''' mbinary
#########################################################################
# File : iteration.py
# Author: mbinary
# Mail: zhuheqin1@gmail.com
# Blog: https://mbinary.xyz
# Github: https://github.com/mbinary
# Created Time: 2018-10-02  21:14
# Description:
#########################################################################
'''

import sympy
import numpy as np
from math import sqrt


def newton(y: sympy.core, x0: float, epsilon: float = 0.00001, maxtime: int = 50) ->(list, list):
    '''
        newton 's iteration method for finding a zeropoint of a func
        y is the func, x0 is the init x val: int float epsilon is the accurrency
    '''
    if epsilon < 0:
        epsilon = -epsilon
    ct = 0
    t = y.free_symbols
    varsymbol = 'x' if len(t) == 0 else t.pop()
    x0 = float(x0)
    y_diff = y.diff()
    li = [x0]
    vals = []
    while 1:
        val = y.subs(varsymbol, x0)
        vals.append(val)
        x = x0 - val/y_diff.subs(varsymbol, x0)
        li.append(x)
        ct += 1
        if ct > maxtime:
            print("after iteration for {} times, I still havn't reach the accurrency.\
                    Maybe this function havsn't zeropoint\n".format(ct))
            return li, val
        if abs(x-x0) < epsilon:
            return li, vals
        x0 = x


def secant(y: sympy.core, x0: float, x1: float, epsilon: float = 0.00001, maxtime: int = 50) ->(list, list):
    '''
        弦截法, 使用newton 差商计算,每次只需计算一次f(x)
        secant method for finding a zeropoint of a func
        y is the func , x0 is the init x val,     epsilon is the accurrency
    '''
    if epsilon < 0:
        epsilon = -epsilon
    ct = 0
    x0, x1 = float(x0), float(x1)
    li = [x0, x1]
    t = y.free_symbols
    varsymbol = 'x' if len(t) == 0 else t.pop()
    last = y.subs(varsymbol, x0)
    vals = [last]
    while 1:
        cur = y.subs(varsymbol, x1)
        vals.append(cur)
        x = x1-cur*(x1-x0)/(cur-last)
        x0, x1 = x1, x
        last = cur
        li.append(x)
        ct += 1
        if ct > maxtime:
            print("after iteration for {} times, I still havn't reach the accurrency.\
                    Maybe this function havsn't zeropoint\n".format(ct))
            return li, vals
        if abs(x0-x1) < epsilon:
            return li, vals
        x0 = x


def solveNonlinearEquations(funcs: [sympy.core], init_dic: dict, epsilon: float = 0.001, maxtime: int = 50)->dict:
    '''solve  nonlinear equations:'''
    li = list(init_dic.keys())
    delta = {i: 0 for i in li}
    ct = 0
    while 1:
        ys = np.array([f.subs(init_dic) for f in funcs], dtype='float')
        mat = np.matrix([[i.diff(x).subs(init_dic) for x in li]
                         for i in funcs], dtype='float')
        delt = np.linalg.solve(mat, -ys)
        for i, j in enumerate(delt):
            init_dic[li[i]] += j
            delta[li[i]] = j
        if ct > maxtime:
            print("after iteration for {} times, I still havn't reach the accurrency.\
                    Maybe this function havsn't zeropoint\n".format(ct))
            return init_dic
        if sqrt(sum(i**2 for i in delta.values())) < epsilon:
            return init_dic


if __name__ == '__main__':
    x, y, z = sympy.symbols('x y z')

    res, res2 = newton(x**5-9, 2, 0.01)
    print(res, res2)

    res, res2 = secant(x**3-3*x-2, 1, 3, 1e-3)
    print(res, res2)

    funcs = [x**2+y**2-1, x**3-y]
    init = {x: 0.8, y: 0.6}
    res_dic = solveNonlinearEquations(funcs, init, 0.001)
    print(res_dic)