physbo.gp.cov.gauss のソースコード

# -*- coding:utf-8 -*-
import numpy as np
from scipy import spatial
from . import _src
from ._src.enhance_gauss import grad_width64


[ドキュメント]class gauss: """ gaussian kernel """ def __init__( self, num_dim, width=3, scale=1, ard=False, max_width=1e6, min_width=1e-6, max_scale=1e6, min_scale=1e-6, ): """ Parameters ---------- num_dim: int width: float scale: float ard: bool flag to use Automatic Relevance Determination (ARD). max_width: float Maximum value of width min_width: float Minimum value of width max_scale: float Maximum value of scale min_scale: float Minimum value of scale """ self.ard = ard self.num_dim = num_dim self.scale = scale self.max_ln_width = np.log(max_width) self.min_ln_width = np.log(min_width) self.max_ln_scale = np.log(max_scale) self.min_ln_scale = np.log(min_scale) if self.ard: # with ARD self.num_params = num_dim + 1 if isinstance(width, np.ndarray) and len(width) == self.num_dim: self.width = width else: self.width = width * np.ones(self.num_dim) else: # without ARD self.width = width self.num_params = 2 params = self.cat_params(self.width, self.scale) self.set_params(params)
[ドキュメント] def print_params(self): """ show the current kernel parameters """ print(" Parameters of Gaussian kernel \n ") print(" width = ", +self.width) print(" scale = ", +self.scale) print(" scale2 = ", +self.scale ** 2) print(" \n")
[ドキュメント] def prepare(self, params=None): """ Setting parameters Parameters ---------- params: numpy.ndarray parameters Returns ------- params: numpy.ndarray width: int scale: int """ if params is None: params = self.params width = self.width scale = self.scale else: params = self.supp_params(params) width, scale = self.decomp_params(params) return params, width, scale
[ドキュメント] def get_grad(self, X, params=None): """ Getting gradiant values of X Parameters ---------- X: numpy.ndarray N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate. params: numpy.ndarray Returns ------- grad: numpy.ndarray """ num_data = X.shape[0] params, width, scale = self.prepare(params) G = self.get_cov(X, params=params) grad = np.zeros((self.num_params, num_data, num_data)) if self.ard: grad[0 : self.num_params - 1, :, :] = grad_width64(X, width, G) else: pairwise_dists = spatial.distance.pdist(X / width, "euclidean") grad[0, :, :] = G * spatial.distance.squareform(pairwise_dists ** 2) grad[-1, :, :] = 2 * G return grad
[ドキュメント] def get_cov(self, X, Z=None, params=None, diag=False): """ compute the covariant matrix Parameters ---------- X: numpy.ndarray N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate. Z: numpy.ndarray N x d dimensional matrix. Each row of Z denotes the d-dimensional feature vector of search candidate. params: numpy.ndarray Parameters diag: bool If X is the diagonalization matrix, true. Returns ------- G: numpy.ndarray covariant matrix """ params, width, scale = self.prepare(params) scale2 = scale ** 2 if Z is None: if diag: G = scale2 * np.ones(X.shape[0]) else: pairwise_dists = spatial.distance.squareform( spatial.distance.pdist(X / width, "euclidean") ** 2 ) G = np.exp(-0.5 * pairwise_dists) * scale2 else: pairwise_dists = ( spatial.distance.cdist(X / width, Z / width, "euclidean") ** 2 ) G = np.exp(-0.5 * pairwise_dists) * scale2 return G
[ドキュメント] def set_params(self, params): """ set kernel parameters Parameters ---------- params: numpy.ndarray Parameters for optimization. """ params = self.supp_params(params) self.params = params self.width, self.scale = self.decomp_params(params)
