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# Copyright (c) 2013-2017, Christian Therien
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# naive_bayes.py - This file is part of the PySptools package.
#
import numpy as np
from sklearn.naive_bayes import GaussianNB
from .base import HyperBaseClassifier
[docs]class HyperGaussianNB(GaussianNB, HyperBaseClassifier):
"""
Apply scikit-learn GaussianNB on a hypercube.
For the __init__ class contructor parameters: `see the sklearn.naive_bayes.GaussianNB class parameters`
The class is intrumented to be use with the scikit-learn cross validation.
It use the plot and display methods from the class Output.
"""
def __init__(self, priors=None):
super(HyperGaussianNB, self).__init__(priors=priors)
HyperBaseClassifier.__init__(self, 'HyperGaussianNB')
[docs] def fit(self, X, y, sample_weight=None):
"""
Same as the sklearn.naive_bayes.GaussianNB fit call.
Parameters:
X: `numpy array`
A vector (n_samples, n_features) where each element *n_features* is a spectrum.
y: `numpy array`
Target values (n_samples,). A zero value is the background. A value of one or more is a class value.
"""
super(HyperGaussianNB, self)._set_n_clusters(int(np.max(y)))
super(HyperGaussianNB, self).fit(X, y, sample_weight=sample_weight)
[docs] def fit_rois(self, M, ROIs):
"""
Fit the HS cube M with the use of ROIs.
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
ROIs: `ROIs type`
Regions of interest instance.
"""
X, y = self._fit_rois(M, ROIs)
super(HyperGaussianNB, self).fit(X, y)
[docs] def classify(self, M):
"""
Classify a hyperspectral cube.
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
Returns: `numpy array`
A class map (m x n x 1).
"""
img = self._convert2D(M)
cls = super(HyperGaussianNB, self).predict(img)
cmap = self._convert3d(cls, M.shape[0], M.shape[1])
super(HyperGaussianNB, self)._set_cmap(cmap)
return self.cmap