y = np.array([1,2,3])
D = image_data[0][:,1]
print (D.ndim)
print (D)
beta = sp.least_squares(D, y)
print (beta)
'''
##############################
D = sp.build_dictionary(image_data)
y = np.random.randint(1, 71, size = 10)
#print (D)
#print (y)
beta, indices = sp.choose_atoms(D, y)
print (beta)
print (indices)
##############################Built list of files to iterate through####################################################
'''
##Big laptop
#os.chdir("C:\\Users\\Jack2\\Google Drive\\URMP\\jc2\\MNIST_Load")
##Little laptop
os.chdir("C:\\Users\\Jack\\Google Drive\\URMP\\jc2\\MNIST_Load")
files = os.listdir()
file_list = []
for i in files:
file_path = file_list[0]
dictionary_data = mnist.load_images(file_path,50)
y = mnist.load_images(file_list[0], 1, 100)[0].flatten()
D = sp.build_dictionary(dictionary_data)
#print ("D.shape: ", D.shape)
approx = None
##Generate sparse code
current_beta = None
next_beta = None
indices = None
condition = True
while condition:
current_beta, indices = sp.choose_atoms(D, y, indices)
next_beta, indices = sp.choose_atoms(D, y, indices)
print (current_beta.shape, next_beta.shape)
#print ("current_beta: ", current_beta, type(current_beta))
#print ("next_beta: ", next_beta, type(next_beta))
#print (type(current_beta == next_beta))
#print (current_beta == next_beta)
#print ((current_beta == next_beta).all())
if isinstance(current_beta == next_beta, bool):
condition = not(current_beta == next_beta)
else:
condition = not((current_beta == next_beta).all())
#print (condition)
# print (condition, condition.all(), condition.any())
