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51 lines
1.9 KiB
51 lines
1.9 KiB
11 months ago
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# def make_func(letters,result):
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# global func_x
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# # print(letters,result)
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# draw_axis(-100, 100, 20)
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# letter = str(letters).split(',')
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# n = len(letter)-1
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# decimals = [round(random.uniform(-10, 10), 1) for _ in range(n)]
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# target_result = functional_formula(letter[1:],result,decimals)
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# # output.configure(state="normal")
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# # output.delete("1.0", tk.END) # 删除当前输出框中的所有文本
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# # output.insert(tk.END, target_result)
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# # output.configure(state="disabled")
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# code_str = fitting('x', target_result)
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# # print(code_str)
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# target_func = create_func(code_str) # 获取当前函数func
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# # # print(target_result)
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# # # 生成带噪声的待拟合数据
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# # np.random.seed(0)
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# # x_data = np.linspace(-100, 100, 50)
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# # y_data = target_func(x_data) + np.random.normal(0, 0.5, 50)
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# # # print(y_data)
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# # code_str = fitting(letters, result)
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# # print(code_str)
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# # func_x = create_func(code_str) # 获取当前函数func
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# #
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# # # 初始参数的估计值
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# # initial_coeffs = [1.0 for _ in range(n)]
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# # # 使用最小二乘法拟合函数
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# # result = leastsq(residuals, initial_coeffs, args=(y_data, x_data))
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# # best_coeffs = result[0]
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# # # 打印拟合得到的系数
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# # print("拟合得到的系数：", best_coeffs)
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#
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# # 绘制拟合结果
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# x_fit = np.linspace(-100, 100, 50)
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# y_fit = target_func(x_fit)
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#
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# # plt.plot(x_data, y_data, 'bo', label='原始数据')
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# plt.plot(x_fit, y_fit, 'r-', label='拟合曲线')
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# plt.legend()
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# plt.xlabel('x')
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# plt.ylabel('y')
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# # plt.show()
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# plt.savefig('new_line.png')
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# # 清除当前图形状态
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# plt.clf()
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# show_image()
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#
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# # 定义误差函数，即最小二乘法的目标函数
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# def residuals(coeffs, y, x):
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# return y - func_x(x, *coeffs)
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