density_forest.py 文件源码

def build_forest(self):

        forest = {}

        for t in range(self.f_size):
            forest[t] = Tree(self, rho=self.rho)
            forest[t].tree_leaf_plots(fname='tree_opt%s.png'%t)


        path = os.getcwd() + '/plots/'
        mkdir_p(path)


        fig = plt.figure(figsize=(10,10))
        ax = fig.add_subplot(111)


        color = ['lightcoral', 'dodgerblue', 'mediumseagreen', 'darkorange']
        for t in range(self.f_size):
            for c, n in enumerate(forest[t].leaf_nodes):

                [[i1, i2], [j1, j2]] = n.quad
                x1, x2 = self.grid[0][i1], self.grid[0][i2]
                y1, y2 = self.grid[1][j1], self.grid[1][j2]

                ax.fill_between([x1,x2], y1, y2, alpha=.15, color=color[c])

        pd.DataFrame(self.data, columns=['x', 'y']).plot(ax=ax, x='x', y='y', kind='scatter', lw=0, alpha=.6, s=20, c='k')
        plt.savefig(path + 'combined.png', format='png')
        plt.close()



        return forest


    # Implement Online L-curve optimization like EWMA to get rid of input depth