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https://github.com/SheffieldML/GPy.git
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Implemented MLP gradients with respect to X.
This commit is contained in:
Neil Lawrence
parent
b127c96bf2
commit
84b7156d23
4 changed files with 32 additions and 46 deletions
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@ -363,18 +363,18 @@ def stick_play(range=None, frame_rate=15):
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GPy.util.visualize.data_play(Y, data_show, frame_rate)
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return Y
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def stick():
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def stick(kernel=None):
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data = GPy.util.datasets.osu_run1()
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# optimize
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m = GPy.models.GPLVM(data['Y'], 2)
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m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
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m.optimize(messages=1, max_f_eval=10000)
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m._set_params(m._get_params())
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plt.clf
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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if GPy.util.visualize.visual_available:
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plt.clf
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -27,7 +27,6 @@ class MLP(Kernpart):
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"""
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def __init__(self, input_dim, variance=1., weight_variance=None, bias_variance=100., ARD=False):
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ARD = False
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self.input_dim = input_dim
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self.ARD = ARD
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if not ARD:
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@ -44,6 +43,7 @@ class MLP(Kernpart):
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assert weight_variance.size == self.input_dim, "bad number of weight variances"
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else:
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weight_variance = np.ones(self.input_dim)
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raise NotImplementedError
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self.name='mlp'
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self._set_params(np.hstack((variance, weight_variance.flatten(), bias_variance)))
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@ -104,47 +104,27 @@ class MLP(Kernpart):
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target[0] += np.sum(self._K_dvar*dL_dK)
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def dK_dX(self, dL_dK, X, X2, target):
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"""Derivative of the covariance matrix with respect to X"""
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self._K_computations(X, X2)
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arg = self._K_asin_arg
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post_div = np.sqrt(1-arg*arg)
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numer = self._K_numer
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denom = self._K_denom
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vec2 = (X2*X2).sum(1)*self.weight_variance + self.bias_variance + 1.
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denom3 = denom*denom*denom
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target += (((X2[None,:, :]/denom[None, :, None]+vec2[None, None, :]*X[:, :, None]*numer/denom)/post_div[:, :, None]) * dL_dK[:, :, None]).sum(1)
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target *= four_over_tau*self.weight_variance*self.variance
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raise NotImplementedError
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gX = np.zeros((X2.shape[0], X.shape[1], X.shape[0]))
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for i in range(X.shape[0]):
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gX[:, :, i] = self._dK_dX_point(dL_dK, X, X2, target, i)
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target += four_over_tau*self.weight_variance*self.variance*((X2[None, :, :]/denom[:, :, None] - vec2[None, :, None]*X[:, None, :]*(numer/denom3)[:, :, None])*(dL_dK/np.sqrt(1-arg*arg))[:, :, None]).sum(1)
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def _dK_dX_point(self, dL_dK, X, X2, target, i):
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"""Gradient with respect to one point of X"""
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inner_prod = self._K_inner_prod[i, :].T
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numer = self._K_numer[i, :].T
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denom = self._K_denom[i, :].T
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arg = self._K_asin_arg[i, :].T
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vec1 = (X[i, :]*X[i, :]).sum()*self.weight_variance + self.bias_variance + 1.
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vec2 = (X2*X2).sum(1)*self.weight_variance + self.bias_variance + 1.
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#denom = np.sqrt(np.outer(vec2,vec1))
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#arg = numer/denom
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gX = np.zeros(X2.shape)
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denom3 = denom*denom*denom
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gX = np.zeros((X2.shape[0], X2.shape[1]))
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for j in range(X2.shape[1]):
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gX[:, j] = X2[:, j]/denom - vec2*X[i, j]*numer/denom3
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gX[:, j] = four_over_tau*self.weight_variance*self.variance*gX[:, j]/np.sqrt(1-arg*arg)
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def dKdiag_dX(self, dL_dKdiag, X, target):
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"""Gradient of diagonal of covariance with respect to X"""
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self._K_diag_computations(X)
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arg = self._K_diag_asin_arg
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denom = self._K_diag_denom
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numer = self._K_diag_numer
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target += four_over_tau*2.*self.weight_variance*self.variance*X*(1/denom*(1 - arg)*dL_dKdiag/(np.sqrt(1-arg*arg)))[:, None]
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def _K_computations(self, X, X2):
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"""Pre-computations for the covariance matrix (used for computing the covariance and its gradients."""
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if self.ARD:
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pass
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else:
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@ -165,9 +145,11 @@ class MLP(Kernpart):
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self._K_dvar = four_over_tau*np.arcsin(self._K_asin_arg)
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def _K_diag_computations(self, X):
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"""Pre-computations concerning the diagonal terms (used for computation of diagonal and its gradients)."""
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if self.ARD:
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pass
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else:
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self._K_diag_numer = (X*X).sum(1)*self.weight_variance + self.bias_variance
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self._K_diag_denom = self._K_diag_numer+1.
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self._K_diag_dvar = four_over_tau*np.arcsin(self._K_diag_numer/self._K_diag_denom)
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self._K_diag_asin_arg = self._K_diag_numer/self._K_diag_denom
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self._K_diag_dvar = four_over_tau*np.arcsin(self._K_diag_asin_arg)
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@ -39,7 +39,6 @@ class POLY(Kernpart):
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"""
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def __init__(self, input_dim, variance=1., weight_variance=None, bias_variance=1., degree=2, ARD=False):
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ARD = False
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self.input_dim = input_dim
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self.ARD = ARD
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if not ARD:
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@ -56,6 +55,7 @@ class POLY(Kernpart):
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assert weight_variance.size == self.input_dim, "bad number of weight variances"
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else:
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weight_variance = np.ones(self.input_dim)
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raise NotImplementedError
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self.degree=degree
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self.name='poly_deg' + str(self.degree)
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self._set_params(np.hstack((variance, weight_variance.flatten(), bias_variance)))
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@ -5,7 +5,13 @@ import numpy as np
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import matplotlib as mpl
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import time
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import Image
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# import visual
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try:
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import visual
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visual_available = True
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except ImportError:
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visual_available = False
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class data_show:
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"""
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@ -24,7 +30,6 @@ class data_show:
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def close(self):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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class vpython_show(data_show):
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"""
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the vpython_show class is a base class for all visualization methods that use vpython to display. It is initialized with a scene. If the scene is set to None it creates a scene window.
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@ -318,7 +323,7 @@ class mocap_data_show_vpython(vpython_show):
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for i in range(self.vals.shape[0]):
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self.spheres.append(visual.sphere(pos=(self.vals[i, 0], self.vals[i, 2], self.vals[i, 1]), radius=self.radius))
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self.scene.visible=True
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def draw_edges(self):
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self.rods = []
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self.line_handle = []
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@ -435,7 +440,6 @@ class mocap_data_show(matplotlib_show):
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self.axes.set_ylim(self.y_lim)
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self.axes.set_zlim(self.z_lim)
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class stick_show(mocap_data_show_vpython):
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"""Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
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def __init__(self, vals, connect=None, scene=None):
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