wehub-resource-sync
63 lines
1.6 KiB
Python
Executable File
63 lines
1.6 KiB
Python
Executable File
import numpy as np
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class SigmoidalFeature(object):
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"""
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Sigmoidal features
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1 / (1 + exp((m - x) @ c)
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"""
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def __init__(self, mean, coef=1):
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"""
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construct sigmoidal features
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Parameters
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----------
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mean : (n_features, ndim) or (n_features,) ndarray
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center of sigmoid function
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coef : (ndim,) ndarray or int or float
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coefficient to be multplied with the distance
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"""
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if mean.ndim == 1:
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mean = mean[:, None]
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else:
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assert mean.ndim == 2
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if isinstance(coef, int) or isinstance(coef, float):
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if np.size(mean, 1) == 1:
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coef = np.array([coef])
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else:
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raise ValueError("mismatch of dimension")
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else:
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assert coef.ndim == 1
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assert np.size(mean, 1) == len(coef)
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self.mean = mean
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self.coef = coef
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def _sigmoid(self, x, mean):
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return np.tanh((x - mean) @ self.coef * 0.5) * 0.5 + 0.5
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def transform(self, x):
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"""
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transform input array with sigmoidal features
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Parameters
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----------
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x : (sample_size, ndim) or (sample_size,) ndarray
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input array
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Returns
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-------
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output : (sample_size, n_features) ndarray
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sigmoidal features
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"""
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if x.ndim == 1:
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x = x[:, None]
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else:
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assert x.ndim == 2
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assert np.size(x, 1) == np.size(self.mean, 1)
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basis = [np.ones(len(x))]
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for m in self.mean:
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basis.append(self._sigmoid(x, m))
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return np.asarray(basis).transpose()
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