wehub-resource-sync
56 lines
1.4 KiB
Python
Executable File
56 lines
1.4 KiB
Python
Executable File
import numpy as np
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class GaussianFeature(object):
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"""
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Gaussian feature
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gaussian function = exp(-0.5 * (x - m) / v)
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"""
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def __init__(self, mean, var):
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"""
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construct gaussian 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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places to locate gaussian function at
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var : float
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variance of the gaussian function
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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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assert isinstance(var, float) or isinstance(var, int)
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self.mean = mean
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self.var = var
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def _gauss(self, x, mean):
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return np.exp(-0.5 * np.sum(np.square(x - mean), axis=-1) / self.var)
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def transform(self, x):
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"""
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transform input array with gaussian features
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Parameters
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----------
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x : (sample_size, ndim) or (sample_size,)
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input array
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Returns
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-------
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output : (sample_size, n_features)
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gaussian 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._gauss(x, m))
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return np.asarray(basis).transpose()
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