import pca
import numpy as np
import compress
# X = np.array([[1, 1], [1, 0], [2, 2], [2, 1], [2, 4], [3, 4], [
# 3, 3], [3, 2], [4, 4], [4, 5], [5, 5], [5, 7], [5, 4]])
# Z = pca.compute_Z(X, True, True)
# COV = pca.compute_covariance_matrix(Z)
# # print(COV)
# L, PCS = pca.find_pcs(COV)
# Zstar = pca.project_data(Z, PCS, L, 1, 0)
# print(PCS)
# print(Zstar)
X = compress.load_data('Data/Train/')
compress.compress_images(X, 10)
compress.compress_images(X, 100)
compress.compress_images(X, 500)
compress.compress_images(X, 1000)
compress.compress_images(X, 2000)
import compress import numpy as np # Real training train = 'Data/Train/' small = 'Data/small/' X = compress.load_data(small) compress.compress_images(X, 100)
import pca
import compress
import numpy as np
img = compress.load_data('Data/Train/')
X_compressed = compress.compress_images(img, 2000)
compress.save_data(X_compressed)
exit()
#X = np.array([[-1,-1],[-1,1],[1,-1],[1,1]])
# X = np.array([[1,1],[1,0],[2,2],[2,1],[2,4],[3,4],[3,3],[3,2],[4,4],[4,5],[5,5],[5,7],[5,4]])
X = np.array([[90, 60, 90], [90, 90, 30], [60, 60, 60], [60, 60, 90],
[30, 30, 30]])
#X = np.array([[2.5,2.4],[.5,.7],[2.2,2.9],[1.9,2.2],[3.1,3],[2.3,2.7],[2,1.6],[1,1.1],[1.5,1.6],[1.1,.9]])
#X = np.array([[0,8],[8,9],[12,11],[20,12]])
Z = pca.compute_Z(X, True, False)
print("Z:")
print(Z)
print()
COV = pca.compute_covariance_matrix(Z)
COV = np.array([[5, 1], [4, 5]])
print("COV:")
print(COV)
print()
L, U = pca.find_pcs(COV)
print("L:")
print(L)
print("U:")
print(U)
print()