#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : qichun tang
# @Date : 2020-12-27
# @Contact : qichun.tang@bupt.edu.cn
import matplotlib.pyplot as plt
[文档]def plot_convergence(
x, y1, y2,
xlabel="Number of iterations $n$",
ylabel=r"$\min f(x)$ after $n$ iterations",
ax=None, name=None, alpha=0.2, yscale=None,
color=None, true_minimum=None,
**kwargs):
"""Plot one or several convergence traces.
Parameters
----------
args[i] : `OptimizeResult`, list of `OptimizeResult`, or tuple
The result(s) for which to plot the convergence trace.
- if `OptimizeResult`, then draw the corresponding single trace;
- if list of `OptimizeResult`, then draw the corresponding convergence
traces in transparency, along with the average convergence trace;
- if tuple, then `args[i][0]` should be a string label and `args[i][1]`
an `OptimizeResult` or a list of `OptimizeResult`.
ax : `Axes`, optional
The matplotlib axes on which to draw the plot, or `None` to create
a new one.
true_minimum : float, optional
The true minimum value of the function, if known.
yscale : None or string, optional
The scale for the y-axis.
Returns
-------
ax : `Axes`
The matplotlib axes.
"""
if ax is None:
ax = plt.gca()
ax.set_title("Convergence plot")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid()
if yscale is not None:
ax.set_yscale(yscale)
ax.plot(x, y1, c=color, label=name, **kwargs)
ax.scatter(x, y2, c=color, alpha=alpha)
if true_minimum:
ax.axhline(true_minimum, linestyle="--",
color="r", lw=1,
label="True minimum")
if true_minimum or name:
ax.legend(loc="best")
return ax
