Source code for pysptools.skl.linear_model

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# linear_model.py - This file is part of the PySptools package.
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import numpy as np
from sklearn.linear_model import LogisticRegression
from .base import HyperBaseClassifier


[docs]class HyperLogisticRegression(LogisticRegression, HyperBaseClassifier): """ Apply scikit-learn LogisticRegression on a hypercube. For the __init__ class contructor parameters: `see the sklearn.linear_model.LogisticRegression 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, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr', verbose=0, warm_start=False, n_jobs=1): super(HyperLogisticRegression, self).__init__(penalty=penalty, dual=dual, tol=tol, C=C, fit_intercept=fit_intercept, intercept_scaling=intercept_scaling, class_weight=class_weight, random_state=random_state, solver=solver, max_iter=max_iter, multi_class=multi_class, verbose=verbose, warm_start=warm_start, n_jobs=n_jobs) HyperBaseClassifier.__init__(self, 'HyperLogisticRegression')
[docs] def fit(self, X, y): """ Same as the sklearn.linear_model.HyperLogisticRegression 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(HyperLogisticRegression, self)._set_n_clusters(int(np.max(y))) super(HyperLogisticRegression, self).fit(X, y)
[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(HyperLogisticRegression, 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(HyperLogisticRegression, self).predict(img) cmap = self._convert3d(cls, M.shape[0], M.shape[1]) super(HyperLogisticRegression, self)._set_cmap(cmap) return self.cmap