97 lines
2.5 KiB
Python
97 lines
2.5 KiB
Python
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# -*- coding: utf-8 -*-
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# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
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# (mostly translation, see implementation details)
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# License: BSD 3 clause
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"""
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The built-in regression models submodule for the gaussian_process module.
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"""
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import numpy as np
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from ..utils import deprecated
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@deprecated("The function constant of regression_models is "
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"deprecated in version 0.19.1 and will be removed in 0.22.")
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def constant(x):
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"""
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Zero order polynomial (constant, p = 1) regression model.
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x --> f(x) = 1
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Parameters
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----------
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x : array_like
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An array with shape (n_eval, n_features) giving the locations x at
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which the regression model should be evaluated.
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Returns
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-------
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f : array_like
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An array with shape (n_eval, p) with the values of the regression
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model.
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"""
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x = np.asarray(x, dtype=np.float64)
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n_eval = x.shape[0]
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f = np.ones([n_eval, 1])
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return f
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@deprecated("The function linear of regression_models is "
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"deprecated in version 0.19.1 and will be removed in 0.22.")
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def linear(x):
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"""
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First order polynomial (linear, p = n+1) regression model.
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x --> f(x) = [ 1, x_1, ..., x_n ].T
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Parameters
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----------
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x : array_like
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An array with shape (n_eval, n_features) giving the locations x at
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which the regression model should be evaluated.
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Returns
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-------
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f : array_like
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An array with shape (n_eval, p) with the values of the regression
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model.
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"""
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x = np.asarray(x, dtype=np.float64)
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n_eval = x.shape[0]
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f = np.hstack([np.ones([n_eval, 1]), x])
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return f
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@deprecated("The function quadratic of regression_models is "
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"deprecated in version 0.19.1 and will be removed in 0.22.")
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def quadratic(x):
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"""
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Second order polynomial (quadratic, p = n*(n-1)/2+n+1) regression model.
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x --> f(x) = [ 1, { x_i, i = 1,...,n }, { x_i * x_j, (i,j) = 1,...,n } ].T
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i > j
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Parameters
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----------
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x : array_like
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An array with shape (n_eval, n_features) giving the locations x at
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which the regression model should be evaluated.
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Returns
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-------
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f : array_like
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An array with shape (n_eval, p) with the values of the regression
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model.
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"""
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x = np.asarray(x, dtype=np.float64)
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n_eval, n_features = x.shape
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f = np.hstack([np.ones([n_eval, 1]), x])
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for k in range(n_features):
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f = np.hstack([f, x[:, k, np.newaxis] * x[:, k:]])
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return f
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