From 0b32bdf9a67eeb1c8650b63638cd08ab4bb12fc9 Mon Sep 17 00:00:00 2001
From: beckdaniel <daniel.e.beck@gmail.com>
Date: Thu, 7 Apr 2016 17:32:35 +0100
Subject: [PATCH] removed logtanh, put into a new branch

---
 GPy/util/warping_functions.py | 108 ----------------------------------
 1 file changed, 108 deletions(-)

diff --git a/GPy/util/warping_functions.py b/GPy/util/warping_functions.py
index c4e8f04c..7bc0050f 100644
--- a/GPy/util/warping_functions.py
+++ b/GPy/util/warping_functions.py
@@ -183,114 +183,6 @@ class TanhFunction(WarpingFunction):
         self.d.gradient[:] = warping_grads[0, -1]
 
 
-class LogTanhFunction(WarpingFunction):
-    """
-    Trying to get the best of both worlds...
-    """
-    def __init__(self, n_terms=3, initial_y=None):
-        """
-        n_terms specifies the number of tanh terms to be used
-        """
-        self.n_terms = n_terms
-        self.num_parameters = 3 * self.n_terms + 1
-        self.psi = np.ones((self.n_terms, 3))
-        super(LogTanhFunction, self).__init__(name='warp_tanh')
-        self.psi = Param('psi', self.psi)
-        self.psi[:, :2].constrain_positive()
-        self.d = Param('%s' % ('d'), 1.0, Logexp())
-        self.link_parameter(self.psi)
-        self.link_parameter(self.d)
-        self.initial_y = initial_y
-
-    def f(self, y):
-        """
-        Transform y with f using parameter vector psi
-        psi = [[a,b,c]]
-
-        :math:`f = (y * d) + \\sum_{terms} a * tanh(b *(y + c))`
-        """
-        if (y <= 0.0).any():
-            sys.stderr.write("Negative y found!, raising exception.\n")
-            raise ValueError
-        d = self.d
-        mpsi = self.psi
-        z = d * np.log(y)
-        for i in range(len(mpsi)):
-            a, b, c = mpsi[i]
-            z += a * np.tanh(b * (np.log(y) + c))
-        return z
-
-    def fgrad_y(self, y, return_precalc=False):
-        """
-        gradient of f w.r.t to y ([N x 1])
-
-        :returns: Nx1 vector of derivatives, unless return_precalc is true, 
-        then it also returns the precomputed stuff
-        """
-        d = self.d
-        mpsi = self.psi
-
-        # vectorized version
-        S = (mpsi[:,1] * (np.log(y)[:,:,None] + mpsi[:,2])).T
-        R = np.tanh(S)
-        D = 1 - (R ** 2)
-
-        GRAD = (d + (mpsi[:,0:1][:,:,None] * mpsi[:,1:2][:,:,None] * D).sum(axis=0)).T / y
-
-        if return_precalc:
-            return GRAD, S, R, D
-
-        return GRAD
-
-    def fgrad_y_psi(self, y, return_covar_chain=False):
-        """
-        gradient of f w.r.t to y and psi
-
-        :returns: NxIx4 tensor of partial derivatives
-        """
-        mpsi = self.psi
-
-        w, s, r, d = self.fgrad_y(y, return_precalc=True)
-        gradients = np.zeros((y.shape[0], y.shape[1], len(mpsi), 4))
-        for i in range(len(mpsi)):
-            a,b,c  = mpsi[i]
-            gradients[:, :, i, 0] = (b * (1.0/np.cosh(s[i])) ** 2).T / y
-            gradients[:, :, i, 1] = a * (d[i] - 2.0 * s[i] * r[i] * (1.0/np.cosh(s[i])) ** 2).T / y
-            gradients[:, :, i, 2] = (-2.0 * a * (b ** 2) * r[i] * ((1.0 / np.cosh(s[i])) ** 2)).T / y
-        gradients[:, :, 0, 3] = 1.0 / y
-
-        if return_covar_chain:
-            covar_grad_chain = np.zeros((y.shape[0], y.shape[1], len(mpsi), 4))
-            for i in range(len(mpsi)):
-                a,b,c = mpsi[i]
-                covar_grad_chain[:, :, i, 0] = (r[i]).T
-                covar_grad_chain[:, :, i, 1] = (a * (np.log(y) + c) * ((1.0 / np.cosh(s[i])) ** 2).T)
-                covar_grad_chain[:, :, i, 2] = a * b * ((1.0 / np.cosh(s[i])) ** 2).T
-            covar_grad_chain[:, :, 0, 3] = np.log(y)
-            return gradients, covar_grad_chain
-
-        return gradients
-
-    def _get_param_names(self):
-        variables = ['a', 'b', 'c', 'd']
-        names = sum([['warp_tanh_%s_t%i' % (variables[n],q) for n in range(3)] 
-                     for q in range(self.n_terms)],[])
-        names.append('warp_tanh')
-        return names
-
-    def update_grads(self, Y_untransformed, Kiy):
-        grad_y = self.fgrad_y(Y_untransformed)
-        grad_y_psi, grad_psi = self.fgrad_y_psi(Y_untransformed,
-                                                return_covar_chain=True)
-        djac_dpsi = ((1.0 / grad_y[:, :, None, None]) * grad_y_psi).sum(axis=0).sum(axis=0)
-        dquad_dpsi = (Kiy[:, None, None, None] * grad_psi).sum(axis=0).sum(axis=0)
-
-        warping_grads = -dquad_dpsi + djac_dpsi
-
-        self.psi.gradient[:] = warping_grads[:, :-1]
-        self.d.gradient[:] = warping_grads[0, -1]
-
-
 class LogFunction(WarpingFunction):
     """
     Easy wrapper for applying a fixed log warping function to