import Problem2 as p2
import cvxopt as opt
import numpy as np
# parameters
name = '2'
print '======Training======'
# load data from csv files
train = loadtxt('data/data'+name+'_train.csv')
# use deep copy here to make cvxopt happy
X = train[:, 0:2].copy()
Y = train[:, 2:3].copy()
# Define parameters
C = 0.1
K = p2.gaussian_gram(X, gamma = 2)
def column_kernel(SVM_X,x):
'''
Given an array of X values and a new x to predict,
computes the vector whose i^th entry is k(SVM_X[i],x)
'''
def k(y):
#return np.dot(x,y) # returns the identity kernel
#return (1+np.dot(x,y)) # returns the linear basis kernel
gamma = 2
sqr_diff = np.linalg.norm(x-y)**2
sqr_diff *= -1.0*gamma
return np.exp(sqr_diff)
def wrapper_gaussian(data, C, gamma):
# print '======Training======'
# load data from csv files
# train = loadtxt('data/data'+name+'_train.csv')
# use deep copy here to make cvxopt happy
X = data["Xtrain"]
Y = data["Ytrain"]
# Define parameters
K = p2.gaussian_gram(X, gamma)
def column_kernel(SVM_X, x):
'''
Given an array of X values and a new x to predict,
computes the vector whose i^th entry is k(SVM_X[i],x)
'''
def k(y):
#return np.dot(x,y) # returns the identity kernel
#return (1+np.dot(x,y)) # returns the linear basis kernel
sqr_diff = np.linalg.norm(x - y)**2
sqr_diff *= -1.0 * gamma
return np.exp(sqr_diff)
return np.apply_along_axis(k, 1, SVM_X).reshape(-1, 1)
# Carry out training, primal and/or dual
a, SVM_alpha, SVM_X, SVM_Y, support = p2.dual_SVM(X, Y, C, K)
def get_prediction_constants():
ay = SVM_alpha * SVM_Y
# get gram matrix for only support X values
SVM_K = K[support]
SVM_K = SVM_K.T[support]
SVM_K = SVM_K.T
# compute bias
bias = np.nansum(
[SVM_Y[i] - np.dot(ay.T, SVM_K[i])
for i in range(len(SVM_Y))]) / len(SVM_Y)
return ay, bias
# Define the predictSVM(x) function, which uses trained parameters
# Define the predict_gaussianSVM(x) function, which uses trained parameters, alpha
ay, bias = get_prediction_constants()
def predictSVM(x):
'''
The predicted value is given by h(x) = sign( sum_{support vectors} alpha_i y_i k(x_i,x) )
'''
debug = False
x = x.reshape(1, -1)
if debug: print 'Classify x: ', x
# predict new Y output
kernel = column_kernel(SVM_X, x)
y = np.dot(ay.T, kernel)
if debug:
print 'New y ', y + bias
return y + bias
def classification_error(X_train, Y_train):
''' Computes the error of the classifier on some training set'''
n, d = X_train.shape
incorrect = 0.0
for i in range(n):
if predictSVM(X_train[i]) * Y_train[i] < 0:
incorrect += 1
return incorrect / n
train_err = classification_error(X, Y)
# # plot training results
# plotDecisionBoundary(X, Y, predictSVM, [-1, 0, 1], title = 'SVM Train on dataset '+str(name)+' with C = '+str(C))
# pl.savefig('prob3compategaussian_kernelSVMtrain_'+str(name)+'_with C='+str(C)+'_gamma='+str(gamma)+'.png')
#
# print '======Validation======'
# # load data from csv files
# validate = loadtxt('data/data'+name+'_validate.csv')
X = data["Xvalidate"]
Y = data["Yvalidate"]
validation_err = classification_error(X, Y)
#
# validation_err = classification_error(X, Y)
#
# # plot validation results
# plotDecisionBoundary(X, Y, predictSVM, [-1, 0, 1], title = 'SVM Validate on dataset '+str(name)+' with C = '+str(C)+' gamma = '+str(gamma))
# pl.savefig('prob2gaussian_kernelSVMvalidate_'+str(name)+'_with C='+str(C)+'_gamma='+str(gamma)+'.png')
#
#
# f = open('errors for gaussian kernel dataset '+str(name)+' with C = '+str(C)+' gamma = '+str(gamma)+'.txt', 'w')
# f.write('Train err: ')
# f.write(str(train_err))
# f.write('\n')
# f.write('Validate err: ')
# f.write(str(validation_err))
# f.write('\n')
# f.write('Number of SVMs: ')
# f.write(str(len(SVM_Y)))
# f.close()
#
# print 'Done plotting...'
X = data["Xtest"]
Y = data["Ytest"]
testing_error = classification_error(X, Y)
return [C, gamma, validation_err, testing_error]
