regression.py 文件源码

def fit(self, x, y, i=0):
        # if gaussian processes are being used, data dimensionality needs to be reduced before fitting
        if self.method[i] == 'GP':
            if self.reduce_dim == 'FastICA':
                print('Reducing dimensionality with ICA')
                do_ica = FastICA(n_components=self.n_components)
                self.do_reduce_dim = do_ica.fit(x)
            if self.reduce_dim == 'PCA':
                print('Reducing dimensionality with PCA')
                do_pca = PCA(n_components=self.n_components)
                self.do_reduce_dim = do_pca.fit(x)

            x = self.do_reduce_dim.transform(x)
        #try:
            print('Training model...')
        try:
            self.model.fit(x, y)
            self.goodfit = True
            print(self.model)
        except:
            self.goodfit = False
            if self.method[i] == 'GP':
                print('Model failed to train! (For GP this does not always indicate a problem, especially for low numbers of components.)')
                pass
            else:
                print('Model failed to train!')
                traceback.print_stack()

        if self.ransac:
            self.outliers = np.logical_not(self.model.inlier_mask_)
            print(str(np.sum(self.outliers)) + ' outliers removed with RANSAC')