[ドキュメント] def supp_params(self, params): """ Set maximum (minimum) values for parameters when the parameter is greater(less) than this value. Parameters ---------- params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- params: numpy.ndarray """ index = np.where(params[0:-1] > self.max_ln_width) params[index[0]] = self.max_ln_width index = np.where(params[0:-1] < self.min_ln_width) params[index[0]] = self.min_ln_width if params[-1] > self.max_ln_scale: params[-1] = self.max_ln_scale if params[-1] < self.min_ln_scale: params[-1] = self.min_ln_scale return params
[ドキュメント] def decomp_params(self, params): """ decompose the parameters defined on the log region into width and scale parameters Parameters ---------- params: numpy.ndarray parameters Returns ------- width: float scale: float """ width = np.exp(params[0:-1]) scale = np.exp(params[-1]) return width, scale
[ドキュメント] def save(self, file_name): """ save the gaussian kernel Parameters ---------- file_name: str file name to save the information of the kernel """ kwarg = { "name": "gauss", "params": self.params, "ard": self.ard, "num_dim": self.num_dim, "max_ln_scale": self.max_ln_scale, "min_ln_scale": self.min_ln_scale, "max_ln_width": self.max_ln_width, "min_ln_width": self.min_ln_width, "num_params": self.num_params, } with open(file_name, "wb") as f: np.savez(f, **kwarg)
[ドキュメント] def load(self, file_name): """ Recovering the Gaussian kernel from file Parameters ---------- file_name: str file name to load the information of the kernel """ temp = np.load(file_name) self.num_dim = temp["num_dim"] self.ard = temp["ard"] self.max_ln_scale = temp["max_ln_scale"] self.min_ln_scale = temp["min_ln_scale"] self.max_ln_width = temp["max_ln_width"] self.min_ln_width = temp["min_ln_width"] params = temp["params"] self.set_params(params)
[ドキュメント] def get_params_bound(self): """ Getting boundary array. Returns ------- bound: list A num_params-dimensional array with the tuple (min_params, max_params). """ if self.ard: bound = [ (self.min_ln_width, self.max_ln_width) for i in range(0, self.num_dim) ] else: bound = [(self.min_ln_width, self.max_ln_width)] bound.append((self.min_ln_scale, self.max_ln_scale)) return bound
[ドキュメント] def cat_params(self, width, scale): """ Taking the logarithm of width and scale parameters and concatinate them into one ndarray Parameters ---------- width: int scale: int Returns ------- params: numpy.ndarray Parameters """ params = np.zeros(self.num_params) params[0:-1] = np.log(width) params[-1] = np.log(scale) return params
[ドキュメント] def rand_expans(self, num_basis, params=None): """ Kernel Expansion Parameters ---------- num_basis: int total number of basis params: numpy.ndarray Parameters Returns ------- tupple (W, b, amp) """ params, width, scale = self.prepare(params) scale2 = scale ** 2 amp = np.sqrt((2 * scale2) / num_basis) W = np.random.randn(num_basis, self.num_dim) / width b = np.random.rand(num_basis) * 2 * np.pi return (W, b, amp)
[ドキュメント] def get_cand_params(self, X, t): """ Getting candidate parameters. Parameters ---------- X: numpy.ndarray N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate. t: numpy.ndarray N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized). Returns ------- params: numpy.ndarray """ if self.ard: # with ARD width = np.zeros(self.num_dim) scale = np.std(t) u = np.random.uniform(0.4, 0.8) width = u * (np.max(X, 0) - np.min(X, 0)) * np.sqrt(self.num_dim) index = np.where(np.abs(width) < 1e-6) width[index[0]] = 1e-6 params = np.append(np.log(width), np.log(scale)) else: # without ARD num_data = X.shape[0] M = max(2000, int(np.floor(num_data / 5))) dist = np.zeros(M) for m in range(M): a = np.random.randint(0, X.shape[0], 2) dist[m] = np.linalg.norm(X[a[0], :] - X[a[1], :]) dist = np.sort(dist) tmp = int(np.floor(M / 10)) n = np.random.randint(0, 5) width = dist[(2 * n + 1) * tmp] scale = np.std(t) params = np.append(np.log(width + 1e-8), np.log(scale)) return params