def line_search(dt, p, gamma, M, y, C):
"""
returns dt
"""
# see class notes
dt = 2.0 * dt
# initial energy and initial gamma do not change throughout
E = _energy(gamma, M, y, C)
while True:
gamma_new = project(gamma - dt * p, y, C)
E_new = _energy(gamma_new, M, y, C)
if E >= E_new + .001 * (gamma - gamma_new).T.dot(gamma - gamma_new):
return dt
else:
dt = dt / 2 # take a smaller timestep and try again
def kernel_classify(M, y, C, tol=.001):
"""
input M, y, C
returns γ
NOTE
for hard margin just pass C = numpy.inf
gradient descent w/ log functions
"""
# please get to the bottom of the dimensionality issues from before
#if len(y.shape) == 1:
# numpy.expand_dims(y, axis=0)
gamma = numpy.zeros((M.shape[0], 1))
dt, itmax = .001, 20000
it = 1
while True:
it += 1
p = gradient(gamma, M, y)
dt = line_search(dt, p, gamma, M, y, C)
gamma_new = project(gamma - dt * p, y, C)
q = (gamma_new - gamma) / dt
if (norm(q) < tol):
break
elif (it > itmax):
break
else:
gamma = gamma_new
return gamma_new
argv = sys.argv
arg = sys.argv[1]
if arg == 'project':
input_file = argv[2]
k = int(argv[3])
A = _parser(input_file, identifier='A')
b = _parser(input_file, identifier='b')
logging.debug(f"Projecting with k: {k}")
logging.debug(f"A : {A}")
logging.debug(f"b : {b}")
n = len(A[0])
A, b = project(A, b, k, n)
logging.debug(f"Result from project: ")
logging.debug(f"\t A: {A}")
logging.debug(f"\t b : {b}")
output_file = argv[4]
_write(output_file, **{'A': A, 'b': b})
elif arg == 'image':
input_file_1 = argv[2]
input_file_2 = argv[3]
output_file = argv[4]
A = _parser(input_file_1, identifier='A')
b = _parser(input_file_1, identifier='b')