Exemplos de mlogit_warp em Python

Exemplo n.º 1

Arquivo: regression.py Projeto: jdtuck/fdasrsf_python

def mlogit_warp_grad(alpha, beta, time, q, y, max_itr=8000, tol=1e-10,
                     delta=0.008, display=0):
    """
    calculates optimal warping for functional multinomial logistic regression

    :param alpha: scalar
    :param beta: numpy ndarray of shape (M,N) of N functions with M samples
    :param time: vector of size M describing the sample points
    :param q: numpy ndarray of shape (M,N) of N functions with M samples
    :param y: numpy ndarray of shape (1,N) responses
    :param max_itr: maximum number of iterations (Default=8000)
    :param tol: stopping tolerance (Default=1e-10)
    :param delta: gradient step size (Default=0.008)
    :param display: display iterations (Default=0)

    :rtype: tuple of numpy array
    :return gam_old: warping function

    """

    gam_old = mw.mlogit_warp(np.ascontiguousarray(alpha),
                             np.ascontiguousarray(beta),
                             time, np.ascontiguousarray(q),
                             np.ascontiguousarray(y, dtype=np.int32), max_itr,
                             tol, delta, display)

    return gam_old

Exemplo n.º 2

Arquivo: regression.py Projeto: venachescu/fdasrsf_python

def mlogit_warp_grad(alpha,
                     beta,
                     time,
                     q,
                     y,
                     max_itr=8000,
                     tol=1e-10,
                     delta=0.008,
                     display=0):
    """
    calculates optimal warping for functional multinomial logistic regression

    :param alpha: scalar
    :param beta: numpy ndarray of shape (M,N) of N functions with M samples
    :param time: vector of size M describing the sample points
    :param q: numpy ndarray of shape (M,N) of N functions with M samples
    :param y: numpy ndarray of shape (1,N) responses
    :param max_itr: maximum number of iterations (Default=8000)
    :param tol: stopping tolerance (Default=1e-10)
    :param delta: gradient step size (Default=0.008)
    :param display: display iterations (Default=0)

    :rtype: tuple of numpy array
    :return gam_old: warping function

    """

    gam_old = mw.mlogit_warp(np.ascontiguousarray(alpha),
                             np.ascontiguousarray(beta), time,
                             np.ascontiguousarray(q),
                             np.ascontiguousarray(y, dtype=np.int32), max_itr,
                             tol, delta, display)

    return gam_old

Exemplo n.º 3

Arquivo: debugc_warp_mlogistic.py Projeto: vnmabus/fdasrsf_python

import numpy as np
import fdasrsf as fs
import mlogit_warp as mw
import h5py

fun = h5py.File(
    '/Users/jdtucker/Documents/Research/fdasrsf/debug/debug_data.h5')
q = fun['q'][:]
y = fun['y'][:]
time = fun['time'][:]
alpha = fun['alpha'][:]
beta = fun['beta'][:]

max_itr = 10000  # 4000
tol = 1e-10
delta = .01
display = 1

gam_old = mw.mlogit_warp(np.ascontiguousarray(alpha),
                         np.ascontiguousarray(beta), time,
                         np.ascontiguousarray(q),
                         np.ascontiguousarray(y, dtype=np.int32), max_itr, tol,
                         delta, display)

Exemplo n.º 4

Arquivo: debugc_warp_mlogistic.py Projeto: glemaitre/fdasrsf

import numpy as np
import fdasrsf as fs
import mlogit_warp as mw
import h5py

fun = h5py.File('/Users/jdtucker/Documents/Research/fdasrsf/debug/debug_data.h5')
q = fun['q'][:]
y = fun['y'][:]
time = fun['time'][:]
alpha = fun['alpha'][:]
beta = fun['beta'][:]

max_itr = 10000  # 4000
tol = 1e-10
delta = .01
display = 1

gam_old = mw.mlogit_warp(np.ascontiguousarray(alpha),
                         np.ascontiguousarray(beta),
                         time, np.ascontiguousarray(q),
                         np.ascontiguousarray(y, dtype=np.int32), max_itr,
                         tol, delta, display)