#!/usr/bin/env python
'''
Created on Aug 31, 2012
@author: masumadmin
'''
from PCA import PCA
from Utility import read_data
if __name__ == '__main__':
pca= PCA(3)
pca.train(read_data('data/iris_sans_class.arff'))
print "ev1: ",pca.eigenvalues[0]
print "ev2: ",pca.eigenvalues[1]
print "ev3: ",pca.eigenvalues[2]
print pca.principal_components
print pca.reduce([0.36158967923198376, -0.08226888783524387, 0.8565721047950943, 0.35884392603772075])
print pca.expand([1.0, -7.091680853665849e-10, -1.7341683644644945e-13])
print "OK"
'''
Created on Sep 11, 2012
@author: masumadmin
'''
from Utility import read_data
from LinearRegression import LinearRegression
if __name__ == '__main__':
lr= LinearRegression()
lr.train(read_data('data/linear_in.arff'), read_data('data/linear_out.arff'))
y = lr.predict([1,1,1])
print "y1: ", y[0]
print "y2: ", y[1]
'''
Created on Sep 18, 2012
@author: masumadmin
'''
from Utility import read_data
from NFoldValidation import NFoldValidation
from PCA02 import PCA02
from MYSVGWriter import MYSVGWriter
if __name__ == '__main__':
matrix, cat_cols, nom_cols = read_data('data/credit-a.arff')
num_folds=3
nfold = NFoldValidation(matrix, cat_cols, nom_cols,num_folds)
print 'total error in linear regression:', nfold.run()
components=5
pca02= PCA02(matrix, cat_cols, nom_cols, components)
pca02.train()
#plot3.svg
svg = MYSVGWriter(500, 500, 0, 0, 100, 100)
start_position=4
for i in range(len( pca02.eigenvalues)):
svg.rect(start_position, 0, 3, 27*pca02.eigenvalues[i] , 0x008080)
start_position+= 4
