mean and unit var. [True]
rescale_X - if True, scale columns of X to lie in [-1, +1] [False]
kernel_fn - if not None, apply kernel fn to X default [None]
The returned preproc object has several more fields added to it,
which are used by preprocessor_apply_to_test
"""
# Set defaults
try:
preproc.standardize_X
except AttributeError:
preproc.standardize_X = True
try:
preproc.rescale_X
except AttributeError:
preproc.rescale_X = False
try:
preproc.kernel_fn
except AttributeError:
preproc.kernel_fn = None
try:
preproc.poly
except AttributeError:
preproc.poly = None
try:
preproc.add_ones
except AttributeError:
preproc.add_ones = None
if preproc.standardize_X:
X, preproc.Xmu = util.center_cols(X)
X, preproc.Xstnd = util.mk_unit_variance(X)
if preproc.rescale_X:
try:
preproc.Xscale
except AttributeError:
preproc.Xscale = [-1, 1]
X = util.rescale_data(X, preproc.Xscale[0], preproc.Xscale[1])
if preproc.kernel_fn is not None:
preproc.basis = X
X = preproc.kernel_fn(X, preproc.basis)
if preproc.poly is not None:
assert preproc.poly > 0, 'polynomial degree must be greater than 0'
X = util.degexpand(X, preproc.poly, False)
if preproc.add_ones:
X = util.add_ones(X)
return preproc, X
def preprocessor_apply_to_test(preproc, X):
"""Transform the test data in the same way as the training data"""
try:
X = util.center_cols(X, preproc.Xmu)
except AttributeError:
pass
try:
X = util.mk_unit_variance(X, preproc.Xstnd)
except AttributeError:
pass
try:
X = util.rescale_data(X, preproc.Xscale[0], preproc.Xscale[1])
except AttributeError:
pass
try:
if preproc.kernel_fn is not None:
X = preproc.kernel_fn(X, preproc.basis)
except AttributeError:
pass
try:
if preproc.poly is not None:
X = util.degexpand(X, preproc.poly, False)
except AttributeError:
pass
try:
if preproc.add_ones:
X = util.add_ones(X)
except AttributeError:
pass
return X
def contoursSSEDemo(): N = 21 x,y,_,_,_,_ = util.poly_data_make(sampling='thibaux', n=N) X = util.add_ones(x) return X,y
#!/usr/bin/env python
# Fit linear and quadratic surfaces to data
# Based on code by Romain Thibaux <*****@*****.**>
import matplotlib.pyplot as pl
import numpy as np
import utils.util as util
from mpl_toolkits.mplot3d import Axes3D
data = util.load_mat('moteData/moteData.mat')
X = data['X']
y = data['y']
X_pad = util.add_ones(X)
for use_quad in (False, True):
phi = X_pad
if use_quad:
phi = np.column_stack((X_pad, X**2))
fig = pl.figure()
ax = Axes3D(fig)
ax.set_zlim(15, 19)
ax.scatter(X[:, 0], X[:, 1], y)
xrange = np.linspace(min(X[:, 0]), max(X[:, 0]), 10)
yrange = np.linspace(min(X[:, 1]), max(X[:, 1]), 10)
xx, yy = np.meshgrid(xrange, yrange)
flatxx = xx.reshape((100, 1))
import matplotlib.pyplot as pl
import numpy as np
import utils.util as util
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize
np.random.seed(0)
#Generating synthetic data:
N = 21
wTrue = np.array([1.45, 0.92])
X = util.add_ones(np.random.uniform(-2, 2, N))
y = wTrue[0] * X[:, 0] + wTrue[1] * X[:, 1] + np.random.normal(0, .1, N)
# #Uncomment if we wish to use data generated from poly_data_make
# x, y,_ ,_ ,_ ,_ = util.poly_data_make(sampling='thibaux', n=N)
# X = util.add_ones(x)
#Plot SSE surface over parameter space.
v = np.arange(-1, 3, .1)
W0, W1 = np.meshgrid(v, v)
SS = np.array([
sum((w0 * X[:, 0] + w1 * X[:, 1] - y)**2)
for w0, w1 in zip(np.ravel(W0), np.ravel(W1))
])
SS = SS.reshape(W0.shape)
fig = pl.figure()
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(W0, W1, SS)
pl.savefig('figures/linregSurfSSEPy.pdf')
pl.draw()
#!/usr/bin/env python
# Fit linear and quadratic surfaces to data
# Based on code by Romain Thibaux <*****@*****.**>
import matplotlib.pyplot as pl
import numpy as np
import utils.util as util
from mpl_toolkits.mplot3d import Axes3D
data = util.load_mat('moteData/moteData.mat')
X = data['X']
y = data['y']
X_pad = util.add_ones(X)
for use_quad in (False, True):
phi = X_pad
if use_quad:
phi = np.column_stack((X_pad, X**2))
fig = pl.figure()
ax = Axes3D(fig)
ax.set_zlim(15, 19)
ax.scatter(X[:,0], X[:,1], y)
xrange = np.linspace(min(X[:,0]), max(X[:,0]), 10)
yrange = np.linspace(min(X[:,1]), max(X[:,1]), 10)
xx, yy = np.meshgrid(xrange, yrange)
flatxx = xx.reshape((100, 1))
