# Bruce A. Maxwell
# Spring 2015
# CS 251 Project 6
#
# Test file for the PCAData class
#
import numpy as np
import data

# This is the proper set of eigenvalues and eigenvectors for the small
# data set of four points.
headers = ['A','B']

# original data
orgdata = np.matrix([ [1,2],
                      [2,4],
                      [5,9.5],
                      [4,8.5] ])

# means of the original data
means = np.matrix([ 3.,  6.])

# eigenvalues of the original data
evals = np.matrix([16.13395443, 0.03271224])

# eigenvectors of the original data as rows
evecs = np.matrix([[ 0.4527601,   0.89163238],
                   [-0.89163238,  0.4527601 ]])

# the original data projected onto the eigenvectors.
# pdata = (evecs * (orgdata - means).T).T
pdata = np.matrix([[-4.4720497,  -0.02777563],
                   [-2.23602485, -0.01388782],
                   [ 4.02623351, -0.19860441],
                   [ 2.68184104,  0.24026787]])


# create a PCAData object
pcad = data.PCAData( headers, pdata, evals, evecs, means )

# Test all of the various new functions
print "Eigenvalues:"
print pcad.get_eigenvalues()

print "\nEigenvectors:"
print pcad.get_eigenvectors()

print "\nMeans:"
print pcad.get_data_means()

print "\nOriginal Headers:"
print pcad.get_data_headers()

# Test old functions
print "\nProjected data:"
print pcad.get_data( pcad.get_headers() )

print "\nRaw headers:"
print pcad.get_raw_headers()

print "\nRaw types:"
print pcad.get_raw_types()

print "\nNumber of rows:"
print pcad.get_raw_num_rows()