# File Name: linregPolyVsDegree.py
# Description:
# Linear Regression with Polynomial Basis of different degrees
# based on code code by Romain Thibaux
# (Lecture 2 from http://www.cs.berkeley.edu/~asimma/294-fall06/)
from utils import preprocessor_create
from utils import poly_data_make
from SupervisedModels.linearRegression import linreg_fit
from SupervisedModels.linearRegression import linreg_fit_bayes
from SupervisedModels.linearRegression import linreg_predict
import numpy as np
import pylab as pl
N = 21
xtrain, ytrain, xtest, _, ytest, _ = poly_data_make(sampling='thibaux', n=N)
degs = np.arange(1, 22)
Nm = len(degs)
# Plot error vs degree
mseTrain = np.zeros(Nm)
mseTest = np.zeros(Nm)
for m in xrange(len(degs)):
deg = degs[m]
pp = preprocessor_create(rescale_X=True, poly=deg, add_ones=True)
model = linreg_fit(xtrain, ytrain, preproc=pp)
ypredTrain = linreg_predict(model, xtrain)
ypredTest = linreg_predict(model, xtest)
mseTrain[m] = np.mean(np.square(ytrain - ypredTrain))
mseTest[m] = np.mean(np.square(ytest - ypredTest))
# File Name: linregPolyVsDegree.py
# Description:
# Linear Regression with Polynomial Basis of different degrees
# based on code code by Romain Thibaux
# (Lecture 2 from http://www.cs.berkeley.edu/~asimma/294-fall06/)
from utils import preprocessor_create
from utils import poly_data_make
from SupervisedModels.linearRegression import linreg_fit
from SupervisedModels.linearRegression import linreg_fit_bayes
from SupervisedModels.linearRegression import linreg_predict
import numpy as np
import pylab as pl
N = 21
xtrain, ytrain, xtest, _, ytest, _ = poly_data_make(sampling='thibaux', n=N)
degs = np.arange(1, 22)
Nm = len(degs)
# Plot error vs degree
mseTrain = np.zeros(Nm)
mseTest = np.zeros(Nm)
for m in xrange(len(degs)):
deg = degs[m]
pp = preprocessor_create(rescale_X=True, poly=deg, add_ones=True)
model = linreg_fit(xtrain, ytrain, preproc=pp)
ypredTrain = linreg_predict(model, xtrain)
ypredTest = linreg_predict(model, xtest)
mseTrain[m] = np.mean(np.square(ytrain - ypredTrain))
mseTest[m] = np.mean(np.square(ytest - ypredTest))
def contoursSSEDemo(): N = 21 x,y,_,_,_,_ = poly_data_make(sampling='thibaux', n=N) X = np.concatenate((np.ones((N,1)), x.reshape(N,1)), axis=1) return X,y
def contoursSSEDemo():
N = 21
x, y, _, _, _, _ = poly_data_make(sampling='thibaux', n=N)
X = np.concatenate((np.ones((N, 1)), x.reshape(N, 1)), axis=1)
return X, y
# Author: Srinivas Vasudevan # E-mail: [email protected] # # File Name: linregResiduals.py # Description: # Linear regression on data with residuals. # # Last Modified: # 2015-11-28 import matplotlib.pyplot as pl import numpy as np from utils import poly_data_make N = 21 x, y, _, _, _, _ = poly_data_make(sampling='thibaux', n=N) X = np.concatenate((np.ones((N,1)), x.reshape(N,1)), axis=1) w = np.linalg.lstsq(X, y)[0] y_estim = np.dot(X,w) pl.plot(X[:,1], y, 'o') pl.plot(X[:,1], y_estim, '-') pl.savefig('linregResidualsNoBars.png') pl.show() for x0, y0, y_hat in zip(X[:,1], y, y_estim): pl.plot([x0, x0],[y0, y_hat],'k-') pl.plot(X[:,1], y, 'o') pl.plot(X[:,1], y_estim, '-') pl.savefig('linregResidualsBars.png')
# Author: Srinivas Vasudevan # E-mail: [email protected] # # File Name: linregResiduals.py # Description: # Linear regression on data with residuals. # # Last Modified: # 2015-11-28 import matplotlib.pyplot as pl import numpy as np from utils import poly_data_make N = 21 x, y, _, _, _, _ = poly_data_make(sampling='thibaux', n=N) X = np.concatenate((np.ones((N, 1)), x.reshape(N, 1)), axis=1) w = np.linalg.lstsq(X, y)[0] y_estim = np.dot(X, w) pl.plot(X[:, 1], y, 'o') pl.plot(X[:, 1], y_estim, '-') pl.savefig('linregResidualsNoBars.png') pl.show() for x0, y0, y_hat in zip(X[:, 1], y, y_estim): pl.plot([x0, x0], [y0, y_hat], 'k-') pl.plot(X[:, 1], y, 'o') pl.plot(X[:, 1], y_estim, '-') pl.savefig('linregResidualsBars.png')