def mlogit_warp_grad(alpha, nu, q, y, max_itr=8000, tol=1e-4,
deltaO=0.008, deltag=0.008, display=0):
"""
calculates optimal warping for functional multinomial 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
:param max_itr: maximum number of iterations (Default=8000)
:param tol: stopping tolerance (Default=1e-10)
:param deltaO: gradient step size for rotation (Default=0.008)
:param deltag: gradient step size for warping (Default=0.008)
:param display: display iterations (Default=0)
:rtype: tuple of numpy array
:return gam_old: warping function
"""
alpha = alpha/norm(alpha)
q, scale = cf.scale_curve(q) # q/norm(q)
for ii in range(0, nu.shape[2]):
nu[:, :, ii], scale = cf.scale_curve(nu[:, :, ii]) # nu/norm(nu)
gam_old, O_old = mw.ocmlogit_warp(np.ascontiguousarray(alpha),
np.ascontiguousarray(nu),
np.ascontiguousarray(q),
np.ascontiguousarray(y, dtype=np.int32),
max_itr, tol, deltaO, deltag, display)
return (gam_old, O_old)
import numpy as np
from scipy.linalg import norm
import fdasrsf as fs
import ocmlogit_warp as mw
import h5py
fun = h5py.File('/home/dtucker/fdasrsf/debug_data_oc_mlogit.h5')
q = fun['q'][:]
y = fun['y'][:]
alpha = fun['alpha'][:]
nu = fun['nu'][:]
max_itr = 8000 # 4000
tol = 1e-4
deltag = .05
deltaO = .08
display = 1
alpha = alpha / norm(alpha)
q, scale = fs.scale_curve(q) # q/norm(q)
for ii in range(0, nu.shape[2]):
nu[:, :, ii], scale = fs.scale_curve(nu[:, :, ii]) # nu/norm(nu)
gam_old, O_old = mw.ocmlogit_warp(np.ascontiguousarray(alpha),
np.ascontiguousarray(nu),
np.ascontiguousarray(q),
np.ascontiguousarray(y, dtype=np.int32),
max_itr, tol, deltaO, deltag, display)
import numpy as np
from scipy.linalg import norm
import fdasrsf as fs
import ocmlogit_warp as mw
import h5py
fun = h5py.File('/home/dtucker/fdasrsf/debug_data_oc_mlogit.h5')
q = fun['q'][:]
y = fun['y'][:]
alpha = fun['alpha'][:]
nu = fun['nu'][:]
max_itr = 8000 # 4000
tol = 1e-4
deltag = .05
deltaO = .08
display = 1
alpha = alpha/norm(alpha)
q, scale = fs.scale_curve(q) # q/norm(q)
for ii in range(0, nu.shape[2]):
nu[:, :, ii], scale = fs.scale_curve(nu[:, :, ii]) # nu/norm(nu)
gam_old, O_old = mw.ocmlogit_warp(np.ascontiguousarray(alpha),
np.ascontiguousarray(nu),
np.ascontiguousarray(q),
np.ascontiguousarray(y, dtype=np.int32), max_itr,
tol, deltaO, deltag, display)
