def logistic_warp(alpha,
nu,
q,
y,
deltaO=.1,
deltag=.05,
max_itr=8000,
tol=1e-4,
display=0,
method=1):
"""
calculates optimal warping for function logistic regression
:param alpha: scalar
:param nu: numpy ndarray of shape (M,N) of M functions with N samples
:param q: numpy ndarray of shape (M,N) of M functions with N samples
:param y: numpy ndarray of shape (1,N) of M functions with N samples
responses
:rtype: numpy array
:return gamma: warping function
"""
if method == 1:
tau = 0
# q, scale = cf.scale_curve(q)
q = q / norm(q)
# nu, scale = cf.scale_curve(nu)
# alpha = alpha/scale
gam_old, O_old = lw.oclogit_warp(
np.ascontiguousarray(alpha), np.ascontiguousarray(nu),
np.ascontiguousarray(q), np.ascontiguousarray(y, dtype=np.int32),
max_itr, tol, deltaO, deltag, display)
elif method == 2:
betanu = cf.q_to_curve(nu)
beta = cf.q_to_curve(q)
T = beta.shape[1]
if y == 1:
beta1, O_old, tau = cf.find_rotation_and_seed_coord(betanu, beta)
q = cf.curve_to_q(beta1)[0]
gam_old = cf.optimum_reparam_curve(nu, q)
elif y == -1:
beta1, O_old, tau = cf.find_rotation_and_seed_coord(
-1 * betanu, beta)
q = cf.curve_to_q(beta1)[0]
gam_old = cf.optimum_reparam_curve(-1 * nu, q)
return (gam_old, O_old, tau)
def logistic_warp(alpha, nu, q, y, deltaO=.1, deltag=.05, max_itr=8000,
tol=1e-4, display=0, method=1):
"""
calculates optimal warping for function logistic regression
:param alpha: scalar
:param nu: numpy ndarray of shape (M,N) of M functions with N samples
:param q: numpy ndarray of shape (M,N) of M functions with N samples
:param y: numpy ndarray of shape (1,N) of M functions with N samples
responses
:rtype: numpy array
:return gamma: warping function
"""
if method == 1:
tau = 0
# q, scale = cf.scale_curve(q)
q = q/norm(q)
# nu, scale = cf.scale_curve(nu)
# alpha = alpha/scale
gam_old, O_old = lw.oclogit_warp(np.ascontiguousarray(alpha),
np.ascontiguousarray(nu),
np.ascontiguousarray(q),
np.ascontiguousarray(y, dtype=np.int32),
max_itr, tol, deltaO, deltag, display)
elif method == 2:
betanu = cf.q_to_curve(nu)
beta = cf.q_to_curve(q)
T = beta.shape[1]
if y == 1:
beta1, O_old, tau = cf.find_rotation_and_seed_coord(betanu, beta)
q = cf.curve_to_q(beta1)
gam_old = cf.optimum_reparam_curve(nu, q)
elif y == -1:
beta1, O_old, tau = cf.find_rotation_and_seed_coord(-1 * betanu, beta)
q = cf.curve_to_q(beta1)
gam_old = cf.optimum_reparam_curve(-1 * nu, q)
return (gam_old, O_old, tau)
import numpy as np
from scipy.linalg import norm
import fdasrsf as fs
import oclogit_warp as lw
import h5py
fun = h5py.File('/home/dtucker/fdasrsf/debug/debug_data_oc_logit.h5')
q = fun['q'][:]
y = fun['y'].value
alpha = fun['alpha'].value
nu = fun['nu'][:]
max_itr = 9000 # 4000
tol = 1e-4
deltag = .05
deltaO = .1
display = 1
q, scale = fs.scale_curve(q) # q/norm(q)
nu, scale = fs.scale_curve(nu) # nu/norm(nu)
gam_old, O_old = lw.oclogit_warp(np.ascontiguousarray(alpha),
np.ascontiguousarray(nu),
np.ascontiguousarray(q),
np.ascontiguousarray(y, dtype=np.int32),
max_itr, tol, deltaO, deltag, display)
